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Ta petlja nikada nije savršeno funkcionisala, ali je smer bio razumljiv. U 2026. petlju je sve teže pročitati jer se dve promene dešavaju istovremeno: AI infrastruktura se agresivno takmiči za silicijum i memoriju, dok AI tehnike direktno ulaze u način na koji igre stvaraju frejmove.\u003C\u002Fp>\n\u003Caside class=\"editorjs-callout editorjs-callout--info my-6 rounded-xl border p-5 border-blue-300 bg-blue-50 dark:border-blue-900 dark:bg-blue-950\u002F20\" role=\"note\">\u003Cstrong class=\"block mb-2 text-gray-900 dark:text-gray-100\">Direktan odgovor\u003C\u002Fstrong>\u003Cdiv class=\"text-gray-700 dark:text-gray-200\">\u003Cstrong>Stari ciklus nadogradnje za gejming je pod pritiskom, ali ga ne zamenjuju jednostavno skuplji GPU-ovi.\u003C\u002Fstrong> AI menja obe strane jednačine: povećava konkurenciju za poluprovodničke i memorijske resurse, a istovremeno omogućava tehnike neuronskog renderovanja koje mogu zameniti uskladištene podatke ili konvencionalni render rad inferencijom. Važna mogućnost je da gejming može odgovoriti na strožu hardversku ekonomiju time što će postati računarski selektivniji, a ne samo zahtevati veće GPU-ove.\u003C\u002Fdiv>\u003C\u002Faside>\n\u003Caside class=\"editorjs-callout editorjs-callout--note my-6 rounded-xl border p-5 border-gray-300 bg-gray-50 dark:border-gray-700 dark:bg-gray-900\u002F40\" role=\"note\">\u003Cstrong class=\"block mb-2 text-gray-900 dark:text-gray-100\">Kako čitati ovaj članak\u003C\u002Fstrong>\u003Cdiv class=\"text-gray-700 dark:text-gray-200\">Ovo je \u003Cstrong>hipoteza potkrepljena dokazima\u003C\u002Fstrong>. Dokumentovani tržišni podaci i tehnologije koje su u prodaji ili najavljene označeni su kao činjenice ili industrijski signali. Veze između njih su tehničko zaključivanje. Arhitektura budućnosti opisana kasnije je hipoteza Figure Rocks, a ne tvrdnja o nenajavljenom planu NVIDIA, AMD, Intel, Microsoft ili konzola.\u003C\u002Fdiv>\u003C\u002Faside>\n\u003Cnav class=\"editorjs-toc\" data-editorjs-toc=\"true\" aria-label=\"Sadržaj\">\u003Cstrong class=\"editorjs-toc__title\">Sadržaj\u003C\u002Fstrong>\u003Col class=\"editorjs-toc__list editorjs-toc__list--depth-0\">\u003Cli class=\"editorjs-toc__item\">\u003Ca href=\"#section-5\" class=\"editorjs-toc__link\">Nesrazmera u obimu je već vidljiva\u003C\u002Fa>\u003C\u002Fli>\u003Cli class=\"editorjs-toc__item\">\u003Ca href=\"#section-9\" class=\"editorjs-toc__link\">Memorija je mesto gde pritisak postaje konkretan\u003C\u002Fa>\u003C\u002Fli>\u003Cli class=\"editorjs-toc__item\">\u003Ca href=\"#section-13\" class=\"editorjs-toc__link\">U isto vreme, sam frejm se menja\u003C\u002Fa>\u003C\u002Fli>\u003Cli class=\"editorjs-toc__item\">\u003Ca href=\"#section-19\" class=\"editorjs-toc__link\">Pitanje 5090 je korisno samo ako ga pravilno formulišemo\u003C\u002Fa>\u003C\u002Fli>\u003Cli class=\"editorjs-toc__item\">\u003Ca href=\"#section-22\" class=\"editorjs-toc__link\">Memorija počinje da postaje računski problem, a ne samo problem kapaciteta\u003C\u002Fa>\u003C\u002Fli>\u003Cli class=\"editorjs-toc__item\">\u003Ca href=\"#section-28\" class=\"editorjs-toc__link\">Kako bi tehnički odgovor mogao da funkcioniše\u003C\u002Fa>\u003C\u002Fli>\u003Cli class=\"editorjs-toc__item\">\u003Ca href=\"#section-31\" class=\"editorjs-toc__link\">Igra ima jednu prednost koju opšta radna opterećenja često nemaju: već zna deo svoje budućnosti\u003C\u002Fa>\u003C\u002Fli>\u003Cli class=\"editorjs-toc__item\">\u003Ca href=\"#section-35\" class=\"editorjs-toc__link\">Zašto arhitektura nalik Spark postaje zanimljiva\u003C\u002Fa>\u003C\u002Fli>\u003Cli class=\"editorjs-toc__item\">\u003Ca href=\"#section-39\" class=\"editorjs-toc__link\">Šta bi moralo da bude tačno da bi ova tranzicija uspela?\u003C\u002Fa>\u003C\u002Fli>\u003Cli class=\"editorjs-toc__item\">\u003Ca href=\"#section-41\" class=\"editorjs-toc__link\">Šta kupac treba da očekuje od Spark-olike tranzicije?\u003C\u002Fa>\u003C\u002Fli>\u003Cli class=\"editorjs-toc__item\">\u003Ca href=\"#section-43\" class=\"editorjs-toc__link\">Šta bi opovrglo ovu hipotezu?\u003C\u002Fa>\u003C\u002Fli>\u003Cli class=\"editorjs-toc__item\">\u003Ca href=\"#section-46\" class=\"editorjs-toc__link\">Praktičan zaključak za gejmera danas\u003C\u002Fa>\u003C\u002Fli>\u003Cli class=\"editorjs-toc__item\">\u003Ca href=\"#section-49\" class=\"editorjs-toc__link\">Primarni izvori\u003C\u002Fa>\u003C\u002Fli>\u003Cli class=\"editorjs-toc__item\">\u003Ca href=\"#section-69\" class=\"editorjs-toc__link\">Nesrazmera u obimu je već vidljiva\u003C\u002Fa>\u003C\u002Fli>\u003Cli class=\"editorjs-toc__item\">\u003Ca href=\"#section-73\" class=\"editorjs-toc__link\">Memorija je mesto gde pritisak postaje konkretan\u003C\u002Fa>\u003C\u002Fli>\u003Cli class=\"editorjs-toc__item\">\u003Ca href=\"#section-77\" class=\"editorjs-toc__link\">U isto vreme, sam okvir se menja\u003C\u002Fa>\u003C\u002Fli>\u003Cli class=\"editorjs-toc__item\">\u003Ca href=\"#section-83\" class=\"editorjs-toc__link\">Pitanje 5090 je korisno samo ako ga pravilno formulišemo\u003C\u002Fa>\u003C\u002Fli>\u003Cli class=\"editorjs-toc__item\">\u003Ca href=\"#section-86\" class=\"editorjs-toc__link\">Memorija počinje da postaje računarski problem, a ne samo problem kapaciteta\u003C\u002Fa>\u003C\u002Fli>\u003Cli class=\"editorjs-toc__item\">\u003Ca href=\"#section-92\" class=\"editorjs-toc__link\">Kako bi tehnički odgovor mogao da funkcioniše\u003C\u002Fa>\u003C\u002Fli>\u003Cli class=\"editorjs-toc__item\">\u003Ca href=\"#section-95\" class=\"editorjs-toc__link\">Igra ima jednu prednost koju opšti radni zadaci često nemaju: već zna deo svoje budućnosti\u003C\u002Fa>\u003C\u002Fli>\u003Cli class=\"editorjs-toc__item\">\u003Ca href=\"#section-99\" class=\"editorjs-toc__link\">Zašto arhitektura nalik Spark-u postaje zanimljiva\u003C\u002Fa>\u003C\u002Fli>\u003Cli class=\"editorjs-toc__item\">\u003Ca href=\"#section-103\" class=\"editorjs-toc__link\">Šta bi moralo da bude tačno da bi ova tranzicija uspela?\u003C\u002Fa>\u003C\u002Fli>\u003Cli class=\"editorjs-toc__item\">\u003Ca href=\"#section-105\" class=\"editorjs-toc__link\">Šta kupac treba da očekuje od tranzicije nalik Spark-u?\u003C\u002Fa>\u003C\u002Fli>\u003Cli class=\"editorjs-toc__item\">\u003Ca href=\"#section-107\" class=\"editorjs-toc__link\">Šta bi opovrglo ovu hipotezu?\u003C\u002Fa>\u003C\u002Fli>\u003Cli class=\"editorjs-toc__item\">\u003Ca href=\"#section-110\" class=\"editorjs-toc__link\">Praktičan zaključak za gejmera danas\u003C\u002Fa>\u003C\u002Fli>\u003Cli class=\"editorjs-toc__item\">\u003Ca href=\"#section-113\" class=\"editorjs-toc__link\">Primarni izvori\u003C\u002Fa>\u003C\u002Fli>\u003C\u002Fol>\u003C\u002Fnav>\n\u003Ch2 id=\"section-5\">Nesrazmera u obimu je već vidljiva\u003C\u002Fh2>\n\u003Cp>Newzoo predviđa globalno tržište igara na 213,9 milijardi dolara u 2026, što je rast od 6,1% u odnosu na prethodnu godinu. Asocijacija poluprovodničke industrije objavila je 791,7 milijardi dolara globalne prodaje poluprovodnika za 2025, rast od 25,6%, i projektovala oko 1 bilion dolara za 2026. Posao igara i posao poluprovodnika i dalje zavise jedan od drugog, ali njihovi podsticaji postaju sve manje simetrični: isti napredni proizvodni ekosistem koji omogućava vrhunski gejming sada služi AI i data-center kupcima sa mnogo većim budžetima za infrastrukturu. Ovo nisu direktno uporedive kategorije proizvoda; oni uspostavljaju razmeru uzvodne računarske ekonomije oko gejminga.\u003C\u002Fp>\n\u003Cdiv class=\"overflow-x-auto\">\u003Ctable class=\"w-full border-collapse\">\u003Cthead>\u003Ctr>\u003Cth class=\"border border-gray-300 px-4 py-2 text-left font-semibold\">Referentna tačka\u003C\u002Fth>\u003Cth class=\"border border-gray-300 px-4 py-2 text-left font-semibold\">Objavljena ili predviđena vrednost\u003C\u002Fth>\u003Cth class=\"border border-gray-300 px-4 py-2 text-left font-semibold\">Zašto je to ovde važno\u003C\u002Fth>\u003C\u002Ftr>\u003C\u002Fthead>\u003Ctbody>\u003Ctr>\u003Ctd class=\"border border-gray-300 px-4 py-2\">Globalno tržište igara, prognoza za 2026\u003C\u002Ftd>\u003Ctd class=\"border border-gray-300 px-4 py-2\">213,9 mlrd $, +6,1% YoY\u003C\u002Ftd>\u003Ctd class=\"border border-gray-300 px-4 py-2\">Veliko tržište zabave za potrošače, ali nizvodno od snabdevanja naprednim hardverom\u003C\u002Ftd>\u003C\u002Ftr>\u003Ctr>\u003Ctd class=\"border border-gray-300 px-4 py-2\">PC igre, prognoza za 2026\u003C\u002Ftd>\u003Ctd class=\"border border-gray-300 px-4 py-2\">45,9 mlrd $, +5,3% YoY\u003C\u002Ftd>\u003Ctd class=\"border border-gray-300 px-4 py-2\">Segment gejminga najdirektnije izložen ekonomiji PC komponenti\u003C\u002Ftd>\u003C\u002Ftr>\u003Ctr>\u003Ctd class=\"border border-gray-300 px-4 py-2\">Globalna prodaja poluprovodnika, 2025\u003C\u002Ftd>\u003Ctd class=\"border border-gray-300 px-4 py-2\">791,7 mlrd $, +25,6% YoY\u003C\u002Ftd>\u003Ctd class=\"border border-gray-300 px-4 py-2\">Pokazuje razmeru uzvodne industrije koja služi gejmingu, AI, mobilnim uređajima, automobilima i cloudu\u003C\u002Ftd>\u003C\u002Ftr>\u003Ctr>\u003Ctd class=\"border border-gray-300 px-4 py-2\">Globalna poluprovodnička industrija, projekcija za 2026\u003C\u002Ftd>\u003Ctd class=\"border border-gray-300 px-4 py-2\">~1 bilion $\u003C\u002Ftd>\u003Ctd class=\"border border-gray-300 px-4 py-2\">Povećava oportunitetni trošak posvećivanja vrhunskog silicijuma samo potrošačkom gejmingu\u003C\u002Ftd>\u003C\u002Ftr>\u003C\u002Ftbody>\u003C\u002Ftable>\u003C\u002Fdiv>\n\u003Caside class=\"editorjs-callout editorjs-callout--warning my-6 rounded-xl border p-5 border-amber-300 bg-amber-50 dark:border-amber-900 dark:bg-amber-950\u002F20\" role=\"note\">\u003Cstrong class=\"block mb-2 text-gray-900 dark:text-gray-100\">Šta ovi tržišni brojevi dokazuju — a šta ne\u003C\u002Fstrong>\u003Cdiv class=\"text-gray-700 dark:text-gray-200\">Brojke uspostavljaju \u003Cstrong>ekonomski kontekst, a ne direktnu uzročnost\u003C\u002Fstrong>. One ne dokazuju da je određeni wafer, GPU ili memorijski paket uzet gejmeru i preusmeren na AI. Takođe ne znače da su gejming i poluprovodnici dve najveće industrije na svetu. Uži, odbranjivi stav je da tržište igara vredno preko 200 milijardi dolara zavisi od uzvodne poluprovodničke ekonomije koja se približava bilionu dolara godišnje, gde se AI infrastruktura sada takmiči za naprednu računarsku snagu i memoriju pod veoma različitim budžetskim i maržnim uslovima.\u003C\u002Fdiv>\u003C\u002Faside>\n\u003Ch2 id=\"section-9\">Memorija je mesto gde pritisak postaje konkretan\u003C\u002Fh2>\n\u003Cp>TrendForce je više puta izvestio da dobavljači DRAM-a preusmeravaju kapacitete ka serverskim i HBM proizvodima kako AI potražnja raste. U martu 2026. predvideo je da će ugovorne cene konvencionalnog DRAM-a porasti 58–63% kvartalno u Q2 i naveo da ograničena GDDR alokacija ograničava snabdevanje grafičkom memorijom. Do Q3, TrendForce je saopštio da potrošački kupci dostižu granicu pristupačnosti, iako su AI i serverska potražnja ostale jake.\u003C\u002Fp>\n\u003Csection class=\"editorjs-process my-6\">\u003Ch3 class=\"mb-3 text-lg font-semibold\">Pritisak alokacije resursa\u003C\u002Fh3>\u003Cdiv class=\"grid grid-cols-1 md:grid-cols-2 xl:grid-cols-3 gap-4\">\u003Cdiv class=\"editorjs-process__step min-w-0  rounded-xl border border-gray-200 dark:border-gray-700 p-4\">\u003Cdiv class=\"text-xs font-semibold text-gray-500 dark:text-gray-400\">1\u003C\u002Fdiv>\u003Cdiv class=\"mt-1 font-semibold text-gray-900 dark:text-gray-100\">1. AI infrastruktura se širi\u003C\u002Fdiv>\u003Cdiv class=\"mt-1 text-sm text-gray-600 dark:text-gray-300\">Cloud provajderi i AI graditelji obezbeđuju serversku memoriju, HBM, enterprise SSD-ove i naprednu računarsku snagu.\u003C\u002Fdiv>\u003C\u002Fdiv>\u003Cdiv class=\"editorjs-process__step min-w-0  rounded-xl border border-gray-200 dark:border-gray-700 p-4\">\u003Cdiv class=\"text-xs font-semibold text-gray-500 dark:text-gray-400\">2\u003C\u002Fdiv>\u003Cdiv class=\"mt-1 font-semibold text-gray-900 dark:text-gray-100\">2. Dobavljači optimizuju asortiman proizvoda\u003C\u002Fdiv>\u003Cdiv class=\"mt-1 text-sm text-gray-600 dark:text-gray-300\">Kapacitet se premešta ka proizvodima sa jačom potražnjom, maržama ili dugoročnim ugovorima.\u003C\u002Fdiv>\u003C\u002Fdiv>\u003Cdiv class=\"editorjs-process__step min-w-0  rounded-xl border border-gray-200 dark:border-gray-700 p-4\">\u003Cdiv class=\"text-xs font-semibold text-gray-500 dark:text-gray-400\">3\u003C\u002Fdiv>\u003Cdiv class=\"mt-1 font-semibold text-gray-900 dark:text-gray-100\">3. Potrošačka alokacija se zateže\u003C\u002Fdiv>\u003Cdiv class=\"mt-1 text-sm text-gray-600 dark:text-gray-300\">Kupci PC, grafičke i potrošačke memorije suočavaju se sa višim cenama ili ograničenom dostupnošću.\u003C\u002Fdiv>\u003C\u002Fdiv>\u003Cdiv class=\"editorjs-process__step min-w-0  rounded-xl border border-gray-200 dark:border-gray-700 p-4\">\u003Cdiv class=\"text-xs font-semibold text-gray-500 dark:text-gray-400\">4\u003C\u002Fdiv>\u003Cdiv class=\"mt-1 font-semibold text-gray-900 dark:text-gray-100\">4. Gejming dobavljači apsorbuju ili prenose trošak\u003C\u002Fdiv>\u003Cdiv class=\"mt-1 text-sm text-gray-600 dark:text-gray-300\">Dizajn ploče, kapacitet VRAM-a i cena sistema postaju teži kompromisi.\u003C\u002Fdiv>\u003C\u002Fdiv>\u003Cdiv class=\"editorjs-process__step min-w-0  rounded-xl border border-gray-200 dark:border-gray-700 p-4\">\u003Cdiv class=\"text-xs font-semibold text-gray-500 dark:text-gray-400\">5\u003C\u002Fdiv>\u003Cdiv class=\"mt-1 font-semibold text-gray-900 dark:text-gray-100\">5. Stari ciklus nadogradnje slabi\u003C\u002Fdiv>\u003Cdiv class=\"mt-1 text-sm text-gray-600 dark:text-gray-300\">Brži GPU je i dalje koristan, ali je brute-force skaliranje ekonomski teže ponoviti na istim potrošačkim cenovnim tačkama.\u003C\u002Fdiv>\u003C\u002Fdiv>\u003C\u002Fdiv>\u003C\u002Fsection>\n\u003Caside class=\"editorjs-callout editorjs-callout--warning my-6 rounded-xl border p-5 border-amber-300 bg-amber-50 dark:border-amber-900 dark:bg-amber-950\u002F20\" role=\"note\">\u003Cstrong class=\"block mb-2 text-gray-900 dark:text-gray-100\">Signal iz septembra 2026, a ne priča o staroj nestašici\u003C\u002Fstrong>\u003Cdiv class=\"text-gray-700 dark:text-gray-200\">TrendForce je 30. septembra 2026. izvestio da dobavljači i dalje daju prioritet kapacitetima naprednog procesa za visokoperformantne serverske proizvode, sa projektovanim rastom ugovornih cena konvencionalnog DRAM-a od dodatnih 10–15% kvartalno u Q4. Njihov DRAM bilten takođe navodi da HBM nastavlja da potiskuje opšti DRAM kapacitet. Pritisak opisan ovde stoga ostaje vidljiv krajem 2026; nije zasnovan samo na privremenom skoku s početka godine.\u003C\u002Fdiv>\u003C\u002Faside>\n\u003Ch2 id=\"section-13\">U isto vreme, sam frejm se menja\u003C\u002Fh2>\n\u003Cp>Microsoft sada opisuje mašinsko učenje kao nešto što postaje temelj real-time grafike. DirectX dodaje ML putanje na nivou šejdera i modela, tako da neuronska kompresija tekstura, denoising, super rezolucija i drugi naučeni poslovi mogu stajati unutar grafičkog steka umesto da budu prikačeni kao izolovane funkcije. NVIDIA istraživanje DLSS 5 gura ideju dalje uvodeći generativnu fazu neuronskog renderovanja koja dopunjuje konvencionalno renderovanje naučenim priorima izgleda. Neuronsko renderovanje ne znači da konvencionalno renderovanje nestaje. Model koji se pojavljuje je hibridni: renderuj neke informacije direktno, inferiraj ili rekonstruiši druge kada je to jeftinije ili korisnije.\u003C\u002Fp>\n\u003Caside class=\"editorjs-referral my-6\">\u003Ca href=\"https:\u002F\u002Ffigure.rocks\u002Fsr\u002Fblog\u002Fdlss-5-is-not-just-upscaling-what-3d-guided-neural-rendering-actually-changes\" class=\"flex flex-col sm:flex-row gap-4 rounded-xl border border-gray-200 dark:border-gray-700 p-4 transition hover:border-primary-500\">\u003Cdiv class=\"min-w-0 flex-1\">\u003Cstrong class=\"block text-lg text-gray-900 dark:text-gray-100\">DLSS 5 nije samo upscaling: šta generativno neuronsko renderovanje zaista menja\u003C\u002Fstrong>\u003Cp class=\"mt-2 text-sm text-gray-600 dark:text-gray-300\">Zašto važna promena nije još jedan režim kvaliteta, već nova faza u načinu na koji se vizuelne informacije mogu proizvesti.\u003C\u002Fp>\u003Cspan class=\"mt-3 inline-flex text-sm font-medium text-primary-600 dark:text-primary-400\">Pročitajte analizu DLSS 5 →\u003C\u002Fspan>\u003C\u002Fdiv>\u003C\u002Fa>\u003C\u002Faside>\n\u003Csection class=\"editorjs-comparison my-6\">\u003Ch3 class=\"mb-3 text-lg font-semibold\">Tradicionalno skaliranje naspram skaliranja u neuronskoj eri\u003C\u002Fh3>\u003Cdiv class=\"overflow-x-auto\">\u003Ctable class=\"w-full border-collapse\">\u003Cthead>\u003Ctr>\u003Cth class=\"border border-gray-300 bg-gray-50 px-4 py-3 text-left dark:border-gray-700 dark:bg-gray-900\">\u003C\u002Fth>\u003Cth class=\"border border-gray-300 bg-gray-50 px-4 py-3 text-left font-semibold dark:border-gray-700 dark:bg-gray-900\">Tradicionalni put\u003C\u002Fth>\u003Cth class=\"border border-gray-300 bg-gray-50 px-4 py-3 text-left font-semibold dark:border-gray-700 dark:bg-gray-900\">Put neuronske ere\u003C\u002Fth>\u003C\u002Ftr>\u003C\u002Fthead>\u003Ctbody>\u003Ctr>\u003Cth class=\"border border-gray-300 px-4 py-3 text-left font-semibold dark:border-gray-700\">Više vizuelne vernosti\u003C\u002Fth>\u003Ctd class=\"border border-gray-300 px-4 py-3 dark:border-gray-700\">Čuvaj više detalja i renderuj više rada\u003C\u002Ftd>\u003Ctd class=\"border border-gray-300 px-4 py-3 dark:border-gray-700\">Čuvaj\u002Frenderuj selektivno, rekonstruiši ili inferiraj neke detalje\u003C\u002Ftd>\u003C\u002Ftr>\u003Ctr>\u003Cth class=\"border border-gray-300 px-4 py-3 text-left font-semibold dark:border-gray-700\">Odgovor memorije\u003C\u002Fth>\u003Ctd class=\"border border-gray-300 px-4 py-3 dark:border-gray-700\">Dodaj kapacitet i propusni opseg\u003C\u002Ftd>\u003Ctd class=\"border border-gray-300 px-4 py-3 dark:border-gray-700\">Dodaj kapacitet gde je potrebno, ali i kompresuj i smanji premeštanje\u003C\u002Ftd>\u003C\u002Ftr>\u003Ctr>\u003Cth class=\"border border-gray-300 px-4 py-3 text-left font-semibold dark:border-gray-700\">Odgovor performansi\u003C\u002Fth>\u003Ctd class=\"border border-gray-300 px-4 py-3 dark:border-gray-700\">Više šejderskog\u002FRT propusnog opsega\u003C\u002Ftd>\u003Ctd class=\"border border-gray-300 px-4 py-3 dark:border-gray-700\">Mešaj šejdersko, RT i matrično\u002FTensor računanje\u003C\u002Ftd>\u003C\u002Ftr>\u003Ctr>\u003Cth class=\"border border-gray-300 px-4 py-3 text-left font-semibold dark:border-gray-700\">Uloga softvera\u003C\u002Fth>\u003Ctd class=\"border border-gray-300 px-4 py-3 dark:border-gray-700\">Engine zakazuje uglavnom konvencionalni grafički rad\u003C\u002Ftd>\u003Ctd class=\"border border-gray-300 px-4 py-3 dark:border-gray-700\">Engine sve više zakazuje renderovanje, premeštanje podataka i ML poslove\u003C\u002Ftd>\u003C\u002Ftr>\u003C\u002Ftbody>\u003C\u002Ftable>\u003C\u002Fdiv>\u003C\u002Fsection>\n\u003Caside class=\"editorjs-callout editorjs-callout--success my-6 rounded-xl border p-5 border-emerald-300 bg-emerald-50 dark:border-emerald-900 dark:bg-emerald-950\u002F20\" role=\"note\">\u003Cstrong class=\"block mb-2 text-gray-900 dark:text-gray-100\">Centralna hipoteza Figure Rocks\u003C\u002Fstrong>\u003Cdiv class=\"text-gray-700 dark:text-gray-200\">\u003Cstrong>AI možda slabi ekonomiju brute-force gejming hardvera dok istovremeno pruža tehnike koje brute-force skaliranje čine manje neophodnim.\u003C\u002Fstrong> To ne znači da će buduće igre zahtevati manje hardvera. Znači da više rezultata može doći iz odlučivanja šta uskladištiti, preneti, renderovati, rekonstruisati ili inferirati. Figure Rocks skraćeno: \u003Cstrong>uskladišti manje → prenesi manje → predvidi više → renderuj manje → inferiraj više.\u003C\u002Fstrong> Ovo je hipoteza o tome odakle će performanse sve više dolaziti, a ne tvrdnja da će svaka buduća igra koristiti jedan pipeline.\u003C\u002Fdiv>\u003C\u002Faside>\n\u003Cdiv class=\"ce-delimiter cdx-block my-8\">\u003C\u002Fdiv>\n\u003Ch2 id=\"section-19\">Pitanje 5090 je korisno samo ako ga pravilno formulišemo\u003C\u002Fh2>\n\u003Cp>RTX 5090 je koristan primer upravo zato što su dokazi pomešani. Na dan 18. oktobar 2026, NVIDIA i dalje navodi GeForce RTX 5090 kao aktuelan proizvod. Istovremeno, ponašanje prodavaca i izveštaji u štampi dokumentuju neuobičajeno slabu dostupnost i nepotvrđene tvrdnje o obustavi proizvodnje. To je tržišni signal, a ne zvanična najava kraja proizvodnje. Odbranjivo pitanje stoga nije da li je NVIDIA tajno ukinula jednu karticu, već da li oportunitetni trošak dodeljivanja vrhunskog silicijuma klase Blackwell entuzijastičkom gejmingu raste kada slična arhitektura može da služi profesionalnoj vizualizaciji i AI tržištima sa veoma različitom ekonomikom.\u003C\u002Fp>\n\u003Caside class=\"editorjs-callout editorjs-callout--note my-6 rounded-xl border p-5 border-gray-300 bg-gray-50 dark:border-gray-700 dark:bg-gray-900\u002F40\" role=\"note\">\u003Cstrong class=\"block mb-2 text-gray-900 dark:text-gray-100\">ČINJENICA \u002F SIGNAL \u002F HIPOTEZA\u003C\u002Fstrong>\u003Cdiv class=\"text-gray-700 dark:text-gray-200\">\u003Cstrong>ČINJENICA:\u003C\u002Fstrong> NVIDIA i dalje javno navodi RTX 5090, a arhitektura Blackwell obuhvata gejming i profesionalne proizvode.\u003Cbr>\u003Cstrong>INDUSTRIJSKI SIGNAL:\u003C\u002Fstrong> nestanak iz prodavnica, ekstremna oskudica i izveštaji o mogućoj obustavi proizvodnje postoje, ali NVIDIA nije potvrdila EOL ili obustavu proizvodnje RTX 5090.\u003Cbr>\u003Cstrong>TEHNIČKI ZAKLJUČAK:\u003C\u002Fstrong> vrhunsko jezgro ima više ekonomski vrednih destinacija.\u003Cbr>\u003Cstrong>HIPOTEZA FIGURE ROCKS:\u003C\u002Fstrong> AI i profesionalna potražnja mogu otežati održavanje tradicionalnog entuzijastičkog očekivanja — da najveće praktično jezgro ostane široko dostupno kao gejming flagship.\u003C\u002Fdiv>\u003C\u002Faside>\n\u003Ch2 id=\"section-22\">Memorija počinje da postaje računski problem, a ne samo problem kapaciteta\u003C\u002Fh2>\n\u003Cp>Postoje najmanje četiri načina da se napadne usko grlo memorije bez prostog povećanja sirove DRAM brzine: kompresija reprezentacije, rekonstrukcija detalja računanjem, predviđanje koji će podaci biti potrebni sledeći i zadržavanje pravih podataka blizu izvršne jedinice. Nijedan od njih ne čini fizički memorijski čip magično bržim. Oni menjaju to koliko korisnog rada sistem dobija iz konačnog budžeta kapaciteta i propusnog opsega.\u003C\u002Fp>\n\u003Cdiv class=\"overflow-x-auto\">\u003Ctable class=\"w-full border-collapse\">\u003Cthead>\u003Ctr>\u003Cth class=\"border border-gray-300 px-4 py-2 text-left font-semibold\">Tehnika\u003C\u002Fth>\u003Cth class=\"border border-gray-300 px-4 py-2 text-left font-semibold\">Šta se menja\u003C\u002Fth>\u003Cth class=\"border border-gray-300 px-4 py-2 text-left font-semibold\">Šta se ne menja automatski\u003C\u002Fth>\u003C\u002Ftr>\u003C\u002Fthead>\u003Ctbody>\u003Ctr>\u003Ctd class=\"border border-gray-300 px-4 py-2\">Kompresija\u003C\u002Ftd>\u003Ctd class=\"border border-gray-300 px-4 py-2\">Manje fizičkih bajtova predstavlja iste logičke podatke\u003C\u002Ftd>\u003Ctd class=\"border border-gray-300 px-4 py-2\">DRAM takt i širina magistrale\u003C\u002Ftd>\u003C\u002Ftr>\u003Ctr>\u003Ctd class=\"border border-gray-300 px-4 py-2\">Neuronska rekonstrukcija\u003C\u002Ftd>\u003Ctd class=\"border border-gray-300 px-4 py-2\">Neki detalji se generišu iz kompaktne reprezentacije\u003C\u002Ftd>\u003Ctd class=\"border border-gray-300 px-4 py-2\">Trošak ne nestaje; inferencija ima sopstveni računski trošak i latenciju\u003C\u002Ftd>\u003C\u002Ftr>\u003Ctr>\u003Ctd class=\"border border-gray-300 px-4 py-2\">Prediktivno prefetch-ovanje\u003C\u002Ftd>\u003Ctd class=\"border border-gray-300 px-4 py-2\">Podaci stižu pre nego što se radno opterećenje koje ih zahteva zaglavi\u003C\u002Ftd>\u003Ctd class=\"border border-gray-300 px-4 py-2\">Ukupan fizički propusni opseg\u003C\u002Ftd>\u003C\u002Ftr>\u003Ctr>\u003Ctd class=\"border border-gray-300 px-4 py-2\">Rezidentnost \u002F objedinjeni pristup\u003C\u002Ftd>\u003Ctd class=\"border border-gray-300 px-4 py-2\">Manje nepotrebnog migriranja ili dupliciranja\u003C\u002Ftd>\u003Ctd class=\"border border-gray-300 px-4 py-2\">Ograničenja kontencije i latencije\u003C\u002Ftd>\u003C\u002Ftr>\u003C\u002Ftbody>\u003C\u002Ftable>\u003C\u002Fdiv>\n\u003Caside class=\"editorjs-callout editorjs-callout--info my-6 rounded-xl border p-5 border-blue-300 bg-blue-50 dark:border-blue-900 dark:bg-blue-950\u002F20\" role=\"note\">\u003Cstrong class=\"block mb-2 text-gray-900 dark:text-gray-100\">Istraživački oslonac: efektivni kapacitet nije sirova brzina\u003C\u002Fstrong>\u003Cdiv class=\"text-gray-700 dark:text-gray-200\">NVIDIA Research Buddy Compression je prijavio prosečan \u003Cstrong>2,2× odnos kompresije za HPC radna opterećenja i 1,5× za radna opterećenja dubokog učenja\u003C\u002Fstrong>, uz usporavanje od 1–2% u toj studiji. Taj rezultat nije gejming benchmark. Njegova relevantnost ovde je arhitektonska: kompresovana memorija može povećati efektivni kapacitet i smanjiti neki saobraćaj bez promene fizičkog DRAM takta.\u003C\u002Fdiv>\u003C\u002Faside>\n\u003Caside class=\"editorjs-callout editorjs-callout--success my-6 rounded-xl border p-5 border-emerald-300 bg-emerald-50 dark:border-emerald-900 dark:bg-emerald-950\u002F20\" role=\"note\">\u003Cstrong class=\"block mb-2 text-gray-900 dark:text-gray-100\">Gejming-specifični dokaz: neuronska kompresija tekstura\u003C\u002Fstrong>\u003Cdiv class=\"text-gray-700 dark:text-gray-200\">NVIDIA Research rad na materijalnim teksturama NTC demonstrirao je \u003Cstrong>4× veću linearnu rezoluciju tekstura — 16× više teksela — uz 30% manje memorije\u003C\u002Fstrong> u poređenju sa BC High. To ne znači da svaka igra dobija 16× memorijski dobitak. To demonstrira mehanizam koji je ovde važan: neki vizuelni detalji mogu se kompaktnije predstaviti i rekonstruisati računanjem umesto da se konvencionalno čuvaju pri istoj vernosti.\u003C\u002Fdiv>\u003C\u002Faside>\n\u003Caside class=\"editorjs-callout editorjs-callout--warning my-6 rounded-xl border p-5 border-amber-300 bg-amber-50 dark:border-amber-900 dark:bg-amber-950\u002F20\" role=\"note\">\u003Cstrong class=\"block mb-2 text-gray-900 dark:text-gray-100\">Šta broj „460ד ne znači\u003C\u002Fstrong>\u003Cdiv class=\"text-gray-700 dark:text-gray-200\">Brojka od 460× koja se ponekad povezuje sa NVIDIA sistemima odnosi se na poređenje kapaciteta memorije dostupne GPU-u kroz DGX generacije, \u003Cstrong>a ne na odnos kompresije memorije od 460×\u003C\u002Fstrong>. Ovaj članak ga namerno ne koristi kao dokaz za kompresiju.\u003C\u002Fdiv>\u003C\u002Faside>\n\u003Ch2 id=\"section-28\">Kako bi tehnički odgovor mogao da funkcioniše\u003C\u002Fh2>\n\u003Csection class=\"editorjs-process my-6\">\u003Ch3 class=\"mb-3 text-lg font-semibold\">Jedan mogući budući okvir za renderovanje\u003C\u002Fh3>\u003Cdiv class=\"grid grid-cols-1 md:grid-cols-2 xl:grid-cols-3 gap-4\">\u003Cdiv class=\"editorjs-process__step min-w-0  rounded-xl border border-gray-200 dark:border-gray-700 p-4\">\u003Cdiv class=\"text-xs font-semibold text-gray-500 dark:text-gray-400\">1\u003C\u002Fdiv>\u003Cdiv class=\"mt-1 font-semibold text-gray-900 dark:text-gray-100\">Kompresuj\u003C\u002Fdiv>\u003Cdiv class=\"mt-1 text-sm text-gray-600 dark:text-gray-300\">Drži teksture ili naučene reprezentacije manjim tamo gde kvalitet dozvoljava.\u003C\u002Fdiv>\u003C\u002Fdiv>\u003Cdiv class=\"editorjs-process__step min-w-0  rounded-xl border border-gray-200 dark:border-gray-700 p-4\">\u003Cdiv class=\"text-xs font-semibold text-gray-500 dark:text-gray-400\">2\u003C\u002Fdiv>\u003Cdiv class=\"mt-1 font-semibold text-gray-900 dark:text-gray-100\">Predvidi\u003C\u002Fdiv>\u003Cdiv class=\"mt-1 text-sm text-gray-600 dark:text-gray-300\">Koristi determinističko stanje igre i naučene modele da proceniš koji će podaci biti potrebni sledeći.\u003C\u002Fdiv>\u003C\u002Fdiv>\u003Cdiv class=\"editorjs-process__step min-w-0  rounded-xl border border-gray-200 dark:border-gray-700 p-4\">\u003Cdiv class=\"text-xs font-semibold text-gray-500 dark:text-gray-400\">3\u003C\u002Fdiv>\u003Cdiv class=\"mt-1 font-semibold text-gray-900 dark:text-gray-100\">Prefetch-uj\u003C\u002Fdiv>\u003Cdiv class=\"mt-1 text-sm text-gray-600 dark:text-gray-300\">Premesti korisne podatke pre nego što se GPU zaglavi čekajući ih.\u003C\u002Fdiv>\u003C\u002Fdiv>\u003Cdiv class=\"editorjs-process__step min-w-0  rounded-xl border border-gray-200 dark:border-gray-700 p-4\">\u003Cdiv class=\"text-xs font-semibold text-gray-500 dark:text-gray-400\">4\u003C\u002Fdiv>\u003Cdiv class=\"mt-1 font-semibold text-gray-900 dark:text-gray-100\">Renderuj\u003C\u002Fdiv>\u003Cdiv class=\"mt-1 text-sm text-gray-600 dark:text-gray-300\">Troši konvencionalni grafički rad tamo gde najviše menja vidljivi rezultat.\u003C\u002Fdiv>\u003C\u002Fdiv>\u003Cdiv class=\"editorjs-process__step min-w-0  rounded-xl border border-gray-200 dark:border-gray-700 p-4\">\u003Cdiv class=\"text-xs font-semibold text-gray-500 dark:text-gray-400\">5\u003C\u002Fdiv>\u003Cdiv class=\"mt-1 font-semibold text-gray-900 dark:text-gray-100\">Izvrši inferenciju\u003C\u002Fdiv>\u003Cdiv class=\"mt-1 text-sm text-gray-600 dark:text-gray-300\">Koristi neuronsku rekonstrukciju ili generisanje tamo gde je inferencija jeftinija od direktnog čuvanja ili simulacije svega.\u003C\u002Fdiv>\u003C\u002Fdiv>\u003Cdiv class=\"editorjs-process__step min-w-0  rounded-xl border border-gray-200 dark:border-gray-700 p-4\">\u003Cdiv class=\"text-xs font-semibold text-gray-500 dark:text-gray-400\">6\u003C\u002Fdiv>\u003Cdiv class=\"mt-1 font-semibold text-gray-900 dark:text-gray-100\">Prikaži\u003C\u002Fdiv>\u003Cdiv class=\"mt-1 text-sm text-gray-600 dark:text-gray-300\">Isporuči kadar uz održavanje latencije, artefakata i tempa kadrova u prihvatljivim granicama.\u003C\u002Fdiv>\u003C\u002Fdiv>\u003C\u002Fdiv>\u003C\u002Fsection>\n\u003Cp>Ovaj pipeline nije jedan najavljeni proizvod. Svaka komponenta već postoji u nekom obliku, ali integrisani lanac je hipoteza. Neuronska kompresija tekstura je pokazala da naučene reprezentacije mogu zameniti memoriju računanjem. Istraživanje GPU prefetch-ovanja je pokazalo da naučeno predviđanje može poboljšati stopu pogodaka memorijskih stranica. DirectX formalizuje ML unutar grafičkog programskog modela. DLSS 5 pokazuje da generativna faza ulazi u renderovanje u realnom vremenu.\u003C\u002Fp>\n\u003Ch2 id=\"section-31\">Igra ima jednu prednost koju opšta radna opterećenja često nemaju: već zna deo svoje budućnosti\u003C\u002Fh2>\n\u003Cp>Gejming engine zna transformaciju kamere, brzinu igrača, trenutnu ćeliju sveta, susedne streaming ćelije, stanje animacije, aktivne entitete, LOD tranzicije, zavisnosti šejdera i često verovatne putanje kretanja. Taj deterministički kontekst ne otkriva budućnost savršeno, ali daje prediktoru bogatiju početnu tačku od samog generičkog memorijskog traga.\u003C\u002Fp>\n\u003Csection class=\"editorjs-process my-6\">\u003Ch3 class=\"mb-3 text-lg font-semibold\">Primer prediktivne gejming memorije\u003C\u002Fh3>\u003Cdiv class=\"grid grid-cols-1 md:grid-cols-2 xl:grid-cols-3 gap-4\">\u003Cdiv class=\"editorjs-process__step min-w-0  rounded-xl border border-gray-200 dark:border-gray-700 p-4\">\u003Cdiv class=\"text-xs font-semibold text-gray-500 dark:text-gray-400\">1\u003C\u002Fdiv>\u003Cdiv class=\"mt-1 font-semibold text-gray-900 dark:text-gray-100\">1. Posmatraj determinističko stanje\u003C\u002Fdiv>\u003Cdiv class=\"mt-1 text-sm text-gray-600 dark:text-gray-300\">Smer kamere, vektor kretanja, trenutna soba, vidljivi portali i poznate zavisnosti asseta već postoje u stanju engine-a.\u003C\u002Fdiv>\u003C\u002Fdiv>\u003Cdiv class=\"editorjs-process__step min-w-0  rounded-xl border border-gray-200 dark:border-gray-700 p-4\">\u003Cdiv class=\"text-xs font-semibold text-gray-500 dark:text-gray-400\">2\u003C\u002Fdiv>\u003Cdiv class=\"mt-1 font-semibold text-gray-900 dark:text-gray-100\">2. Procenti radni set bliske budućnosti\u003C\u002Fdiv>\u003Cdiv class=\"mt-1 text-sm text-gray-600 dark:text-gray-300\">Naučeni prediktor može rangirati verovatne sledeće sobe, teksture, geometriju, šejdere ili neuronske reprezentacije.\u003C\u002Fdiv>\u003C\u002Fdiv>\u003Cdiv class=\"editorjs-process__step min-w-0  rounded-xl border border-gray-200 dark:border-gray-700 p-4\">\u003Cdiv class=\"text-xs font-semibold text-gray-500 dark:text-gray-400\">3\u003C\u002Fdiv>\u003Cdiv class=\"mt-1 font-semibold text-gray-900 dark:text-gray-100\">3. Prefetch-uj tokom dostupnog propusnog opsega\u003C\u002Fdiv>\u003Cdiv class=\"mt-1 text-sm text-gray-600 dark:text-gray-300\">Premesti verovatne resurse dok memorijski sistem ima rezervu umesto da čekaš na obavezni promašaj.\u003C\u002Fdiv>\u003C\u002Fdiv>\u003Cdiv class=\"editorjs-process__step min-w-0  rounded-xl border border-gray-200 dark:border-gray-700 p-4\">\u003Cdiv class=\"text-xs font-semibold text-gray-500 dark:text-gray-400\">4\u003C\u002Fdiv>\u003Cdiv class=\"mt-1 font-semibold text-gray-900 dark:text-gray-100\">4. Zadrži determinističke garancije\u003C\u002Fdiv>\u003Cdiv class=\"mt-1 text-sm text-gray-600 dark:text-gray-300\">Kritični asseti i dalje koriste autorizovana pravila; predviđanje dopunjuje, a ne zamenjuje ispravnost.\u003C\u002Fdiv>\u003C\u002Fdiv>\u003Cdiv class=\"editorjs-process__step min-w-0  rounded-xl border border-gray-200 dark:border-gray-700 p-4\">\u003Cdiv class=\"text-xs font-semibold text-gray-500 dark:text-gray-400\">5\u003C\u002Fdiv>\u003Cdiv class=\"mt-1 font-semibold text-gray-900 dark:text-gray-100\">5. Meri trošak promašaja\u003C\u002Fdiv>\u003Cdiv class=\"mt-1 text-sm text-gray-600 dark:text-gray-300\">Ako predviđanje troši propusni opseg ili izbacuje korisne podatke, pouzdanost ili dubina prefetch-a moraju da se smanje.\u003C\u002Fdiv>\u003C\u002Fdiv>\u003C\u002Fdiv>\u003C\u002Fsection>\n\u003Caside class=\"editorjs-callout editorjs-callout--note my-6 rounded-xl border p-5 border-gray-300 bg-gray-50 dark:border-gray-700 dark:bg-gray-900\u002F40\" role=\"note\">\u003Cstrong class=\"block mb-2 text-gray-900 dark:text-gray-100\">Ova ideja ima istraživački presedan, ali još ne i gejming dokaz\u003C\u002Fstrong>\u003Cdiv class=\"text-gray-700 dark:text-gray-200\">Objavljena studija o objedinjenoj virtuelnoj memoriji CPU–GPU prijavila je \u003Cstrong>10,89% poboljšanja performansi\u003C\u002Fstrong>, porast stope pogodaka stranica sa \u003Cstrong>76,10% na 89,02%\u003C\u002Fstrong> i \u003Cstrong>11,05% manje saobraćaja na CPU–GPU interkonekciji\u003C\u002Fstrong> korišćenjem naučenog prefetch-ovanja. Rad iz 2026. na AAAI o asinhronom prefetch-ovanju KV keša prijavio je \u003Cstrong>2,15× efikasnost attention kernela i do 1,97× end-to-end propusnost\u003C\u002Fstrong> na svom LLM radnom opterećenju. Nijedan rezultat ne treba kopirati na igre; oba demonstriraju da predviđanje i prefetch mogu sakriti trošak memorije u stvarnim GPU radnim opterećenjima.\u003C\u002Fdiv>\u003C\u002Faside>\n\u003Ch2 id=\"section-35\">Zašto arhitektura nalik Spark postaje zanimljiva\u003C\u002Fh2>\n\u003Cp>DGX Spark nije gejming PC, i tretirati ga kao takav sakrilo bi korisnu lekciju. Njegov GB10 dizajn kombinuje Arm CPU jezgra, Blackwell GPU, Tensor i RT hardver, i do 128 GB koherentne objedinjene sistemske memorije. Ta arhitektura postavlja drugačije pitanje od klasičnog desktopa: umesto čistog razdvajanja sistema na CPU memoriju i diskretni VRAM, šta se dešava kada više računskih engine-a deli veliki memorijski pool?\u003C\u002Fp>\n\u003Cdiv class=\"overflow-x-auto\">\u003Ctable class=\"w-full border-collapse\">\u003Cthead>\u003Ctr>\u003Cth class=\"border border-gray-300 px-4 py-2 text-left font-semibold\">Svojstvo\u003C\u002Fth>\u003Cth class=\"border border-gray-300 px-4 py-2 text-left font-semibold\">DGX Spark referenca\u003C\u002Fth>\u003Cth class=\"border border-gray-300 px-4 py-2 text-left font-semibold\">Implikacija za gejming ako bi princip bio prilagođen\u003C\u002Fth>\u003C\u002Ftr>\u003C\u002Fthead>\u003Ctbody>\u003Ctr>\u003Ctd class=\"border border-gray-300 px-4 py-2\">Memorija\u003C\u002Ftd>\u003Ctd class=\"border border-gray-300 px-4 py-2\">Do 128 GB koherentne objedinjene LPDDR5X\u003C\u002Ftd>\u003Ctd class=\"border border-gray-300 px-4 py-2\">Veliki zajednički pool mogao bi da smanji dupliranje i omogući lokalnom AI + grafici pristup zajedničkim podacima\u003C\u002Ftd>\u003C\u002Ftr>\u003Ctr>\u003Ctd class=\"border border-gray-300 px-4 py-2\">Propusni opseg memorije\u003C\u002Ftd>\u003Ctd class=\"border border-gray-300 px-4 py-2\">273 GB\u002Fs\u003C\u002Ftd>\u003Ctd class=\"border border-gray-300 px-4 py-2\">Kapacitet nije dovoljan; vrhunska grafika i dalje zahteva agresivno upravljanje propusnim opsegom i latencijom\u003C\u002Ftd>\u003C\u002Ftr>\u003Ctr>\u003Ctd class=\"border border-gray-300 px-4 py-2\">Sastav računanja\u003C\u002Ftd>\u003Ctd class=\"border border-gray-300 px-4 py-2\">CPU + Blackwell GPU + Tensor + RT\u003C\u002Ftd>\u003Ctd class=\"border border-gray-300 px-4 py-2\">Budući gejming radni zadatak mogao bi biti podeljen na više specijalizovanih engine-a\u003C\u002Ftd>\u003C\u002Ftr>\u003Ctr>\u003Ctd class=\"border border-gray-300 px-4 py-2\">Format\u002Fpotrošnja\u003C\u002Ftd>\u003Ctd class=\"border border-gray-300 px-4 py-2\">Kompaktan GB10 sistem\u003C\u002Ftd>\u003Ctd class=\"border border-gray-300 px-4 py-2\">Integracija može poboljšati efikasnost, ali može smanjiti modularnu nadogradnju\u003C\u002Ftd>\u003C\u002Ftr>\u003C\u002Ftbody>\u003C\u002Ftable>\u003C\u002Fdiv>\n\u003Caside class=\"editorjs-callout editorjs-callout--warning my-6 rounded-xl border p-5 border-amber-300 bg-amber-50 dark:border-amber-900 dark:bg-amber-950\u002F20\" role=\"note\">\u003Cstrong class=\"block mb-2 text-gray-900 dark:text-gray-100\">Kapacitet nije propusni opseg\u003C\u002Fstrong>\u003Cdiv class=\"text-gray-700 dark:text-gray-200\">Veliki objedinjeni pool \u003Cstrong>ne\u003C\u002Fstrong> zamenjuje automatski propusni opseg vrhunske diskretne GPU. DGX Spark navodi 273 GB\u002Fs propusnog opsega memorije, dok NVIDIA RTX PRO 6000 Blackwell Workstation Edition navodi 1.792 GB\u002Fs. To su veoma različiti proizvodi i poređenje nije gejming benchmark; ono samo čini arhitektonsko ograničenje vidljivim. Spark-oliki gejming dizajn morao bi da reši kontenciju, latenciju i propusni opseg — ne samo da obezbedi više zajedničkih gigabajta.\u003C\u002Fdiv>\u003C\u002Faside>\n\u003Ch2 id=\"section-39\">Šta bi moralo da bude tačno da bi ova tranzicija uspela?\u003C\u002Fh2>\n\u003Csection class=\"editorjs-process my-6\">\u003Ch3 class=\"mb-3 text-lg font-semibold\">Test održivosti neuronskog gejminga\u003C\u002Fh3>\u003Cdiv class=\"grid grid-cols-1 md:grid-cols-2 xl:grid-cols-3 gap-4\">\u003Cdiv class=\"editorjs-process__step min-w-0  rounded-xl border border-gray-200 dark:border-gray-700 p-4\">\u003Cdiv class=\"text-xs font-semibold text-gray-500 dark:text-gray-400\">1\u003C\u002Fdiv>\u003Cdiv class=\"mt-1 font-semibold text-gray-900 dark:text-gray-100\">1. Inferencija mora da uštedi više nego što košta\u003C\u002Fdiv>\u003Cdiv class=\"mt-1 text-sm text-gray-600 dark:text-gray-300\">Neuronski rad mora da ukloni dovoljno troškova skladištenja, propusnog opsega ili konvencionalnog renderovanja da opravda sopstveno računanje i latenciju.\u003C\u002Fdiv>\u003C\u002Fdiv>\u003Cdiv class=\"editorjs-process__step min-w-0  rounded-xl border border-gray-200 dark:border-gray-700 p-4\">\u003Cdiv class=\"text-xs font-semibold text-gray-500 dark:text-gray-400\">2\u003C\u002Fdiv>\u003Cdiv class=\"mt-1 font-semibold text-gray-900 dark:text-gray-100\">2. Engine mora da izloži korisno stanje\u003C\u002Fdiv>\u003Cdiv class=\"mt-1 text-sm text-gray-600 dark:text-gray-300\">Predviđanje radi bolje kada sistem zna smer kamere, zavisnosti asseta, kretanje i verovatnu vidljivost.\u003C\u002Fdiv>\u003C\u002Fdiv>\u003Cdiv class=\"editorjs-process__step min-w-0  rounded-xl border border-gray-200 dark:border-gray-700 p-4\">\u003Cdiv class=\"text-xs font-semibold text-gray-500 dark:text-gray-400\">3\u003C\u002Fdiv>\u003Cdiv class=\"mt-1 font-semibold text-gray-900 dark:text-gray-100\">3. Kretanje memorije mora da bude namerno\u003C\u002Fdiv>\u003Cdiv class=\"mt-1 text-sm text-gray-600 dark:text-gray-300\">Kompresija bez dobre politike rezidentnosti i prefetch-a i dalje može da zastane.\u003C\u002Fdiv>\u003C\u002Fdiv>\u003Cdiv class=\"editorjs-process__step min-w-0  rounded-xl border border-gray-200 dark:border-gray-700 p-4\">\u003Cdiv class=\"text-xs font-semibold text-gray-500 dark:text-gray-400\">4\u003C\u002Fdiv>\u003Cdiv class=\"mt-1 font-semibold text-gray-900 dark:text-gray-100\">4. Kvalitet mora da ostane stabilan\u003C\u002Fdiv>\u003Cdiv class=\"mt-1 text-sm text-gray-600 dark:text-gray-300\">Generisani detalji ne smeju da unesu neprihvatljive artefakte, vremensku nestabilnost ili netačne informacije o igri.\u003C\u002Fdiv>\u003C\u002Fdiv>\u003Cdiv class=\"editorjs-process__step min-w-0  rounded-xl border border-gray-200 dark:border-gray-700 p-4\">\u003Cdiv class=\"text-xs font-semibold text-gray-500 dark:text-gray-400\">5\u003C\u002Fdiv>\u003Cdiv class=\"mt-1 font-semibold text-gray-900 dark:text-gray-100\">5. Latencija mora da ostane ograničena\u003C\u002Fdiv>\u003Cdiv class=\"mt-1 text-sm text-gray-600 dark:text-gray-300\">Lepši frejm nije automatski responzivnija igra.\u003C\u002Fdiv>\u003C\u002Fdiv>\u003Cdiv class=\"editorjs-process__step min-w-0  rounded-xl border border-gray-200 dark:border-gray-700 p-4\">\u003Cdiv class=\"text-xs font-semibold text-gray-500 dark:text-gray-400\">6\u003C\u002Fdiv>\u003Cdiv class=\"mt-1 font-semibold text-gray-900 dark:text-gray-100\">6. Stack mora da preživi razlike između proizvođača\u003C\u002Fdiv>\u003Cdiv class=\"mt-1 text-sm text-gray-600 dark:text-gray-300\">Korisne tehnike zahtevaju API-je, drajvere i engine putanje koji se ne fragmentišu u jednokratne demo-e.\u003C\u002Fdiv>\u003C\u002Fdiv>\u003C\u002Fdiv>\u003C\u002Fsection>\n\u003Ch2 id=\"section-41\">Šta kupac treba da očekuje od Spark-olike tranzicije?\u003C\u002Fh2>\n\u003Csection class=\"editorjs-comparison my-6\">\u003Ch3 class=\"mb-3 text-lg font-semibold\">Potencijalna korist vs stvarni trošak\u003C\u002Fh3>\u003Cdiv class=\"overflow-x-auto\">\u003Ctable class=\"w-full border-collapse\">\u003Cthead>\u003Ctr>\u003Cth class=\"border border-gray-300 bg-gray-50 px-4 py-3 text-left dark:border-gray-700 dark:bg-gray-900\">\u003C\u002Fth>\u003Cth class=\"border border-gray-300 bg-gray-50 px-4 py-3 text-left font-semibold dark:border-gray-700 dark:bg-gray-900\">Potencijalna korist\u003C\u002Fth>\u003Cth class=\"border border-gray-300 bg-gray-50 px-4 py-3 text-left font-semibold dark:border-gray-700 dark:bg-gray-900\">Trošak \u002F rizik\u003C\u002Fth>\u003C\u002Ftr>\u003C\u002Fthead>\u003Ctbody>\u003Ctr>\u003Cth class=\"border border-gray-300 px-4 py-3 text-left font-semibold dark:border-gray-700\">Veliki koherentni pool\u003C\u002Fth>\u003Ctd class=\"border border-gray-300 px-4 py-3 dark:border-gray-700\">Fleksibilnije deljenje između CPU, grafike i lokalnog AI\u003C\u002Ftd>\u003Ctd class=\"border border-gray-300 px-4 py-3 dark:border-gray-700\">Deljeni klijenti mogu da se takmiče za isti propusni opseg\u003C\u002Ftd>\u003C\u002Ftr>\u003Ctr>\u003Cth class=\"border border-gray-300 px-4 py-3 text-left font-semibold dark:border-gray-700\">Manje dupliranja\u003C\u002Fth>\u003Ctd class=\"border border-gray-300 px-4 py-3 dark:border-gray-700\">Manje kopija između odvojenih pool-ova sistemske RAM i VRAM\u003C\u002Ftd>\u003Ctd class=\"border border-gray-300 px-4 py-3 dark:border-gray-700\">Koherencija i zakazivanje dodaju složenost\u003C\u002Ftd>\u003C\u002Ftr>\u003Ctr>\u003Cth class=\"border border-gray-300 px-4 py-3 text-left font-semibold dark:border-gray-700\">Više prostora za lokalni AI\u003C\u002Fth>\u003Ctd class=\"border border-gray-300 px-4 py-3 dark:border-gray-700\">Veći modeli, agenti ili neuronski asseti mogu koegzistirati lokalno\u003C\u002Ftd>\u003Ctd class=\"border border-gray-300 px-4 py-3 dark:border-gray-700\">AI može da se takmiči sa grafikom za računanje i memoriju\u003C\u002Ftd>\u003C\u002Ftr>\u003Ctr>\u003Cth class=\"border border-gray-300 px-4 py-3 text-left font-semibold dark:border-gray-700\">Optimizacija na nivou sistema\u003C\u002Fth>\u003Ctd class=\"border border-gray-300 px-4 py-3 dark:border-gray-700\">Platforma može zajedno da optimizuje smeštanje podataka i akceleratore\u003C\u002Ftd>\u003Ctd class=\"border border-gray-300 px-4 py-3 dark:border-gray-700\">Čvršća integracija može smanjiti mogućnost nadogradnje i povećati zaključanost\u003C\u002Ftd>\u003C\u002Ftr>\u003Ctr>\u003Cth class=\"border border-gray-300 px-4 py-3 text-left font-semibold dark:border-gray-700\">Nova fleksibilnost radnih zadataka\u003C\u002Fth>\u003Ctd class=\"border border-gray-300 px-4 py-3 dark:border-gray-700\">Mašina može bolje da stari ako se buduće igre okrenu neuronskim radnim zadacima\u003C\u002Ftd>\u003Ctd class=\"border border-gray-300 px-4 py-3 dark:border-gray-700\">Samo ako engine-i i runtime-i zaista koriste te mogućnosti\u003C\u002Ftd>\u003C\u002Ftr>\u003C\u002Ftbody>\u003C\u002Ftable>\u003C\u002Fdiv>\u003C\u002Fsection>\n\u003Ch2 id=\"section-43\">Šta bi opovrglo ovu hipotezu?\u003C\u002Fh2>\n\u003Cp>Ova teza je namerno opovrgljiva. Tabela ispod definiše signale tržišta, softvera, hardvera i usvajanja igara koji bi vremenom ojačali ili oslabili argument.\u003C\u002Fp>\n\u003Csection class=\"editorjs-comparison my-6\">\u003Ch3 class=\"mb-3 text-lg font-semibold\">Dokazi koji jačaju ili slabe tezu\u003C\u002Fh3>\u003Cdiv class=\"overflow-x-auto\">\u003Ctable class=\"w-full border-collapse\">\u003Cthead>\u003Ctr>\u003Cth class=\"border border-gray-300 bg-gray-50 px-4 py-3 text-left dark:border-gray-700 dark:bg-gray-900\">\u003C\u002Fth>\u003Cth class=\"border border-gray-300 bg-gray-50 px-4 py-3 text-left font-semibold dark:border-gray-700 dark:bg-gray-900\">Ojačalo bi je\u003C\u002Fth>\u003Cth class=\"border border-gray-300 bg-gray-50 px-4 py-3 text-left font-semibold dark:border-gray-700 dark:bg-gray-900\">Oslabilo bi je\u003C\u002Fth>\u003C\u002Ftr>\u003C\u002Fthead>\u003Ctbody>\u003Ctr>\u003Cth class=\"border border-gray-300 px-4 py-3 text-left font-semibold dark:border-gray-700\">Memorijska ekonomija\u003C\u002Fth>\u003Ctd class=\"border border-gray-300 px-4 py-3 dark:border-gray-700\">Prioritet servera\u002FHBM nastavlja da pritiska potrošačku alokaciju\u003C\u002Ftd>\u003Ctd class=\"border border-gray-300 px-4 py-3 dark:border-gray-700\">Potrošački kapacitet se širi i pritisak na cene se normalizuje\u003C\u002Ftd>\u003C\u002Ftr>\u003Ctr>\u003Cth class=\"border border-gray-300 px-4 py-3 text-left font-semibold dark:border-gray-700\">Grafički stack\u003C\u002Fth>\u003Ctd class=\"border border-gray-300 px-4 py-3 dark:border-gray-700\">ML primitivi postaju normalne engine\u002FDirectX funkcije\u003C\u002Ftd>\u003Ctd class=\"border border-gray-300 px-4 py-3 dark:border-gray-700\">Neuronska grafika ostaje opciona post-obrada specifična za proizvođača\u003C\u002Ftd>\u003C\u002Ftr>\u003Ctr>\u003Cth class=\"border border-gray-300 px-4 py-3 text-left font-semibold dark:border-gray-700\">Dizajn hardvera\u003C\u002Fth>\u003Ctd class=\"border border-gray-300 px-4 py-3 dark:border-gray-700\">Više integrisanog heterogenog računanja i veći koherentni memorijski pool-ovi\u003C\u002Ftd>\u003Ctd class=\"border border-gray-300 px-4 py-3 dark:border-gray-700\">Diskretna GPU + odvojena RAM ostaje jasno superiornija po ceni, fleksibilnosti i latenciji\u003C\u002Ftd>\u003C\u002Ftr>\u003Ctr>\u003Cth class=\"border border-gray-300 px-4 py-3 text-left font-semibold dark:border-gray-700\">Usvajanje u igrama\u003C\u002Fth>\u003Ctd class=\"border border-gray-300 px-4 py-3 dark:border-gray-700\">Studiji široko isporučuju neuronsku kompresiju, generisanje i prediktivne putanje podataka\u003C\u002Ftd>\u003Ctd class=\"border border-gray-300 px-4 py-3 dark:border-gray-700\">Tehnologija ostaje ograničena na demo-e ili nekoliko vodećih igara\u003C\u002Ftd>\u003C\u002Ftr>\u003C\u002Ftbody>\u003C\u002Ftable>\u003C\u002Fdiv>\u003C\u002Fsection>\n\u003Ch2 id=\"section-46\">Praktičan zaključak za gejmera danas\u003C\u002Fh2>\n\u003Cp>Ne kupujte hardver zato što bi spekulativna arhitektura mogla da stigne. Kupujte za igre, rezoluciju, ciljnu latenciju i radne zadatke koje zaista imate. Ali ne pretpostavljajte da se buduća otpornost i dalje može svesti na jedan broj kao što je raster performansa ili kapacitet VRAM-a. Smer softverskog stack-a sugeriše da ponašanje memorije, neuronsko računanje i podrška engine-a mogu postati sve važniji delovi jednačine vrednosti.\u003C\u002Fp>\n\u003Caside class=\"editorjs-referral my-6\">\u003Ca href=\"https:\u002F\u002Ffigure.rocks\u002Fsr\u002Fblog\u002Fvram-usage-is-not-vram-requirement-why-a-full-memory-meter-does-not-tell-the-whole-story\" class=\"flex flex-col sm:flex-row gap-4 rounded-xl border border-gray-200 dark:border-gray-700 p-4 transition hover:border-primary-500\">\u003Cdiv class=\"min-w-0 flex-1\">\u003Cstrong class=\"block text-lg text-gray-900 dark:text-gray-100\">Korišćenje VRAM-a nije zahtev za VRAM: Zašto puni memorijski metar ne govori celu priču\u003C\u002Fstrong>\u003Cp class=\"mt-2 text-sm text-gray-600 dark:text-gray-300\">Praktično objašnjenje radnih skupova, rezidentnosti i zašto alocirani broj VRAM-a nije isto što i tvrdi zahtev.\u003C\u002Fp>\u003Cspan class=\"mt-3 inline-flex text-sm font-medium text-primary-600 dark:text-primary-400\">Pročitajte VRAM vodič →\u003C\u002Fspan>\u003C\u002Fdiv>\u003C\u002Fa>\u003C\u002Faside>\n\u003Ch2 id=\"section-49\">Primarni izvori\u003C\u002Fh2>\n\u003Ca href=\"https:\u002F\u002Fnewzoo.com\u002Farticles\u002F2026-global-games-market-key-numbers\" target=\"_blank\" rel=\"noopener noreferrer\" class=\"editorjs-link-tool block border border-gray-200 dark:border-gray-700 rounded-lg p-4 transition text-gray-900 dark:text-gray-100 hover:border-primary-500 hover:bg-primary-50 dark:hover:bg-gray-900 hover:text-gray-900 dark:hover:text-gray-100\">\u003Cstrong class=\"block font-semibold\">Newzoo — Ključni brojevi globalnog tržišta igara 2026\u003C\u002Fstrong>\u003Cp class=\"text-sm text-gray-600 dark:text-gray-400\">Prognoza tržišta igara za 2026. korišćena za poređenje razmera.\u003C\u002Fp>\u003C\u002Fa>\n\u003Ca href=\"https:\u002F\u002Fwww.semiconductors.org\u002Fglobal-annual-semiconductor-sales-increase-25-6-to-791-7-billion-in-2025\u002F\" target=\"_blank\" rel=\"noopener noreferrer\" class=\"editorjs-link-tool block border border-gray-200 dark:border-gray-700 rounded-lg p-4 transition text-gray-900 dark:text-gray-100 hover:border-primary-500 hover:bg-primary-50 dark:hover:bg-gray-900 hover:text-gray-900 dark:hover:text-gray-100\">\u003Cstrong class=\"block font-semibold\">Semiconductor Industry Association — Globalna prodaja 2025\u003C\u002Fstrong>\u003Cp class=\"text-sm text-gray-600 dark:text-gray-400\">Prodaja poluprovodnika u 2025. i projekcija udruženja od oko 1 bilion dolara za 2026.\u003C\u002Fp>\u003C\u002Fa>\n\u003Ca href=\"https:\u002F\u002Fwww.trendforce.com\u002Fpresscenter\u002Fnews\u002F20260930-13258.html\" target=\"_blank\" rel=\"noopener noreferrer\" class=\"editorjs-link-tool block border border-gray-200 dark:border-gray-700 rounded-lg p-4 transition text-gray-900 dark:text-gray-100 hover:border-primary-500 hover:bg-primary-50 dark:hover:bg-gray-900 hover:text-gray-900 dark:hover:text-gray-100\">\u003Cstrong class=\"block font-semibold\">TrendForce — Potražnja za AI serverima održava rast cena memorije u 4Q26\u003C\u002Fstrong>\u003Cp class=\"text-sm text-gray-600 dark:text-gray-400\">Dokazi s kraja 2026. za tekuću alokaciju kapaciteta sa prioritetom servera i pritisak na potrošače.\u003C\u002Fp>\u003C\u002Fa>\n\u003Ca href=\"https:\u002F\u002Fresearch.nvidia.com\u002Fpublication\u002F2019-04_buddy-compression-enabling-larger-memory-deep-learning-and-hpc-workloads-gpus\" target=\"_blank\" rel=\"noopener noreferrer\" class=\"editorjs-link-tool block border border-gray-200 dark:border-gray-700 rounded-lg p-4 transition text-gray-900 dark:text-gray-100 hover:border-primary-500 hover:bg-primary-50 dark:hover:bg-gray-900 hover:text-gray-900 dark:hover:text-gray-100\">\u003Cstrong class=\"block font-semibold\">NVIDIA Research — Buddy Compression\u003C\u002Fstrong>\u003Cp class=\"text-sm text-gray-600 dark:text-gray-400\">Istraživački izvor za prosečne stope kompresije od 2,2× za HPC i 1,5× za DL.\u003C\u002Fp>\u003C\u002Fa>\n\u003Ca href=\"https:\u002F\u002Fwww.sciencedirect.com\u002Fscience\u002Farticle\u002Fabs\u002Fpii\u002FS0743731522002490\" target=\"_blank\" rel=\"noopener noreferrer\" class=\"editorjs-link-tool block border border-gray-200 dark:border-gray-700 rounded-lg p-4 transition text-gray-900 dark:text-gray-100 hover:border-primary-500 hover:bg-primary-50 dark:hover:bg-gray-900 hover:text-gray-900 dark:hover:text-gray-100\">\u003Cstrong class=\"block font-semibold\">CPU–GPU UVM prefetching zasnovan na dubokom učenju\u003C\u002Fstrong>\u003Cp class=\"text-sm text-gray-600 dark:text-gray-400\">Istraživački izvor za rezultate naučenog page-prefetching-a.\u003C\u002Fp>\u003C\u002Fa>\n\u003Ca href=\"https:\u002F\u002Fojs.aaai.org\u002Findex.php\u002FAAAI\u002Farticle\u002Fview\u002F39224\" target=\"_blank\" rel=\"noopener noreferrer\" class=\"editorjs-link-tool block border border-gray-200 dark:border-gray-700 rounded-lg p-4 transition text-gray-900 dark:text-gray-100 hover:border-primary-500 hover:bg-primary-50 dark:hover:bg-gray-900 hover:text-gray-900 dark:hover:text-gray-100\">\u003Cstrong class=\"block font-semibold\">AAAI 2026 — Asinhrono prefetch-ovanje KV keša\u003C\u002Fstrong>\u003Cp class=\"text-sm text-gray-600 dark:text-gray-400\">Istraživački izvor koji demonstrira skrivanje latencije kroz proaktivno prefetch-ovanje GPU keša.\u003C\u002Fp>\u003C\u002Fa>\n\u003Ca href=\"https:\u002F\u002Fwww.trendforce.com\u002Fpresscenter\u002Fnews\u002F20260331-12995.html\" target=\"_blank\" rel=\"noopener noreferrer\" class=\"editorjs-link-tool block border border-gray-200 dark:border-gray-700 rounded-lg p-4 transition text-gray-900 dark:text-gray-100 hover:border-primary-500 hover:bg-primary-50 dark:hover:bg-gray-900 hover:text-gray-900 dark:hover:text-gray-100\">\u003Cstrong class=\"block font-semibold\">TrendForce — Potražnja za AI serverima i cene memorije u 2Q26\u003C\u002Fstrong>\u003Cp class=\"text-sm text-gray-600 dark:text-gray-400\">Trenutni dokazi za prioritizaciju servera\u002FHBM, ograničenja GDDR-a i pritisak na potrošače.\u003C\u002Fp>\u003C\u002Fa>\n\u003Ca href=\"https:\u002F\u002Fdevblogs.microsoft.com\u002Fdirectx\u002Fevolving-directx-for-the-ml-era-on-windows\u002F\" target=\"_blank\" rel=\"noopener noreferrer\" class=\"editorjs-link-tool block border border-gray-200 dark:border-gray-700 rounded-lg p-4 transition text-gray-900 dark:text-gray-100 hover:border-primary-500 hover:bg-primary-50 dark:hover:bg-gray-900 hover:text-gray-900 dark:hover:text-gray-100\">\u003Cstrong class=\"block font-semibold\">Microsoft DirectX — Evolucija DirectX-a za ML eru\u003C\u002Fstrong>\u003Cp class=\"text-sm text-gray-600 dark:text-gray-400\">Zvanični GDC 2026 pregled ML-a na nivou šejdera i modela u DirectX-u.\u003C\u002Fp>\u003C\u002Fa>\n\u003Ca href=\"https:\u002F\u002Fresearch.nvidia.com\u002Flabs\u002Fadlr\u002FDLSS5\u002F\" target=\"_blank\" rel=\"noopener noreferrer\" class=\"editorjs-link-tool block border border-gray-200 dark:border-gray-700 rounded-lg p-4 transition text-gray-900 dark:text-gray-100 hover:border-primary-500 hover:bg-primary-50 dark:hover:bg-gray-900 hover:text-gray-900 dark:hover:text-gray-100\">\u003Cstrong class=\"block font-semibold\">NVIDIA Research — DLSS 5: Generativno neuronsko renderovanje\u003C\u002Fstrong>\u003Cp class=\"text-sm text-gray-600 dark:text-gray-400\">Primarna istraživačka stranica koja opisuje DLSS 5 kao fazu generativnog renderovanja u realnom vremenu.\u003C\u002Fp>\u003C\u002Fa>\n\u003Ca href=\"https:\u002F\u002Fwww.nvidia.com\u002Fen-us\u002Fproducts\u002Fworkstations\u002Fdgx-spark\u002F\" target=\"_blank\" rel=\"noopener noreferrer\" class=\"editorjs-link-tool block border border-gray-200 dark:border-gray-700 rounded-lg p-4 transition text-gray-900 dark:text-gray-100 hover:border-primary-500 hover:bg-primary-50 dark:hover:bg-gray-900 hover:text-gray-900 dark:hover:text-gray-100\">\u003Cstrong class=\"block font-semibold\">NVIDIA — DGX Spark specifikacije\u003C\u002Fstrong>\u003Cp class=\"text-sm text-gray-600 dark:text-gray-400\">Zvanične specifikacije arhitekture, objedinjene memorije i propusnog opsega.\u003C\u002Fp>\u003C\u002Fa>\n\u003Ca href=\"https:\u002F\u002Fwww.nvidia.com\u002Fen-us\u002Fgeforce\u002Fgraphics-cards\u002F50-series\u002Frtx-5090\u002F\" target=\"_blank\" rel=\"noopener noreferrer\" class=\"editorjs-link-tool block border border-gray-200 dark:border-gray-700 rounded-lg p-4 transition text-gray-900 dark:text-gray-100 hover:border-primary-500 hover:bg-primary-50 dark:hover:bg-gray-900 hover:text-gray-900 dark:hover:text-gray-100\">\u003Cstrong class=\"block font-semibold\">NVIDIA — GeForce RTX 5090\u003C\u002Fstrong>\u003Cp class=\"text-sm text-gray-600 dark:text-gray-400\">Zvanična aktuelna stranica proizvoda RTX 5090 koja se koristi za razlikovanje potvrđenog statusa proizvoda od glasina o obustavi proizvodnje.\u003C\u002Fp>\u003C\u002Fa>\n\u003Ca href=\"https:\u002F\u002Fwww.tomshardware.com\u002Fpc-components\u002Fgpus\u002Fnvidia-rtx-5090-disappears-from-micro-center-amid-rumors-of-a-production-halt-retailer-flags-flagship-as-not-carried-as-stock-mysteriously-vanishes\" target=\"_blank\" rel=\"noopener noreferrer\" class=\"editorjs-link-tool block border border-gray-200 dark:border-gray-700 rounded-lg p-4 transition text-gray-900 dark:text-gray-100 hover:border-primary-500 hover:bg-primary-50 dark:hover:bg-gray-900 hover:text-gray-900 dark:hover:text-gray-100\">\u003Cstrong class=\"block font-semibold\">Tom’s Hardware — Nestanak RTX 5090 iz maloprodaje i glasine o obustavi proizvodnje\u003C\u002Fstrong>\u003Cp class=\"text-sm text-gray-600 dark:text-gray-400\">Trenutni tržišni izveštaj koji se koristi samo kao signal iz industrije; eksplicitno napominje da NVIDIA nije potvrdila obustavu proizvodnje.\u003C\u002Fp>\u003C\u002Fa>\n\u003Ca href=\"https:\u002F\u002Fresearch.nvidia.com\u002Flabs\u002Frtr\u002Fneural_texture_compression\u002F\" target=\"_blank\" rel=\"noopener noreferrer\" class=\"editorjs-link-tool block border border-gray-200 dark:border-gray-700 rounded-lg p-4 transition text-gray-900 dark:text-gray-100 hover:border-primary-500 hover:bg-primary-50 dark:hover:bg-gray-900 hover:text-gray-900 dark:hover:text-gray-100\">\u003Cstrong class=\"block font-semibold\">NVIDIA Research — Neuronska kompresija tekstura materijala sa slučajnim pristupom\u003C\u002Fstrong>\u003Cp class=\"text-sm text-gray-600 dark:text-gray-400\">Primarni izvor za poređenje 4× linearne rezolucije \u002F 16× teksela uz 30% manje memorije u odnosu na BC High.\u003C\u002Fp>\u003C\u002Fa>\n\u003Ca href=\"https:\u002F\u002Fwww.nvidia.com\u002Fde-de\u002Fproducts\u002Fworkstations\u002Fprofessional-desktop-gpus\u002Frtx-pro-6000\u002F\" target=\"_blank\" rel=\"noopener noreferrer\" class=\"editorjs-link-tool block border border-gray-200 dark:border-gray-700 rounded-lg p-4 transition text-gray-900 dark:text-gray-100 hover:border-primary-500 hover:bg-primary-50 dark:hover:bg-gray-900 hover:text-gray-900 dark:hover:text-gray-100\">\u003Cstrong class=\"block font-semibold\">NVIDIA — RTX PRO 6000 Blackwell Workstation Edition\u003C\u002Fstrong>\u003Cp class=\"text-sm text-gray-600 dark:text-gray-400\">Zvanični izvor za 96 GB GDDR7 i 1.792 GB\u002Fs memorijskog propusnog opsega koji se koristi kao referentna tačka za propusni opseg.\u003C\u002Fp>\u003C\u002Fa>\n\u003Csection class=\"editorjs-glossary my-6 rounded-xl border border-gray-200 dark:border-gray-700 p-5\">\u003Ch3 class=\"mb-3 text-lg font-semibold\">Ključni koncepti\u003C\u002Fh3>\u003Cdl>\u003Cdiv id=\"neural-rendering\" class=\"border-t border-gray-200 dark:border-gray-700 py-3 first:border-t-0\">\u003Cdt class=\"font-semibold text-gray-900 dark:text-gray-100\">Neuronsko renderovanje\u003C\u002Fdt>\u003Cdd class=\"mt-1 text-gray-600 dark:text-gray-300\">Metode renderovanja koje koriste obučene modele kao deo procesa stvaranja, rekonstrukcije ili unapređenja vizuelnog izlaza.\u003C\u002Fdd>\u003C\u002Fdiv>\u003Cdiv id=\"heterogeneous-compute\" class=\"border-t border-gray-200 dark:border-gray-700 py-3 first:border-t-0\">\u003Cdt class=\"font-semibold text-gray-900 dark:text-gray-100\">Heterogeno računanje\u003C\u002Fdt>\u003Cdd class=\"mt-1 text-gray-600 dark:text-gray-300\">Sistem koji dodeljuje različite radne zadatke različitim računarskim jedinicama kao što su CPU, GPU, RT i matrični\u002FTensor hardver.\u003C\u002Fdd>\u003C\u002Fdiv>\u003Cdiv id=\"effective-memory\" class=\"border-t border-gray-200 dark:border-gray-700 py-3 first:border-t-0\">\u003Cdt class=\"font-semibold text-gray-900 dark:text-gray-100\">Efektivna memorija\u003C\u002Fdt>\u003Cdd class=\"mt-1 text-gray-600 dark:text-gray-300\">Praktičan opis količine korisnih podataka koje memorijski sistem može da zadrži i dostavi računarskim jedinicama nakon uzimanja u obzir kompresije, rezidentnosti i efikasnosti premeštanja. Nije formalna zamena za fizički kapacitet.\u003C\u002Fdd>\u003C\u002Fdiv>\u003Cdiv id=\"predictive-memory\" class=\"border-t border-gray-200 dark:border-gray-700 py-3 first:border-t-0\">\u003Cdt class=\"font-semibold text-gray-900 dark:text-gray-100\">Prediktivna memorija\u003C\u002Fdt>\u003Cdd class=\"mt-1 text-gray-600 dark:text-gray-300\">Figure Rocks skraćenica za kombinovanje poznatog stanja igre i naučene predikcije radi pripreme verovatno potrebnih podataka pre zastoja. To je hipoteza, a ne industrijski standard.\u003C\u002Fdd>\u003C\u002Fdiv>\u003C\u002Fdl>\u003C\u002Fsection>\n\u003Cp>Decenijama je entuzijastičko PC gejmingovanje sledilo poznatu pogodbu: igre su tražile više računarske snage, generacije GPU-a su je pružale, a igrači su nadograđivali kada jaz postane dovoljno velik. Ta petlja nikada nije savršeno funkcionisala, ali je smer bio razumljiv. U 2026. godini tu petlju je sve teže pročitati jer se dve promene dešavaju istovremeno: AI infrastruktura se agresivno takmiči za silicijum i memoriju, dok AI tehnike direktno ulaze u način na koji igre stvaraju kadrove.\u003C\u002Fp>\n\u003Caside class=\"editorjs-callout editorjs-callout--info my-6 rounded-xl border p-5 border-blue-300 bg-blue-50 dark:border-blue-900 dark:bg-blue-950\u002F20\" role=\"note\">\u003Cstrong class=\"block mb-2 text-gray-900 dark:text-gray-100\">Direktan odgovor\u003C\u002Fstrong>\u003Cdiv class=\"text-gray-700 dark:text-gray-200\">\u003Cstrong>Stari ciklus nadogradnje za gejming je pod pritiskom, ali ga ne zamenjuju jednostavno skuplji GPU-ovi.\u003C\u002Fstrong> AI menja obe strane jednačine: povećava konkurenciju za poluprovodničke i memorijske resurse, a takođe omogućava tehnike neuronskog renderovanja koje mogu da zamene uskladištene podatke ili konvencionalni rad renderovanja inferencijom. Važna mogućnost je da gejming može odgovoriti na strožu hardversku ekonomiju time što će postati računarski selektivniji, a ne samo zahtevanjem većih GPU-ova.\u003C\u002Fdiv>\u003C\u002Faside>\n\u003Caside class=\"editorjs-callout editorjs-callout--note my-6 rounded-xl border p-5 border-gray-300 bg-gray-50 dark:border-gray-700 dark:bg-gray-900\u002F40\" role=\"note\">\u003Cstrong class=\"block mb-2 text-gray-900 dark:text-gray-100\">Kako čitati ovaj članak\u003C\u002Fstrong>\u003Cdiv class=\"text-gray-700 dark:text-gray-200\">Ovo je \u003Cstrong>hipoteza potkrepljena dokazima\u003C\u002Fstrong>. Dokumentovani tržišni podaci i tehnologije koje su u prodaji ili najavljene označeni su kao činjenice ili signali iz industrije. Veze između njih su tehničko zaključivanje. Arhitektura budućnosti opisana kasnije je hipoteza Figure Rocks, a ne tvrdnja o nenajavljenom planu razvoja NVIDIA, AMD, Intel, Microsoft ili konzola.\u003C\u002Fdiv>\u003C\u002Faside>\n\u003Cnav class=\"editorjs-toc\" data-editorjs-toc=\"true\" aria-label=\"Sadržaj\">\u003Cstrong class=\"editorjs-toc__title\">Sadržaj\u003C\u002Fstrong>\u003Col class=\"editorjs-toc__list editorjs-toc__list--depth-0\">\u003Cli class=\"editorjs-toc__item\">\u003Ca href=\"#section-5\" class=\"editorjs-toc__link\">Nesrazmera u obimu je već vidljiva\u003C\u002Fa>\u003C\u002Fli>\u003Cli class=\"editorjs-toc__item\">\u003Ca href=\"#section-9\" class=\"editorjs-toc__link\">Memorija je mesto gde pritisak postaje konkretan\u003C\u002Fa>\u003C\u002Fli>\u003Cli class=\"editorjs-toc__item\">\u003Ca href=\"#section-13\" class=\"editorjs-toc__link\">U isto vreme, sam frejm se menja\u003C\u002Fa>\u003C\u002Fli>\u003Cli class=\"editorjs-toc__item\">\u003Ca href=\"#section-19\" class=\"editorjs-toc__link\">Pitanje 5090 je korisno samo ako ga pravilno formulišemo\u003C\u002Fa>\u003C\u002Fli>\u003Cli class=\"editorjs-toc__item\">\u003Ca href=\"#section-22\" class=\"editorjs-toc__link\">Memorija počinje da postaje računski problem, a ne samo problem kapaciteta\u003C\u002Fa>\u003C\u002Fli>\u003Cli class=\"editorjs-toc__item\">\u003Ca href=\"#section-28\" class=\"editorjs-toc__link\">Kako bi tehnički odgovor mogao da funkcioniše\u003C\u002Fa>\u003C\u002Fli>\u003Cli class=\"editorjs-toc__item\">\u003Ca href=\"#section-31\" class=\"editorjs-toc__link\">Igra ima jednu prednost koju opšta radna opterećenja često nemaju: već zna deo svoje budućnosti\u003C\u002Fa>\u003C\u002Fli>\u003Cli class=\"editorjs-toc__item\">\u003Ca href=\"#section-35\" class=\"editorjs-toc__link\">Zašto arhitektura nalik Spark postaje zanimljiva\u003C\u002Fa>\u003C\u002Fli>\u003Cli class=\"editorjs-toc__item\">\u003Ca href=\"#section-39\" class=\"editorjs-toc__link\">Šta bi moralo da bude tačno da bi ova tranzicija uspela?\u003C\u002Fa>\u003C\u002Fli>\u003Cli class=\"editorjs-toc__item\">\u003Ca href=\"#section-41\" class=\"editorjs-toc__link\">Šta kupac treba da očekuje od Spark-olike tranzicije?\u003C\u002Fa>\u003C\u002Fli>\u003Cli class=\"editorjs-toc__item\">\u003Ca href=\"#section-43\" class=\"editorjs-toc__link\">Šta bi opovrglo ovu hipotezu?\u003C\u002Fa>\u003C\u002Fli>\u003Cli class=\"editorjs-toc__item\">\u003Ca href=\"#section-46\" class=\"editorjs-toc__link\">Praktičan zaključak za gejmera danas\u003C\u002Fa>\u003C\u002Fli>\u003Cli class=\"editorjs-toc__item\">\u003Ca href=\"#section-49\" class=\"editorjs-toc__link\">Primarni izvori\u003C\u002Fa>\u003C\u002Fli>\u003Cli class=\"editorjs-toc__item\">\u003Ca href=\"#section-69\" class=\"editorjs-toc__link\">Nesrazmera u obimu je već vidljiva\u003C\u002Fa>\u003C\u002Fli>\u003Cli class=\"editorjs-toc__item\">\u003Ca href=\"#section-73\" class=\"editorjs-toc__link\">Memorija je mesto gde pritisak postaje konkretan\u003C\u002Fa>\u003C\u002Fli>\u003Cli class=\"editorjs-toc__item\">\u003Ca href=\"#section-77\" class=\"editorjs-toc__link\">U isto vreme, sam okvir se menja\u003C\u002Fa>\u003C\u002Fli>\u003Cli class=\"editorjs-toc__item\">\u003Ca href=\"#section-83\" class=\"editorjs-toc__link\">Pitanje 5090 je korisno samo ako ga pravilno formulišemo\u003C\u002Fa>\u003C\u002Fli>\u003Cli class=\"editorjs-toc__item\">\u003Ca href=\"#section-86\" class=\"editorjs-toc__link\">Memorija počinje da postaje računarski problem, a ne samo problem kapaciteta\u003C\u002Fa>\u003C\u002Fli>\u003Cli class=\"editorjs-toc__item\">\u003Ca href=\"#section-92\" class=\"editorjs-toc__link\">Kako bi tehnički odgovor mogao da funkcioniše\u003C\u002Fa>\u003C\u002Fli>\u003Cli class=\"editorjs-toc__item\">\u003Ca href=\"#section-95\" class=\"editorjs-toc__link\">Igra ima jednu prednost koju opšti radni zadaci često nemaju: već zna deo svoje budućnosti\u003C\u002Fa>\u003C\u002Fli>\u003Cli class=\"editorjs-toc__item\">\u003Ca href=\"#section-99\" class=\"editorjs-toc__link\">Zašto arhitektura nalik Spark-u postaje zanimljiva\u003C\u002Fa>\u003C\u002Fli>\u003Cli class=\"editorjs-toc__item\">\u003Ca href=\"#section-103\" class=\"editorjs-toc__link\">Šta bi moralo da bude tačno da bi ova tranzicija uspela?\u003C\u002Fa>\u003C\u002Fli>\u003Cli class=\"editorjs-toc__item\">\u003Ca href=\"#section-105\" class=\"editorjs-toc__link\">Šta kupac treba da očekuje od tranzicije nalik Spark-u?\u003C\u002Fa>\u003C\u002Fli>\u003Cli class=\"editorjs-toc__item\">\u003Ca href=\"#section-107\" class=\"editorjs-toc__link\">Šta bi opovrglo ovu hipotezu?\u003C\u002Fa>\u003C\u002Fli>\u003Cli class=\"editorjs-toc__item\">\u003Ca href=\"#section-110\" class=\"editorjs-toc__link\">Praktičan zaključak za gejmera danas\u003C\u002Fa>\u003C\u002Fli>\u003Cli class=\"editorjs-toc__item\">\u003Ca href=\"#section-113\" class=\"editorjs-toc__link\">Primarni izvori\u003C\u002Fa>\u003C\u002Fli>\u003C\u002Fol>\u003C\u002Fnav>\n\u003Ch2 id=\"section-69\">Nesrazmera u obimu je već vidljiva\u003C\u002Fh2>\n\u003Cp>Newzoo predviđa globalno tržište igara na 213,9 milijardi dolara u 2026. godini, što je rast od 6,1% u odnosu na prethodnu godinu. Semiconductor Industry Association je izvestila o 791,7 milijardi dolara globalne prodaje poluprovodnika u 2025. godini, što je rast od 25,6%, i projektovala oko 1 bilion dolara za 2026. Posao igara i posao poluprovodnika i dalje zavise jedan od drugog, ali njihovi podsticaji postaju sve manje simetrični: isti napredni proizvodni ekosistem koji omogućava premium gejming sada služi AI i data-center kupcima sa mnogo većim budžetima za infrastrukturu. To nisu direktno uporedive kategorije proizvoda; oni uspostavljaju razmeru uzvodne računarske ekonomije oko gejminga.\u003C\u002Fp>\n\u003Cdiv class=\"overflow-x-auto\">\u003Ctable class=\"w-full border-collapse\">\u003Cthead>\u003Ctr>\u003Cth class=\"border border-gray-300 px-4 py-2 text-left font-semibold\">Referentna tačka\u003C\u002Fth>\u003Cth class=\"border border-gray-300 px-4 py-2 text-left font-semibold\">Prijavljena ili predviđena vrednost\u003C\u002Fth>\u003Cth class=\"border border-gray-300 px-4 py-2 text-left font-semibold\">Zašto je to ovde važno\u003C\u002Fth>\u003C\u002Ftr>\u003C\u002Fthead>\u003Ctbody>\u003Ctr>\u003Ctd class=\"border border-gray-300 px-4 py-2\">Globalno tržište igara, prognoza za 2026.\u003C\u002Ftd>\u003Ctd class=\"border border-gray-300 px-4 py-2\">213,9 mlrd $, +6,1% YoY\u003C\u002Ftd>\u003Ctd class=\"border border-gray-300 px-4 py-2\">Veliko tržište potrošačke zabave, ali nizvodno od snabdevanja naprednim hardverom\u003C\u002Ftd>\u003C\u002Ftr>\u003Ctr>\u003Ctd class=\"border border-gray-300 px-4 py-2\">PC igre, prognoza za 2026.\u003C\u002Ftd>\u003Ctd class=\"border border-gray-300 px-4 py-2\">45,9 mlrd $, +5,3% YoY\u003C\u002Ftd>\u003Ctd class=\"border border-gray-300 px-4 py-2\">Segment gejminga najdirektnije izložen ekonomiji PC komponenti\u003C\u002Ftd>\u003C\u002Ftr>\u003Ctr>\u003Ctd class=\"border border-gray-300 px-4 py-2\">Globalna prodaja poluprovodnika, 2025.\u003C\u002Ftd>\u003Ctd class=\"border border-gray-300 px-4 py-2\">791,7 mlrd $, +25,6% YoY\u003C\u002Ftd>\u003Ctd class=\"border border-gray-300 px-4 py-2\">Pokazuje razmeru uzvodne industrije koja služi gejmingu, AI-ju, mobilnim uređajima, automobilima i oblaku\u003C\u002Ftd>\u003C\u002Ftr>\u003Ctr>\u003Ctd class=\"border border-gray-300 px-4 py-2\">Globalna poluprovodnička industrija, projekcija za 2026.\u003C\u002Ftd>\u003Ctd class=\"border border-gray-300 px-4 py-2\">~1 bilion $\u003C\u002Ftd>\u003Ctd class=\"border border-gray-300 px-4 py-2\">Povećava oportunitetni trošak dodeljivanja premium silicijuma samo potrošačkom gejmingu\u003C\u002Ftd>\u003C\u002Ftr>\u003C\u002Ftbody>\u003C\u002Ftable>\u003C\u002Fdiv>\n\u003Caside class=\"editorjs-callout editorjs-callout--warning my-6 rounded-xl border p-5 border-amber-300 bg-amber-50 dark:border-amber-900 dark:bg-amber-950\u002F20\" role=\"note\">\u003Cstrong class=\"block mb-2 text-gray-900 dark:text-gray-100\">Šta ovi tržišni brojevi dokazuju — a šta ne\u003C\u002Fstrong>\u003Cdiv class=\"text-gray-700 dark:text-gray-200\">Brojke uspostavljaju \u003Cstrong>ekonomski kontekst, a ne direktnu uzročnost\u003C\u002Fstrong>. One ne dokazuju da je određeni wafer, GPU ili memorijski paket uzet igraču i preusmeren na AI. Takođe ne znače da su gejming i poluprovodnici dve najveće industrije na svetu. Uži, odbranjiv argument je da tržište igara vredno preko 200 milijardi dolara zavisi od uzvodne poluprovodničke ekonomije koja se približava bilionu dolara godišnje, gde se AI infrastruktura sada takmiči za naprednu računarsku snagu i memoriju pod veoma različitim budžetskim uslovima i maržama.\u003C\u002Fdiv>\u003C\u002Faside>\n\u003Ch2 id=\"section-73\">Memorija je mesto gde pritisak postaje konkretan\u003C\u002Fh2>\n\u003Cp>TrendForce je više puta izvestio da dobavljači DRAM-a preusmeravaju kapacitete ka serverskim i HBM proizvodima kako AI potražnja raste. U martu 2026. predvideo je da će ugovorne cene konvencionalnog DRAM-a porasti 58–63% kvartalno u Q2 i naveo da ograničena GDDR alokacija ograničava snabdevanje grafičkom memorijom. Do Q3, TrendForce je saopštio da potrošački kupci dostižu granicu priuštivosti iako su AI i serverska potražnja ostale jake.\u003C\u002Fp>\n\u003Csection class=\"editorjs-process my-6\">\u003Ch3 class=\"mb-3 text-lg font-semibold\">Pritisak alokacije resursa\u003C\u002Fh3>\u003Cdiv class=\"grid grid-cols-1 md:grid-cols-2 xl:grid-cols-3 gap-4\">\u003Cdiv class=\"editorjs-process__step min-w-0  rounded-xl border border-gray-200 dark:border-gray-700 p-4\">\u003Cdiv class=\"text-xs font-semibold text-gray-500 dark:text-gray-400\">1\u003C\u002Fdiv>\u003Cdiv class=\"mt-1 font-semibold text-gray-900 dark:text-gray-100\">1. AI infrastruktura se širi\u003C\u002Fdiv>\u003Cdiv class=\"mt-1 text-sm text-gray-600 dark:text-gray-300\">Cloud provajderi i AI graditelji obezbeđuju serversku memoriju, HBM, enterprise SSD-ove i napredno računarstvo.\u003C\u002Fdiv>\u003C\u002Fdiv>\u003Cdiv class=\"editorjs-process__step min-w-0  rounded-xl border border-gray-200 dark:border-gray-700 p-4\">\u003Cdiv class=\"text-xs font-semibold text-gray-500 dark:text-gray-400\">2\u003C\u002Fdiv>\u003Cdiv class=\"mt-1 font-semibold text-gray-900 dark:text-gray-100\">2. Dobavljači optimizuju asortiman proizvoda\u003C\u002Fdiv>\u003Cdiv class=\"mt-1 text-sm text-gray-600 dark:text-gray-300\">Kapacitet se premešta ka proizvodima sa jačom potražnjom, maržama ili dugoročnim ugovorima.\u003C\u002Fdiv>\u003C\u002Fdiv>\u003Cdiv class=\"editorjs-process__step min-w-0  rounded-xl border border-gray-200 dark:border-gray-700 p-4\">\u003Cdiv class=\"text-xs font-semibold text-gray-500 dark:text-gray-400\">3\u003C\u002Fdiv>\u003Cdiv class=\"mt-1 font-semibold text-gray-900 dark:text-gray-100\">3. Potrošačka alokacija se zateže\u003C\u002Fdiv>\u003Cdiv class=\"mt-1 text-sm text-gray-600 dark:text-gray-300\">Kupci PC, grafičke i potrošačke memorije suočavaju se sa višim cenama ili ograničenom dostupnošću.\u003C\u002Fdiv>\u003C\u002Fdiv>\u003Cdiv class=\"editorjs-process__step min-w-0  rounded-xl border border-gray-200 dark:border-gray-700 p-4\">\u003Cdiv class=\"text-xs font-semibold text-gray-500 dark:text-gray-400\">4\u003C\u002Fdiv>\u003Cdiv class=\"mt-1 font-semibold text-gray-900 dark:text-gray-100\">4. Proizvođači gejming opreme apsorbuju ili prenose troškove\u003C\u002Fdiv>\u003Cdiv class=\"mt-1 text-sm text-gray-600 dark:text-gray-300\">Dizajn ploče, VRAM kapacitet i cena sistema postaju teži kompromisi.\u003C\u002Fdiv>\u003C\u002Fdiv>\u003Cdiv class=\"editorjs-process__step min-w-0  rounded-xl border border-gray-200 dark:border-gray-700 p-4\">\u003Cdiv class=\"text-xs font-semibold text-gray-500 dark:text-gray-400\">5\u003C\u002Fdiv>\u003Cdiv class=\"mt-1 font-semibold text-gray-900 dark:text-gray-100\">5. Stari ciklus nadogradnje slabi\u003C\u002Fdiv>\u003Cdiv class=\"mt-1 text-sm text-gray-600 dark:text-gray-300\">Brži GPU je i dalje koristan, ali brute-force skaliranje postaje ekonomski teže ponoviti na istim potrošačkim cenovnim tačkama.\u003C\u002Fdiv>\u003C\u002Fdiv>\u003C\u002Fdiv>\u003C\u002Fsection>\n\u003Caside class=\"editorjs-callout editorjs-callout--warning my-6 rounded-xl border p-5 border-amber-300 bg-amber-50 dark:border-amber-900 dark:bg-amber-950\u002F20\" role=\"note\">\u003Cstrong class=\"block mb-2 text-gray-900 dark:text-gray-100\">Signal iz septembra 2026, a ne stara priča o nestašici\u003C\u002Fstrong>\u003Cdiv class=\"text-gray-700 dark:text-gray-200\">TrendForce je 30. septembra 2026. izvestio da dobavljači i dalje daju prioritet kapacitetima naprednih procesa za visokoperformantne serverske proizvode, sa projektovanim rastom ugovornih cena konvencionalnog DRAM-a od dodatnih 10–15% kvartalno u Q4. Njihov DRAM bilten takođe navodi da HBM nastavlja da potiskuje opšti DRAM kapacitet. Pritisak opisan ovde stoga ostaje vidljiv krajem 2026; nije zasnovan samo na privremenom skoku početkom godine.\u003C\u002Fdiv>\u003C\u002Faside>\n\u003Ch2 id=\"section-77\">U isto vreme, sam okvir se menja\u003C\u002Fh2>\n\u003Cp>Microsoft sada opisuje mašinsko učenje kao nešto što postaje temelj real-time grafike. DirectX dodaje ML putanje na nivou šejdera i modela, tako da neuronska kompresija tekstura, denoising, super rezolucija i drugi naučeni zadaci mogu da budu unutar grafičkog steka umesto da budu prikačeni kao izolovane funkcije. NVIDIA-ino istraživanje DLSS 5 gura ideju dalje uvodeći generativnu neuronsku fazu renderovanja koja dopunjuje konvencionalno renderovanje naučenim priorima izgleda. Neuronsko renderovanje ne znači da konvencionalno renderovanje nestaje. Model koji se pojavljuje je hibridni: renderuj neke informacije direktno, izvedi ili rekonstruiši druge informacije kada je to jeftinije ili korisnije.\u003C\u002Fp>\n\u003Caside class=\"editorjs-referral my-6\">\u003Ca href=\"https:\u002F\u002Ffigure.rocks\u002Fsr\u002Fblog\u002Fdlss-5-is-not-just-upscaling-what-3d-guided-neural-rendering-actually-changes\" class=\"flex flex-col sm:flex-row gap-4 rounded-xl border border-gray-200 dark:border-gray-700 p-4 transition hover:border-primary-500\">\u003Cdiv class=\"min-w-0 flex-1\">\u003Cstrong class=\"block text-lg text-gray-900 dark:text-gray-100\">DLSS 5 nije samo upscaling: šta generativno neuronsko renderovanje zaista menja\u003C\u002Fstrong>\u003Cp class=\"mt-2 text-sm text-gray-600 dark:text-gray-300\">Zašto važna promena nije još jedan režim kvaliteta, već nova faza u načinu na koji se vizuelne informacije mogu proizvesti.\u003C\u002Fp>\u003Cspan class=\"mt-3 inline-flex text-sm font-medium text-primary-600 dark:text-primary-400\">Pročitajte analizu DLSS 5 →\u003C\u002Fspan>\u003C\u002Fdiv>\u003C\u002Fa>\u003C\u002Faside>\n\u003Csection class=\"editorjs-comparison my-6\">\u003Ch3 class=\"mb-3 text-lg font-semibold\">Tradicionalno skaliranje vs skaliranje u neuronskoj eri\u003C\u002Fh3>\u003Cdiv class=\"overflow-x-auto\">\u003Ctable class=\"w-full border-collapse\">\u003Cthead>\u003Ctr>\u003Cth class=\"border border-gray-300 bg-gray-50 px-4 py-3 text-left dark:border-gray-700 dark:bg-gray-900\">\u003C\u002Fth>\u003Cth class=\"border border-gray-300 bg-gray-50 px-4 py-3 text-left font-semibold dark:border-gray-700 dark:bg-gray-900\">Tradicionalni put\u003C\u002Fth>\u003Cth class=\"border border-gray-300 bg-gray-50 px-4 py-3 text-left font-semibold dark:border-gray-700 dark:bg-gray-900\">Put neuronske ere\u003C\u002Fth>\u003C\u002Ftr>\u003C\u002Fthead>\u003Ctbody>\u003Ctr>\u003Cth class=\"border border-gray-300 px-4 py-3 text-left font-semibold dark:border-gray-700\">Više vizuelne vernosti\u003C\u002Fth>\u003Ctd class=\"border border-gray-300 px-4 py-3 dark:border-gray-700\">Čuvaj više detalja i renderuj više posla\u003C\u002Ftd>\u003Ctd class=\"border border-gray-300 px-4 py-3 dark:border-gray-700\">Čuvaj\u002Frenderuj selektivno, rekonstruiši ili izvedi neke detalje\u003C\u002Ftd>\u003C\u002Ftr>\u003Ctr>\u003Cth class=\"border border-gray-300 px-4 py-3 text-left font-semibold dark:border-gray-700\">Odgovor memorije\u003C\u002Fth>\u003Ctd class=\"border border-gray-300 px-4 py-3 dark:border-gray-700\">Dodaj kapacitet i propusni opseg\u003C\u002Ftd>\u003Ctd class=\"border border-gray-300 px-4 py-3 dark:border-gray-700\">Dodaj kapacitet gde je potrebno, ali i kompresuj i smanji premeštanje\u003C\u002Ftd>\u003C\u002Ftr>\u003Ctr>\u003Cth class=\"border border-gray-300 px-4 py-3 text-left font-semibold dark:border-gray-700\">Odgovor performansi\u003C\u002Fth>\u003Ctd class=\"border border-gray-300 px-4 py-3 dark:border-gray-700\">Više šejder\u002FRT propusnosti\u003C\u002Ftd>\u003Ctd class=\"border border-gray-300 px-4 py-3 dark:border-gray-700\">Mešaj šejdere, RT i matrično\u002FTensor računarstvo\u003C\u002Ftd>\u003C\u002Ftr>\u003Ctr>\u003Cth class=\"border border-gray-300 px-4 py-3 text-left font-semibold dark:border-gray-700\">Uloga softvera\u003C\u002Fth>\u003Ctd class=\"border border-gray-300 px-4 py-3 dark:border-gray-700\">Engine zakazuje uglavnom konvencionalni grafički rad\u003C\u002Ftd>\u003Ctd class=\"border border-gray-300 px-4 py-3 dark:border-gray-700\">Engine sve više zakazuje renderovanje, premeštanje podataka i ML zadatke\u003C\u002Ftd>\u003C\u002Ftr>\u003C\u002Ftbody>\u003C\u002Ftable>\u003C\u002Fdiv>\u003C\u002Fsection>\n\u003Caside class=\"editorjs-callout editorjs-callout--success my-6 rounded-xl border p-5 border-emerald-300 bg-emerald-50 dark:border-emerald-900 dark:bg-emerald-950\u002F20\" role=\"note\">\u003Cstrong class=\"block mb-2 text-gray-900 dark:text-gray-100\">Centralna hipoteza Figure Rocks\u003C\u002Fstrong>\u003Cdiv class=\"text-gray-700 dark:text-gray-200\">\u003Cstrong>AI možda slabi ekonomiku brute-force gejming hardvera dok istovremeno pruža tehnike koje čine brute-force skaliranje manje neophodnim.\u003C\u002Fstrong> To ne znači da će buduće igre zahtevati manje hardvera. Znači da više rezultata može doći iz odlučivanja šta sačuvati, preneti, renderovati, rekonstruisati ili izvesti. Figure Rocks skraćeno: \u003Cstrong>čuvaj manje → premeštaj manje → predviđaj više → renderuj manje → izvodi više.\u003C\u002Fstrong> Ovo je hipoteza o tome odakle će performanse sve više dolaziti, a ne tvrdnja da će svaka buduća igra koristiti jedan pipeline.\u003C\u002Fdiv>\u003C\u002Faside>\n\u003Cdiv class=\"ce-delimiter cdx-block my-8\">\u003C\u002Fdiv>\n\u003Ch2 id=\"section-83\">Pitanje 5090 je korisno samo ako ga pravilno formulišemo\u003C\u002Fh2>\n\u003Cp>RTX 5090 je koristan primer upravo zato što su dokazi pomešani. Na dan 18. oktobar 2026, NVIDIA i dalje navodi GeForce RTX 5090 kao aktuelan proizvod. Istovremeno, ponašanje prodavaca i izveštaji u štampi dokumentovali su neuobičajeno lošu dostupnost i nepotvrđene tvrdnje o obustavi proizvodnje. To je tržišni signal, a ne zvanična najava kraja proizvodnje. Odbranjivo pitanje stoga nije da li je NVIDIA tajno ukinula jednu karticu, već da li oportunitetni trošak dodeljivanja vrhunskog Blackwell silicijuma entuzijastičkom gejmingu raste kada slična arhitektura može da služi profesionalnoj vizualizaciji i AI tržištima sa veoma različitom ekonomikom.\u003C\u002Fp>\n\u003Caside class=\"editorjs-callout editorjs-callout--note my-6 rounded-xl border p-5 border-gray-300 bg-gray-50 dark:border-gray-700 dark:bg-gray-900\u002F40\" role=\"note\">\u003Cstrong class=\"block mb-2 text-gray-900 dark:text-gray-100\">ČINJENICA \u002F SIGNAL \u002F HIPOTEZA\u003C\u002Fstrong>\u003Cdiv class=\"text-gray-700 dark:text-gray-200\">\u003Cstrong>ČINJENICA:\u003C\u002Fstrong> NVIDIA i dalje javno navodi RTX 5090, a Blackwell arhitektura pokriva gejming i profesionalne proizvode.\u003Cbr>\u003Cstrong>INDUSTRIJSKI SIGNAL:\u003C\u002Fstrong> nestanak iz prodavnica, ekstremna oskudica i izveštaji o mogućoj obustavi proizvodnje postoje, ali NVIDIA nije potvrdila EOL ili obustavu proizvodnje RTX 5090.\u003Cbr>\u003Cstrong>TEHNIČKI ZAKLJUČAK:\u003C\u002Fstrong> vrhunski čip ima više ekonomski vrednih destinacija.\u003Cbr>\u003Cstrong>HIPOTEZA FIGURE ROCKS:\u003C\u002Fstrong> AI i profesionalna potražnja mogu otežati održavanje tradicionalnog entuzijastičkog očekivanja — da najveći praktični čip ostaje široko dostupan kao gejming flagship.\u003C\u002Fdiv>\u003C\u002Faside>\n\u003Ch2 id=\"section-86\">Memorija počinje da postaje računarski problem, a ne samo problem kapaciteta\u003C\u002Fh2>\n\u003Cp>Postoje najmanje četiri načina da se napadne usko grlo memorije bez jednostavnog povećanja sirove DRAM brzine: kompresuj reprezentaciju, rekonstruiši detalje računanjem, predvidi koji će podaci biti potrebni sledeći i drži prave podatke rezidentnim blizu izvršne jedinice. Nijedan od njih ne čini fizički memorijski čip magično bržim. Oni menjaju koliko korisnog rada sistem dobija iz konačnog kapaciteta i budžeta propusnog opsega.\u003C\u002Fp>\n\u003Cdiv class=\"overflow-x-auto\">\u003Ctable class=\"w-full border-collapse\">\u003Cthead>\u003Ctr>\u003Cth class=\"border border-gray-300 px-4 py-2 text-left font-semibold\">Tehnika\u003C\u002Fth>\u003Cth class=\"border border-gray-300 px-4 py-2 text-left font-semibold\">Šta se menja\u003C\u002Fth>\u003Cth class=\"border border-gray-300 px-4 py-2 text-left font-semibold\">Šta se ne menja automatski\u003C\u002Fth>\u003C\u002Ftr>\u003C\u002Fthead>\u003Ctbody>\u003Ctr>\u003Ctd class=\"border border-gray-300 px-4 py-2\">Kompresija\u003C\u002Ftd>\u003Ctd class=\"border border-gray-300 px-4 py-2\">Manje fizičkih bajtova predstavlja iste logičke podatke\u003C\u002Ftd>\u003Ctd class=\"border border-gray-300 px-4 py-2\">DRAM takt i širina magistrale\u003C\u002Ftd>\u003C\u002Ftr>\u003Ctr>\u003Ctd class=\"border border-gray-300 px-4 py-2\">Neuronska rekonstrukcija\u003C\u002Ftd>\u003Ctd class=\"border border-gray-300 px-4 py-2\">Neki detalji se generišu iz kompaktne reprezentacije\u003C\u002Ftd>\u003Ctd class=\"border border-gray-300 px-4 py-2\">Trošak ne nestaje; inferencija ima sopstveni trošak računanja\u002Flatencije\u003C\u002Ftd>\u003C\u002Ftr>\u003Ctr>\u003Ctd class=\"border border-gray-300 px-4 py-2\">Prediktivno prefetch\u003C\u002Ftd>\u003Ctd class=\"border border-gray-300 px-4 py-2\">Podaci stižu pre nego što se zadatak koji ih zahteva zaglavi\u003C\u002Ftd>\u003Ctd class=\"border border-gray-300 px-4 py-2\">Ukupan fizički propusni opseg\u003C\u002Ftd>\u003C\u002Ftr>\u003Ctr>\u003Ctd class=\"border border-gray-300 px-4 py-2\">Rezidentnost \u002F objedinjen pristup\u003C\u002Ftd>\u003Ctd class=\"border border-gray-300 px-4 py-2\">Manje nepotrebnog premeštanja ili dupliranja\u003C\u002Ftd>\u003Ctd class=\"border border-gray-300 px-4 py-2\">Ograničenja kontencije i latencije\u003C\u002Ftd>\u003C\u002Ftr>\u003C\u002Ftbody>\u003C\u002Ftable>\u003C\u002Fdiv>\n\u003Caside class=\"editorjs-callout editorjs-callout--info my-6 rounded-xl border p-5 border-blue-300 bg-blue-50 dark:border-blue-900 dark:bg-blue-950\u002F20\" role=\"note\">\u003Cstrong class=\"block mb-2 text-gray-900 dark:text-gray-100\">Istraživački oslonac: efektivni kapacitet nije sirova brzina\u003C\u002Fstrong>\u003Cdiv class=\"text-gray-700 dark:text-gray-200\">NVIDIA Research Buddy Compression izvestio je o prosečnom \u003Cstrong>2,2× kompresionom odnosu za HPC zadatke i 1,5× za zadatke dubokog učenja\u003C\u002Fstrong>, uz 1–2% usporavanja u toj studiji. Taj rezultat nije gejming benchmark. Njegova relevantnost ovde je arhitektonska: kompresovana memorija može povećati efektivni kapacitet i smanjiti neki saobraćaj bez promene fizičkog DRAM takta.\u003C\u002Fdiv>\u003C\u002Faside>\n\u003Caside class=\"editorjs-callout editorjs-callout--success my-6 rounded-xl border p-5 border-emerald-300 bg-emerald-50 dark:border-emerald-900 dark:bg-emerald-950\u002F20\" role=\"note\">\u003Cstrong class=\"block mb-2 text-gray-900 dark:text-gray-100\">Gejming-specifični dokaz: neuronska kompresija tekstura\u003C\u002Fstrong>\u003Cdiv class=\"text-gray-700 dark:text-gray-200\">NVIDIA Research rad na materijalnim teksturama NTC pokazao je \u003Cstrong>4× veću linearnu rezoluciju tekstura — 16× više teksela — uz 30% manje memorije\u003C\u002Fstrong> u poređenju sa BC High. To ne znači da svaka igra dobija 16× memorijski dobitak. To demonstrira mehanizam koji je ovde važan: neki vizuelni detalji mogu se kompaktnije predstaviti i rekonstruisati računanjem umesto da se konvencionalno čuvaju pri istoj vernosti.\u003C\u002Fdiv>\u003C\u002Faside>\n\u003Caside class=\"editorjs-callout editorjs-callout--warning my-6 rounded-xl border p-5 border-amber-300 bg-amber-50 dark:border-amber-900 dark:bg-amber-950\u002F20\" role=\"note\">\u003Cstrong class=\"block mb-2 text-gray-900 dark:text-gray-100\">Šta broj „460ד ne znači\u003C\u002Fstrong>\u003Cdiv class=\"text-gray-700 dark:text-gray-200\">Brojka od 460× koja se ponekad povezuje sa NVIDIA sistemima odnosi se na poređenje kapaciteta memorije dostupne GPU-u kroz DGX generacije, \u003Cstrong>a ne na odnos kompresije memorije od 460×\u003C\u002Fstrong>. Ovaj članak ga namerno ne koristi kao dokaz za kompresiju.\u003C\u002Fdiv>\u003C\u002Faside>\n\u003Ch2 id=\"section-92\">Kako bi tehnički odgovor mogao da funkcioniše\u003C\u002Fh2>\n\u003Csection class=\"editorjs-process my-6\">\u003Ch3 class=\"mb-3 text-lg font-semibold\">Jedan mogući budući pipeline za frejmove\u003C\u002Fh3>\u003Cdiv class=\"grid grid-cols-1 md:grid-cols-2 xl:grid-cols-3 gap-4\">\u003Cdiv class=\"editorjs-process__step min-w-0  rounded-xl border border-gray-200 dark:border-gray-700 p-4\">\u003Cdiv class=\"text-xs font-semibold text-gray-500 dark:text-gray-400\">1\u003C\u002Fdiv>\u003Cdiv class=\"mt-1 font-semibold text-gray-900 dark:text-gray-100\">Kompresija\u003C\u002Fdiv>\u003Cdiv class=\"mt-1 text-sm text-gray-600 dark:text-gray-300\">Držite teksture ili naučene reprezentacije manjim gde kvalitet to dozvoljava.\u003C\u002Fdiv>\u003C\u002Fdiv>\u003Cdiv class=\"editorjs-process__step min-w-0  rounded-xl border border-gray-200 dark:border-gray-700 p-4\">\u003Cdiv class=\"text-xs font-semibold text-gray-500 dark:text-gray-400\">2\u003C\u002Fdiv>\u003Cdiv class=\"mt-1 font-semibold text-gray-900 dark:text-gray-100\">Predviđanje\u003C\u002Fdiv>\u003Cdiv class=\"mt-1 text-sm text-gray-600 dark:text-gray-300\">Koristite determinističko stanje igre i naučene modele da procenite koji će podaci sledeći biti potrebni.\u003C\u002Fdiv>\u003C\u002Fdiv>\u003Cdiv class=\"editorjs-process__step min-w-0  rounded-xl border border-gray-200 dark:border-gray-700 p-4\">\u003Cdiv class=\"text-xs font-semibold text-gray-500 dark:text-gray-400\">3\u003C\u002Fdiv>\u003Cdiv class=\"mt-1 font-semibold text-gray-900 dark:text-gray-100\">Prefetch\u003C\u002Fdiv>\u003Cdiv class=\"mt-1 text-sm text-gray-600 dark:text-gray-300\">Premeštajte korisne podatke pre nego što se GPU blokira čekajući ih.\u003C\u002Fdiv>\u003C\u002Fdiv>\u003Cdiv class=\"editorjs-process__step min-w-0  rounded-xl border border-gray-200 dark:border-gray-700 p-4\">\u003Cdiv class=\"text-xs font-semibold text-gray-500 dark:text-gray-400\">4\u003C\u002Fdiv>\u003Cdiv class=\"mt-1 font-semibold text-gray-900 dark:text-gray-100\">Renderovanje\u003C\u002Fdiv>\u003Cdiv class=\"mt-1 text-sm text-gray-600 dark:text-gray-300\">Trošite konvencionalni grafički rad tamo gde najviše menja vidljivi rezultat.\u003C\u002Fdiv>\u003C\u002Fdiv>\u003Cdiv class=\"editorjs-process__step min-w-0  rounded-xl border border-gray-200 dark:border-gray-700 p-4\">\u003Cdiv class=\"text-xs font-semibold text-gray-500 dark:text-gray-400\">5\u003C\u002Fdiv>\u003Cdiv class=\"mt-1 font-semibold text-gray-900 dark:text-gray-100\">Inferencija\u003C\u002Fdiv>\u003Cdiv class=\"mt-1 text-sm text-gray-600 dark:text-gray-300\">Koristite neuronsku rekonstrukciju ili generisanje tamo gde je inferencija jeftinija od direktnog skladištenja ili simulacije svega.\u003C\u002Fdiv>\u003C\u002Fdiv>\u003Cdiv class=\"editorjs-process__step min-w-0  rounded-xl border border-gray-200 dark:border-gray-700 p-4\">\u003Cdiv class=\"text-xs font-semibold text-gray-500 dark:text-gray-400\">6\u003C\u002Fdiv>\u003Cdiv class=\"mt-1 font-semibold text-gray-900 dark:text-gray-100\">Prikaz\u003C\u002Fdiv>\u003Cdiv class=\"mt-1 text-sm text-gray-600 dark:text-gray-300\">Isporučite frejm uz održavanje latencije, artefakata i tempa frejmova u prihvatljivim granicama.\u003C\u002Fdiv>\u003C\u002Fdiv>\u003C\u002Fdiv>\u003C\u002Fsection>\n\u003Cp>Ovaj pipeline nije jedan najavljeni proizvod. Svaka komponenta već postoji u nekom obliku, ali integrisani lanac je hipoteza. Neuronska kompresija tekstura je pokazala da naučene reprezentacije mogu da razmene računanje za memoriju. Istraživanje GPU prefetch-a je pokazalo da naučeno predviđanje može da poboljša stopu pogodaka memorijskih stranica. DirectX formalizuje ML unutar grafičkog programskog modela. DLSS 5 pokazuje da generativna faza ulazi u renderovanje u realnom vremenu.\u003C\u002Fp>\n\u003Ch2 id=\"section-95\">Igra ima jednu prednost koju opšti radni zadaci često nemaju: već zna deo svoje budućnosti\u003C\u002Fh2>\n\u003Cp>Game engine zna transformaciju kamere, brzinu igrača, trenutnu ćeliju sveta, susedne streaming ćelije, stanje animacije, aktivne entitete, LOD tranzicije, zavisnosti šejdera i često verovatne putanje kretanja. Taj deterministički kontekst ne otkriva budućnost savršeno, ali daje prediktoru bogatiju početnu tačku od samog generičkog memorijskog traga.\u003C\u002Fp>\n\u003Csection class=\"editorjs-process my-6\">\u003Ch3 class=\"mb-3 text-lg font-semibold\">Primer prediktivne memorije u igrama\u003C\u002Fh3>\u003Cdiv class=\"grid grid-cols-1 md:grid-cols-2 xl:grid-cols-3 gap-4\">\u003Cdiv class=\"editorjs-process__step min-w-0  rounded-xl border border-gray-200 dark:border-gray-700 p-4\">\u003Cdiv class=\"text-xs font-semibold text-gray-500 dark:text-gray-400\">1\u003C\u002Fdiv>\u003Cdiv class=\"mt-1 font-semibold text-gray-900 dark:text-gray-100\">1. Posmatrajte determinističko stanje\u003C\u002Fdiv>\u003Cdiv class=\"mt-1 text-sm text-gray-600 dark:text-gray-300\">Smer kamere, vektor kretanja, trenutna soba, vidljivi portali i poznate zavisnosti asseta već postoje u stanju engine-a.\u003C\u002Fdiv>\u003C\u002Fdiv>\u003Cdiv class=\"editorjs-process__step min-w-0  rounded-xl border border-gray-200 dark:border-gray-700 p-4\">\u003Cdiv class=\"text-xs font-semibold text-gray-500 dark:text-gray-400\">2\u003C\u002Fdiv>\u003Cdiv class=\"mt-1 font-semibold text-gray-900 dark:text-gray-100\">2. Procenite radni set bliske budućnosti\u003C\u002Fdiv>\u003Cdiv class=\"mt-1 text-sm text-gray-600 dark:text-gray-300\">Naučeni prediktor može da rangira verovatne sledeće sobe, teksture, geometriju, šejdere ili neuronske reprezentacije.\u003C\u002Fdiv>\u003C\u002Fdiv>\u003Cdiv class=\"editorjs-process__step min-w-0  rounded-xl border border-gray-200 dark:border-gray-700 p-4\">\u003Cdiv class=\"text-xs font-semibold text-gray-500 dark:text-gray-400\">3\u003C\u002Fdiv>\u003Cdiv class=\"mt-1 font-semibold text-gray-900 dark:text-gray-100\">3. Prefetch tokom dostupnog propusnog opsega\u003C\u002Fdiv>\u003Cdiv class=\"mt-1 text-sm text-gray-600 dark:text-gray-300\">Premeštajte verovatne resurse dok memorijski sistem ima rezervu, umesto da čekate strogi promašaj zahteva.\u003C\u002Fdiv>\u003C\u002Fdiv>\u003Cdiv class=\"editorjs-process__step min-w-0  rounded-xl border border-gray-200 dark:border-gray-700 p-4\">\u003Cdiv class=\"text-xs font-semibold text-gray-500 dark:text-gray-400\">4\u003C\u002Fdiv>\u003Cdiv class=\"mt-1 font-semibold text-gray-900 dark:text-gray-100\">4. Očuvajte determinističke garancije\u003C\u002Fdiv>\u003Cdiv class=\"mt-1 text-sm text-gray-600 dark:text-gray-300\">Kritični asseti i dalje koriste autorizovana pravila; predviđanje dopunjuje, a ne zamenjuje ispravnost.\u003C\u002Fdiv>\u003C\u002Fdiv>\u003Cdiv class=\"editorjs-process__step min-w-0  rounded-xl border border-gray-200 dark:border-gray-700 p-4\">\u003Cdiv class=\"text-xs font-semibold text-gray-500 dark:text-gray-400\">5\u003C\u002Fdiv>\u003Cdiv class=\"mt-1 font-semibold text-gray-900 dark:text-gray-100\">5. Merite cenu promašaja\u003C\u002Fdiv>\u003Cdiv class=\"mt-1 text-sm text-gray-600 dark:text-gray-300\">Ako predviđanje troši propusni opseg ili izbacuje korisne podatke, pouzdanost ili dubina prefetch-a moraju da se smanje.\u003C\u002Fdiv>\u003C\u002Fdiv>\u003C\u002Fdiv>\u003C\u002Fsection>\n\u003Caside class=\"editorjs-callout editorjs-callout--note my-6 rounded-xl border p-5 border-gray-300 bg-gray-50 dark:border-gray-700 dark:bg-gray-900\u002F40\" role=\"note\">\u003Cstrong class=\"block mb-2 text-gray-900 dark:text-gray-100\">Ova ideja ima istraživački presedan, ali još ne i dokaz u igrama\u003C\u002Fstrong>\u003Cdiv class=\"text-gray-700 dark:text-gray-200\">Objavljena studija o unifikovanoj virtuelnoj memoriji CPU–GPU izvestila je o \u003Cstrong>poboljšanju performansi od 10,89%\u003C\u002Fstrong>, porastu stope pogodaka stranica sa \u003Cstrong>76,10% na 89,02%\u003C\u002Fstrong> i \u003Cstrong>11,05% manjem saobraćaju između CPU i GPU\u003C\u002Fstrong> korišćenjem naučenog prefetch-a. Rad sa AAAI konferencije iz 2026. o asinhronom prefetch-u KV keša izvestio je o \u003Cstrong>2,15× efikasnosti attention kernela i do 1,97× end-to-end propusnosti\u003C\u002Fstrong> na svom LLM radnom zadatku. Nijedan rezultat ne treba kopirati na igre; oba pokazuju da predviđanje i prefetch mogu da sakriju memorijski trošak u realnim GPU radnim zadacima.\u003C\u002Fdiv>\u003C\u002Faside>\n\u003Ch2 id=\"section-99\">Zašto arhitektura nalik Spark-u postaje zanimljiva\u003C\u002Fh2>\n\u003Cp>DGX Spark nije gejming PC, i tretirati ga kao takav sakrilo bi korisnu lekciju. Njegov GB10 dizajn kombinuje Arm CPU jezgra, Blackwell GPU, Tensor i RT hardver, i do 128 GB koherentne unifikovane sistemske memorije. Ta arhitektura postavlja drugačije pitanje od klasičnog desktopa: umesto čistog razdvajanja sistema na CPU memoriju i diskretni VRAM, šta se dešava kada više računarskih engine-a deli veliki memorijski pool?\u003C\u002Fp>\n\u003Cdiv class=\"overflow-x-auto\">\u003Ctable class=\"w-full border-collapse\">\u003Cthead>\u003Ctr>\u003Cth class=\"border border-gray-300 px-4 py-2 text-left font-semibold\">Svojstvo\u003C\u002Fth>\u003Cth class=\"border border-gray-300 px-4 py-2 text-left font-semibold\">DGX Spark referenca\u003C\u002Fth>\u003Cth class=\"border border-gray-300 px-4 py-2 text-left font-semibold\">Implikacija za igre ako bi princip bio prilagođen\u003C\u002Fth>\u003C\u002Ftr>\u003C\u002Fthead>\u003Ctbody>\u003Ctr>\u003Ctd class=\"border border-gray-300 px-4 py-2\">Memorija\u003C\u002Ftd>\u003Ctd class=\"border border-gray-300 px-4 py-2\">Do 128 GB koherentne unifikovane LPDDR5X\u003C\u002Ftd>\u003Ctd class=\"border border-gray-300 px-4 py-2\">Veliki deljeni pool mogao bi da smanji dupliranje i omogući lokalnoj AI i grafici pristup zajedničkim podacima\u003C\u002Ftd>\u003C\u002Ftr>\u003Ctr>\u003Ctd class=\"border border-gray-300 px-4 py-2\">Memorijski propusni opseg\u003C\u002Ftd>\u003Ctd class=\"border border-gray-300 px-4 py-2\">273 GB\u002Fs\u003C\u002Ftd>\u003Ctd class=\"border border-gray-300 px-4 py-2\">Kapacitet nije dovoljan; vrhunska grafika i dalje zahteva agresivno upravljanje propusnim opsegom i latencijom\u003C\u002Ftd>\u003C\u002Ftr>\u003Ctr>\u003Ctd class=\"border border-gray-300 px-4 py-2\">Sastav računanja\u003C\u002Ftd>\u003Ctd class=\"border border-gray-300 px-4 py-2\">CPU + Blackwell GPU + Tensor + RT\u003C\u002Ftd>\u003Ctd class=\"border border-gray-300 px-4 py-2\">Budući radni zadatak igre mogao bi da se podeli na više specijalizovanih engine-a\u003C\u002Ftd>\u003C\u002Ftr>\u003Ctr>\u003Ctd class=\"border border-gray-300 px-4 py-2\">Format\u002Fpotrošnja\u003C\u002Ftd>\u003Ctd class=\"border border-gray-300 px-4 py-2\">Kompaktan GB10 sistem\u003C\u002Ftd>\u003Ctd class=\"border border-gray-300 px-4 py-2\">Integracija može da poboljša efikasnost, ali može da smanji modularnu nadogradnju\u003C\u002Ftd>\u003C\u002Ftr>\u003C\u002Ftbody>\u003C\u002Ftable>\u003C\u002Fdiv>\n\u003Caside class=\"editorjs-callout editorjs-callout--warning my-6 rounded-xl border p-5 border-amber-300 bg-amber-50 dark:border-amber-900 dark:bg-amber-950\u002F20\" role=\"note\">\u003Cstrong class=\"block mb-2 text-gray-900 dark:text-gray-100\">Kapacitet nije propusni opseg\u003C\u002Fstrong>\u003Cdiv class=\"text-gray-700 dark:text-gray-200\">Veliki unifikovani pool \u003Cstrong>ne\u003C\u002Fstrong> zamenjuje automatski propusni opseg vrhunskog diskretnog GPU-a. DGX Spark navodi 273 GB\u002Fs memorijskog propusnog opsega, dok NVIDIA RTX PRO 6000 Blackwell Workstation Edition navodi 1.792 GB\u002Fs. To su veoma različiti proizvodi i poređenje nije gejming benchmark; ono samo čini arhitektonsko ograničenje vidljivim. Gejming dizajn nalik Spark-u morao bi da reši kontenciju, latenciju i propusni opseg — ne samo da obezbedi više deljenih gigabajta.\u003C\u002Fdiv>\u003C\u002Faside>\n\u003Ch2 id=\"section-103\">Šta bi moralo da bude tačno da bi ova tranzicija uspela?\u003C\u002Fh2>\n\u003Csection class=\"editorjs-process my-6\">\u003Ch3 class=\"mb-3 text-lg font-semibold\">Test održivosti neuronskog gejminga\u003C\u002Fh3>\u003Cdiv class=\"grid grid-cols-1 md:grid-cols-2 xl:grid-cols-3 gap-4\">\u003Cdiv class=\"editorjs-process__step min-w-0  rounded-xl border border-gray-200 dark:border-gray-700 p-4\">\u003Cdiv class=\"text-xs font-semibold text-gray-500 dark:text-gray-400\">1\u003C\u002Fdiv>\u003Cdiv class=\"mt-1 font-semibold text-gray-900 dark:text-gray-100\">1. Inferencija mora da uštedi više nego što košta\u003C\u002Fdiv>\u003Cdiv class=\"mt-1 text-sm text-gray-600 dark:text-gray-300\">Neuronski rad mora da ukloni dovoljno troška skladištenja, propusnog opsega ili konvencionalnog renderovanja da opravda sopstveno računanje i latenciju.\u003C\u002Fdiv>\u003C\u002Fdiv>\u003Cdiv class=\"editorjs-process__step min-w-0  rounded-xl border border-gray-200 dark:border-gray-700 p-4\">\u003Cdiv class=\"text-xs font-semibold text-gray-500 dark:text-gray-400\">2\u003C\u002Fdiv>\u003Cdiv class=\"mt-1 font-semibold text-gray-900 dark:text-gray-100\">2. Engine mora da izloži korisno stanje\u003C\u002Fdiv>\u003Cdiv class=\"mt-1 text-sm text-gray-600 dark:text-gray-300\">Predviđanje radi bolje kada sistem zna smer kamere, zavisnosti asseta, kretanje i verovatnu vidljivost.\u003C\u002Fdiv>\u003C\u002Fdiv>\u003Cdiv class=\"editorjs-process__step min-w-0  rounded-xl border border-gray-200 dark:border-gray-700 p-4\">\u003Cdiv class=\"text-xs font-semibold text-gray-500 dark:text-gray-400\">3\u003C\u002Fdiv>\u003Cdiv class=\"mt-1 font-semibold text-gray-900 dark:text-gray-100\">3. Memorijsko kretanje mora da postane namerno\u003C\u002Fdiv>\u003Cdiv class=\"mt-1 text-sm text-gray-600 dark:text-gray-300\">Kompresija bez dobre politike rezidentnosti i prefetch-a i dalje može da izazove zastoj.\u003C\u002Fdiv>\u003C\u002Fdiv>\u003Cdiv class=\"editorjs-process__step min-w-0  rounded-xl border border-gray-200 dark:border-gray-700 p-4\">\u003Cdiv class=\"text-xs font-semibold text-gray-500 dark:text-gray-400\">4\u003C\u002Fdiv>\u003Cdiv class=\"mt-1 font-semibold text-gray-900 dark:text-gray-100\">4. Kvalitet mora da ostane stabilan\u003C\u002Fdiv>\u003Cdiv class=\"mt-1 text-sm text-gray-600 dark:text-gray-300\">Generisani detalji ne smeju da unesu neprihvatljive artefakte, vremensku nestabilnost ili netačne informacije o igri.\u003C\u002Fdiv>\u003C\u002Fdiv>\u003Cdiv class=\"editorjs-process__step min-w-0  rounded-xl border border-gray-200 dark:border-gray-700 p-4\">\u003Cdiv class=\"text-xs font-semibold text-gray-500 dark:text-gray-400\">5\u003C\u002Fdiv>\u003Cdiv class=\"mt-1 font-semibold text-gray-900 dark:text-gray-100\">5. Latencija mora da ostane ograničena\u003C\u002Fdiv>\u003Cdiv class=\"mt-1 text-sm text-gray-600 dark:text-gray-300\">Lepši frejm nije automatski responzivnija igra.\u003C\u002Fdiv>\u003C\u002Fdiv>\u003Cdiv class=\"editorjs-process__step min-w-0  rounded-xl border border-gray-200 dark:border-gray-700 p-4\">\u003Cdiv class=\"text-xs font-semibold text-gray-500 dark:text-gray-400\">6\u003C\u002Fdiv>\u003Cdiv class=\"mt-1 font-semibold text-gray-900 dark:text-gray-100\">6. Stack mora da preživi razlike između proizvođača\u003C\u002Fdiv>\u003Cdiv class=\"mt-1 text-sm text-gray-600 dark:text-gray-300\">Korisne tehnike zahtevaju API-je, drajvere i engine putanje koji se ne fragmentišu u jednokratne demo primere.\u003C\u002Fdiv>\u003C\u002Fdiv>\u003C\u002Fdiv>\u003C\u002Fsection>\n\u003Ch2 id=\"section-105\">Šta kupac treba da očekuje od tranzicije nalik Spark-u?\u003C\u002Fh2>\n\u003Csection class=\"editorjs-comparison my-6\">\u003Ch3 class=\"mb-3 text-lg font-semibold\">Potencijalna korist naspram stvarne cene\u003C\u002Fh3>\u003Cdiv class=\"overflow-x-auto\">\u003Ctable class=\"w-full border-collapse\">\u003Cthead>\u003Ctr>\u003Cth class=\"border border-gray-300 bg-gray-50 px-4 py-3 text-left dark:border-gray-700 dark:bg-gray-900\">\u003C\u002Fth>\u003Cth class=\"border border-gray-300 bg-gray-50 px-4 py-3 text-left font-semibold dark:border-gray-700 dark:bg-gray-900\">Potencijalni dobitak\u003C\u002Fth>\u003Cth class=\"border border-gray-300 bg-gray-50 px-4 py-3 text-left font-semibold dark:border-gray-700 dark:bg-gray-900\">Cena \u002F rizik\u003C\u002Fth>\u003C\u002Ftr>\u003C\u002Fthead>\u003Ctbody>\u003Ctr>\u003Cth class=\"border border-gray-300 px-4 py-3 text-left font-semibold dark:border-gray-700\">Veliki koherentni pool\u003C\u002Fth>\u003Ctd class=\"border border-gray-300 px-4 py-3 dark:border-gray-700\">Fleksibilnije deljenje između CPU, grafike i lokalne AI\u003C\u002Ftd>\u003Ctd class=\"border border-gray-300 px-4 py-3 dark:border-gray-700\">Deljeni klijenti mogu da se takmiče za isti propusni opseg\u003C\u002Ftd>\u003C\u002Ftr>\u003Ctr>\u003Cth class=\"border border-gray-300 px-4 py-3 text-left font-semibold dark:border-gray-700\">Manje dupliranja\u003C\u002Fth>\u003Ctd class=\"border border-gray-300 px-4 py-3 dark:border-gray-700\">Manje kopija između odvojenih pool-ova sistemskog RAM-a i VRAM-a\u003C\u002Ftd>\u003Ctd class=\"border border-gray-300 px-4 py-3 dark:border-gray-700\">Koherencija i zakazivanje dodaju složenost\u003C\u002Ftd>\u003C\u002Ftr>\u003Ctr>\u003Cth class=\"border border-gray-300 px-4 py-3 text-left font-semibold dark:border-gray-700\">Više prostora za lokalnu AI\u003C\u002Fth>\u003Ctd class=\"border border-gray-300 px-4 py-3 dark:border-gray-700\">Veći modeli, agenti ili neuronski asseti mogu da koegzistiraju lokalno\u003C\u002Ftd>\u003Ctd class=\"border border-gray-300 px-4 py-3 dark:border-gray-700\">AI može da se takmiči sa grafikom za računanje i memoriju\u003C\u002Ftd>\u003C\u002Ftr>\u003Ctr>\u003Cth class=\"border border-gray-300 px-4 py-3 text-left font-semibold dark:border-gray-700\">Optimizacija na nivou sistema\u003C\u002Fth>\u003Ctd class=\"border border-gray-300 px-4 py-3 dark:border-gray-700\">Platforma može zajedno da optimizuje smeštanje podataka i akceleratore\u003C\u002Ftd>\u003Ctd class=\"border border-gray-300 px-4 py-3 dark:border-gray-700\">Čvršća integracija može da smanji mogućnost nadogradnje i poveća zaključanost\u003C\u002Ftd>\u003C\u002Ftr>\u003Ctr>\u003Cth class=\"border border-gray-300 px-4 py-3 text-left font-semibold dark:border-gray-700\">Nova fleksibilnost radnih zadataka\u003C\u002Fth>\u003Ctd class=\"border border-gray-300 px-4 py-3 dark:border-gray-700\">Mašina može bolje da stari ako se buduće igre okrenu neuronskim radnim zadacima\u003C\u002Ftd>\u003Ctd class=\"border border-gray-300 px-4 py-3 dark:border-gray-700\">Samo ako engine-i i runtime-i zaista koriste te mogućnosti\u003C\u002Ftd>\u003C\u002Ftr>\u003C\u002Ftbody>\u003C\u002Ftable>\u003C\u002Fdiv>\u003C\u002Fsection>\n\u003Ch2 id=\"section-107\">Šta bi opovrglo ovu hipotezu?\u003C\u002Fh2>\n\u003Cp>Ova teza je namerno opovrgljiva. Tabela ispod definiše signale na tržištu, softveru, hardveru i usvajanju u igrama koji bi vremenom ojačali ili oslabili argument.\u003C\u002Fp>\n\u003Csection class=\"editorjs-comparison my-6\">\u003Ch3 class=\"mb-3 text-lg font-semibold\">Dokazi koji jačaju ili slabe tezu\u003C\u002Fh3>\u003Cdiv class=\"overflow-x-auto\">\u003Ctable class=\"w-full border-collapse\">\u003Cthead>\u003Ctr>\u003Cth class=\"border border-gray-300 bg-gray-50 px-4 py-3 text-left dark:border-gray-700 dark:bg-gray-900\">\u003C\u002Fth>\u003Cth class=\"border border-gray-300 bg-gray-50 px-4 py-3 text-left font-semibold dark:border-gray-700 dark:bg-gray-900\">Ojačalo bi je\u003C\u002Fth>\u003Cth class=\"border border-gray-300 bg-gray-50 px-4 py-3 text-left font-semibold dark:border-gray-700 dark:bg-gray-900\">Oslaabilo bi je\u003C\u002Fth>\u003C\u002Ftr>\u003C\u002Fthead>\u003Ctbody>\u003Ctr>\u003Cth class=\"border border-gray-300 px-4 py-3 text-left font-semibold dark:border-gray-700\">Ekonomija memorije\u003C\u002Fth>\u003Ctd class=\"border border-gray-300 px-4 py-3 dark:border-gray-700\">Prioritet servera\u002FHBM nastavlja da potiskuje potrošačku alokaciju\u003C\u002Ftd>\u003Ctd class=\"border border-gray-300 px-4 py-3 dark:border-gray-700\">Potrošački kapacitet se širi i pritisak na cene se normalizuje\u003C\u002Ftd>\u003C\u002Ftr>\u003Ctr>\u003Cth class=\"border border-gray-300 px-4 py-3 text-left font-semibold dark:border-gray-700\">Grafički stek\u003C\u002Fth>\u003Ctd class=\"border border-gray-300 px-4 py-3 dark:border-gray-700\">ML primitivi postaju uobičajene funkcije engine-a\u002FDirectX-a\u003C\u002Ftd>\u003Ctd class=\"border border-gray-300 px-4 py-3 dark:border-gray-700\">Neuralna grafika ostaje opciona naknadna obrada specifična za proizvođača\u003C\u002Ftd>\u003C\u002Ftr>\u003Ctr>\u003Cth class=\"border border-gray-300 px-4 py-3 text-left font-semibold dark:border-gray-700\">Dizajn hardvera\u003C\u002Fth>\u003Ctd class=\"border border-gray-300 px-4 py-3 dark:border-gray-700\">Više integrisanog heterogenog računanja i veći koherentni memorijski bazeni\u003C\u002Ftd>\u003Ctd class=\"border border-gray-300 px-4 py-3 dark:border-gray-700\">Diskretna GPU + odvojena RAM memorija ostaje jasno superiornija po ceni, fleksibilnosti i latenciji\u003C\u002Ftd>\u003C\u002Ftr>\u003Ctr>\u003Cth class=\"border border-gray-300 px-4 py-3 text-left font-semibold dark:border-gray-700\">Prihvatanje u igrama\u003C\u002Fth>\u003Ctd class=\"border border-gray-300 px-4 py-3 dark:border-gray-700\">Studiji široko isporučuju neuralnu kompresiju, generisanje i prediktivne tokove podataka\u003C\u002Ftd>\u003Ctd class=\"border border-gray-300 px-4 py-3 dark:border-gray-700\">Tehnologija ostaje ograničena na demo verzije ili nekoliko vodećih igara\u003C\u002Ftd>\u003C\u002Ftr>\u003C\u002Ftbody>\u003C\u002Ftable>\u003C\u002Fdiv>\u003C\u002Fsection>\n\u003Ch2 id=\"section-110\">Praktičan zaključak za gejmera danas\u003C\u002Fh2>\n\u003Cp>Ne kupujte hardver zato što bi mogla da stigne spekulativna arhitektura. Kupujte za igre, rezoluciju, ciljnu latenciju i radne zadatke koje zaista imate. Ali ne pretpostavljajte da se buduća otpornost i dalje može svesti na jedan broj kao što je raster performansa ili kapacitet VRAM-a. Smer softverskog steka sugeriše da bi ponašanje memorije, neuralno računanje i podrška engine-a mogli postati sve važniji delovi jednačine vrednosti.\u003C\u002Fp>\n\u003Caside class=\"editorjs-referral my-6\">\u003Ca href=\"https:\u002F\u002Ffigure.rocks\u002Fsr\u002Fblog\u002Fvram-usage-is-not-vram-requirement-why-a-full-memory-meter-does-not-tell-the-whole-story\" class=\"flex flex-col sm:flex-row gap-4 rounded-xl border border-gray-200 dark:border-gray-700 p-4 transition hover:border-primary-500\">\u003Cdiv class=\"min-w-0 flex-1\">\u003Cstrong class=\"block text-lg text-gray-900 dark:text-gray-100\">Korišćenje VRAM-a nije zahtev za VRAM: Zašto pun memetar ne govori celu priču\u003C\u002Fstrong>\u003Cp class=\"mt-2 text-sm text-gray-600 dark:text-gray-300\">Praktično objašnjenje radnih skupova, rezidentnosti i zašto alocirani broj VRAM-a nije isto što i tvrdi zahtev.\u003C\u002Fp>\u003Cspan class=\"mt-3 inline-flex text-sm font-medium text-primary-600 dark:text-primary-400\">Pročitajte vodič o VRAM-u →\u003C\u002Fspan>\u003C\u002Fdiv>\u003C\u002Fa>\u003C\u002Faside>\n\u003Ch2 id=\"section-113\">Primarni izvori\u003C\u002Fh2>\n\u003Ca href=\"https:\u002F\u002Fnewzoo.com\u002Farticles\u002F2026-global-games-market-key-numbers\" target=\"_blank\" rel=\"noopener noreferrer\" class=\"editorjs-link-tool block border border-gray-200 dark:border-gray-700 rounded-lg p-4 transition text-gray-900 dark:text-gray-100 hover:border-primary-500 hover:bg-primary-50 dark:hover:bg-gray-900 hover:text-gray-900 dark:hover:text-gray-100\">\u003Cstrong class=\"block font-semibold\">Newzoo — Ključni brojevi globalnog tržišta igara za 2026.\u003C\u002Fstrong>\u003Cp class=\"text-sm text-gray-600 dark:text-gray-400\">Prognoza tržišta igara za 2026. korišćena za poređenje razmera.\u003C\u002Fp>\u003C\u002Fa>\n\u003Ca href=\"https:\u002F\u002Fwww.semiconductors.org\u002Fglobal-annual-semiconductor-sales-increase-25-6-to-791-7-billion-in-2025\u002F\" target=\"_blank\" rel=\"noopener noreferrer\" class=\"editorjs-link-tool block border border-gray-200 dark:border-gray-700 rounded-lg p-4 transition text-gray-900 dark:text-gray-100 hover:border-primary-500 hover:bg-primary-50 dark:hover:bg-gray-900 hover:text-gray-900 dark:hover:text-gray-100\">\u003Cstrong class=\"block font-semibold\">Semiconductor Industry Association — Globalna prodaja u 2025.\u003C\u002Fstrong>\u003Cp class=\"text-sm text-gray-600 dark:text-gray-400\">Prodaja poluprovodnika u 2025. i projekcija udruženja od oko 1 bilion dolara za 2026.\u003C\u002Fp>\u003C\u002Fa>\n\u003Ca href=\"https:\u002F\u002Fwww.trendforce.com\u002Fpresscenter\u002Fnews\u002F20260930-13258.html\" target=\"_blank\" rel=\"noopener noreferrer\" class=\"editorjs-link-tool block border border-gray-200 dark:border-gray-700 rounded-lg p-4 transition text-gray-900 dark:text-gray-100 hover:border-primary-500 hover:bg-primary-50 dark:hover:bg-gray-900 hover:text-gray-900 dark:hover:text-gray-100\">\u003Cstrong class=\"block font-semibold\">TrendForce — Potražnja za AI serverima održava rast cena memorije u 4Q26\u003C\u002Fstrong>\u003Cp class=\"text-sm text-gray-600 dark:text-gray-400\">Dokazi s kraja 2026. o tekućoj alokaciji kapaciteta sa prioritetom servera i pritisku na potrošače.\u003C\u002Fp>\u003C\u002Fa>\n\u003Ca href=\"https:\u002F\u002Fresearch.nvidia.com\u002Fpublication\u002F2019-04_buddy-compression-enabling-larger-memory-deep-learning-and-hpc-workloads-gpus\" target=\"_blank\" rel=\"noopener noreferrer\" class=\"editorjs-link-tool block border border-gray-200 dark:border-gray-700 rounded-lg p-4 transition text-gray-900 dark:text-gray-100 hover:border-primary-500 hover:bg-primary-50 dark:hover:bg-gray-900 hover:text-gray-900 dark:hover:text-gray-100\">\u003Cstrong class=\"block font-semibold\">NVIDIA Research — Buddy Compression\u003C\u002Fstrong>\u003Cp class=\"text-sm text-gray-600 dark:text-gray-400\">Istraživački izvor za prosečne stope kompresije od 2,2× za HPC i 1,5× za DL.\u003C\u002Fp>\u003C\u002Fa>\n\u003Ca href=\"https:\u002F\u002Fwww.sciencedirect.com\u002Fscience\u002Farticle\u002Fabs\u002Fpii\u002FS0743731522002490\" target=\"_blank\" rel=\"noopener noreferrer\" class=\"editorjs-link-tool block border border-gray-200 dark:border-gray-700 rounded-lg p-4 transition text-gray-900 dark:text-gray-100 hover:border-primary-500 hover:bg-primary-50 dark:hover:bg-gray-900 hover:text-gray-900 dark:hover:text-gray-100\">\u003Cstrong class=\"block font-semibold\">UVM prefetching CPU–GPU zasnovan na dubokom učenju\u003C\u002Fstrong>\u003Cp class=\"text-sm text-gray-600 dark:text-gray-400\">Istraživački izvor za rezultate naučenog prefetching-a stranica.\u003C\u002Fp>\u003C\u002Fa>\n\u003Ca href=\"https:\u002F\u002Fojs.aaai.org\u002Findex.php\u002FAAAI\u002Farticle\u002Fview\u002F39224\" target=\"_blank\" rel=\"noopener noreferrer\" class=\"editorjs-link-tool block border border-gray-200 dark:border-gray-700 rounded-lg p-4 transition text-gray-900 dark:text-gray-100 hover:border-primary-500 hover:bg-primary-50 dark:hover:bg-gray-900 hover:text-gray-900 dark:hover:text-gray-100\">\u003Cstrong class=\"block font-semibold\">AAAI 2026 — Asinhroni KV cache prefetching\u003C\u002Fstrong>\u003Cp class=\"text-sm text-gray-600 dark:text-gray-400\">Istraživački izvor koji demonstrira skrivanje latencije kroz proaktivni prefetching GPU keša.\u003C\u002Fp>\u003C\u002Fa>\n\u003Ca href=\"https:\u002F\u002Fwww.trendforce.com\u002Fpresscenter\u002Fnews\u002F20260331-12995.html\" target=\"_blank\" rel=\"noopener noreferrer\" class=\"editorjs-link-tool block border border-gray-200 dark:border-gray-700 rounded-lg p-4 transition text-gray-900 dark:text-gray-100 hover:border-primary-500 hover:bg-primary-50 dark:hover:bg-gray-900 hover:text-gray-900 dark:hover:text-gray-100\">\u003Cstrong class=\"block font-semibold\">TrendForce — Potražnja za AI serverima i cene memorije u 2Q26\u003C\u002Fstrong>\u003Cp class=\"text-sm text-gray-600 dark:text-gray-400\">Trenutni dokazi za prioritizaciju servera\u002FHBM, ograničenja GDDR-a i pritisak na potrošače.\u003C\u002Fp>\u003C\u002Fa>\n\u003Ca href=\"https:\u002F\u002Fdevblogs.microsoft.com\u002Fdirectx\u002Fevolving-directx-for-the-ml-era-on-windows\u002F\" target=\"_blank\" rel=\"noopener noreferrer\" class=\"editorjs-link-tool block border border-gray-200 dark:border-gray-700 rounded-lg p-4 transition text-gray-900 dark:text-gray-100 hover:border-primary-500 hover:bg-primary-50 dark:hover:bg-gray-900 hover:text-gray-900 dark:hover:text-gray-100\">\u003Cstrong class=\"block font-semibold\">Microsoft DirectX — Evolucija DirectX-a za ML eru\u003C\u002Fstrong>\u003Cp class=\"text-sm text-gray-600 dark:text-gray-400\">Zvanični pregled GDC 2026 o ML-u na nivou šejdera i modela u DirectX-u.\u003C\u002Fp>\u003C\u002Fa>\n\u003Ca href=\"https:\u002F\u002Fresearch.nvidia.com\u002Flabs\u002Fadlr\u002FDLSS5\u002F\" target=\"_blank\" rel=\"noopener noreferrer\" class=\"editorjs-link-tool block border border-gray-200 dark:border-gray-700 rounded-lg p-4 transition text-gray-900 dark:text-gray-100 hover:border-primary-500 hover:bg-primary-50 dark:hover:bg-gray-900 hover:text-gray-900 dark:hover:text-gray-100\">\u003Cstrong class=\"block font-semibold\">NVIDIA Research — DLSS 5: Generativno neuralno renderovanje\u003C\u002Fstrong>\u003Cp class=\"text-sm text-gray-600 dark:text-gray-400\">Primarna istraživačka stranica koja opisuje DLSS 5 kao fazu generativnog renderovanja u realnom vremenu.\u003C\u002Fp>\u003C\u002Fa>\n\u003Ca href=\"https:\u002F\u002Fwww.nvidia.com\u002Fen-us\u002Fproducts\u002Fworkstations\u002Fdgx-spark\u002F\" target=\"_blank\" rel=\"noopener noreferrer\" class=\"editorjs-link-tool block border border-gray-200 dark:border-gray-700 rounded-lg p-4 transition text-gray-900 dark:text-gray-100 hover:border-primary-500 hover:bg-primary-50 dark:hover:bg-gray-900 hover:text-gray-900 dark:hover:text-gray-100\">\u003Cstrong class=\"block font-semibold\">NVIDIA — DGX Spark specifikacije\u003C\u002Fstrong>\u003Cp class=\"text-sm text-gray-600 dark:text-gray-400\">Zvanične specifikacije arhitekture, unifikovane memorije i propusnog opsega.\u003C\u002Fp>\u003C\u002Fa>\n\u003Ca href=\"https:\u002F\u002Fwww.nvidia.com\u002Fen-us\u002Fgeforce\u002Fgraphics-cards\u002F50-series\u002Frtx-5090\u002F\" target=\"_blank\" rel=\"noopener noreferrer\" class=\"editorjs-link-tool block border border-gray-200 dark:border-gray-700 rounded-lg p-4 transition text-gray-900 dark:text-gray-100 hover:border-primary-500 hover:bg-primary-50 dark:hover:bg-gray-900 hover:text-gray-900 dark:hover:text-gray-100\">\u003Cstrong class=\"block font-semibold\">NVIDIA — GeForce RTX 5090\u003C\u002Fstrong>\u003Cp class=\"text-sm text-gray-600 dark:text-gray-400\">Zvanična trenutna stranica proizvoda RTX 5090 korišćena za razlikovanje potvrđenog statusa proizvoda od glasina o obustavi proizvodnje.\u003C\u002Fp>\u003C\u002Fa>\n\u003Ca href=\"https:\u002F\u002Fwww.tomshardware.com\u002Fpc-components\u002Fgpus\u002Fnvidia-rtx-5090-disappears-from-micro-center-amid-rumors-of-a-production-halt-retailer-flags-flagship-as-not-carried-as-stock-mysteriously-vanishes\" target=\"_blank\" rel=\"noopener noreferrer\" class=\"editorjs-link-tool block border border-gray-200 dark:border-gray-700 rounded-lg p-4 transition text-gray-900 dark:text-gray-100 hover:border-primary-500 hover:bg-primary-50 dark:hover:bg-gray-900 hover:text-gray-900 dark:hover:text-gray-100\">\u003Cstrong class=\"block font-semibold\">Tom’s Hardware — Nestanak RTX 5090 iz maloprodaje i glasine o obustavi proizvodnje\u003C\u002Fstrong>\u003Cp class=\"text-sm text-gray-600 dark:text-gray-400\">Trenutni tržišni izveštaj korišćen samo kao industrijski signal; eksplicitno napominje da NVIDIA nije potvrdila obustavu proizvodnje.\u003C\u002Fp>\u003C\u002Fa>\n\u003Ca href=\"https:\u002F\u002Fresearch.nvidia.com\u002Flabs\u002Frtr\u002Fneural_texture_compression\u002F\" target=\"_blank\" rel=\"noopener noreferrer\" class=\"editorjs-link-tool block border border-gray-200 dark:border-gray-700 rounded-lg p-4 transition text-gray-900 dark:text-gray-100 hover:border-primary-500 hover:bg-primary-50 dark:hover:bg-gray-900 hover:text-gray-900 dark:hover:text-gray-100\">\u003Cstrong class=\"block font-semibold\">NVIDIA Research — Neuralna kompresija materijalnih tekstura sa slučajnim pristupom\u003C\u002Fstrong>\u003Cp class=\"text-sm text-gray-600 dark:text-gray-400\">Primarni izvor za poređenje 4× linearne rezolucije \u002F 16× teksela uz 30% manje memorije u odnosu na BC High.\u003C\u002Fp>\u003C\u002Fa>\n\u003Ca href=\"https:\u002F\u002Fwww.nvidia.com\u002Fde-de\u002Fproducts\u002Fworkstations\u002Fprofessional-desktop-gpus\u002Frtx-pro-6000\u002F\" target=\"_blank\" rel=\"noopener noreferrer\" class=\"editorjs-link-tool block border border-gray-200 dark:border-gray-700 rounded-lg p-4 transition text-gray-900 dark:text-gray-100 hover:border-primary-500 hover:bg-primary-50 dark:hover:bg-gray-900 hover:text-gray-900 dark:hover:text-gray-100\">\u003Cstrong class=\"block font-semibold\">NVIDIA — RTX PRO 6000 Blackwell Workstation Edition\u003C\u002Fstrong>\u003Cp class=\"text-sm text-gray-600 dark:text-gray-400\">Zvanični izvor za 96 GB GDDR7 i memorijsku propusnost od 1.792 GB\u002Fs koja se koristi kao referentna tačka za propusnost.\u003C\u002Fp>\u003C\u002Fa>\n\u003Csection class=\"editorjs-glossary my-6 rounded-xl border border-gray-200 dark:border-gray-700 p-5\">\u003Ch3 class=\"mb-3 text-lg font-semibold\">Ključni pojmovi\u003C\u002Fh3>\u003Cdl>\u003Cdiv id=\"neural-rendering\" class=\"border-t border-gray-200 dark:border-gray-700 py-3 first:border-t-0\">\u003Cdt class=\"font-semibold text-gray-900 dark:text-gray-100\">Neuronsko renderovanje\u003C\u002Fdt>\u003Cdd class=\"mt-1 text-gray-600 dark:text-gray-300\">Metode renderovanja koje koriste obučene modele kao deo procesa stvaranja, rekonstrukcije ili unapređenja vizuelnog izlaza.\u003C\u002Fdd>\u003C\u002Fdiv>\u003Cdiv id=\"heterogeneous-compute\" class=\"border-t border-gray-200 dark:border-gray-700 py-3 first:border-t-0\">\u003Cdt class=\"font-semibold text-gray-900 dark:text-gray-100\">Heterogeno računanje\u003C\u002Fdt>\u003Cdd class=\"mt-1 text-gray-600 dark:text-gray-300\">Sistem koji dodeljuje različite radne zadatke različitim računarskim jedinicama kao što su CPU, GPU, RT i matrični\u002FTensor hardver.\u003C\u002Fdd>\u003C\u002Fdiv>\u003Cdiv id=\"effective-memory\" class=\"border-t border-gray-200 dark:border-gray-700 py-3 first:border-t-0\">\u003Cdt class=\"font-semibold text-gray-900 dark:text-gray-100\">Efektivna memorija\u003C\u002Fdt>\u003Cdd class=\"mt-1 text-gray-600 dark:text-gray-300\">Praktičan opis toga koliko korisnih podataka memorijski sistem može da zadrži i dostavi računarskim jedinicama nakon uzimanja u obzir kompresije, rezidentnosti i efikasnosti premeštanja. Nije formalna zamena za fizički kapacitet.\u003C\u002Fdd>\u003C\u002Fdiv>\u003Cdiv id=\"predictive-memory\" class=\"border-t border-gray-200 dark:border-gray-700 py-3 first:border-t-0\">\u003Cdt class=\"font-semibold text-gray-900 dark:text-gray-100\">Prediktivna memorija\u003C\u002Fdt>\u003Cdd class=\"mt-1 text-gray-600 dark:text-gray-300\">Figure Rocks skraćeni izraz za kombinovanje poznatog stanja igre i naučene predikcije radi pripreme verovatno potrebnih podataka pre zastoja. To je hipoteza, a ne industrijski standard.\u003C\u002Fdd>\u003C\u002Fdiv>\u003C\u002Fdl>\u003C\u002Fsection>",{"time":538,"blocks":539,"version":1624},1792336868226,[540,546,554,561,568,574,579,606,613,618,623,646,652,657,662,671,709,716,721,726,731,737,742,747,772,778,784,790,795,819,824,829,834,855,861,866,871,896,902,907,931,936,979,984,989,1025,1030,1035,1043,1048,1058,1067,1076,1085,1094,1103,1112,1121,1130,1139,1148,1157,1166,1175,1198,1202,1206,1210,1213,1216,1220,1237,1241,1244,1248,1261,1266,1270,1274,1277,1298,1302,1305,1308,1312,1316,1320,1324,1338,1342,1346,1349,1352,1374,1378,1382,1386,1403,1408,1412,1416,1431,1435,1438,1452,1456,1481,1484,1488,1514,1517,1521,1526,1529,1535,1541,1547,1552,1559,1566,1571,1577,1583,1589,1595,1601,1607,1613],{"id":541,"data":542,"type":544,"tunes":545},"p-0001",{"text":543},"Decenijama je PC gejming za entuzijaste sledio poznatu pogodbu: igre su tražile više računarske snage, generacije GPU-a su je pružale, a igrači su nadograđivali opremu kada jaz postane dovoljno veliki. Ta petlja nikada nije savršeno funkcionisala, ali je smer bio razumljiv. U 2026. petlju je sve teže pročitati jer se dve promene dešavaju istovremeno: AI infrastruktura se agresivno takmiči za silicijum i memoriju, dok AI tehnike direktno ulaze u način na koji igre stvaraju frejmove.","paragraph",{},{"id":547,"data":548,"type":552,"tunes":553},"c-0002",{"body":549,"title":550,"variant":551},"\u003Cstrong>Stari ciklus nadogradnje za gejming je pod pritiskom, ali ga ne zamenjuju jednostavno skuplji GPU-ovi.\u003C\u002Fstrong> AI menja obe strane jednačine: povećava konkurenciju za poluprovodničke i memorijske resurse, a istovremeno omogućava tehnike neuronskog renderovanja koje mogu zameniti uskladištene podatke ili konvencionalni render rad inferencijom. Važna mogućnost je da gejming može odgovoriti na strožu hardversku ekonomiju time što će postati računarski selektivniji, a ne samo zahtevati veće GPU-ove.","Direktan odgovor","info","callout",{},{"id":555,"data":556,"type":552,"tunes":560},"c-0003",{"body":557,"title":558,"variant":559},"Ovo je \u003Cstrong>hipoteza potkrepljena dokazima\u003C\u002Fstrong>. Dokumentovani tržišni podaci i tehnologije koje su u prodaji ili najavljene označeni su kao činjenice ili industrijski signali. Veze između njih su tehničko zaključivanje. Arhitektura budućnosti opisana kasnije je hipoteza Figure Rocks, a ne tvrdnja o nenajavljenom planu NVIDIA, AMD, Intel, Microsoft ili konzola.","Kako čitati ovaj članak","note",{},{"id":562,"data":563,"type":566,"tunes":567},"toc-0004",{"title":564,"maxLevel":565,"minLevel":47},"Sadržaj",3,"tableOfContents",{},{"id":569,"data":570,"type":572,"tunes":573},"h-0005",{"text":571,"level":47},"Nesrazmera u obimu je već vidljiva","header",{},{"id":575,"data":576,"type":544,"tunes":578},"p-0006",{"text":577},"Newzoo predviđa globalno tržište igara na 213,9 milijardi dolara u 2026, što je rast od 6,1% u odnosu na prethodnu godinu. Asocijacija poluprovodničke industrije objavila je 791,7 milijardi dolara globalne prodaje poluprovodnika za 2025, rast od 25,6%, i projektovala oko 1 bilion dolara za 2026. Posao igara i posao poluprovodnika i dalje zavise jedan od drugog, ali njihovi podsticaji postaju sve manje simetrični: isti napredni proizvodni ekosistem koji omogućava vrhunski gejming sada služi AI i data-center kupcima sa mnogo većim budžetima za infrastrukturu. Ovo nisu direktno uporedive kategorije proizvoda; oni uspostavljaju razmeru uzvodne računarske ekonomije oko gejminga.",{},{"id":580,"data":581,"type":604,"tunes":605},"t-0007",{"content":582,"stretched":603,"withHeadings":15},[583,587,591,595,599],[584,585,586],"Referentna tačka","Objavljena ili predviđena vrednost","Zašto je to ovde važno",[588,589,590],"Globalno tržište igara, prognoza za 2026","213,9 mlrd $, +6,1% YoY","Veliko tržište zabave za potrošače, ali nizvodno od snabdevanja naprednim hardverom",[592,593,594],"PC igre, prognoza za 2026","45,9 mlrd $, +5,3% YoY","Segment gejminga najdirektnije izložen ekonomiji PC komponenti",[596,597,598],"Globalna prodaja poluprovodnika, 2025","791,7 mlrd $, +25,6% YoY","Pokazuje razmeru uzvodne industrije koja služi gejmingu, AI, mobilnim uređajima, automobilima i cloudu",[600,601,602],"Globalna poluprovodnička industrija, projekcija za 2026","~1 bilion $","Povećava oportunitetni trošak posvećivanja vrhunskog silicijuma samo potrošačkom gejmingu",false,"table",{},{"id":607,"data":608,"type":552,"tunes":612},"c-0008",{"body":609,"title":610,"variant":611},"Brojke uspostavljaju \u003Cstrong>ekonomski kontekst, a ne direktnu uzročnost\u003C\u002Fstrong>. One ne dokazuju da je određeni wafer, GPU ili memorijski paket uzet gejmeru i preusmeren na AI. Takođe ne znače da su gejming i poluprovodnici dve najveće industrije na svetu. Uži, odbranjivi stav je da tržište igara vredno preko 200 milijardi dolara zavisi od uzvodne poluprovodničke ekonomije koja se približava bilionu dolara godišnje, gde se AI infrastruktura sada takmiči za naprednu računarsku snagu i memoriju pod veoma različitim budžetskim i maržnim uslovima.","Šta ovi tržišni brojevi dokazuju — a šta ne","warning",{},{"id":614,"data":615,"type":572,"tunes":617},"h-0009",{"text":616,"level":47},"Memorija je mesto gde pritisak postaje konkretan",{},{"id":619,"data":620,"type":544,"tunes":622},"p-0010",{"text":621},"TrendForce je više puta izvestio da dobavljači DRAM-a preusmeravaju kapacitete ka serverskim i HBM proizvodima kako AI potražnja raste. U martu 2026. predvideo je da će ugovorne cene konvencionalnog DRAM-a porasti 58–63% kvartalno u Q2 i naveo da ograničena GDDR alokacija ograničava snabdevanje grafičkom memorijom. Do Q3, TrendForce je saopštio da potrošački kupci dostižu granicu pristupačnosti, iako su AI i serverska potražnja ostale jake.",{},{"id":624,"data":625,"type":644,"tunes":645},"flow-0011",{"steps":626,"title":642,"orientation":643},[627,630,633,636,639],{"label":628,"description":629},"1. AI infrastruktura se širi","Cloud provajderi i AI graditelji obezbeđuju serversku memoriju, HBM, enterprise SSD-ove i naprednu računarsku snagu.",{"label":631,"description":632},"2. Dobavljači optimizuju asortiman proizvoda","Kapacitet se premešta ka proizvodima sa jačom potražnjom, maržama ili dugoročnim ugovorima.",{"label":634,"description":635},"3. Potrošačka alokacija se zateže","Kupci PC, grafičke i potrošačke memorije suočavaju se sa višim cenama ili ograničenom dostupnošću.",{"label":637,"description":638},"4. Gejming dobavljači apsorbuju ili prenose trošak","Dizajn ploče, kapacitet VRAM-a i cena sistema postaju teži kompromisi.",{"label":640,"description":641},"5. Stari ciklus nadogradnje slabi","Brži GPU je i dalje koristan, ali je brute-force skaliranje ekonomski teže ponoviti na istim potrošačkim cenovnim tačkama.","Pritisak alokacije resursa","auto","processFlow",{},{"id":647,"data":648,"type":552,"tunes":651},"v3-sep-signal",{"body":649,"title":650,"variant":611},"TrendForce je 30. septembra 2026. izvestio da dobavljači i dalje daju prioritet kapacitetima naprednog procesa za visokoperformantne serverske proizvode, sa projektovanim rastom ugovornih cena konvencionalnog DRAM-a od dodatnih 10–15% kvartalno u Q4. Njihov DRAM bilten takođe navodi da HBM nastavlja da potiskuje opšti DRAM kapacitet. Pritisak opisan ovde stoga ostaje vidljiv krajem 2026; nije zasnovan samo na privremenom skoku s početka godine.","Signal iz septembra 2026, a ne priča o staroj nestašici",{},{"id":653,"data":654,"type":572,"tunes":656},"h-0012",{"text":655,"level":47},"U isto vreme, sam frejm se menja",{},{"id":658,"data":659,"type":544,"tunes":661},"p-0013",{"text":660},"Microsoft sada opisuje mašinsko učenje kao nešto što postaje temelj real-time grafike. DirectX dodaje ML putanje na nivou šejdera i modela, tako da neuronska kompresija tekstura, denoising, super rezolucija i drugi naučeni poslovi mogu stajati unutar grafičkog steka umesto da budu prikačeni kao izolovane funkcije. NVIDIA istraživanje DLSS 5 gura ideju dalje uvodeći generativnu fazu neuronskog renderovanja koja dopunjuje konvencionalno renderovanje naučenim priorima izgleda. Neuronsko renderovanje ne znači da konvencionalno renderovanje nestaje. Model koji se pojavljuje je hibridni: renderuj neke informacije direktno, inferiraj ili rekonstruiši druge kada je to jeftinije ili korisnije.",{},{"id":663,"data":664,"type":669,"tunes":670},"ref-0014",{"url":665,"title":666,"excerpt":667,"ctaLabel":668},"https:\u002F\u002Ffigure.rocks\u002Fsr\u002Fblog\u002Fdlss-5-is-not-just-upscaling-what-3d-guided-neural-rendering-actually-changes","DLSS 5 nije samo upscaling: šta generativno neuronsko renderovanje zaista menja","Zašto važna promena nije još jedan režim kvaliteta, već nova faza u načinu na koji se vizuelne informacije mogu proizvesti.","Pročitajte analizu DLSS 5","referralArticle",{},{"id":672,"data":673,"type":707,"tunes":708},"cmp-0015",{"rows":674,"title":699,"layout":604,"columns":700},[675,681,687,693],{"id":676,"label":677,"values":678},"fidelity","Više vizuelne vernosti",{"new":679,"old":680},"Čuvaj\u002Frenderuj selektivno, rekonstruiši ili inferiraj neke detalje","Čuvaj više detalja i renderuj više rada",{"id":682,"label":683,"values":684},"memory","Odgovor memorije",{"new":685,"old":686},"Dodaj kapacitet gde je potrebno, ali i kompresuj i smanji premeštanje","Dodaj kapacitet i propusni opseg",{"id":688,"label":689,"values":690},"performance","Odgovor performansi",{"new":691,"old":692},"Mešaj šejdersko, RT i matrično\u002FTensor računanje","Više šejderskog\u002FRT propusnog opsega",{"id":694,"label":695,"values":696},"software","Uloga softvera",{"new":697,"old":698},"Engine sve više zakazuje renderovanje, premeštanje podataka i ML poslove","Engine zakazuje uglavnom konvencionalni grafički rad","Tradicionalno skaliranje naspram skaliranja u neuronskoj eri",[701,704],{"id":702,"label":703},"old","Tradicionalni put",{"id":705,"label":706},"new","Put neuronske ere","comparison",{},{"id":710,"data":711,"type":552,"tunes":715},"c-0016",{"body":712,"title":713,"variant":714},"\u003Cstrong>AI možda slabi ekonomiju brute-force gejming hardvera dok istovremeno pruža tehnike koje brute-force skaliranje čine manje neophodnim.\u003C\u002Fstrong> To ne znači da će buduće igre zahtevati manje hardvera. Znači da više rezultata može doći iz odlučivanja šta uskladištiti, preneti, renderovati, rekonstruisati ili inferirati. Figure Rocks skraćeno: \u003Cstrong>uskladišti manje → prenesi manje → predvidi više → renderuj manje → inferiraj više.\u003C\u002Fstrong> Ovo je hipoteza o tome odakle će performanse sve više dolaziti, a ne tvrdnja da će svaka buduća igra koristiti jedan pipeline.","Centralna hipoteza Figure Rocks","success",{},{"id":717,"data":718,"type":719,"tunes":720},"d-0017",{},"delimiter",{},{"id":722,"data":723,"type":572,"tunes":725},"h-0018",{"text":724,"level":47},"Pitanje 5090 je korisno samo ako ga pravilno formulišemo",{},{"id":727,"data":728,"type":544,"tunes":730},"p-0019",{"text":729},"RTX 5090 je koristan primer upravo zato što su dokazi pomešani. Na dan 18. oktobar 2026, NVIDIA i dalje navodi GeForce RTX 5090 kao aktuelan proizvod. Istovremeno, ponašanje prodavaca i izveštaji u štampi dokumentuju neuobičajeno slabu dostupnost i nepotvrđene tvrdnje o obustavi proizvodnje. To je tržišni signal, a ne zvanična najava kraja proizvodnje. Odbranjivo pitanje stoga nije da li je NVIDIA tajno ukinula jednu karticu, već da li oportunitetni trošak dodeljivanja vrhunskog silicijuma klase Blackwell entuzijastičkom gejmingu raste kada slična arhitektura može da služi profesionalnoj vizualizaciji i AI tržištima sa veoma različitom ekonomikom.",{},{"id":732,"data":733,"type":552,"tunes":736},"c-0020",{"body":734,"title":735,"variant":559},"\u003Cstrong>ČINJENICA:\u003C\u002Fstrong> NVIDIA i dalje javno navodi RTX 5090, a arhitektura Blackwell obuhvata gejming i profesionalne proizvode.\u003Cbr>\u003Cstrong>INDUSTRIJSKI SIGNAL:\u003C\u002Fstrong> nestanak iz prodavnica, ekstremna oskudica i izveštaji o mogućoj obustavi proizvodnje postoje, ali NVIDIA nije potvrdila EOL ili obustavu proizvodnje RTX 5090.\u003Cbr>\u003Cstrong>TEHNIČKI ZAKLJUČAK:\u003C\u002Fstrong> vrhunsko jezgro ima više ekonomski vrednih destinacija.\u003Cbr>\u003Cstrong>HIPOTEZA FIGURE ROCKS:\u003C\u002Fstrong> AI i profesionalna potražnja mogu otežati održavanje tradicionalnog entuzijastičkog očekivanja — da najveće praktično jezgro ostane široko dostupno kao gejming flagship.","ČINJENICA \u002F SIGNAL \u002F HIPOTEZA",{},{"id":738,"data":739,"type":572,"tunes":741},"v3-memory-compute",{"text":740,"level":47},"Memorija počinje da postaje računski problem, a ne samo problem kapaciteta",{},{"id":743,"data":744,"type":544,"tunes":746},"v3-memory-compute-p1",{"text":745},"Postoje najmanje četiri načina da se napadne usko grlo memorije bez prostog povećanja sirove DRAM brzine: kompresija reprezentacije, rekonstrukcija detalja računanjem, predviđanje koji će podaci biti potrebni sledeći i zadržavanje pravih podataka blizu izvršne jedinice. Nijedan od njih ne čini fizički memorijski čip magično bržim. Oni menjaju to koliko korisnog rada sistem dobija iz konačnog budžeta kapaciteta i propusnog opsega.",{},{"id":748,"data":749,"type":604,"tunes":771},"v3-four-memory-levers",{"content":750,"stretched":603,"withHeadings":15},[751,755,759,763,767],[752,753,754],"Tehnika","Šta se menja","Šta se ne menja automatski",[756,757,758],"Kompresija","Manje fizičkih bajtova predstavlja iste logičke podatke","DRAM takt i širina magistrale",[760,761,762],"Neuronska rekonstrukcija","Neki detalji se generišu iz kompaktne reprezentacije","Trošak ne nestaje; inferencija ima sopstveni računski trošak i latenciju",[764,765,766],"Prediktivno prefetch-ovanje","Podaci stižu pre nego što se radno opterećenje koje ih zahteva zaglavi","Ukupan fizički propusni opseg",[768,769,770],"Rezidentnost \u002F objedinjeni pristup","Manje nepotrebnog migriranja ili dupliciranja","Ograničenja kontencije i latencije",{},{"id":773,"data":774,"type":552,"tunes":777},"v3-buddy-data",{"body":775,"title":776,"variant":551},"NVIDIA Research Buddy Compression je prijavio prosečan \u003Cstrong>2,2× odnos kompresije za HPC radna opterećenja i 1,5× za radna opterećenja dubokog učenja\u003C\u002Fstrong>, uz usporavanje od 1–2% u toj studiji. Taj rezultat nije gejming benchmark. Njegova relevantnost ovde je arhitektonska: kompresovana memorija može povećati efektivni kapacitet i smanjiti neki saobraćaj bez promene fizičkog DRAM takta.","Istraživački oslonac: efektivni kapacitet nije sirova brzina",{},{"id":779,"data":780,"type":552,"tunes":783},"v5-ntc-evidence",{"body":781,"title":782,"variant":714},"NVIDIA Research rad na materijalnim teksturama NTC demonstrirao je \u003Cstrong>4× veću linearnu rezoluciju tekstura — 16× više teksela — uz 30% manje memorije\u003C\u002Fstrong> u poređenju sa BC High. To ne znači da svaka igra dobija 16× memorijski dobitak. To demonstrira mehanizam koji je ovde važan: neki vizuelni detalji mogu se kompaktnije predstaviti i rekonstruisati računanjem umesto da se konvencionalno čuvaju pri istoj vernosti.","Gejming-specifični dokaz: neuronska kompresija tekstura",{},{"id":785,"data":786,"type":552,"tunes":789},"v3-460-correction",{"body":787,"title":788,"variant":611},"Brojka od 460× koja se ponekad povezuje sa NVIDIA sistemima odnosi se na poređenje kapaciteta memorije dostupne GPU-u kroz DGX generacije, \u003Cstrong>a ne na odnos kompresije memorije od 460×\u003C\u002Fstrong>. Ovaj članak ga namerno ne koristi kao dokaz za kompresiju.","Šta broj „460ד ne znači",{},{"id":791,"data":792,"type":572,"tunes":794},"h-0021",{"text":793,"level":47},"Kako bi tehnički odgovor mogao da funkcioniše",{},{"id":796,"data":797,"type":644,"tunes":818},"flow-0022",{"steps":798,"title":817,"orientation":643},[799,802,805,808,811,814],{"label":800,"description":801},"Kompresuj","Drži teksture ili naučene reprezentacije manjim tamo gde kvalitet dozvoljava.",{"label":803,"description":804},"Predvidi","Koristi determinističko stanje igre i naučene modele da proceniš koji će podaci biti potrebni sledeći.",{"label":806,"description":807},"Prefetch-uj","Premesti korisne podatke pre nego što se GPU zaglavi čekajući ih.",{"label":809,"description":810},"Renderuj","Troši konvencionalni grafički rad tamo gde najviše menja vidljivi rezultat.",{"label":812,"description":813},"Izvrši inferenciju","Koristi neuronsku rekonstrukciju ili generisanje tamo gde je inferencija jeftinija od direktnog čuvanja ili simulacije svega.",{"label":815,"description":816},"Prikaži","Isporuči kadar uz održavanje latencije, artefakata i tempa kadrova u prihvatljivim granicama.","Jedan mogući budući okvir za renderovanje",{},{"id":820,"data":821,"type":544,"tunes":823},"p-0023",{"text":822},"Ovaj pipeline nije jedan najavljeni proizvod. Svaka komponenta već postoji u nekom obliku, ali integrisani lanac je hipoteza. Neuronska kompresija tekstura je pokazala da naučene reprezentacije mogu zameniti memoriju računanjem. Istraživanje GPU prefetch-ovanja je pokazalo da naučeno predviđanje može poboljšati stopu pogodaka memorijskih stranica. DirectX formalizuje ML unutar grafičkog programskog modela. DLSS 5 pokazuje da generativna faza ulazi u renderovanje u realnom vremenu.",{},{"id":825,"data":826,"type":572,"tunes":828},"v3-predictive-loop",{"text":827,"level":47},"Igra ima jednu prednost koju opšta radna opterećenja često nemaju: već zna deo svoje budućnosti",{},{"id":830,"data":831,"type":544,"tunes":833},"v3-predictive-loop-p1",{"text":832},"Gejming engine zna transformaciju kamere, brzinu igrača, trenutnu ćeliju sveta, susedne streaming ćelije, stanje animacije, aktivne entitete, LOD tranzicije, zavisnosti šejdera i često verovatne putanje kretanja. Taj deterministički kontekst ne otkriva budućnost savršeno, ali daje prediktoru bogatiju početnu tačku od samog generičkog memorijskog traga.",{},{"id":835,"data":836,"type":644,"tunes":854},"v3-predictive-door",{"steps":837,"title":853,"orientation":643},[838,841,844,847,850],{"label":839,"description":840},"1. Posmatraj determinističko stanje","Smer kamere, vektor kretanja, trenutna soba, vidljivi portali i poznate zavisnosti asseta već postoje u stanju engine-a.",{"label":842,"description":843},"2. Procenti radni set bliske budućnosti","Naučeni prediktor može rangirati verovatne sledeće sobe, teksture, geometriju, šejdere ili neuronske reprezentacije.",{"label":845,"description":846},"3. Prefetch-uj tokom dostupnog propusnog opsega","Premesti verovatne resurse dok memorijski sistem ima rezervu umesto da čekaš na obavezni promašaj.",{"label":848,"description":849},"4. Zadrži determinističke garancije","Kritični asseti i dalje koriste autorizovana pravila; predviđanje dopunjuje, a ne zamenjuje ispravnost.",{"label":851,"description":852},"5. Meri trošak promašaja","Ako predviđanje troši propusni opseg ili izbacuje korisne podatke, pouzdanost ili dubina prefetch-a moraju da se smanje.","Primer prediktivne gejming memorije",{},{"id":856,"data":857,"type":552,"tunes":860},"v3-learned-evidence",{"body":858,"title":859,"variant":559},"Objavljena studija o objedinjenoj virtuelnoj memoriji CPU–GPU prijavila je \u003Cstrong>10,89% poboljšanja performansi\u003C\u002Fstrong>, porast stope pogodaka stranica sa \u003Cstrong>76,10% na 89,02%\u003C\u002Fstrong> i \u003Cstrong>11,05% manje saobraćaja na CPU–GPU interkonekciji\u003C\u002Fstrong> korišćenjem naučenog prefetch-ovanja. Rad iz 2026. na AAAI o asinhronom prefetch-ovanju KV keša prijavio je \u003Cstrong>2,15× efikasnost attention kernela i do 1,97× end-to-end propusnost\u003C\u002Fstrong> na svom LLM radnom opterećenju. Nijedan rezultat ne treba kopirati na igre; oba demonstriraju da predviđanje i prefetch mogu sakriti trošak memorije u stvarnim GPU radnim opterećenjima.","Ova ideja ima istraživački presedan, ali još ne i gejming dokaz",{},{"id":862,"data":863,"type":572,"tunes":865},"h-0024",{"text":864,"level":47},"Zašto arhitektura nalik Spark postaje zanimljiva",{},{"id":867,"data":868,"type":544,"tunes":870},"p-0025",{"text":869},"DGX Spark nije gejming PC, i tretirati ga kao takav sakrilo bi korisnu lekciju. Njegov GB10 dizajn kombinuje Arm CPU jezgra, Blackwell GPU, Tensor i RT hardver, i do 128 GB koherentne objedinjene sistemske memorije. Ta arhitektura postavlja drugačije pitanje od klasičnog desktopa: umesto čistog razdvajanja sistema na CPU memoriju i diskretni VRAM, šta se dešava kada više računskih engine-a deli veliki memorijski pool?",{},{"id":872,"data":873,"type":604,"tunes":895},"t-0026",{"content":874,"stretched":603,"withHeadings":15},[875,879,883,887,891],[876,877,878],"Svojstvo","DGX Spark referenca","Implikacija za gejming ako bi princip bio prilagođen",[880,881,882],"Memorija","Do 128 GB koherentne objedinjene LPDDR5X","Veliki zajednički pool mogao bi da smanji dupliranje i omogući lokalnom AI + grafici pristup zajedničkim podacima",[884,885,886],"Propusni opseg memorije","273 GB\u002Fs","Kapacitet nije dovoljan; vrhunska grafika i dalje zahteva agresivno upravljanje propusnim opsegom i latencijom",[888,889,890],"Sastav računanja","CPU + Blackwell GPU + Tensor + RT","Budući gejming radni zadatak mogao bi biti podeljen na više specijalizovanih engine-a",[892,893,894],"Format\u002Fpotrošnja","Kompaktan GB10 sistem","Integracija može poboljšati efikasnost, ali može smanjiti modularnu nadogradnju",{},{"id":897,"data":898,"type":552,"tunes":901},"c-0027",{"body":899,"title":900,"variant":611},"Veliki objedinjeni pool \u003Cstrong>ne\u003C\u002Fstrong> zamenjuje automatski propusni opseg vrhunske diskretne GPU. DGX Spark navodi 273 GB\u002Fs propusnog opsega memorije, dok NVIDIA RTX PRO 6000 Blackwell Workstation Edition navodi 1.792 GB\u002Fs. To su veoma različiti proizvodi i poređenje nije gejming benchmark; ono samo čini arhitektonsko ograničenje vidljivim. Spark-oliki gejming dizajn morao bi da reši kontenciju, latenciju i propusni opseg — ne samo da obezbedi više zajedničkih gigabajta.","Kapacitet nije propusni opseg",{},{"id":903,"data":904,"type":572,"tunes":906},"h-0028",{"text":905,"level":47},"Šta bi moralo da bude tačno da bi ova tranzicija uspela?",{},{"id":908,"data":909,"type":644,"tunes":930},"flow-0029",{"steps":910,"title":929,"orientation":643},[911,914,917,920,923,926],{"label":912,"description":913},"1. Inferencija mora da uštedi više nego što košta","Neuronski rad mora da ukloni dovoljno troškova skladištenja, propusnog opsega ili konvencionalnog renderovanja da opravda sopstveno računanje i latenciju.",{"label":915,"description":916},"2. Engine mora da izloži korisno stanje","Predviđanje radi bolje kada sistem zna smer kamere, zavisnosti asseta, kretanje i verovatnu vidljivost.",{"label":918,"description":919},"3. Kretanje memorije mora da bude namerno","Kompresija bez dobre politike rezidentnosti i prefetch-a i dalje može da zastane.",{"label":921,"description":922},"4. Kvalitet mora da ostane stabilan","Generisani detalji ne smeju da unesu neprihvatljive artefakte, vremensku nestabilnost ili netačne informacije o igri.",{"label":924,"description":925},"5. Latencija mora da ostane ograničena","Lepši frejm nije automatski responzivnija igra.",{"label":927,"description":928},"6. Stack mora da preživi razlike između proizvođača","Korisne tehnike zahtevaju API-je, drajvere i engine putanje koji se ne fragmentišu u jednokratne demo-e.","Test održivosti neuronskog gejminga",{},{"id":932,"data":933,"type":572,"tunes":935},"v3-reader-spark-expect",{"text":934,"level":47},"Šta kupac treba da očekuje od Spark-olike tranzicije?",{},{"id":937,"data":938,"type":707,"tunes":978},"v3-buyer-transition",{"rows":939,"title":970,"layout":604,"columns":971},[940,946,952,958,964],{"id":941,"label":942,"values":943},"pool","Veliki koherentni pool",{"cost":944,"gain":945},"Deljeni klijenti mogu da se takmiče za isti propusni opseg","Fleksibilnije deljenje između CPU, grafike i lokalnog AI",{"id":947,"label":948,"values":949},"movement","Manje dupliranja",{"cost":950,"gain":951},"Koherencija i zakazivanje dodaju složenost","Manje kopija između odvojenih pool-ova sistemske RAM i VRAM",{"id":953,"label":954,"values":955},"ai","Više prostora za lokalni AI",{"cost":956,"gain":957},"AI može da se takmiči sa grafikom za računanje i memoriju","Veći modeli, agenti ili neuronski asseti mogu koegzistirati lokalno",{"id":959,"label":960,"values":961},"efficiency","Optimizacija na nivou sistema",{"cost":962,"gain":963},"Čvršća integracija može smanjiti mogućnost nadogradnje i povećati zaključanost","Platforma može zajedno da optimizuje smeštanje podataka i akceleratore",{"id":965,"label":966,"values":967},"longevity","Nova fleksibilnost radnih zadataka",{"cost":968,"gain":969},"Samo ako engine-i i runtime-i zaista koriste te mogućnosti","Mašina može bolje da stari ako se buduće igre okrenu neuronskim radnim zadacima","Potencijalna korist vs stvarni trošak",[972,975],{"id":973,"label":974},"gain","Potencijalna korist",{"id":976,"label":977},"cost","Trošak \u002F rizik",{},{"id":980,"data":981,"type":572,"tunes":983},"h-0030",{"text":982,"level":47},"Šta bi opovrglo ovu hipotezu?",{},{"id":985,"data":986,"type":544,"tunes":988},"p-0031",{"text":987},"Ova teza je namerno opovrgljiva. Tabela ispod definiše signale tržišta, softvera, hardvera i usvajanja igara koji bi vremenom ojačali ili oslabili argument.",{},{"id":990,"data":991,"type":707,"tunes":1024},"cmp-0032",{"rows":992,"title":1016,"layout":604,"columns":1017},[993,998,1004,1010],{"id":682,"label":994,"values":995},"Memorijska ekonomija",{"weakens":996,"strengthens":997},"Potrošački kapacitet se širi i pritisak na cene se normalizuje","Prioritet servera\u002FHBM nastavlja da pritiska potrošačku alokaciju",{"id":999,"label":1000,"values":1001},"graphics","Grafički stack",{"weakens":1002,"strengthens":1003},"Neuronska grafika ostaje opciona post-obrada specifična za proizvođača","ML primitivi postaju normalne engine\u002FDirectX funkcije",{"id":1005,"label":1006,"values":1007},"hardware","Dizajn hardvera",{"weakens":1008,"strengthens":1009},"Diskretna GPU + odvojena RAM ostaje jasno superiornija po ceni, fleksibilnosti i latenciji","Više integrisanog heterogenog računanja i veći koherentni memorijski pool-ovi",{"id":1011,"label":1012,"values":1013},"games","Usvajanje u igrama",{"weakens":1014,"strengthens":1015},"Tehnologija ostaje ograničena na demo-e ili nekoliko vodećih igara","Studiji široko isporučuju neuronsku kompresiju, generisanje i prediktivne putanje podataka","Dokazi koji jačaju ili slabe tezu",[1018,1021],{"id":1019,"label":1020},"strengthens","Ojačalo bi je",{"id":1022,"label":1023},"weakens","Oslabilo bi je",{},{"id":1026,"data":1027,"type":572,"tunes":1029},"h-0033",{"text":1028,"level":47},"Praktičan zaključak za gejmera danas",{},{"id":1031,"data":1032,"type":544,"tunes":1034},"p-0034",{"text":1033},"Ne kupujte hardver zato što bi spekulativna arhitektura mogla da stigne. Kupujte za igre, rezoluciju, ciljnu latenciju i radne zadatke koje zaista imate. Ali ne pretpostavljajte da se buduća otpornost i dalje može svesti na jedan broj kao što je raster performansa ili kapacitet VRAM-a. Smer softverskog stack-a sugeriše da ponašanje memorije, neuronsko računanje i podrška engine-a mogu postati sve važniji delovi jednačine vrednosti.",{},{"id":1036,"data":1037,"type":669,"tunes":1042},"ref-0035",{"url":1038,"title":1039,"excerpt":1040,"ctaLabel":1041},"https:\u002F\u002Ffigure.rocks\u002Fsr\u002Fblog\u002Fvram-usage-is-not-vram-requirement-why-a-full-memory-meter-does-not-tell-the-whole-story","Korišćenje VRAM-a nije zahtev za VRAM: Zašto puni memorijski metar ne govori celu priču","Praktično objašnjenje radnih skupova, rezidentnosti i zašto alocirani broj VRAM-a nije isto što i tvrdi zahtev.","Pročitajte VRAM vodič",{},{"id":1044,"data":1045,"type":572,"tunes":1047},"h-0036",{"text":1046,"level":47},"Primarni izvori",{},{"id":1049,"data":1050,"type":1056,"tunes":1057},"v3-src-newzoo",{"link":1051,"meta":1052},"https:\u002F\u002Fnewzoo.com\u002Farticles\u002F2026-global-games-market-key-numbers",{"image":1053,"title":1054,"description":1055},{"url":13},"Newzoo — Ključni brojevi globalnog tržišta igara 2026","Prognoza tržišta igara za 2026. korišćena za poređenje razmera.","linkTool",{},{"id":1059,"data":1060,"type":1056,"tunes":1066},"src-0038",{"link":1061,"meta":1062},"https:\u002F\u002Fwww.semiconductors.org\u002Fglobal-annual-semiconductor-sales-increase-25-6-to-791-7-billion-in-2025\u002F",{"image":1063,"title":1064,"description":1065},{"url":13},"Semiconductor Industry Association — Globalna prodaja 2025","Prodaja poluprovodnika u 2025. i projekcija udruženja od oko 1 bilion dolara za 2026.",{},{"id":1068,"data":1069,"type":1056,"tunes":1075},"v3-src-trend-sep",{"link":1070,"meta":1071},"https:\u002F\u002Fwww.trendforce.com\u002Fpresscenter\u002Fnews\u002F20260930-13258.html",{"image":1072,"title":1073,"description":1074},{"url":13},"TrendForce — Potražnja za AI serverima održava rast cena memorije u 4Q26","Dokazi s kraja 2026. za tekuću alokaciju kapaciteta sa prioritetom servera i pritisak na potrošače.",{},{"id":1077,"data":1078,"type":1056,"tunes":1084},"v3-src-buddy",{"link":1079,"meta":1080},"https:\u002F\u002Fresearch.nvidia.com\u002Fpublication\u002F2019-04_buddy-compression-enabling-larger-memory-deep-learning-and-hpc-workloads-gpus",{"image":1081,"title":1082,"description":1083},{"url":13},"NVIDIA Research — Buddy Compression","Istraživački izvor za prosečne stope kompresije od 2,2× za HPC i 1,5× za DL.",{},{"id":1086,"data":1087,"type":1056,"tunes":1093},"v3-src-uvm",{"link":1088,"meta":1089},"https:\u002F\u002Fwww.sciencedirect.com\u002Fscience\u002Farticle\u002Fabs\u002Fpii\u002FS0743731522002490",{"image":1090,"title":1091,"description":1092},{"url":13},"CPU–GPU UVM prefetching zasnovan na dubokom učenju","Istraživački izvor za rezultate naučenog page-prefetching-a.",{},{"id":1095,"data":1096,"type":1056,"tunes":1102},"v3-src-aaai",{"link":1097,"meta":1098},"https:\u002F\u002Fojs.aaai.org\u002Findex.php\u002FAAAI\u002Farticle\u002Fview\u002F39224",{"image":1099,"title":1100,"description":1101},{"url":13},"AAAI 2026 — Asinhrono prefetch-ovanje KV keša","Istraživački izvor koji demonstrira skrivanje latencije kroz proaktivno prefetch-ovanje GPU keša.",{},{"id":1104,"data":1105,"type":1056,"tunes":1111},"src-0039",{"link":1106,"meta":1107},"https:\u002F\u002Fwww.trendforce.com\u002Fpresscenter\u002Fnews\u002F20260331-12995.html",{"image":1108,"title":1109,"description":1110},{"url":13},"TrendForce — Potražnja za AI serverima i cene memorije u 2Q26","Trenutni dokazi za prioritizaciju servera\u002FHBM, ograničenja GDDR-a i pritisak na potrošače.",{},{"id":1113,"data":1114,"type":1056,"tunes":1120},"src-0040",{"link":1115,"meta":1116},"https:\u002F\u002Fdevblogs.microsoft.com\u002Fdirectx\u002Fevolving-directx-for-the-ml-era-on-windows\u002F",{"image":1117,"title":1118,"description":1119},{"url":13},"Microsoft DirectX — Evolucija DirectX-a za ML eru","Zvanični GDC 2026 pregled ML-a na nivou šejdera i modela u DirectX-u.",{},{"id":1122,"data":1123,"type":1056,"tunes":1129},"src-0041",{"link":1124,"meta":1125},"https:\u002F\u002Fresearch.nvidia.com\u002Flabs\u002Fadlr\u002FDLSS5\u002F",{"image":1126,"title":1127,"description":1128},{"url":13},"NVIDIA Research — DLSS 5: Generativno neuronsko renderovanje","Primarna istraživačka stranica koja opisuje DLSS 5 kao fazu generativnog renderovanja u realnom vremenu.",{},{"id":1131,"data":1132,"type":1056,"tunes":1138},"src-0042",{"link":1133,"meta":1134},"https:\u002F\u002Fwww.nvidia.com\u002Fen-us\u002Fproducts\u002Fworkstations\u002Fdgx-spark\u002F",{"image":1135,"title":1136,"description":1137},{"url":13},"NVIDIA — DGX Spark specifikacije","Zvanične specifikacije arhitekture, objedinjene memorije i propusnog opsega.",{},{"id":1140,"data":1141,"type":1056,"tunes":1147},"v5-src-5090-official",{"link":1142,"meta":1143},"https:\u002F\u002Fwww.nvidia.com\u002Fen-us\u002Fgeforce\u002Fgraphics-cards\u002F50-series\u002Frtx-5090\u002F",{"image":1144,"title":1145,"description":1146},{"url":13},"NVIDIA — GeForce RTX 5090","Zvanična aktuelna stranica proizvoda RTX 5090 koja se koristi za razlikovanje potvrđenog statusa proizvoda od glasina o obustavi proizvodnje.",{},{"id":1149,"data":1150,"type":1056,"tunes":1156},"v5-src-5090-signal",{"link":1151,"meta":1152},"https:\u002F\u002Fwww.tomshardware.com\u002Fpc-components\u002Fgpus\u002Fnvidia-rtx-5090-disappears-from-micro-center-amid-rumors-of-a-production-halt-retailer-flags-flagship-as-not-carried-as-stock-mysteriously-vanishes",{"image":1153,"title":1154,"description":1155},{"url":13},"Tom’s Hardware — Nestanak RTX 5090 iz maloprodaje i glasine o obustavi proizvodnje","Trenutni tržišni izveštaj koji se koristi samo kao signal iz industrije; eksplicitno napominje da NVIDIA nije potvrdila obustavu proizvodnje.",{},{"id":1158,"data":1159,"type":1056,"tunes":1165},"v5-src-ntc",{"link":1160,"meta":1161},"https:\u002F\u002Fresearch.nvidia.com\u002Flabs\u002Frtr\u002Fneural_texture_compression\u002F",{"image":1162,"title":1163,"description":1164},{"url":13},"NVIDIA Research — Neuronska kompresija tekstura materijala sa slučajnim pristupom","Primarni izvor za poređenje 4× linearne rezolucije \u002F 16× teksela uz 30% manje memorije u odnosu na BC High.",{},{"id":1167,"data":1168,"type":1056,"tunes":1174},"v5-src-rtxpro6000",{"link":1169,"meta":1170},"https:\u002F\u002Fwww.nvidia.com\u002Fde-de\u002Fproducts\u002Fworkstations\u002Fprofessional-desktop-gpus\u002Frtx-pro-6000\u002F",{"image":1171,"title":1172,"description":1173},{"url":13},"NVIDIA — RTX PRO 6000 Blackwell Workstation Edition","Zvanični izvor za 96 GB GDDR7 i 1.792 GB\u002Fs memorijskog propusnog opsega koji se koristi kao referentna tačka za propusni opseg.",{},{"id":1176,"data":1177,"type":1196,"tunes":1197},"gloss-0044",{"title":1178,"entries":1179},"Ključni koncepti",[1180,1184,1188,1192],{"term":1181,"anchor":1182,"definition":1183},"Neuronsko renderovanje","neural-rendering","Metode renderovanja koje koriste obučene modele kao deo procesa stvaranja, rekonstrukcije ili unapređenja vizuelnog izlaza.",{"term":1185,"anchor":1186,"definition":1187},"Heterogeno računanje","heterogeneous-compute","Sistem koji dodeljuje različite radne zadatke različitim računarskim jedinicama kao što su CPU, GPU, RT i matrični\u002FTensor hardver.",{"term":1189,"anchor":1190,"definition":1191},"Efektivna memorija","effective-memory","Praktičan opis količine korisnih podataka koje memorijski sistem može da zadrži i dostavi računarskim jedinicama nakon uzimanja u obzir kompresije, rezidentnosti i efikasnosti premeštanja. Nije formalna zamena za fizički kapacitet.",{"term":1193,"anchor":1194,"definition":1195},"Prediktivna memorija","predictive-memory","Figure Rocks skraćenica za kombinovanje poznatog stanja igre i naučene predikcije radi pripreme verovatno potrebnih podataka pre zastoja. To je hipoteza, a ne industrijski standard.","glossary",{},{"id":541,"data":1199,"type":544,"tunes":1201},{"text":1200},"Decenijama je entuzijastičko PC gejmingovanje sledilo poznatu pogodbu: igre su tražile više računarske snage, generacije GPU-a su je pružale, a igrači su nadograđivali kada jaz postane dovoljno velik. Ta petlja nikada nije savršeno funkcionisala, ali je smer bio razumljiv. U 2026. godini tu petlju je sve teže pročitati jer se dve promene dešavaju istovremeno: AI infrastruktura se agresivno takmiči za silicijum i memoriju, dok AI tehnike direktno ulaze u način na koji igre stvaraju kadrove.",{},{"id":547,"data":1203,"type":552,"tunes":1205},{"body":1204,"title":550,"variant":551},"\u003Cstrong>Stari ciklus nadogradnje za gejming je pod pritiskom, ali ga ne zamenjuju jednostavno skuplji GPU-ovi.\u003C\u002Fstrong> AI menja obe strane jednačine: povećava konkurenciju za poluprovodničke i memorijske resurse, a takođe omogućava tehnike neuronskog renderovanja koje mogu da zamene uskladištene podatke ili konvencionalni rad renderovanja inferencijom. Važna mogućnost je da gejming može odgovoriti na strožu hardversku ekonomiju time što će postati računarski selektivniji, a ne samo zahtevanjem većih GPU-ova.",{},{"id":555,"data":1207,"type":552,"tunes":1209},{"body":1208,"title":558,"variant":559},"Ovo je \u003Cstrong>hipoteza potkrepljena dokazima\u003C\u002Fstrong>. Dokumentovani tržišni podaci i tehnologije koje su u prodaji ili najavljene označeni su kao činjenice ili signali iz industrije. Veze između njih su tehničko zaključivanje. Arhitektura budućnosti opisana kasnije je hipoteza Figure Rocks, a ne tvrdnja o nenajavljenom planu razvoja NVIDIA, AMD, Intel, Microsoft ili konzola.",{},{"id":562,"data":1211,"type":566,"tunes":1212},{"title":564,"maxLevel":565,"minLevel":47},{},{"id":569,"data":1214,"type":572,"tunes":1215},{"text":571,"level":47},{},{"id":575,"data":1217,"type":544,"tunes":1219},{"text":1218},"Newzoo predviđa globalno tržište igara na 213,9 milijardi dolara u 2026. godini, što je rast od 6,1% u odnosu na prethodnu godinu. Semiconductor Industry Association je izvestila o 791,7 milijardi dolara globalne prodaje poluprovodnika u 2025. godini, što je rast od 25,6%, i projektovala oko 1 bilion dolara za 2026. Posao igara i posao poluprovodnika i dalje zavise jedan od drugog, ali njihovi podsticaji postaju sve manje simetrični: isti napredni proizvodni ekosistem koji omogućava premium gejming sada služi AI i data-center kupcima sa mnogo većim budžetima za infrastrukturu. To nisu direktno uporedive kategorije proizvoda; oni uspostavljaju razmeru uzvodne računarske ekonomije oko gejminga.",{},{"id":580,"data":1221,"type":604,"tunes":1236},{"content":1222,"stretched":603,"withHeadings":15},[1223,1225,1228,1230,1233],[584,1224,586],"Prijavljena ili predviđena vrednost",[1226,589,1227],"Globalno tržište igara, prognoza za 2026.","Veliko tržište potrošačke zabave, ali nizvodno od snabdevanja naprednim hardverom",[1229,593,594],"PC igre, prognoza za 2026.",[1231,597,1232],"Globalna prodaja poluprovodnika, 2025.","Pokazuje razmeru uzvodne industrije koja služi gejmingu, AI-ju, mobilnim uređajima, automobilima i oblaku",[1234,601,1235],"Globalna poluprovodnička industrija, projekcija za 2026.","Povećava oportunitetni trošak dodeljivanja premium silicijuma samo potrošačkom gejmingu",{},{"id":607,"data":1238,"type":552,"tunes":1240},{"body":1239,"title":610,"variant":611},"Brojke uspostavljaju \u003Cstrong>ekonomski kontekst, a ne direktnu uzročnost\u003C\u002Fstrong>. One ne dokazuju da je određeni wafer, GPU ili memorijski paket uzet igraču i preusmeren na AI. Takođe ne znače da su gejming i poluprovodnici dve najveće industrije na svetu. Uži, odbranjiv argument je da tržište igara vredno preko 200 milijardi dolara zavisi od uzvodne poluprovodničke ekonomije koja se približava bilionu dolara godišnje, gde se AI infrastruktura sada takmiči za naprednu računarsku snagu i memoriju pod veoma različitim budžetskim uslovima i maržama.",{},{"id":614,"data":1242,"type":572,"tunes":1243},{"text":616,"level":47},{},{"id":619,"data":1245,"type":544,"tunes":1247},{"text":1246},"TrendForce je više puta izvestio da dobavljači DRAM-a preusmeravaju kapacitete ka serverskim i HBM proizvodima kako AI potražnja raste. U martu 2026. predvideo je da će ugovorne cene konvencionalnog DRAM-a porasti 58–63% kvartalno u Q2 i naveo da ograničena GDDR alokacija ograničava snabdevanje grafičkom memorijom. Do Q3, TrendForce je saopštio da potrošački kupci dostižu granicu priuštivosti iako su AI i serverska potražnja ostale jake.",{},{"id":624,"data":1249,"type":644,"tunes":1260},{"steps":1250,"title":642,"orientation":643},[1251,1253,1254,1255,1258],{"label":628,"description":1252},"Cloud provajderi i AI graditelji obezbeđuju serversku memoriju, HBM, enterprise SSD-ove i napredno računarstvo.",{"label":631,"description":632},{"label":634,"description":635},{"label":1256,"description":1257},"4. Proizvođači gejming opreme apsorbuju ili prenose troškove","Dizajn ploče, VRAM kapacitet i cena sistema postaju teži kompromisi.",{"label":640,"description":1259},"Brži GPU je i dalje koristan, ali brute-force skaliranje postaje ekonomski teže ponoviti na istim potrošačkim cenovnim tačkama.",{},{"id":647,"data":1262,"type":552,"tunes":1265},{"body":1263,"title":1264,"variant":611},"TrendForce je 30. septembra 2026. izvestio da dobavljači i dalje daju prioritet kapacitetima naprednih procesa za visokoperformantne serverske proizvode, sa projektovanim rastom ugovornih cena konvencionalnog DRAM-a od dodatnih 10–15% kvartalno u Q4. Njihov DRAM bilten takođe navodi da HBM nastavlja da potiskuje opšti DRAM kapacitet. Pritisak opisan ovde stoga ostaje vidljiv krajem 2026; nije zasnovan samo na privremenom skoku početkom godine.","Signal iz septembra 2026, a ne stara priča o nestašici",{},{"id":653,"data":1267,"type":572,"tunes":1269},{"text":1268,"level":47},"U isto vreme, sam okvir se menja",{},{"id":658,"data":1271,"type":544,"tunes":1273},{"text":1272},"Microsoft sada opisuje mašinsko učenje kao nešto što postaje temelj real-time grafike. DirectX dodaje ML putanje na nivou šejdera i modela, tako da neuronska kompresija tekstura, denoising, super rezolucija i drugi naučeni zadaci mogu da budu unutar grafičkog steka umesto da budu prikačeni kao izolovane funkcije. NVIDIA-ino istraživanje DLSS 5 gura ideju dalje uvodeći generativnu neuronsku fazu renderovanja koja dopunjuje konvencionalno renderovanje naučenim priorima izgleda. Neuronsko renderovanje ne znači da konvencionalno renderovanje nestaje. Model koji se pojavljuje je hibridni: renderuj neke informacije direktno, izvedi ili rekonstruiši druge informacije kada je to jeftinije ili korisnije.",{},{"id":663,"data":1275,"type":669,"tunes":1276},{"url":665,"title":666,"excerpt":667,"ctaLabel":668},{},{"id":672,"data":1278,"type":707,"tunes":1297},{"rows":1279,"title":1293,"layout":604,"columns":1294},[1280,1284,1286,1290],{"id":676,"label":677,"values":1281},{"new":1282,"old":1283},"Čuvaj\u002Frenderuj selektivno, rekonstruiši ili izvedi neke detalje","Čuvaj više detalja i renderuj više posla",{"id":682,"label":683,"values":1285},{"new":685,"old":686},{"id":688,"label":689,"values":1287},{"new":1288,"old":1289},"Mešaj šejdere, RT i matrično\u002FTensor računarstvo","Više šejder\u002FRT propusnosti",{"id":694,"label":695,"values":1291},{"new":1292,"old":698},"Engine sve više zakazuje renderovanje, premeštanje podataka i ML zadatke","Tradicionalno skaliranje vs skaliranje u neuronskoj eri",[1295,1296],{"id":702,"label":703},{"id":705,"label":706},{},{"id":710,"data":1299,"type":552,"tunes":1301},{"body":1300,"title":713,"variant":714},"\u003Cstrong>AI možda slabi ekonomiku brute-force gejming hardvera dok istovremeno pruža tehnike koje čine brute-force skaliranje manje neophodnim.\u003C\u002Fstrong> To ne znači da će buduće igre zahtevati manje hardvera. Znači da više rezultata može doći iz odlučivanja šta sačuvati, preneti, renderovati, rekonstruisati ili izvesti. Figure Rocks skraćeno: \u003Cstrong>čuvaj manje → premeštaj manje → predviđaj više → renderuj manje → izvodi više.\u003C\u002Fstrong> Ovo je hipoteza o tome odakle će performanse sve više dolaziti, a ne tvrdnja da će svaka buduća igra koristiti jedan pipeline.",{},{"id":717,"data":1303,"type":719,"tunes":1304},{},{},{"id":722,"data":1306,"type":572,"tunes":1307},{"text":724,"level":47},{},{"id":727,"data":1309,"type":544,"tunes":1311},{"text":1310},"RTX 5090 je koristan primer upravo zato što su dokazi pomešani. Na dan 18. oktobar 2026, NVIDIA i dalje navodi GeForce RTX 5090 kao aktuelan proizvod. Istovremeno, ponašanje prodavaca i izveštaji u štampi dokumentovali su neuobičajeno lošu dostupnost i nepotvrđene tvrdnje o obustavi proizvodnje. To je tržišni signal, a ne zvanična najava kraja proizvodnje. Odbranjivo pitanje stoga nije da li je NVIDIA tajno ukinula jednu karticu, već da li oportunitetni trošak dodeljivanja vrhunskog Blackwell silicijuma entuzijastičkom gejmingu raste kada slična arhitektura može da služi profesionalnoj vizualizaciji i AI tržištima sa veoma različitom ekonomikom.",{},{"id":732,"data":1313,"type":552,"tunes":1315},{"body":1314,"title":735,"variant":559},"\u003Cstrong>ČINJENICA:\u003C\u002Fstrong> NVIDIA i dalje javno navodi RTX 5090, a Blackwell arhitektura pokriva gejming i profesionalne proizvode.\u003Cbr>\u003Cstrong>INDUSTRIJSKI SIGNAL:\u003C\u002Fstrong> nestanak iz prodavnica, ekstremna oskudica i izveštaji o mogućoj obustavi proizvodnje postoje, ali NVIDIA nije potvrdila EOL ili obustavu proizvodnje RTX 5090.\u003Cbr>\u003Cstrong>TEHNIČKI ZAKLJUČAK:\u003C\u002Fstrong> vrhunski čip ima više ekonomski vrednih destinacija.\u003Cbr>\u003Cstrong>HIPOTEZA FIGURE ROCKS:\u003C\u002Fstrong> AI i profesionalna potražnja mogu otežati održavanje tradicionalnog entuzijastičkog očekivanja — da najveći praktični čip ostaje široko dostupan kao gejming flagship.",{},{"id":738,"data":1317,"type":572,"tunes":1319},{"text":1318,"level":47},"Memorija počinje da postaje računarski problem, a ne samo problem kapaciteta",{},{"id":743,"data":1321,"type":544,"tunes":1323},{"text":1322},"Postoje najmanje četiri načina da se napadne usko grlo memorije bez jednostavnog povećanja sirove DRAM brzine: kompresuj reprezentaciju, rekonstruiši detalje računanjem, predvidi koji će podaci biti potrebni sledeći i drži prave podatke rezidentnim blizu izvršne jedinice. Nijedan od njih ne čini fizički memorijski čip magično bržim. Oni menjaju koliko korisnog rada sistem dobija iz konačnog kapaciteta i budžeta propusnog opsega.",{},{"id":748,"data":1325,"type":604,"tunes":1337},{"content":1326,"stretched":603,"withHeadings":15},[1327,1328,1329,1331,1334],[752,753,754],[756,757,758],[760,761,1330],"Trošak ne nestaje; inferencija ima sopstveni trošak računanja\u002Flatencije",[1332,1333,766],"Prediktivno prefetch","Podaci stižu pre nego što se zadatak koji ih zahteva zaglavi",[1335,1336,770],"Rezidentnost \u002F objedinjen pristup","Manje nepotrebnog premeštanja ili dupliranja",{},{"id":773,"data":1339,"type":552,"tunes":1341},{"body":1340,"title":776,"variant":551},"NVIDIA Research Buddy Compression izvestio je o prosečnom \u003Cstrong>2,2× kompresionom odnosu za HPC zadatke i 1,5× za zadatke dubokog učenja\u003C\u002Fstrong>, uz 1–2% usporavanja u toj studiji. Taj rezultat nije gejming benchmark. Njegova relevantnost ovde je arhitektonska: kompresovana memorija može povećati efektivni kapacitet i smanjiti neki saobraćaj bez promene fizičkog DRAM takta.",{},{"id":779,"data":1343,"type":552,"tunes":1345},{"body":1344,"title":782,"variant":714},"NVIDIA Research rad na materijalnim teksturama NTC pokazao je \u003Cstrong>4× veću linearnu rezoluciju tekstura — 16× više teksela — uz 30% manje memorije\u003C\u002Fstrong> u poređenju sa BC High. To ne znači da svaka igra dobija 16× memorijski dobitak. To demonstrira mehanizam koji je ovde važan: neki vizuelni detalji mogu se kompaktnije predstaviti i rekonstruisati računanjem umesto da se konvencionalno čuvaju pri istoj vernosti.",{},{"id":785,"data":1347,"type":552,"tunes":1348},{"body":787,"title":788,"variant":611},{},{"id":791,"data":1350,"type":572,"tunes":1351},{"text":793,"level":47},{},{"id":796,"data":1353,"type":644,"tunes":1373},{"steps":1354,"title":1372,"orientation":643},[1355,1357,1360,1363,1366,1369],{"label":756,"description":1356},"Držite teksture ili naučene reprezentacije manjim gde kvalitet to dozvoljava.",{"label":1358,"description":1359},"Predviđanje","Koristite determinističko stanje igre i naučene modele da procenite koji će podaci sledeći biti potrebni.",{"label":1361,"description":1362},"Prefetch","Premeštajte korisne podatke pre nego što se GPU blokira čekajući ih.",{"label":1364,"description":1365},"Renderovanje","Trošite konvencionalni grafički rad tamo gde najviše menja vidljivi rezultat.",{"label":1367,"description":1368},"Inferencija","Koristite neuronsku rekonstrukciju ili generisanje tamo gde je inferencija jeftinija od direktnog skladištenja ili simulacije svega.",{"label":1370,"description":1371},"Prikaz","Isporučite frejm uz održavanje latencije, artefakata i tempa frejmova u prihvatljivim granicama.","Jedan mogući budući pipeline za frejmove",{},{"id":820,"data":1375,"type":544,"tunes":1377},{"text":1376},"Ovaj pipeline nije jedan najavljeni proizvod. Svaka komponenta već postoji u nekom obliku, ali integrisani lanac je hipoteza. Neuronska kompresija tekstura je pokazala da naučene reprezentacije mogu da razmene računanje za memoriju. Istraživanje GPU prefetch-a je pokazalo da naučeno predviđanje može da poboljša stopu pogodaka memorijskih stranica. DirectX formalizuje ML unutar grafičkog programskog modela. DLSS 5 pokazuje da generativna faza ulazi u renderovanje u realnom vremenu.",{},{"id":825,"data":1379,"type":572,"tunes":1381},{"text":1380,"level":47},"Igra ima jednu prednost koju opšti radni zadaci često nemaju: već zna deo svoje budućnosti",{},{"id":830,"data":1383,"type":544,"tunes":1385},{"text":1384},"Game engine zna transformaciju kamere, brzinu igrača, trenutnu ćeliju sveta, susedne streaming ćelije, stanje animacije, aktivne entitete, LOD tranzicije, zavisnosti šejdera i često verovatne putanje kretanja. Taj deterministički kontekst ne otkriva budućnost savršeno, ali daje prediktoru bogatiju početnu tačku od samog generičkog memorijskog traga.",{},{"id":835,"data":1387,"type":644,"tunes":1402},{"steps":1388,"title":1401,"orientation":643},[1389,1391,1394,1397,1399],{"label":1390,"description":840},"1. Posmatrajte determinističko stanje",{"label":1392,"description":1393},"2. Procenite radni set bliske budućnosti","Naučeni prediktor može da rangira verovatne sledeće sobe, teksture, geometriju, šejdere ili neuronske reprezentacije.",{"label":1395,"description":1396},"3. Prefetch tokom dostupnog propusnog opsega","Premeštajte verovatne resurse dok memorijski sistem ima rezervu, umesto da čekate strogi promašaj zahteva.",{"label":1398,"description":849},"4. Očuvajte determinističke garancije",{"label":1400,"description":852},"5. Merite cenu promašaja","Primer prediktivne memorije u igrama",{},{"id":856,"data":1404,"type":552,"tunes":1407},{"body":1405,"title":1406,"variant":559},"Objavljena studija o unifikovanoj virtuelnoj memoriji CPU–GPU izvestila je o \u003Cstrong>poboljšanju performansi od 10,89%\u003C\u002Fstrong>, porastu stope pogodaka stranica sa \u003Cstrong>76,10% na 89,02%\u003C\u002Fstrong> i \u003Cstrong>11,05% manjem saobraćaju između CPU i GPU\u003C\u002Fstrong> korišćenjem naučenog prefetch-a. Rad sa AAAI konferencije iz 2026. o asinhronom prefetch-u KV keša izvestio je o \u003Cstrong>2,15× efikasnosti attention kernela i do 1,97× end-to-end propusnosti\u003C\u002Fstrong> na svom LLM radnom zadatku. Nijedan rezultat ne treba kopirati na igre; oba pokazuju da predviđanje i prefetch mogu da sakriju memorijski trošak u realnim GPU radnim zadacima.","Ova ideja ima istraživački presedan, ali još ne i dokaz u igrama",{},{"id":862,"data":1409,"type":572,"tunes":1411},{"text":1410,"level":47},"Zašto arhitektura nalik Spark-u postaje zanimljiva",{},{"id":867,"data":1413,"type":544,"tunes":1415},{"text":1414},"DGX Spark nije gejming PC, i tretirati ga kao takav sakrilo bi korisnu lekciju. Njegov GB10 dizajn kombinuje Arm CPU jezgra, Blackwell GPU, Tensor i RT hardver, i do 128 GB koherentne unifikovane sistemske memorije. Ta arhitektura postavlja drugačije pitanje od klasičnog desktopa: umesto čistog razdvajanja sistema na CPU memoriju i diskretni VRAM, šta se dešava kada više računarskih engine-a deli veliki memorijski pool?",{},{"id":872,"data":1417,"type":604,"tunes":1430},{"content":1418,"stretched":603,"withHeadings":15},[1419,1421,1424,1426,1428],[876,877,1420],"Implikacija za igre ako bi princip bio prilagođen",[880,1422,1423],"Do 128 GB koherentne unifikovane LPDDR5X","Veliki deljeni pool mogao bi da smanji dupliranje i omogući lokalnoj AI i grafici pristup zajedničkim podacima",[1425,885,886],"Memorijski propusni opseg",[888,889,1427],"Budući radni zadatak igre mogao bi da se podeli na više specijalizovanih engine-a",[892,893,1429],"Integracija može da poboljša efikasnost, ali može da smanji modularnu nadogradnju",{},{"id":897,"data":1432,"type":552,"tunes":1434},{"body":1433,"title":900,"variant":611},"Veliki unifikovani pool \u003Cstrong>ne\u003C\u002Fstrong> zamenjuje automatski propusni opseg vrhunskog diskretnog GPU-a. DGX Spark navodi 273 GB\u002Fs memorijskog propusnog opsega, dok NVIDIA RTX PRO 6000 Blackwell Workstation Edition navodi 1.792 GB\u002Fs. To su veoma različiti proizvodi i poređenje nije gejming benchmark; ono samo čini arhitektonsko ograničenje vidljivim. Gejming dizajn nalik Spark-u morao bi da reši kontenciju, latenciju i propusni opseg — ne samo da obezbedi više deljenih gigabajta.",{},{"id":903,"data":1436,"type":572,"tunes":1437},{"text":905,"level":47},{},{"id":908,"data":1439,"type":644,"tunes":1451},{"steps":1440,"title":929,"orientation":643},[1441,1443,1444,1447,1448,1449],{"label":912,"description":1442},"Neuronski rad mora da ukloni dovoljno troška skladištenja, propusnog opsega ili konvencionalnog renderovanja da opravda sopstveno računanje i latenciju.",{"label":915,"description":916},{"label":1445,"description":1446},"3. Memorijsko kretanje mora da postane namerno","Kompresija bez dobre politike rezidentnosti i prefetch-a i dalje može da izazove zastoj.",{"label":921,"description":922},{"label":924,"description":925},{"label":927,"description":1450},"Korisne tehnike zahtevaju API-je, drajvere i engine putanje koji se ne fragmentišu u jednokratne demo primere.",{},{"id":932,"data":1453,"type":572,"tunes":1455},{"text":1454,"level":47},"Šta kupac treba da očekuje od tranzicije nalik Spark-u?",{},{"id":937,"data":1457,"type":707,"tunes":1480},{"rows":1458,"title":1474,"layout":604,"columns":1475},[1459,1462,1465,1469,1472],{"id":941,"label":942,"values":1460},{"cost":944,"gain":1461},"Fleksibilnije deljenje između CPU, grafike i lokalne AI",{"id":947,"label":948,"values":1463},{"cost":950,"gain":1464},"Manje kopija između odvojenih pool-ova sistemskog RAM-a i VRAM-a",{"id":953,"label":1466,"values":1467},"Više prostora za lokalnu AI",{"cost":956,"gain":1468},"Veći modeli, agenti ili neuronski asseti mogu da koegzistiraju lokalno",{"id":959,"label":960,"values":1470},{"cost":1471,"gain":963},"Čvršća integracija može da smanji mogućnost nadogradnje i poveća zaključanost",{"id":965,"label":966,"values":1473},{"cost":968,"gain":969},"Potencijalna korist naspram stvarne cene",[1476,1478],{"id":973,"label":1477},"Potencijalni dobitak",{"id":976,"label":1479},"Cena \u002F rizik",{},{"id":980,"data":1482,"type":572,"tunes":1483},{"text":982,"level":47},{},{"id":985,"data":1485,"type":544,"tunes":1487},{"text":1486},"Ova teza je namerno opovrgljiva. Tabela ispod definiše signale na tržištu, softveru, hardveru i usvajanju u igrama koji bi vremenom ojačali ili oslabili argument.",{},{"id":990,"data":1489,"type":707,"tunes":1513},{"rows":1490,"title":1016,"layout":604,"columns":1509},[1491,1495,1500,1504],{"id":682,"label":1492,"values":1493},"Ekonomija memorije",{"weakens":996,"strengthens":1494},"Prioritet servera\u002FHBM nastavlja da potiskuje potrošačku alokaciju",{"id":999,"label":1496,"values":1497},"Grafički stek",{"weakens":1498,"strengthens":1499},"Neuralna grafika ostaje opciona naknadna obrada specifična za proizvođača","ML primitivi postaju uobičajene funkcije engine-a\u002FDirectX-a",{"id":1005,"label":1006,"values":1501},{"weakens":1502,"strengthens":1503},"Diskretna GPU + odvojena RAM memorija ostaje jasno superiornija po ceni, fleksibilnosti i latenciji","Više integrisanog heterogenog računanja i veći koherentni memorijski bazeni",{"id":1011,"label":1505,"values":1506},"Prihvatanje u igrama",{"weakens":1507,"strengthens":1508},"Tehnologija ostaje ograničena na demo verzije ili nekoliko vodećih igara","Studiji široko isporučuju neuralnu kompresiju, generisanje i prediktivne tokove podataka",[1510,1511],{"id":1019,"label":1020},{"id":1022,"label":1512},"Oslaabilo bi je",{},{"id":1026,"data":1515,"type":572,"tunes":1516},{"text":1028,"level":47},{},{"id":1031,"data":1518,"type":544,"tunes":1520},{"text":1519},"Ne kupujte hardver zato što bi mogla da stigne spekulativna arhitektura. Kupujte za igre, rezoluciju, ciljnu latenciju i radne zadatke koje zaista imate. Ali ne pretpostavljajte da se buduća otpornost i dalje može svesti na jedan broj kao što je raster performansa ili kapacitet VRAM-a. Smer softverskog steka sugeriše da bi ponašanje memorije, neuralno računanje i podrška engine-a mogli postati sve važniji delovi jednačine vrednosti.",{},{"id":1036,"data":1522,"type":669,"tunes":1525},{"url":1038,"title":1523,"excerpt":1040,"ctaLabel":1524},"Korišćenje VRAM-a nije zahtev za VRAM: Zašto pun memetar ne govori celu priču","Pročitajte vodič o VRAM-u",{},{"id":1044,"data":1527,"type":572,"tunes":1528},{"text":1046,"level":47},{},{"id":1049,"data":1530,"type":1056,"tunes":1534},{"link":1051,"meta":1531},{"image":1532,"title":1533,"description":1055},{"url":13},"Newzoo — Ključni brojevi globalnog tržišta igara za 2026.",{},{"id":1059,"data":1536,"type":1056,"tunes":1540},{"link":1061,"meta":1537},{"image":1538,"title":1539,"description":1065},{"url":13},"Semiconductor Industry Association — Globalna prodaja u 2025.",{},{"id":1068,"data":1542,"type":1056,"tunes":1546},{"link":1070,"meta":1543},{"image":1544,"title":1073,"description":1545},{"url":13},"Dokazi s kraja 2026. o tekućoj alokaciji kapaciteta sa prioritetom servera i pritisku na potrošače.",{},{"id":1077,"data":1548,"type":1056,"tunes":1551},{"link":1079,"meta":1549},{"image":1550,"title":1082,"description":1083},{"url":13},{},{"id":1086,"data":1553,"type":1056,"tunes":1558},{"link":1088,"meta":1554},{"image":1555,"title":1556,"description":1557},{"url":13},"UVM prefetching CPU–GPU zasnovan na dubokom učenju","Istraživački izvor za rezultate naučenog prefetching-a stranica.",{},{"id":1095,"data":1560,"type":1056,"tunes":1565},{"link":1097,"meta":1561},{"image":1562,"title":1563,"description":1564},{"url":13},"AAAI 2026 — Asinhroni KV cache prefetching","Istraživački izvor koji demonstrira skrivanje latencije kroz proaktivni prefetching GPU keša.",{},{"id":1104,"data":1567,"type":1056,"tunes":1570},{"link":1106,"meta":1568},{"image":1569,"title":1109,"description":1110},{"url":13},{},{"id":1113,"data":1572,"type":1056,"tunes":1576},{"link":1115,"meta":1573},{"image":1574,"title":1118,"description":1575},{"url":13},"Zvanični pregled GDC 2026 o ML-u na nivou šejdera i modela u DirectX-u.",{},{"id":1122,"data":1578,"type":1056,"tunes":1582},{"link":1124,"meta":1579},{"image":1580,"title":1581,"description":1128},{"url":13},"NVIDIA Research — DLSS 5: Generativno neuralno renderovanje",{},{"id":1131,"data":1584,"type":1056,"tunes":1588},{"link":1133,"meta":1585},{"image":1586,"title":1136,"description":1587},{"url":13},"Zvanične specifikacije arhitekture, unifikovane memorije i propusnog opsega.",{},{"id":1140,"data":1590,"type":1056,"tunes":1594},{"link":1142,"meta":1591},{"image":1592,"title":1145,"description":1593},{"url":13},"Zvanična trenutna stranica proizvoda RTX 5090 korišćena za razlikovanje potvrđenog statusa proizvoda od glasina o obustavi proizvodnje.",{},{"id":1149,"data":1596,"type":1056,"tunes":1600},{"link":1151,"meta":1597},{"image":1598,"title":1154,"description":1599},{"url":13},"Trenutni tržišni izveštaj korišćen samo kao industrijski signal; eksplicitno napominje da NVIDIA nije potvrdila obustavu proizvodnje.",{},{"id":1158,"data":1602,"type":1056,"tunes":1606},{"link":1160,"meta":1603},{"image":1604,"title":1605,"description":1164},{"url":13},"NVIDIA Research — Neuralna kompresija materijalnih tekstura sa slučajnim pristupom",{},{"id":1167,"data":1608,"type":1056,"tunes":1612},{"link":1169,"meta":1609},{"image":1610,"title":1172,"description":1611},{"url":13},"Zvanični izvor za 96 GB GDDR7 i memorijsku propusnost od 1.792 GB\u002Fs koja se koristi kao referentna tačka za propusnost.",{},{"id":1176,"data":1614,"type":1196,"tunes":1623},{"title":1615,"entries":1616},"Ključni pojmovi",[1617,1618,1619,1621],{"term":1181,"anchor":1182,"definition":1183},{"term":1185,"anchor":1186,"definition":1187},{"term":1189,"anchor":1190,"definition":1620},"Praktičan opis toga koliko korisnih podataka memorijski sistem može da zadrži i dostavi računarskim jedinicama nakon uzimanja u obzir kompresije, rezidentnosti i efikasnosti premeštanja. Nije formalna zamena za fizički kapacitet.",{"term":1193,"anchor":1194,"definition":1622},"Figure Rocks skraćeni izraz za kombinovanje poznatog stanja igre i naučene predikcije radi pripreme verovatno potrebnih podataka pre zastoja. To je hipoteza, a ne industrijski standard.",{},"2.31","AI čini visokoperformantni silicijum i memoriju vrednijim izvan gejminga, dok istovremeno igrama daje nove načine za renderovanje, kompresiju i predviđanje. Ova analiza postavlja pitanje da li se ta dva trenda stapaju u novi hardverski model.","\u002Fuploads\u002F2026\u002F10\u002Frender-less-infer-more-is-ai-breaking-the-traditional-gaming-hardware-upgrade-cycle-1792323493963-l2x8qb.webp","render-less-infer-more-is-ai-breaking-the-traditional-gaming-hardware-upgrade-cycle-1792323493963-l2x8qb","PUBLISHED","2026-10-18T03:34:00.000Z","2026-10-18T11:34:48.220Z","2026-10-18T15:26:55.786Z",{"en":1633,"de":1634,"sr":1635,"es":1636,"fr":1637,"it":1638,"ru":1639,"zh":1640},"\u002Fblog\u002Frender-less-infer-more-is-ai-breaking-the-traditional-gaming-hardware-upgrade-cycle","\u002Fde\u002Fblog\u002Frender-less-infer-more-is-ai-breaking-the-traditional-gaming-hardware-upgrade-cycle","\u002Fsr\u002Fblog\u002Frender-less-infer-more-is-ai-breaking-the-traditional-gaming-hardware-upgrade-cycle","\u002Fes\u002Fblog\u002Frender-less-infer-more-is-ai-breaking-the-traditional-gaming-hardware-upgrade-cycle","\u002Ffr\u002Fblog\u002Frender-less-infer-more-is-ai-breaking-the-traditional-gaming-hardware-upgrade-cycle","\u002Fit\u002Fblog\u002Frender-less-infer-more-is-ai-breaking-the-traditional-gaming-hardware-upgrade-cycle","\u002Fru\u002Fblog\u002Frender-less-infer-more-is-ai-breaking-the-traditional-gaming-hardware-upgrade-cycle","\u002Fzh\u002Fblog\u002Frender-less-infer-more-is-ai-breaking-the-traditional-gaming-hardware-upgrade-cycle",[1642,1646,1650,1654,1658],{"id":1643,"name":1644,"slug":1645},144,"Технологија","tech",{"id":1647,"name":1648,"slug":1649},166,"Гејминг вести","gaming-news",{"id":1651,"name":1652,"slug":1653},165,"Вести","news",{"id":1655,"name":1656,"slug":1657},170,"Технолошке вести","tech-news",{"id":1659,"name":1660,"slug":705},427,"Ново",{"id":283,"login":1662,"email":13,"displayName":1663},"aleksandar","Aleksandar Stajic",[1665,2514],{"lang":8,"title":1666,"content":1667,"contentJson":1668,"excerpt":2513},"Render Less, Infer More: Is AI Breaking the Traditional Gaming Hardware Upgrade Cycle?","{\"time\":1792336677504,\"blocks\":[{\"id\":\"p-0001\",\"type\":\"paragraph\",\"data\":{\"text\":\"For decades, enthusiast PC gaming followed a familiar bargain: games asked for more compute, GPU generations delivered more compute, and players upgraded when the gap became large enough. That loop never worked perfectly, but the direction was understandable. In 2026, the loop is becoming harder to read because two changes are happening at the same time: AI infrastructure is competing aggressively for silicon and memory, while AI techniques are moving directly into the way games create frames.\"},\"tunes\":{}},{\"id\":\"c-0002\",\"type\":\"callout\",\"data\":{\"variant\":\"info\",\"title\":\"Direct answer\",\"body\":\"\u003Cstrong>The old gaming upgrade cycle is under pressure, but it is not simply being replaced by more expensive GPUs.\u003C\u002Fstrong> AI is changing both sides of the equation: it increases competition for semiconductor and memory resources, and it also enables neural rendering techniques that can trade stored data or conventional rendering work for inference. The important possibility is that gaming may respond to tighter hardware economics by becoming more computationally selective, not merely by demanding larger GPUs.\"},\"tunes\":{}},{\"id\":\"c-0003\",\"type\":\"callout\",\"data\":{\"variant\":\"note\",\"title\":\"How to read this article\",\"body\":\"This is an \u003Cstrong>evidence-backed hypothesis\u003C\u002Fstrong>. Documented market data and shipping or announced technologies are labeled as facts or industry signals. The connections between them are technical inference. The future architecture described later is a Figure Rocks hypothesis, not a claim about an unannounced NVIDIA, AMD, Intel, Microsoft or console roadmap.\"},\"tunes\":{}},{\"id\":\"toc-0004\",\"type\":\"tableOfContents\",\"data\":{\"title\":\"Contents\",\"minLevel\":2,\"maxLevel\":3},\"tunes\":{}},{\"id\":\"h-0005\",\"type\":\"header\",\"data\":{\"text\":\"The scale mismatch is already visible\",\"level\":2},\"tunes\":{}},{\"id\":\"p-0006\",\"type\":\"paragraph\",\"data\":{\"text\":\"Newzoo forecasts the global games market at $213.9 billion in 2026, up 6.1% year over year. The Semiconductor Industry Association reported $791.7 billion in global semiconductor sales for 2025, up 25.6%, and projected roughly $1 trillion for 2026. The games business and semiconductor business still depend on each other, but their incentives are becoming less symmetric: the same advanced manufacturing ecosystem that enables premium gaming now serves AI and data-center buyers with much larger infrastructure budgets. These are not directly comparable product categories; they establish the scale of the upstream compute economy around gaming.\"},\"tunes\":{}},{\"id\":\"t-0007\",\"type\":\"table\",\"data\":{\"withHeadings\":true,\"stretched\":false,\"content\":[[\"Reference point\",\"Reported or forecast value\",\"Why it matters here\"],[\"Global games market, 2026 forecast\",\"$213.9B, +6.1% YoY\",\"Large consumer entertainment market, but downstream of advanced hardware supply\"],[\"PC games, 2026 forecast\",\"$45.9B, +5.3% YoY\",\"The gaming segment most directly exposed to PC component economics\"],[\"Global semiconductor sales, 2025\",\"$791.7B, +25.6% YoY\",\"Shows the scale of the upstream industry serving gaming, AI, mobile, automotive and cloud\"],[\"Global semiconductor industry, 2026 projection\",\"~$1T\",\"Raises the opportunity cost of dedicating premium silicon only to consumer gaming\"]]},\"tunes\":{}},{\"id\":\"c-0008\",\"type\":\"callout\",\"data\":{\"variant\":\"warning\",\"title\":\"What these market numbers do — and do not — prove\",\"body\":\"The figures establish \u003Cstrong>economic context, not direct causation\u003C\u002Fstrong>. They do not prove that a specific wafer, GPU or memory package was taken from a gamer and reassigned to AI. They also do not mean gaming and semiconductors are the two largest industries in the world. The narrower, defensible point is that a $200B+ games market depends on an upstream semiconductor economy approaching trillion-dollar annual scale, where AI infrastructure now competes for advanced compute and memory under very different budget and margin conditions.\"},\"tunes\":{}},{\"id\":\"h-0009\",\"type\":\"header\",\"data\":{\"text\":\"Memory is where the pressure becomes concrete\",\"level\":2},\"tunes\":{}},{\"id\":\"p-0010\",\"type\":\"paragraph\",\"data\":{\"text\":\"TrendForce has repeatedly reported that DRAM suppliers are shifting capacity toward server and HBM products as AI demand grows. In March 2026 it forecast conventional DRAM contract prices rising 58–63% quarter over quarter in Q2 and said limited GDDR allocation was constraining graphics memory supply. By Q3, TrendForce said consumer customers were reaching an affordability limit even as AI and server demand remained strong.\"},\"tunes\":{}},{\"id\":\"flow-0011\",\"type\":\"processFlow\",\"data\":{\"title\":\"The resource-allocation pressure\",\"orientation\":\"auto\",\"steps\":[{\"label\":\"1. AI infrastructure expands\",\"description\":\"Cloud providers and AI builders secure server memory, HBM, enterprise SSDs and advanced compute.\"},{\"label\":\"2. Suppliers optimize product mix\",\"description\":\"Capacity moves toward products with stronger demand, margins or long-term contracts.\"},{\"label\":\"3. Consumer allocation tightens\",\"description\":\"PC, graphics and consumer memory buyers face higher prices or constrained availability.\"},{\"label\":\"4. Gaming vendors absorb or pass through cost\",\"description\":\"Board design, VRAM capacity and system price become harder trade-offs.\"},{\"label\":\"5. The old upgrade cycle weakens\",\"description\":\"A faster GPU is still useful, but brute-force scaling becomes economically harder to repeat at the same consumer price points.\"}]},\"tunes\":{}},{\"id\":\"v3-sep-signal\",\"type\":\"callout\",\"data\":{\"variant\":\"warning\",\"title\":\"A September 2026 signal, not an old shortage story\",\"body\":\"TrendForce reported on September 30, 2026 that suppliers still prioritize advanced-process capacity for high-performance server products, with conventional DRAM contract prices projected to rise another 10–15% QoQ in Q4. Its DRAM bulletin also says HBM continues to crowd out general DRAM capacity. The pressure described here therefore remains visible late in 2026; it is not based only on a temporary early-year spike.\"},\"tunes\":{}},{\"id\":\"h-0012\",\"type\":\"header\",\"data\":{\"text\":\"At exactly the same time, the frame itself is changing\",\"level\":2},\"tunes\":{}},{\"id\":\"p-0013\",\"type\":\"paragraph\",\"data\":{\"text\":\"Microsoft now describes machine learning as becoming foundational to real-time graphics. DirectX is adding shader-level and model-level ML paths so neural texture compression, denoising, super resolution and other learned workloads can sit inside the graphics stack instead of being bolted on as isolated features. NVIDIA's DLSS 5 research pushes the idea further by introducing a generative neural rendering stage that complements conventional rendering with learned appearance priors. Neural rendering does not mean conventional rendering disappears. The emerging model is hybrid: render some information directly, infer or reconstruct other information when doing so is cheaper or more useful.\"},\"tunes\":{}},{\"id\":\"ref-0014\",\"type\":\"referralArticle\",\"data\":{\"url\":\"https:\u002F\u002Ffigure.rocks\u002Fblog\u002Fdlss-5-is-not-just-upscaling-what-3d-guided-neural-rendering-actually-changes\",\"title\":\"DLSS 5 Is Not Just Upscaling: What Generative Neural Rendering Actually Changes\",\"excerpt\":\"Why the important change is not another quality mode, but a new stage in how visual information can be produced.\",\"ctaLabel\":\"Read the DLSS 5 analysis\"},\"tunes\":{}},{\"id\":\"cmp-0015\",\"type\":\"comparison\",\"data\":{\"title\":\"Traditional scaling vs neural-era scaling\",\"layout\":\"table\",\"columns\":[{\"id\":\"old\",\"label\":\"Traditional path\"},{\"id\":\"new\",\"label\":\"Neural-era path\"}],\"rows\":[{\"id\":\"fidelity\",\"label\":\"More visual fidelity\",\"values\":{\"old\":\"Store more detail and render more work\",\"new\":\"Store\u002Frender selectively, reconstruct or infer some detail\"}},{\"id\":\"memory\",\"label\":\"Memory response\",\"values\":{\"old\":\"Add capacity and bandwidth\",\"new\":\"Add capacity where needed, but also compress and reduce movement\"}},{\"id\":\"performance\",\"label\":\"Performance response\",\"values\":{\"old\":\"More shader\u002FRT throughput\",\"new\":\"Mix shader, RT and matrix\u002FTensor-style compute\"}},{\"id\":\"software\",\"label\":\"Software role\",\"values\":{\"old\":\"Engine schedules mostly conventional graphics work\",\"new\":\"Engine increasingly schedules rendering, data movement and ML workloads\"}}]},\"tunes\":{}},{\"id\":\"c-0016\",\"type\":\"callout\",\"data\":{\"variant\":\"success\",\"title\":\"The central Figure Rocks hypothesis\",\"body\":\"\u003Cstrong>AI may be weakening the economics of brute-force gaming hardware while simultaneously supplying techniques that make brute-force scaling less necessary.\u003C\u002Fstrong> That does not mean future games need less hardware. It means more of the result may come from deciding what to store, move, render, reconstruct or infer. Figure Rocks shorthand: \u003Cstrong>store less → move less → predict more → render less → infer more.\u003C\u002Fstrong> This is a hypothesis about where performance gains may increasingly come from, not a claim that every future game will use one pipeline.\"},\"tunes\":{}},{\"id\":\"d-0017\",\"type\":\"delimiter\",\"data\":{},\"tunes\":{}},{\"id\":\"h-0018\",\"type\":\"header\",\"data\":{\"text\":\"The 5090 question is useful only if we phrase it correctly\",\"level\":2},\"tunes\":{}},{\"id\":\"p-0019\",\"type\":\"paragraph\",\"data\":{\"text\":\"The RTX 5090 is a useful example precisely because the evidence is mixed. As of October 18, 2026, NVIDIA still lists the GeForce RTX 5090 as a current product. At the same time, retailer behavior and press reports have documented unusually poor availability and unconfirmed claims of a production halt. That is a market signal, not an official end-of-production announcement. The defensible question is therefore not whether NVIDIA secretly killed one card, but whether the opportunity cost of allocating top-tier Blackwell-class silicon to enthusiast gaming rises when similar architecture can serve professional visualization and AI markets with very different economics.\"},\"tunes\":{}},{\"id\":\"c-0020\",\"type\":\"callout\",\"data\":{\"variant\":\"note\",\"title\":\"FACT \u002F SIGNAL \u002F HYPOTHESIS\",\"body\":\"\u003Cstrong>FACT:\u003C\u002Fstrong> NVIDIA still publicly lists the RTX 5090, and Blackwell architecture spans gaming and professional products.\u003Cbr>\u003Cstrong>INDUSTRY SIGNAL:\u003C\u002Fstrong> retailer disappearance, extreme scarcity and reports of a possible production halt exist, but NVIDIA has not confirmed an RTX 5090 EOL or production stop.\u003Cbr>\u003Cstrong>TECHNICAL INFERENCE:\u003C\u002Fstrong> a high-end die has multiple economically valuable destinations.\u003Cbr>\u003Cstrong>FIGURE ROCKS HYPOTHESIS:\u003C\u002Fstrong> AI and professional demand can make the traditional enthusiast expectation — that the largest practical die remains broadly attainable as a gaming flagship — harder to sustain.\"},\"tunes\":{}},{\"id\":\"v3-memory-compute\",\"type\":\"header\",\"data\":{\"text\":\"Memory is starting to become a compute problem, not only a capacity problem\",\"level\":2},\"tunes\":{}},{\"id\":\"v3-memory-compute-p1\",\"type\":\"paragraph\",\"data\":{\"text\":\"There are at least four ways to attack a memory bottleneck without simply increasing raw DRAM speed: compress the representation, reconstruct detail with compute, predict what data will be needed next, and keep the right data resident close to the execution unit. None of these makes a physical memory chip magically faster. They change how much useful work the system obtains from a finite capacity and bandwidth budget.\"},\"tunes\":{}},{\"id\":\"v3-four-memory-levers\",\"type\":\"table\",\"data\":{\"withHeadings\":true,\"stretched\":false,\"content\":[[\"Technique\",\"What changes\",\"What does not change automatically\"],[\"Compression\",\"Fewer physical bytes represent the same logical data\",\"The DRAM clock and bus width\"],[\"Neural reconstruction\",\"Some detail is generated from a compact representation\",\"The cost disappears; inference has its own compute\u002Flatency cost\"],[\"Predictive prefetch\",\"Data arrives before the requesting workload stalls\",\"Total physical bandwidth\"],[\"Residency \u002F unified access\",\"Less unnecessary migration or duplication\",\"Contention and latency constraints\"]]},\"tunes\":{}},{\"id\":\"v3-buddy-data\",\"type\":\"callout\",\"data\":{\"variant\":\"info\",\"title\":\"Research anchor: effective capacity is not raw speed\",\"body\":\"NVIDIA Research's Buddy Compression reported an average \u003Cstrong>2.2× compression ratio for HPC workloads and 1.5× for deep-learning workloads\u003C\u002Fstrong>, with 1–2% slowdown in that study. That result is not a gaming benchmark. Its relevance here is architectural: compressed memory can increase effective capacity and reduce some traffic without changing the physical DRAM clock.\"},\"tunes\":{}},{\"id\":\"v5-ntc-evidence\",\"type\":\"callout\",\"data\":{\"variant\":\"success\",\"title\":\"Gaming-specific evidence: neural texture compression\",\"body\":\"NVIDIA Research's material-texture NTC work demonstrated \u003Cstrong>4× higher linear texture resolution — 16× more texels — while using 30% less memory\u003C\u002Fstrong> than its BC High comparison. That does not mean every game gets a 16× memory gain. It demonstrates the mechanism that matters here: some visual detail can be represented more compactly and reconstructed with compute instead of being stored conventionally at the same fidelity.\"},\"tunes\":{}},{\"id\":\"v3-460-correction\",\"type\":\"callout\",\"data\":{\"variant\":\"warning\",\"title\":\"What the “460×” number does not mean\",\"body\":\"A 460× figure sometimes associated with NVIDIA systems refers to a comparison in GPU-accessible memory capacity across DGX generations, \u003Cstrong>not a 460× memory-compression ratio\u003C\u002Fstrong>. This article deliberately does not use it as evidence for compression.\"},\"tunes\":{}},{\"id\":\"h-0021\",\"type\":\"header\",\"data\":{\"text\":\"How the technical response could work\",\"level\":2},\"tunes\":{}},{\"id\":\"flow-0022\",\"type\":\"processFlow\",\"data\":{\"title\":\"One possible future frame pipeline\",\"orientation\":\"auto\",\"steps\":[{\"label\":\"Compress\",\"description\":\"Keep textures or learned representations smaller where quality permits.\"},{\"label\":\"Predict\",\"description\":\"Use deterministic game state and learned models to estimate what data will be needed next.\"},{\"label\":\"Prefetch\",\"description\":\"Move useful data before the GPU stalls waiting for it.\"},{\"label\":\"Render\",\"description\":\"Spend conventional graphics work where it changes the visible result most.\"},{\"label\":\"Infer\",\"description\":\"Use neural reconstruction or generation where inference is cheaper than storing or simulating everything directly.\"},{\"label\":\"Present\",\"description\":\"Deliver the frame while keeping latency, artifacts and frame pacing inside acceptable limits.\"}]},\"tunes\":{}},{\"id\":\"p-0023\",\"type\":\"paragraph\",\"data\":{\"text\":\"This pipeline is not one announced product. Every component already exists in some form, but the integrated chain is the hypothesis. Neural texture compression has shown that learned representations can exchange compute for memory. GPU prefetch research has shown that learned prediction can improve memory-page hit rates. DirectX is formalizing ML inside the graphics programming model. DLSS 5 shows a generative stage entering real-time rendering.\"},\"tunes\":{}},{\"id\":\"v3-predictive-loop\",\"type\":\"header\",\"data\":{\"text\":\"A game has one advantage general workloads often do not: it already knows part of its future\",\"level\":2},\"tunes\":{}},{\"id\":\"v3-predictive-loop-p1\",\"type\":\"paragraph\",\"data\":{\"text\":\"A game engine knows the camera transform, player velocity, current world cell, neighboring streaming cells, animation state, active entities, LOD transitions, shader dependencies and often likely traversal paths. That deterministic context does not reveal the future perfectly, but it gives a predictor a richer starting point than a generic memory trace alone.\"},\"tunes\":{}},{\"id\":\"v3-predictive-door\",\"type\":\"processFlow\",\"data\":{\"title\":\"A predictive gaming-memory example\",\"orientation\":\"auto\",\"steps\":[{\"label\":\"1. Observe deterministic state\",\"description\":\"Camera direction, movement vector, current room, visible portals and known asset dependencies already exist in engine state.\"},{\"label\":\"2. Estimate the near-future working set\",\"description\":\"A learned predictor can rank likely next rooms, textures, geometry, shaders or neural representations.\"},{\"label\":\"3. Prefetch during available bandwidth\",\"description\":\"Move likely resources while the memory system has headroom instead of waiting for a hard demand miss.\"},{\"label\":\"4. Keep deterministic guarantees\",\"description\":\"Critical assets still use authored rules; prediction supplements rather than replaces correctness.\"},{\"label\":\"5. Measure the miss cost\",\"description\":\"If prediction wastes bandwidth or evicts useful data, confidence or prefetch depth must fall.\"}]},\"tunes\":{}},{\"id\":\"v3-learned-evidence\",\"type\":\"callout\",\"data\":{\"variant\":\"note\",\"title\":\"This idea has a research precedent, but not yet a gaming proof\",\"body\":\"A published CPU–GPU unified-virtual-memory study reported a \u003Cstrong>10.89% performance improvement\u003C\u002Fstrong>, page-hit rate rising from \u003Cstrong>76.10% to 89.02%\u003C\u002Fstrong>, and \u003Cstrong>11.05% lower CPU–GPU interconnect traffic\u003C\u002Fstrong> using learned prefetching. A 2026 AAAI paper on asynchronous KV-cache prefetching reported \u003Cstrong>2.15× attention-kernel efficiency and up to 1.97× end-to-end throughput\u003C\u002Fstrong> on its LLM workload. Neither result should be copied onto games; both demonstrate that prediction and prefetch can hide memory cost in real GPU workloads.\"},\"tunes\":{}},{\"id\":\"h-0024\",\"type\":\"header\",\"data\":{\"text\":\"Why a Spark-like architecture becomes interesting\",\"level\":2},\"tunes\":{}},{\"id\":\"p-0025\",\"type\":\"paragraph\",\"data\":{\"text\":\"DGX Spark is not a gaming PC, and treating it as one would hide the useful lesson. Its GB10 design combines Arm CPU cores, a Blackwell GPU, Tensor and RT hardware, and up to 128 GB of coherent unified system memory. That architecture asks a different question from a classic desktop: instead of splitting the system cleanly into CPU memory and discrete VRAM, what happens when multiple compute engines share a large memory pool?\"},\"tunes\":{}},{\"id\":\"t-0026\",\"type\":\"table\",\"data\":{\"withHeadings\":true,\"stretched\":false,\"content\":[[\"Property\",\"DGX Spark reference\",\"Gaming implication if the principle were adapted\"],[\"Memory\",\"Up to 128 GB coherent unified LPDDR5X\",\"Large shared pool could reduce duplication and give local AI + graphics access to common data\"],[\"Memory bandwidth\",\"273 GB\u002Fs\",\"Capacity is not enough; high-end graphics still needs aggressive bandwidth and latency management\"],[\"Compute mix\",\"CPU + Blackwell GPU + Tensor + RT\",\"A future game workload could be split across more specialized engines\"],[\"Form factor\u002Fpower\",\"Compact GB10 system\",\"Integration can improve efficiency but may reduce modular upgradeability\"]]},\"tunes\":{}},{\"id\":\"c-0027\",\"type\":\"callout\",\"data\":{\"variant\":\"warning\",\"title\":\"Capacity is not bandwidth\",\"body\":\"A large unified pool does \u003Cstrong>not\u003C\u002Fstrong> automatically replace the bandwidth of a high-end discrete GPU. DGX Spark lists 273 GB\u002Fs of memory bandwidth, while NVIDIA's RTX PRO 6000 Blackwell Workstation Edition lists 1,792 GB\u002Fs. These are very different products and the comparison is not a gaming benchmark; it simply makes the architectural constraint visible. A Spark-like gaming design would need to solve contention, latency and bandwidth — not just provide more shared gigabytes.\"},\"tunes\":{}},{\"id\":\"h-0028\",\"type\":\"header\",\"data\":{\"text\":\"What would have to be true for this transition to work?\",\"level\":2},\"tunes\":{}},{\"id\":\"flow-0029\",\"type\":\"processFlow\",\"data\":{\"title\":\"The Neural Gaming Viability Test\",\"orientation\":\"auto\",\"steps\":[{\"label\":\"1. Inference must save more than it costs\",\"description\":\"Neural work has to remove enough storage, bandwidth or conventional rendering cost to justify its own compute and latency.\"},{\"label\":\"2. The engine must expose useful state\",\"description\":\"Prediction works better when the system knows camera direction, asset dependencies, movement and likely visibility.\"},{\"label\":\"3. Memory movement must become deliberate\",\"description\":\"Compression without good residency and prefetch policy can still stall.\"},{\"label\":\"4. Quality must remain stable\",\"description\":\"Generated detail cannot introduce unacceptable artifacts, temporal instability or incorrect game information.\"},{\"label\":\"5. Latency must stay bounded\",\"description\":\"A prettier frame is not automatically a more responsive game.\"},{\"label\":\"6. The stack must survive vendor differences\",\"description\":\"Useful techniques need APIs, drivers and engine paths that do not fragment into one-off demos.\"}]},\"tunes\":{}},{\"id\":\"v3-reader-spark-expect\",\"type\":\"header\",\"data\":{\"text\":\"What should a buyer expect from a Spark-like transition?\",\"level\":2},\"tunes\":{}},{\"id\":\"v3-buyer-transition\",\"type\":\"comparison\",\"data\":{\"title\":\"Potential benefit vs real cost\",\"layout\":\"table\",\"columns\":[{\"id\":\"gain\",\"label\":\"Potential gain\"},{\"id\":\"cost\",\"label\":\"Cost \u002F risk\"}],\"rows\":[{\"id\":\"pool\",\"label\":\"Large coherent pool\",\"values\":{\"gain\":\"More flexible sharing between CPU, graphics and local AI\",\"cost\":\"Shared clients can contend for the same bandwidth\"}},{\"id\":\"movement\",\"label\":\"Less duplication\",\"values\":{\"gain\":\"Fewer copies between separate system-RAM and VRAM pools\",\"cost\":\"Coherence and scheduling add complexity\"}},{\"id\":\"ai\",\"label\":\"More local AI headroom\",\"values\":{\"gain\":\"Larger models, agents or neural assets can coexist locally\",\"cost\":\"AI can compete with graphics for compute and memory\"}},{\"id\":\"efficiency\",\"label\":\"System-level optimization\",\"values\":{\"gain\":\"The platform can optimize data placement and accelerators together\",\"cost\":\"Tighter integration can reduce upgradeability and increase lock-in\"}},{\"id\":\"longevity\",\"label\":\"New workload flexibility\",\"values\":{\"gain\":\"A machine may age better if future games shift toward neural workloads\",\"cost\":\"Only if engines and runtimes actually use those capabilities\"}}]},\"tunes\":{}},{\"id\":\"h-0030\",\"type\":\"header\",\"data\":{\"text\":\"What would falsify this hypothesis?\",\"level\":2},\"tunes\":{}},{\"id\":\"p-0031\",\"type\":\"paragraph\",\"data\":{\"text\":\"This thesis is deliberately falsifiable. The table below defines the market, software, hardware and game-adoption signals that would make the argument stronger or weaker over time.\"},\"tunes\":{}},{\"id\":\"cmp-0032\",\"type\":\"comparison\",\"data\":{\"title\":\"Evidence that strengthens or weakens the thesis\",\"layout\":\"table\",\"columns\":[{\"id\":\"strengthens\",\"label\":\"Would strengthen it\"},{\"id\":\"weakens\",\"label\":\"Would weaken it\"}],\"rows\":[{\"id\":\"memory\",\"label\":\"Memory economics\",\"values\":{\"strengthens\":\"Server\u002FHBM priority continues to squeeze consumer allocation\",\"weakens\":\"Consumer capacity expands and price pressure normalizes\"}},{\"id\":\"graphics\",\"label\":\"Graphics stack\",\"values\":{\"strengthens\":\"ML primitives become normal engine\u002FDirectX features\",\"weakens\":\"Neural graphics remains vendor-specific optional post-processing\"}},{\"id\":\"hardware\",\"label\":\"Hardware design\",\"values\":{\"strengthens\":\"More integrated heterogeneous compute and larger coherent memory pools\",\"weakens\":\"Discrete GPU + separate RAM remains clearly superior on cost, flexibility and latency\"}},{\"id\":\"games\",\"label\":\"Game adoption\",\"values\":{\"strengthens\":\"Studios ship neural compression, generation and predictive data paths broadly\",\"weakens\":\"Tech remains limited to demos or a few flagship games\"}}]},\"tunes\":{}},{\"id\":\"h-0033\",\"type\":\"header\",\"data\":{\"text\":\"The practical conclusion for a gamer today\",\"level\":2},\"tunes\":{}},{\"id\":\"p-0034\",\"type\":\"paragraph\",\"data\":{\"text\":\"Do not buy hardware because a speculative architecture might arrive. Buy for the games, resolution, latency target and workloads you actually have. But do not assume that future-proofing can still be reduced to one number such as raster performance or VRAM capacity. The direction of the software stack suggests that memory behavior, neural compute and engine support may become increasingly important parts of the value equation.\"},\"tunes\":{}},{\"id\":\"ref-0035\",\"type\":\"referralArticle\",\"data\":{\"url\":\"https:\u002F\u002Ffigure.rocks\u002Fblog\u002Fvram-usage-is-not-vram-requirement-why-a-full-memory-meter-does-not-tell-the-whole-story\",\"title\":\"VRAM Usage Is Not VRAM Requirement: Why a Full Memory Meter Does Not Tell the Whole Story\",\"excerpt\":\"A practical explanation of working sets, residency and why an allocated VRAM number is not the same thing as a hard requirement.\",\"ctaLabel\":\"Read the VRAM guide\"},\"tunes\":{}},{\"id\":\"h-0036\",\"type\":\"header\",\"data\":{\"text\":\"Primary sources\",\"level\":2},\"tunes\":{}},{\"id\":\"v3-src-newzoo\",\"type\":\"linkTool\",\"data\":{\"link\":\"https:\u002F\u002Fnewzoo.com\u002Farticles\u002F2026-global-games-market-key-numbers\",\"meta\":{\"title\":\"Newzoo — 2026 Global Games Market Key Numbers\",\"description\":\"2026 games-market forecast used for the scale comparison.\",\"image\":{\"url\":\"\"}}},\"tunes\":{}},{\"id\":\"src-0038\",\"type\":\"linkTool\",\"data\":{\"link\":\"https:\u002F\u002Fwww.semiconductors.org\u002Fglobal-annual-semiconductor-sales-increase-25-6-to-791-7-billion-in-2025\u002F\",\"meta\":{\"title\":\"Semiconductor Industry Association — 2025 Global Sales\",\"description\":\"2025 semiconductor sales and the association's roughly $1 trillion 2026 projection.\",\"image\":{\"url\":\"\"}}},\"tunes\":{}},{\"id\":\"v3-src-trend-sep\",\"type\":\"linkTool\",\"data\":{\"link\":\"https:\u002F\u002Fwww.trendforce.com\u002Fpresscenter\u002Fnews\u002F20260930-13258.html\",\"meta\":{\"title\":\"TrendForce — AI Server Demand Sustains Memory Price Increases in 4Q26\",\"description\":\"Late-2026 evidence for ongoing server-priority capacity allocation and consumer pressure.\",\"image\":{\"url\":\"\"}}},\"tunes\":{}},{\"id\":\"v3-src-buddy\",\"type\":\"linkTool\",\"data\":{\"link\":\"https:\u002F\u002Fresearch.nvidia.com\u002Fpublication\u002F2019-04_buddy-compression-enabling-larger-memory-deep-learning-and-hpc-workloads-gpus\",\"meta\":{\"title\":\"NVIDIA Research — Buddy Compression\",\"description\":\"Research source for 2.2× HPC and 1.5× DL average compression ratios.\",\"image\":{\"url\":\"\"}}},\"tunes\":{}},{\"id\":\"v3-src-uvm\",\"type\":\"linkTool\",\"data\":{\"link\":\"https:\u002F\u002Fwww.sciencedirect.com\u002Fscience\u002Farticle\u002Fabs\u002Fpii\u002FS0743731522002490\",\"meta\":{\"title\":\"Deep-learning based CPU–GPU UVM prefetching\",\"description\":\"Research source for learned page-prefetching results.\",\"image\":{\"url\":\"\"}}},\"tunes\":{}},{\"id\":\"v3-src-aaai\",\"type\":\"linkTool\",\"data\":{\"link\":\"https:\u002F\u002Fojs.aaai.org\u002Findex.php\u002FAAAI\u002Farticle\u002Fview\u002F39224\",\"meta\":{\"title\":\"AAAI 2026 — Asynchronous KV Cache Prefetching\",\"description\":\"Research source demonstrating latency hiding through proactive GPU-cache prefetching.\",\"image\":{\"url\":\"\"}}},\"tunes\":{}},{\"id\":\"src-0039\",\"type\":\"linkTool\",\"data\":{\"link\":\"https:\u002F\u002Fwww.trendforce.com\u002Fpresscenter\u002Fnews\u002F20260331-12995.html\",\"meta\":{\"title\":\"TrendForce — AI Server Demand and 2Q26 Memory Prices\",\"description\":\"Current evidence for server\u002FHBM prioritization, GDDR constraints and consumer pressure.\",\"image\":{\"url\":\"\"}}},\"tunes\":{}},{\"id\":\"src-0040\",\"type\":\"linkTool\",\"data\":{\"link\":\"https:\u002F\u002Fdevblogs.microsoft.com\u002Fdirectx\u002Fevolving-directx-for-the-ml-era-on-windows\u002F\",\"meta\":{\"title\":\"Microsoft DirectX — Evolving DirectX for the ML Era\",\"description\":\"Official GDC 2026 overview of shader-level and model-level ML in DirectX.\",\"image\":{\"url\":\"\"}}},\"tunes\":{}},{\"id\":\"src-0041\",\"type\":\"linkTool\",\"data\":{\"link\":\"https:\u002F\u002Fresearch.nvidia.com\u002Flabs\u002Fadlr\u002FDLSS5\u002F\",\"meta\":{\"title\":\"NVIDIA Research — DLSS 5: Generative Neural Rendering\",\"description\":\"Primary research page describing DLSS 5 as a real-time generative rendering stage.\",\"image\":{\"url\":\"\"}}},\"tunes\":{}},{\"id\":\"src-0042\",\"type\":\"linkTool\",\"data\":{\"link\":\"https:\u002F\u002Fwww.nvidia.com\u002Fen-us\u002Fproducts\u002Fworkstations\u002Fdgx-spark\u002F\",\"meta\":{\"title\":\"NVIDIA — DGX Spark Specifications\",\"description\":\"Official architecture, unified memory and bandwidth specifications.\",\"image\":{\"url\":\"\"}}},\"tunes\":{}},{\"id\":\"v5-src-5090-official\",\"type\":\"linkTool\",\"data\":{\"link\":\"https:\u002F\u002Fwww.nvidia.com\u002Fen-us\u002Fgeforce\u002Fgraphics-cards\u002F50-series\u002Frtx-5090\u002F\",\"meta\":{\"title\":\"NVIDIA — GeForce RTX 5090\",\"description\":\"Official current RTX 5090 product page used to distinguish confirmed product status from production-halt rumors.\",\"image\":{\"url\":\"\"}}},\"tunes\":{}},{\"id\":\"v5-src-5090-signal\",\"type\":\"linkTool\",\"data\":{\"link\":\"https:\u002F\u002Fwww.tomshardware.com\u002Fpc-components\u002Fgpus\u002Fnvidia-rtx-5090-disappears-from-micro-center-amid-rumors-of-a-production-halt-retailer-flags-flagship-as-not-carried-as-stock-mysteriously-vanishes\",\"meta\":{\"title\":\"Tom’s Hardware — RTX 5090 Retail Disappearance and Production-Halt Rumors\",\"description\":\"Current market report used only as an industry signal; it explicitly notes that NVIDIA has not confirmed a production halt.\",\"image\":{\"url\":\"\"}}},\"tunes\":{}},{\"id\":\"v5-src-ntc\",\"type\":\"linkTool\",\"data\":{\"link\":\"https:\u002F\u002Fresearch.nvidia.com\u002Flabs\u002Frtr\u002Fneural_texture_compression\u002F\",\"meta\":{\"title\":\"NVIDIA Research — Random-Access Neural Compression of Material Textures\",\"description\":\"Primary source for the 4× linear resolution \u002F 16× texel comparison at 30% less memory versus BC High.\",\"image\":{\"url\":\"\"}}},\"tunes\":{}},{\"id\":\"v5-src-rtxpro6000\",\"type\":\"linkTool\",\"data\":{\"link\":\"https:\u002F\u002Fwww.nvidia.com\u002Fde-de\u002Fproducts\u002Fworkstations\u002Fprofessional-desktop-gpus\u002Frtx-pro-6000\u002F\",\"meta\":{\"title\":\"NVIDIA — RTX PRO 6000 Blackwell Workstation Edition\",\"description\":\"Official source for 96 GB GDDR7 and 1,792 GB\u002Fs memory bandwidth used as a bandwidth reference point.\",\"image\":{\"url\":\"\"}}},\"tunes\":{}},{\"id\":\"gloss-0044\",\"type\":\"glossary\",\"data\":{\"title\":\"Key concepts\",\"entries\":[{\"term\":\"Neural rendering\",\"definition\":\"Rendering methods that use trained models as part of producing, reconstructing or enhancing visual output.\",\"anchor\":\"neural-rendering\"},{\"term\":\"Heterogeneous compute\",\"definition\":\"A system that assigns different workloads to different compute engines such as CPU, GPU, RT and matrix\u002FTensor hardware.\",\"anchor\":\"heterogeneous-compute\"},{\"term\":\"Effective memory\",\"definition\":\"A practical description of how much useful data a memory system can keep and feed to compute after compression, residency and movement efficiency are considered. It is not a formal replacement for physical capacity.\",\"anchor\":\"effective-memory\"},{\"term\":\"Predictive memory\",\"definition\":\"Figure Rocks shorthand for combining known game state and learned prediction to prepare likely-needed data before a stall. It is a hypothesis, not an industry standard.\",\"anchor\":\"predictive-memory\"}]},\"tunes\":{}},{\"id\":\"p-0001\",\"type\":\"paragraph\",\"data\":{\"text\":\"For decades, enthusiast PC gaming followed a familiar bargain: games asked for more compute, GPU generations delivered more compute, and players upgraded when the gap became large enough. That loop never worked perfectly, but the direction was understandable. In 2026, the loop is becoming harder to read because two changes are happening at the same time: AI infrastructure is competing aggressively for silicon and memory, while AI techniques are moving directly into the way games create frames.\"},\"tunes\":{}},{\"id\":\"c-0002\",\"type\":\"callout\",\"data\":{\"variant\":\"info\",\"title\":\"Direct answer\",\"body\":\"\u003Cstrong>The old gaming upgrade cycle is under pressure, but it is not simply being replaced by more expensive GPUs.\u003C\u002Fstrong> AI is changing both sides of the equation: it increases competition for semiconductor and memory resources, and it also enables neural rendering techniques that can trade stored data or conventional rendering work for inference. The important possibility is that gaming may respond to tighter hardware economics by becoming more computationally selective, not merely by demanding larger GPUs.\"},\"tunes\":{}},{\"id\":\"c-0003\",\"type\":\"callout\",\"data\":{\"variant\":\"note\",\"title\":\"How to read this article\",\"body\":\"This is an \u003Cstrong>evidence-backed hypothesis\u003C\u002Fstrong>. Documented market data and shipping or announced technologies are labeled as facts or industry signals. The connections between them are technical inference. The future architecture described later is a Figure Rocks hypothesis, not a claim about an unannounced NVIDIA, AMD, Intel, Microsoft or console roadmap.\"},\"tunes\":{}},{\"id\":\"toc-0004\",\"type\":\"tableOfContents\",\"data\":{\"title\":\"Contents\",\"minLevel\":2,\"maxLevel\":3},\"tunes\":{}},{\"id\":\"h-0005\",\"type\":\"header\",\"data\":{\"text\":\"The scale mismatch is already visible\",\"level\":2},\"tunes\":{}},{\"id\":\"p-0006\",\"type\":\"paragraph\",\"data\":{\"text\":\"Newzoo forecasts the global games market at $213.9 billion in 2026, up 6.1% year over year. The Semiconductor Industry Association reported $791.7 billion in global semiconductor sales for 2025, up 25.6%, and projected roughly $1 trillion for 2026. The games business and semiconductor business still depend on each other, but their incentives are becoming less symmetric: the same advanced manufacturing ecosystem that enables premium gaming now serves AI and data-center buyers with much larger infrastructure budgets. These are not directly comparable product categories; they establish the scale of the upstream compute economy around gaming.\"},\"tunes\":{}},{\"id\":\"t-0007\",\"type\":\"table\",\"data\":{\"withHeadings\":true,\"stretched\":false,\"content\":[[\"Reference point\",\"Reported or forecast value\",\"Why it matters here\"],[\"Global games market, 2026 forecast\",\"$213.9B, +6.1% YoY\",\"Large consumer entertainment market, but downstream of advanced hardware supply\"],[\"PC games, 2026 forecast\",\"$45.9B, +5.3% YoY\",\"The gaming segment most directly exposed to PC component economics\"],[\"Global semiconductor sales, 2025\",\"$791.7B, +25.6% YoY\",\"Shows the scale of the upstream industry serving gaming, AI, mobile, automotive and cloud\"],[\"Global semiconductor industry, 2026 projection\",\"~$1T\",\"Raises the opportunity cost of dedicating premium silicon only to consumer gaming\"]]},\"tunes\":{}},{\"id\":\"c-0008\",\"type\":\"callout\",\"data\":{\"variant\":\"warning\",\"title\":\"What these market numbers do — and do not — prove\",\"body\":\"The figures establish \u003Cstrong>economic context, not direct causation\u003C\u002Fstrong>. They do not prove that a specific wafer, GPU or memory package was taken from a gamer and reassigned to AI. They also do not mean gaming and semiconductors are the two largest industries in the world. The narrower, defensible point is that a $200B+ games market depends on an upstream semiconductor economy approaching trillion-dollar annual scale, where AI infrastructure now competes for advanced compute and memory under very different budget and margin conditions.\"},\"tunes\":{}},{\"id\":\"h-0009\",\"type\":\"header\",\"data\":{\"text\":\"Memory is where the pressure becomes concrete\",\"level\":2},\"tunes\":{}},{\"id\":\"p-0010\",\"type\":\"paragraph\",\"data\":{\"text\":\"TrendForce has repeatedly reported that DRAM suppliers are shifting capacity toward server and HBM products as AI demand grows. In March 2026 it forecast conventional DRAM contract prices rising 58–63% quarter over quarter in Q2 and said limited GDDR allocation was constraining graphics memory supply. By Q3, TrendForce said consumer customers were reaching an affordability limit even as AI and server demand remained strong.\"},\"tunes\":{}},{\"id\":\"flow-0011\",\"type\":\"processFlow\",\"data\":{\"title\":\"The resource-allocation pressure\",\"orientation\":\"auto\",\"steps\":[{\"label\":\"1. AI infrastructure expands\",\"description\":\"Cloud providers and AI builders secure server memory, HBM, enterprise SSDs and advanced compute.\"},{\"label\":\"2. Suppliers optimize product mix\",\"description\":\"Capacity moves toward products with stronger demand, margins or long-term contracts.\"},{\"label\":\"3. Consumer allocation tightens\",\"description\":\"PC, graphics and consumer memory buyers face higher prices or constrained availability.\"},{\"label\":\"4. Gaming vendors absorb or pass through cost\",\"description\":\"Board design, VRAM capacity and system price become harder trade-offs.\"},{\"label\":\"5. The old upgrade cycle weakens\",\"description\":\"A faster GPU is still useful, but brute-force scaling becomes economically harder to repeat at the same consumer price points.\"}]},\"tunes\":{}},{\"id\":\"v3-sep-signal\",\"type\":\"callout\",\"data\":{\"variant\":\"warning\",\"title\":\"A September 2026 signal, not an old shortage story\",\"body\":\"TrendForce reported on September 30, 2026 that suppliers still prioritize advanced-process capacity for high-performance server products, with conventional DRAM contract prices projected to rise another 10–15% QoQ in Q4. Its DRAM bulletin also says HBM continues to crowd out general DRAM capacity. The pressure described here therefore remains visible late in 2026; it is not based only on a temporary early-year spike.\"},\"tunes\":{}},{\"id\":\"h-0012\",\"type\":\"header\",\"data\":{\"text\":\"At exactly the same time, the frame itself is changing\",\"level\":2},\"tunes\":{}},{\"id\":\"p-0013\",\"type\":\"paragraph\",\"data\":{\"text\":\"Microsoft now describes machine learning as becoming foundational to real-time graphics. DirectX is adding shader-level and model-level ML paths so neural texture compression, denoising, super resolution and other learned workloads can sit inside the graphics stack instead of being bolted on as isolated features. NVIDIA's DLSS 5 research pushes the idea further by introducing a generative neural rendering stage that complements conventional rendering with learned appearance priors. Neural rendering does not mean conventional rendering disappears. The emerging model is hybrid: render some information directly, infer or reconstruct other information when doing so is cheaper or more useful.\"},\"tunes\":{}},{\"id\":\"ref-0014\",\"type\":\"referralArticle\",\"data\":{\"url\":\"https:\u002F\u002Ffigure.rocks\u002Fblog\u002Fdlss-5-is-not-just-upscaling-what-3d-guided-neural-rendering-actually-changes\",\"title\":\"DLSS 5 Is Not Just Upscaling: What Generative Neural Rendering Actually Changes\",\"excerpt\":\"Why the important change is not another quality mode, but a new stage in how visual information can be produced.\",\"ctaLabel\":\"Read the DLSS 5 analysis\"},\"tunes\":{}},{\"id\":\"cmp-0015\",\"type\":\"comparison\",\"data\":{\"title\":\"Traditional scaling vs neural-era scaling\",\"layout\":\"table\",\"columns\":[{\"id\":\"old\",\"label\":\"Traditional path\"},{\"id\":\"new\",\"label\":\"Neural-era path\"}],\"rows\":[{\"id\":\"fidelity\",\"label\":\"More visual fidelity\",\"values\":{\"old\":\"Store more detail and render more work\",\"new\":\"Store\u002Frender selectively, reconstruct or infer some detail\"}},{\"id\":\"memory\",\"label\":\"Memory response\",\"values\":{\"old\":\"Add capacity and bandwidth\",\"new\":\"Add capacity where needed, but also compress and reduce movement\"}},{\"id\":\"performance\",\"label\":\"Performance response\",\"values\":{\"old\":\"More shader\u002FRT throughput\",\"new\":\"Mix shader, RT and matrix\u002FTensor-style compute\"}},{\"id\":\"software\",\"label\":\"Software role\",\"values\":{\"old\":\"Engine schedules mostly conventional graphics work\",\"new\":\"Engine increasingly schedules rendering, data movement and ML workloads\"}}]},\"tunes\":{}},{\"id\":\"c-0016\",\"type\":\"callout\",\"data\":{\"variant\":\"success\",\"title\":\"The central Figure Rocks hypothesis\",\"body\":\"\u003Cstrong>AI may be weakening the economics of brute-force gaming hardware while simultaneously supplying techniques that make brute-force scaling less necessary.\u003C\u002Fstrong> That does not mean future games need less hardware. It means more of the result may come from deciding what to store, move, render, reconstruct or infer. Figure Rocks shorthand: \u003Cstrong>store less → move less → predict more → render less → infer more.\u003C\u002Fstrong> This is a hypothesis about where performance gains may increasingly come from, not a claim that every future game will use one pipeline.\"},\"tunes\":{}},{\"id\":\"d-0017\",\"type\":\"delimiter\",\"data\":{},\"tunes\":{}},{\"id\":\"h-0018\",\"type\":\"header\",\"data\":{\"text\":\"The 5090 question is useful only if we phrase it correctly\",\"level\":2},\"tunes\":{}},{\"id\":\"p-0019\",\"type\":\"paragraph\",\"data\":{\"text\":\"The RTX 5090 is a useful example precisely because the evidence is mixed. As of October 18, 2026, NVIDIA still lists the GeForce RTX 5090 as a current product. At the same time, retailer behavior and press reports have documented unusually poor availability and unconfirmed claims of a production halt. That is a market signal, not an official end-of-production announcement. The defensible question is therefore not whether NVIDIA secretly killed one card, but whether the opportunity cost of allocating top-tier Blackwell-class silicon to enthusiast gaming rises when similar architecture can serve professional visualization and AI markets with very different economics.\"},\"tunes\":{}},{\"id\":\"c-0020\",\"type\":\"callout\",\"data\":{\"variant\":\"note\",\"title\":\"FACT \u002F SIGNAL \u002F HYPOTHESIS\",\"body\":\"\u003Cstrong>FACT:\u003C\u002Fstrong> NVIDIA still publicly lists the RTX 5090, and Blackwell architecture spans gaming and professional products.\u003Cbr>\u003Cstrong>INDUSTRY SIGNAL:\u003C\u002Fstrong> retailer disappearance, extreme scarcity and reports of a possible production halt exist, but NVIDIA has not confirmed an RTX 5090 EOL or production stop.\u003Cbr>\u003Cstrong>TECHNICAL INFERENCE:\u003C\u002Fstrong> a high-end die has multiple economically valuable destinations.\u003Cbr>\u003Cstrong>FIGURE ROCKS HYPOTHESIS:\u003C\u002Fstrong> AI and professional demand can make the traditional enthusiast expectation — that the largest practical die remains broadly attainable as a gaming flagship — harder to sustain.\"},\"tunes\":{}},{\"id\":\"v3-memory-compute\",\"type\":\"header\",\"data\":{\"text\":\"Memory is starting to become a compute problem, not only a capacity problem\",\"level\":2},\"tunes\":{}},{\"id\":\"v3-memory-compute-p1\",\"type\":\"paragraph\",\"data\":{\"text\":\"There are at least four ways to attack a memory bottleneck without simply increasing raw DRAM speed: compress the representation, reconstruct detail with compute, predict what data will be needed next, and keep the right data resident close to the execution unit. None of these makes a physical memory chip magically faster. They change how much useful work the system obtains from a finite capacity and bandwidth budget.\"},\"tunes\":{}},{\"id\":\"v3-four-memory-levers\",\"type\":\"table\",\"data\":{\"withHeadings\":true,\"stretched\":false,\"content\":[[\"Technique\",\"What changes\",\"What does not change automatically\"],[\"Compression\",\"Fewer physical bytes represent the same logical data\",\"The DRAM clock and bus width\"],[\"Neural reconstruction\",\"Some detail is generated from a compact representation\",\"The cost disappears; inference has its own compute\u002Flatency cost\"],[\"Predictive prefetch\",\"Data arrives before the requesting workload stalls\",\"Total physical bandwidth\"],[\"Residency \u002F unified access\",\"Less unnecessary migration or duplication\",\"Contention and latency constraints\"]]},\"tunes\":{}},{\"id\":\"v3-buddy-data\",\"type\":\"callout\",\"data\":{\"variant\":\"info\",\"title\":\"Research anchor: effective capacity is not raw speed\",\"body\":\"NVIDIA Research's Buddy Compression reported an average \u003Cstrong>2.2× compression ratio for HPC workloads and 1.5× for deep-learning workloads\u003C\u002Fstrong>, with 1–2% slowdown in that study. That result is not a gaming benchmark. Its relevance here is architectural: compressed memory can increase effective capacity and reduce some traffic without changing the physical DRAM clock.\"},\"tunes\":{}},{\"id\":\"v5-ntc-evidence\",\"type\":\"callout\",\"data\":{\"variant\":\"success\",\"title\":\"Gaming-specific evidence: neural texture compression\",\"body\":\"NVIDIA Research's material-texture NTC work demonstrated \u003Cstrong>4× higher linear texture resolution — 16× more texels — while using 30% less memory\u003C\u002Fstrong> than its BC High comparison. That does not mean every game gets a 16× memory gain. It demonstrates the mechanism that matters here: some visual detail can be represented more compactly and reconstructed with compute instead of being stored conventionally at the same fidelity.\"},\"tunes\":{}},{\"id\":\"v3-460-correction\",\"type\":\"callout\",\"data\":{\"variant\":\"warning\",\"title\":\"What the “460×” number does not mean\",\"body\":\"A 460× figure sometimes associated with NVIDIA systems refers to a comparison in GPU-accessible memory capacity across DGX generations, \u003Cstrong>not a 460× memory-compression ratio\u003C\u002Fstrong>. This article deliberately does not use it as evidence for compression.\"},\"tunes\":{}},{\"id\":\"h-0021\",\"type\":\"header\",\"data\":{\"text\":\"How the technical response could work\",\"level\":2},\"tunes\":{}},{\"id\":\"flow-0022\",\"type\":\"processFlow\",\"data\":{\"title\":\"One possible future frame pipeline\",\"orientation\":\"auto\",\"steps\":[{\"label\":\"Compress\",\"description\":\"Keep textures or learned representations smaller where quality permits.\"},{\"label\":\"Predict\",\"description\":\"Use deterministic game state and learned models to estimate what data will be needed next.\"},{\"label\":\"Prefetch\",\"description\":\"Move useful data before the GPU stalls waiting for it.\"},{\"label\":\"Render\",\"description\":\"Spend conventional graphics work where it changes the visible result most.\"},{\"label\":\"Infer\",\"description\":\"Use neural reconstruction or generation where inference is cheaper than storing or simulating everything directly.\"},{\"label\":\"Present\",\"description\":\"Deliver the frame while keeping latency, artifacts and frame pacing inside acceptable limits.\"}]},\"tunes\":{}},{\"id\":\"p-0023\",\"type\":\"paragraph\",\"data\":{\"text\":\"This pipeline is not one announced product. Every component already exists in some form, but the integrated chain is the hypothesis. Neural texture compression has shown that learned representations can exchange compute for memory. GPU prefetch research has shown that learned prediction can improve memory-page hit rates. DirectX is formalizing ML inside the graphics programming model. DLSS 5 shows a generative stage entering real-time rendering.\"},\"tunes\":{}},{\"id\":\"v3-predictive-loop\",\"type\":\"header\",\"data\":{\"text\":\"A game has one advantage general workloads often do not: it already knows part of its future\",\"level\":2},\"tunes\":{}},{\"id\":\"v3-predictive-loop-p1\",\"type\":\"paragraph\",\"data\":{\"text\":\"A game engine knows the camera transform, player velocity, current world cell, neighboring streaming cells, animation state, active entities, LOD transitions, shader dependencies and often likely traversal paths. That deterministic context does not reveal the future perfectly, but it gives a predictor a richer starting point than a generic memory trace alone.\"},\"tunes\":{}},{\"id\":\"v3-predictive-door\",\"type\":\"processFlow\",\"data\":{\"title\":\"A predictive gaming-memory example\",\"orientation\":\"auto\",\"steps\":[{\"label\":\"1. Observe deterministic state\",\"description\":\"Camera direction, movement vector, current room, visible portals and known asset dependencies already exist in engine state.\"},{\"label\":\"2. Estimate the near-future working set\",\"description\":\"A learned predictor can rank likely next rooms, textures, geometry, shaders or neural representations.\"},{\"label\":\"3. Prefetch during available bandwidth\",\"description\":\"Move likely resources while the memory system has headroom instead of waiting for a hard demand miss.\"},{\"label\":\"4. Keep deterministic guarantees\",\"description\":\"Critical assets still use authored rules; prediction supplements rather than replaces correctness.\"},{\"label\":\"5. Measure the miss cost\",\"description\":\"If prediction wastes bandwidth or evicts useful data, confidence or prefetch depth must fall.\"}]},\"tunes\":{}},{\"id\":\"v3-learned-evidence\",\"type\":\"callout\",\"data\":{\"variant\":\"note\",\"title\":\"This idea has a research precedent, but not yet a gaming proof\",\"body\":\"A published CPU–GPU unified-virtual-memory study reported a \u003Cstrong>10.89% performance improvement\u003C\u002Fstrong>, page-hit rate rising from \u003Cstrong>76.10% to 89.02%\u003C\u002Fstrong>, and \u003Cstrong>11.05% lower CPU–GPU interconnect traffic\u003C\u002Fstrong> using learned prefetching. A 2026 AAAI paper on asynchronous KV-cache prefetching reported \u003Cstrong>2.15× attention-kernel efficiency and up to 1.97× end-to-end throughput\u003C\u002Fstrong> on its LLM workload. Neither result should be copied onto games; both demonstrate that prediction and prefetch can hide memory cost in real GPU workloads.\"},\"tunes\":{}},{\"id\":\"h-0024\",\"type\":\"header\",\"data\":{\"text\":\"Why a Spark-like architecture becomes interesting\",\"level\":2},\"tunes\":{}},{\"id\":\"p-0025\",\"type\":\"paragraph\",\"data\":{\"text\":\"DGX Spark is not a gaming PC, and treating it as one would hide the useful lesson. Its GB10 design combines Arm CPU cores, a Blackwell GPU, Tensor and RT hardware, and up to 128 GB of coherent unified system memory. That architecture asks a different question from a classic desktop: instead of splitting the system cleanly into CPU memory and discrete VRAM, what happens when multiple compute engines share a large memory pool?\"},\"tunes\":{}},{\"id\":\"t-0026\",\"type\":\"table\",\"data\":{\"withHeadings\":true,\"stretched\":false,\"content\":[[\"Property\",\"DGX Spark reference\",\"Gaming implication if the principle were adapted\"],[\"Memory\",\"Up to 128 GB coherent unified LPDDR5X\",\"Large shared pool could reduce duplication and give local AI + graphics access to common data\"],[\"Memory bandwidth\",\"273 GB\u002Fs\",\"Capacity is not enough; high-end graphics still needs aggressive bandwidth and latency management\"],[\"Compute mix\",\"CPU + Blackwell GPU + Tensor + RT\",\"A future game workload could be split across more specialized engines\"],[\"Form factor\u002Fpower\",\"Compact GB10 system\",\"Integration can improve efficiency but may reduce modular upgradeability\"]]},\"tunes\":{}},{\"id\":\"c-0027\",\"type\":\"callout\",\"data\":{\"variant\":\"warning\",\"title\":\"Capacity is not bandwidth\",\"body\":\"A large unified pool does \u003Cstrong>not\u003C\u002Fstrong> automatically replace the bandwidth of a high-end discrete GPU. DGX Spark lists 273 GB\u002Fs of memory bandwidth, while NVIDIA's RTX PRO 6000 Blackwell Workstation Edition lists 1,792 GB\u002Fs. These are very different products and the comparison is not a gaming benchmark; it simply makes the architectural constraint visible. A Spark-like gaming design would need to solve contention, latency and bandwidth — not just provide more shared gigabytes.\"},\"tunes\":{}},{\"id\":\"h-0028\",\"type\":\"header\",\"data\":{\"text\":\"What would have to be true for this transition to work?\",\"level\":2},\"tunes\":{}},{\"id\":\"flow-0029\",\"type\":\"processFlow\",\"data\":{\"title\":\"The Neural Gaming Viability Test\",\"orientation\":\"auto\",\"steps\":[{\"label\":\"1. Inference must save more than it costs\",\"description\":\"Neural work has to remove enough storage, bandwidth or conventional rendering cost to justify its own compute and latency.\"},{\"label\":\"2. The engine must expose useful state\",\"description\":\"Prediction works better when the system knows camera direction, asset dependencies, movement and likely visibility.\"},{\"label\":\"3. Memory movement must become deliberate\",\"description\":\"Compression without good residency and prefetch policy can still stall.\"},{\"label\":\"4. Quality must remain stable\",\"description\":\"Generated detail cannot introduce unacceptable artifacts, temporal instability or incorrect game information.\"},{\"label\":\"5. Latency must stay bounded\",\"description\":\"A prettier frame is not automatically a more responsive game.\"},{\"label\":\"6. The stack must survive vendor differences\",\"description\":\"Useful techniques need APIs, drivers and engine paths that do not fragment into one-off demos.\"}]},\"tunes\":{}},{\"id\":\"v3-reader-spark-expect\",\"type\":\"header\",\"data\":{\"text\":\"What should a buyer expect from a Spark-like transition?\",\"level\":2},\"tunes\":{}},{\"id\":\"v3-buyer-transition\",\"type\":\"comparison\",\"data\":{\"title\":\"Potential benefit vs real cost\",\"layout\":\"table\",\"columns\":[{\"id\":\"gain\",\"label\":\"Potential gain\"},{\"id\":\"cost\",\"label\":\"Cost \u002F risk\"}],\"rows\":[{\"id\":\"pool\",\"label\":\"Large coherent pool\",\"values\":{\"gain\":\"More flexible sharing between CPU, graphics and local AI\",\"cost\":\"Shared clients can contend for the same bandwidth\"}},{\"id\":\"movement\",\"label\":\"Less duplication\",\"values\":{\"gain\":\"Fewer copies between separate system-RAM and VRAM pools\",\"cost\":\"Coherence and scheduling add complexity\"}},{\"id\":\"ai\",\"label\":\"More local AI headroom\",\"values\":{\"gain\":\"Larger models, agents or neural assets can coexist locally\",\"cost\":\"AI can compete with graphics for compute and memory\"}},{\"id\":\"efficiency\",\"label\":\"System-level optimization\",\"values\":{\"gain\":\"The platform can optimize data placement and accelerators together\",\"cost\":\"Tighter integration can reduce upgradeability and increase lock-in\"}},{\"id\":\"longevity\",\"label\":\"New workload flexibility\",\"values\":{\"gain\":\"A machine may age better if future games shift toward neural workloads\",\"cost\":\"Only if engines and runtimes actually use those capabilities\"}}]},\"tunes\":{}},{\"id\":\"h-0030\",\"type\":\"header\",\"data\":{\"text\":\"What would falsify this hypothesis?\",\"level\":2},\"tunes\":{}},{\"id\":\"p-0031\",\"type\":\"paragraph\",\"data\":{\"text\":\"This thesis is deliberately falsifiable. The table below defines the market, software, hardware and game-adoption signals that would make the argument stronger or weaker over time.\"},\"tunes\":{}},{\"id\":\"cmp-0032\",\"type\":\"comparison\",\"data\":{\"title\":\"Evidence that strengthens or weakens the thesis\",\"layout\":\"table\",\"columns\":[{\"id\":\"strengthens\",\"label\":\"Would strengthen it\"},{\"id\":\"weakens\",\"label\":\"Would weaken it\"}],\"rows\":[{\"id\":\"memory\",\"label\":\"Memory economics\",\"values\":{\"strengthens\":\"Server\u002FHBM priority continues to squeeze consumer allocation\",\"weakens\":\"Consumer capacity expands and price pressure normalizes\"}},{\"id\":\"graphics\",\"label\":\"Graphics stack\",\"values\":{\"strengthens\":\"ML primitives become normal engine\u002FDirectX features\",\"weakens\":\"Neural graphics remains vendor-specific optional post-processing\"}},{\"id\":\"hardware\",\"label\":\"Hardware design\",\"values\":{\"strengthens\":\"More integrated heterogeneous compute and larger coherent memory pools\",\"weakens\":\"Discrete GPU + separate RAM remains clearly superior on cost, flexibility and latency\"}},{\"id\":\"games\",\"label\":\"Game adoption\",\"values\":{\"strengthens\":\"Studios ship neural compression, generation and predictive data paths broadly\",\"weakens\":\"Tech remains limited to demos or a few flagship games\"}}]},\"tunes\":{}},{\"id\":\"h-0033\",\"type\":\"header\",\"data\":{\"text\":\"The practical conclusion for a gamer today\",\"level\":2},\"tunes\":{}},{\"id\":\"p-0034\",\"type\":\"paragraph\",\"data\":{\"text\":\"Do not buy hardware because a speculative architecture might arrive. Buy for the games, resolution, latency target and workloads you actually have. But do not assume that future-proofing can still be reduced to one number such as raster performance or VRAM capacity. The direction of the software stack suggests that memory behavior, neural compute and engine support may become increasingly important parts of the value equation.\"},\"tunes\":{}},{\"id\":\"ref-0035\",\"type\":\"referralArticle\",\"data\":{\"url\":\"https:\u002F\u002Ffigure.rocks\u002Fblog\u002Fvram-usage-is-not-vram-requirement-why-a-full-memory-meter-does-not-tell-the-whole-story\",\"title\":\"VRAM Usage Is Not VRAM Requirement: Why a Full Memory Meter Does Not Tell the Whole Story\",\"excerpt\":\"A practical explanation of working sets, residency and why an allocated VRAM number is not the same thing as a hard requirement.\",\"ctaLabel\":\"Read the VRAM guide\"},\"tunes\":{}},{\"id\":\"h-0036\",\"type\":\"header\",\"data\":{\"text\":\"Primary sources\",\"level\":2},\"tunes\":{}},{\"id\":\"v3-src-newzoo\",\"type\":\"linkTool\",\"data\":{\"link\":\"https:\u002F\u002Fnewzoo.com\u002Farticles\u002F2026-global-games-market-key-numbers\",\"meta\":{\"title\":\"Newzoo — 2026 Global Games 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pressure.\",\"image\":{\"url\":\"\"}}},\"tunes\":{}},{\"id\":\"v3-src-buddy\",\"type\":\"linkTool\",\"data\":{\"link\":\"https:\u002F\u002Fresearch.nvidia.com\u002Fpublication\u002F2019-04_buddy-compression-enabling-larger-memory-deep-learning-and-hpc-workloads-gpus\",\"meta\":{\"title\":\"NVIDIA Research — Buddy Compression\",\"description\":\"Research source for 2.2× HPC and 1.5× DL average compression ratios.\",\"image\":{\"url\":\"\"}}},\"tunes\":{}},{\"id\":\"v3-src-uvm\",\"type\":\"linkTool\",\"data\":{\"link\":\"https:\u002F\u002Fwww.sciencedirect.com\u002Fscience\u002Farticle\u002Fabs\u002Fpii\u002FS0743731522002490\",\"meta\":{\"title\":\"Deep-learning based CPU–GPU UVM prefetching\",\"description\":\"Research source for learned page-prefetching 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It is not a formal replacement for physical capacity.\",\"anchor\":\"effective-memory\"},{\"term\":\"Predictive memory\",\"definition\":\"Figure Rocks shorthand for combining known game state and learned prediction to prepare likely-needed data before a stall. It is a hypothesis, not an industry standard.\",\"anchor\":\"predictive-memory\"}]},\"tunes\":{}}],\"version\":\"2.31.6\"}",{"time":1669,"blocks":1670,"version":2512},1792336677504,[1671,1675,1680,1685,1689,1693,1697,1721,1726,1730,1734,1754,1759,1763,1767,1774,1804,1809,1812,1816,1820,1825,1829,1833,1857,1862,1867,1872,1876,1898,1902,1906,1910,1930,1935,1939,1943,1965,1970,1974,1997,2001,2036,2040,2044,2074,2078,2082,2089,2093,2100,2107,2114,2120,2127,2134,2141,2148,2155,2162,2168,2175,2182,2188,2205,2208,2211,2214,2217,2220,2223,2232,2235,2238,2241,2250,2253,2256,2259,2262,2277,2280,2283,2286,2289,2292,2295,2298,2307,2310,2313,2316,2319,2329,2332,2335,2338,2347,2350,2353,2356,2365,2368,2371,2381,2384,2401,2404,2407,2422,2425,2428,2431,2434,2439,2444,2449,2454,2459,2464,2469,2474,2479,2484,2489,2494,2499,2504],{"id":541,"data":1672,"type":544,"tunes":1674},{"text":1673},"For decades, enthusiast PC gaming followed a familiar bargain: games asked for more compute, GPU generations delivered more compute, and players upgraded when the gap became large enough. That loop never worked perfectly, but the direction was understandable. In 2026, the loop is becoming harder to read because two changes are happening at the same time: AI infrastructure is competing aggressively for silicon and memory, while AI techniques are moving directly into the way games create frames.",{},{"id":547,"data":1676,"type":552,"tunes":1679},{"body":1677,"title":1678,"variant":551},"\u003Cstrong>The old gaming upgrade cycle is under pressure, but it is not simply being replaced by more expensive GPUs.\u003C\u002Fstrong> AI is changing both sides of the equation: it increases competition for semiconductor and memory resources, and it also enables neural rendering techniques that can trade stored data or conventional rendering work for inference. The important possibility is that gaming may respond to tighter hardware economics by becoming more computationally selective, not merely by demanding larger GPUs.","Direct answer",{},{"id":555,"data":1681,"type":552,"tunes":1684},{"body":1682,"title":1683,"variant":559},"This is an \u003Cstrong>evidence-backed hypothesis\u003C\u002Fstrong>. Documented market data and shipping or announced technologies are labeled as facts or industry signals. The connections between them are technical inference. The future architecture described later is a Figure Rocks hypothesis, not a claim about an unannounced NVIDIA, AMD, Intel, Microsoft or console roadmap.","How to read this article",{},{"id":562,"data":1686,"type":566,"tunes":1688},{"title":1687,"maxLevel":565,"minLevel":47},"Contents",{},{"id":569,"data":1690,"type":572,"tunes":1692},{"text":1691,"level":47},"The scale mismatch is already visible",{},{"id":575,"data":1694,"type":544,"tunes":1696},{"text":1695},"Newzoo forecasts the global games market at $213.9 billion in 2026, up 6.1% year over year. The Semiconductor Industry Association reported $791.7 billion in global semiconductor sales for 2025, up 25.6%, and projected roughly $1 trillion for 2026. The games business and semiconductor business still depend on each other, but their incentives are becoming less symmetric: the same advanced manufacturing ecosystem that enables premium gaming now serves AI and data-center buyers with much larger infrastructure budgets. These are not directly comparable product categories; they establish the scale of the upstream compute economy around gaming.",{},{"id":580,"data":1698,"type":604,"tunes":1720},{"content":1699,"stretched":603,"withHeadings":15},[1700,1704,1708,1712,1716],[1701,1702,1703],"Reference point","Reported or forecast value","Why it matters here",[1705,1706,1707],"Global games market, 2026 forecast","$213.9B, +6.1% YoY","Large consumer entertainment market, but downstream of advanced hardware supply",[1709,1710,1711],"PC games, 2026 forecast","$45.9B, +5.3% YoY","The gaming segment most directly exposed to PC component economics",[1713,1714,1715],"Global semiconductor sales, 2025","$791.7B, +25.6% YoY","Shows the scale of the upstream industry serving gaming, AI, mobile, automotive and cloud",[1717,1718,1719],"Global semiconductor industry, 2026 projection","~$1T","Raises the opportunity cost of dedicating premium silicon only to consumer gaming",{},{"id":607,"data":1722,"type":552,"tunes":1725},{"body":1723,"title":1724,"variant":611},"The figures establish \u003Cstrong>economic context, not direct causation\u003C\u002Fstrong>. They do not prove that a specific wafer, GPU or memory package was taken from a gamer and reassigned to AI. They also do not mean gaming and semiconductors are the two largest industries in the world. The narrower, defensible point is that a $200B+ games market depends on an upstream semiconductor economy approaching trillion-dollar annual scale, where AI infrastructure now competes for advanced compute and memory under very different budget and margin conditions.","What these market numbers do — and do not — prove",{},{"id":614,"data":1727,"type":572,"tunes":1729},{"text":1728,"level":47},"Memory is where the pressure becomes concrete",{},{"id":619,"data":1731,"type":544,"tunes":1733},{"text":1732},"TrendForce has repeatedly reported that DRAM suppliers are shifting capacity toward server and HBM products as AI demand grows. In March 2026 it forecast conventional DRAM contract prices rising 58–63% quarter over quarter in Q2 and said limited GDDR allocation was constraining graphics memory supply. By Q3, TrendForce said consumer customers were reaching an affordability limit even as AI and server demand remained strong.",{},{"id":624,"data":1735,"type":644,"tunes":1753},{"steps":1736,"title":1752,"orientation":643},[1737,1740,1743,1746,1749],{"label":1738,"description":1739},"1. AI infrastructure expands","Cloud providers and AI builders secure server memory, HBM, enterprise SSDs and advanced compute.",{"label":1741,"description":1742},"2. Suppliers optimize product mix","Capacity moves toward products with stronger demand, margins or long-term contracts.",{"label":1744,"description":1745},"3. Consumer allocation tightens","PC, graphics and consumer memory buyers face higher prices or constrained availability.",{"label":1747,"description":1748},"4. Gaming vendors absorb or pass through cost","Board design, VRAM capacity and system price become harder trade-offs.",{"label":1750,"description":1751},"5. The old upgrade cycle weakens","A faster GPU is still useful, but brute-force scaling becomes economically harder to repeat at the same consumer price points.","The resource-allocation pressure",{},{"id":647,"data":1755,"type":552,"tunes":1758},{"body":1756,"title":1757,"variant":611},"TrendForce reported on September 30, 2026 that suppliers still prioritize advanced-process capacity for high-performance server products, with conventional DRAM contract prices projected to rise another 10–15% QoQ in Q4. Its DRAM bulletin also says HBM continues to crowd out general DRAM capacity. The pressure described here therefore remains visible late in 2026; it is not based only on a temporary early-year spike.","A September 2026 signal, not an old shortage story",{},{"id":653,"data":1760,"type":572,"tunes":1762},{"text":1761,"level":47},"At exactly the same time, the frame itself is changing",{},{"id":658,"data":1764,"type":544,"tunes":1766},{"text":1765},"Microsoft now describes machine learning as becoming foundational to real-time graphics. DirectX is adding shader-level and model-level ML paths so neural texture compression, denoising, super resolution and other learned workloads can sit inside the graphics stack instead of being bolted on as isolated features. NVIDIA's DLSS 5 research pushes the idea further by introducing a generative neural rendering stage that complements conventional rendering with learned appearance priors. Neural rendering does not mean conventional rendering disappears. The emerging model is hybrid: render some information directly, infer or reconstruct other information when doing so is cheaper or more useful.",{},{"id":663,"data":1768,"type":669,"tunes":1773},{"url":1769,"title":1770,"excerpt":1771,"ctaLabel":1772},"https:\u002F\u002Ffigure.rocks\u002Fblog\u002Fdlss-5-is-not-just-upscaling-what-3d-guided-neural-rendering-actually-changes","DLSS 5 Is Not Just Upscaling: What Generative Neural Rendering Actually Changes","Why the important change is not another quality mode, but a new stage in how visual information can be produced.","Read the DLSS 5 analysis",{},{"id":672,"data":1775,"type":707,"tunes":1803},{"rows":1776,"title":1797,"layout":604,"columns":1798},[1777,1782,1787,1792],{"id":676,"label":1778,"values":1779},"More visual fidelity",{"new":1780,"old":1781},"Store\u002Frender selectively, reconstruct or infer some detail","Store more detail and render more work",{"id":682,"label":1783,"values":1784},"Memory response",{"new":1785,"old":1786},"Add capacity where needed, but also compress and reduce movement","Add capacity and bandwidth",{"id":688,"label":1788,"values":1789},"Performance response",{"new":1790,"old":1791},"Mix shader, RT and matrix\u002FTensor-style compute","More shader\u002FRT throughput",{"id":694,"label":1793,"values":1794},"Software role",{"new":1795,"old":1796},"Engine increasingly schedules rendering, data movement and ML workloads","Engine schedules mostly conventional graphics work","Traditional scaling vs neural-era scaling",[1799,1801],{"id":702,"label":1800},"Traditional path",{"id":705,"label":1802},"Neural-era path",{},{"id":710,"data":1805,"type":552,"tunes":1808},{"body":1806,"title":1807,"variant":714},"\u003Cstrong>AI may be weakening the economics of brute-force gaming hardware while simultaneously supplying techniques that make brute-force scaling less necessary.\u003C\u002Fstrong> That does not mean future games need less hardware. It means more of the result may come from deciding what to store, move, render, reconstruct or infer. Figure Rocks shorthand: \u003Cstrong>store less → move less → predict more → render less → infer more.\u003C\u002Fstrong> This is a hypothesis about where performance gains may increasingly come from, not a claim that every future game will use one pipeline.","The central Figure Rocks hypothesis",{},{"id":717,"data":1810,"type":719,"tunes":1811},{},{},{"id":722,"data":1813,"type":572,"tunes":1815},{"text":1814,"level":47},"The 5090 question is useful only if we phrase it correctly",{},{"id":727,"data":1817,"type":544,"tunes":1819},{"text":1818},"The RTX 5090 is a useful example precisely because the evidence is mixed. As of October 18, 2026, NVIDIA still lists the GeForce RTX 5090 as a current product. At the same time, retailer behavior and press reports have documented unusually poor availability and unconfirmed claims of a production halt. That is a market signal, not an official end-of-production announcement. The defensible question is therefore not whether NVIDIA secretly killed one card, but whether the opportunity cost of allocating top-tier Blackwell-class silicon to enthusiast gaming rises when similar architecture can serve professional visualization and AI markets with very different economics.",{},{"id":732,"data":1821,"type":552,"tunes":1824},{"body":1822,"title":1823,"variant":559},"\u003Cstrong>FACT:\u003C\u002Fstrong> NVIDIA still publicly lists the RTX 5090, and Blackwell architecture spans gaming and professional products.\u003Cbr>\u003Cstrong>INDUSTRY SIGNAL:\u003C\u002Fstrong> retailer disappearance, extreme scarcity and reports of a possible production halt exist, but NVIDIA has not confirmed an RTX 5090 EOL or production stop.\u003Cbr>\u003Cstrong>TECHNICAL INFERENCE:\u003C\u002Fstrong> a high-end die has multiple economically valuable destinations.\u003Cbr>\u003Cstrong>FIGURE ROCKS HYPOTHESIS:\u003C\u002Fstrong> AI and professional demand can make the traditional enthusiast expectation — that the largest practical die remains broadly attainable as a gaming flagship — harder to sustain.","FACT \u002F SIGNAL \u002F HYPOTHESIS",{},{"id":738,"data":1826,"type":572,"tunes":1828},{"text":1827,"level":47},"Memory is starting to become a compute problem, not only a capacity problem",{},{"id":743,"data":1830,"type":544,"tunes":1832},{"text":1831},"There are at least four ways to attack a memory bottleneck without simply increasing raw DRAM speed: compress the representation, reconstruct detail with compute, predict what data will be needed next, and keep the right data resident close to the execution unit. None of these makes a physical memory chip magically faster. They change how much useful work the system obtains from a finite capacity and bandwidth budget.",{},{"id":748,"data":1834,"type":604,"tunes":1856},{"content":1835,"stretched":603,"withHeadings":15},[1836,1840,1844,1848,1852],[1837,1838,1839],"Technique","What changes","What does not change automatically",[1841,1842,1843],"Compression","Fewer physical bytes represent the same logical data","The DRAM clock and bus width",[1845,1846,1847],"Neural reconstruction","Some detail is generated from a compact representation","The cost disappears; inference has its own compute\u002Flatency cost",[1849,1850,1851],"Predictive prefetch","Data arrives before the requesting workload stalls","Total physical bandwidth",[1853,1854,1855],"Residency \u002F unified access","Less unnecessary migration or duplication","Contention and latency constraints",{},{"id":773,"data":1858,"type":552,"tunes":1861},{"body":1859,"title":1860,"variant":551},"NVIDIA Research's Buddy Compression reported an average \u003Cstrong>2.2× compression ratio for HPC workloads and 1.5× for deep-learning workloads\u003C\u002Fstrong>, with 1–2% slowdown in that study. That result is not a gaming benchmark. Its relevance here is architectural: compressed memory can increase effective capacity and reduce some traffic without changing the physical DRAM clock.","Research anchor: effective capacity is not raw speed",{},{"id":779,"data":1863,"type":552,"tunes":1866},{"body":1864,"title":1865,"variant":714},"NVIDIA Research's material-texture NTC work demonstrated \u003Cstrong>4× higher linear texture resolution — 16× more texels — while using 30% less memory\u003C\u002Fstrong> than its BC High comparison. That does not mean every game gets a 16× memory gain. It demonstrates the mechanism that matters here: some visual detail can be represented more compactly and reconstructed with compute instead of being stored conventionally at the same fidelity.","Gaming-specific evidence: neural texture compression",{},{"id":785,"data":1868,"type":552,"tunes":1871},{"body":1869,"title":1870,"variant":611},"A 460× figure sometimes associated with NVIDIA systems refers to a comparison in GPU-accessible memory capacity across DGX generations, \u003Cstrong>not a 460× memory-compression ratio\u003C\u002Fstrong>. This article deliberately does not use it as evidence for compression.","What the “460×” number does not mean",{},{"id":791,"data":1873,"type":572,"tunes":1875},{"text":1874,"level":47},"How the technical response could work",{},{"id":796,"data":1877,"type":644,"tunes":1897},{"steps":1878,"title":1896,"orientation":643},[1879,1882,1885,1887,1890,1893],{"label":1880,"description":1881},"Compress","Keep textures or learned representations smaller where quality permits.",{"label":1883,"description":1884},"Predict","Use deterministic game state and learned models to estimate what data will be needed next.",{"label":1361,"description":1886},"Move useful data before the GPU stalls waiting for it.",{"label":1888,"description":1889},"Render","Spend conventional graphics work where it changes the visible result most.",{"label":1891,"description":1892},"Infer","Use neural reconstruction or generation where inference is cheaper than storing or simulating everything directly.",{"label":1894,"description":1895},"Present","Deliver the frame while keeping latency, artifacts and frame pacing inside acceptable limits.","One possible future frame pipeline",{},{"id":820,"data":1899,"type":544,"tunes":1901},{"text":1900},"This pipeline is not one announced product. Every component already exists in some form, but the integrated chain is the hypothesis. Neural texture compression has shown that learned representations can exchange compute for memory. GPU prefetch research has shown that learned prediction can improve memory-page hit rates. DirectX is formalizing ML inside the graphics programming model. DLSS 5 shows a generative stage entering real-time rendering.",{},{"id":825,"data":1903,"type":572,"tunes":1905},{"text":1904,"level":47},"A game has one advantage general workloads often do not: it already knows part of its future",{},{"id":830,"data":1907,"type":544,"tunes":1909},{"text":1908},"A game engine knows the camera transform, player velocity, current world cell, neighboring streaming cells, animation state, active entities, LOD transitions, shader dependencies and often likely traversal paths. That deterministic context does not reveal the future perfectly, but it gives a predictor a richer starting point than a generic memory trace alone.",{},{"id":835,"data":1911,"type":644,"tunes":1929},{"steps":1912,"title":1928,"orientation":643},[1913,1916,1919,1922,1925],{"label":1914,"description":1915},"1. Observe deterministic state","Camera direction, movement vector, current room, visible portals and known asset dependencies already exist in engine state.",{"label":1917,"description":1918},"2. Estimate the near-future working set","A learned predictor can rank likely next rooms, textures, geometry, shaders or neural representations.",{"label":1920,"description":1921},"3. Prefetch during available bandwidth","Move likely resources while the memory system has headroom instead of waiting for a hard demand miss.",{"label":1923,"description":1924},"4. Keep deterministic guarantees","Critical assets still use authored rules; prediction supplements rather than replaces correctness.",{"label":1926,"description":1927},"5. Measure the miss cost","If prediction wastes bandwidth or evicts useful data, confidence or prefetch depth must fall.","A predictive gaming-memory example",{},{"id":856,"data":1931,"type":552,"tunes":1934},{"body":1932,"title":1933,"variant":559},"A published CPU–GPU unified-virtual-memory study reported a \u003Cstrong>10.89% performance improvement\u003C\u002Fstrong>, page-hit rate rising from \u003Cstrong>76.10% to 89.02%\u003C\u002Fstrong>, and \u003Cstrong>11.05% lower CPU–GPU interconnect traffic\u003C\u002Fstrong> using learned prefetching. A 2026 AAAI paper on asynchronous KV-cache prefetching reported \u003Cstrong>2.15× attention-kernel efficiency and up to 1.97× end-to-end throughput\u003C\u002Fstrong> on its LLM workload. Neither result should be copied onto games; both demonstrate that prediction and prefetch can hide memory cost in real GPU workloads.","This idea has a research precedent, but not yet a gaming proof",{},{"id":862,"data":1936,"type":572,"tunes":1938},{"text":1937,"level":47},"Why a Spark-like architecture becomes interesting",{},{"id":867,"data":1940,"type":544,"tunes":1942},{"text":1941},"DGX Spark is not a gaming PC, and treating it as one would hide the useful lesson. Its GB10 design combines Arm CPU cores, a Blackwell GPU, Tensor and RT hardware, and up to 128 GB of coherent unified system memory. That architecture asks a different question from a classic desktop: instead of splitting the system cleanly into CPU memory and discrete VRAM, what happens when multiple compute engines share a large memory pool?",{},{"id":872,"data":1944,"type":604,"tunes":1964},{"content":1945,"stretched":603,"withHeadings":15},[1946,1950,1954,1957,1960],[1947,1948,1949],"Property","DGX Spark reference","Gaming implication if the principle were adapted",[1951,1952,1953],"Memory","Up to 128 GB coherent unified LPDDR5X","Large shared pool could reduce duplication and give local AI + graphics access to common data",[1955,885,1956],"Memory bandwidth","Capacity is not enough; high-end graphics still needs aggressive bandwidth and latency management",[1958,889,1959],"Compute mix","A future game workload could be split across more specialized engines",[1961,1962,1963],"Form factor\u002Fpower","Compact GB10 system","Integration can improve efficiency but may reduce modular upgradeability",{},{"id":897,"data":1966,"type":552,"tunes":1969},{"body":1967,"title":1968,"variant":611},"A large unified pool does \u003Cstrong>not\u003C\u002Fstrong> automatically replace the bandwidth of a high-end discrete GPU. DGX Spark lists 273 GB\u002Fs of memory bandwidth, while NVIDIA's RTX PRO 6000 Blackwell Workstation Edition lists 1,792 GB\u002Fs. These are very different products and the comparison is not a gaming benchmark; it simply makes the architectural constraint visible. A Spark-like gaming design would need to solve contention, latency and bandwidth — not just provide more shared gigabytes.","Capacity is not bandwidth",{},{"id":903,"data":1971,"type":572,"tunes":1973},{"text":1972,"level":47},"What would have to be true for this transition to work?",{},{"id":908,"data":1975,"type":644,"tunes":1996},{"steps":1976,"title":1995,"orientation":643},[1977,1980,1983,1986,1989,1992],{"label":1978,"description":1979},"1. Inference must save more than it costs","Neural work has to remove enough storage, bandwidth or conventional rendering cost to justify its own compute and latency.",{"label":1981,"description":1982},"2. The engine must expose useful state","Prediction works better when the system knows camera direction, asset dependencies, movement and likely visibility.",{"label":1984,"description":1985},"3. Memory movement must become deliberate","Compression without good residency and prefetch policy can still stall.",{"label":1987,"description":1988},"4. Quality must remain stable","Generated detail cannot introduce unacceptable artifacts, temporal instability or incorrect game information.",{"label":1990,"description":1991},"5. Latency must stay bounded","A prettier frame is not automatically a more responsive game.",{"label":1993,"description":1994},"6. The stack must survive vendor differences","Useful techniques need APIs, drivers and engine paths that do not fragment into one-off demos.","The Neural Gaming Viability Test",{},{"id":932,"data":1998,"type":572,"tunes":2000},{"text":1999,"level":47},"What should a buyer expect from a Spark-like transition?",{},{"id":937,"data":2002,"type":707,"tunes":2035},{"rows":2003,"title":2029,"layout":604,"columns":2030},[2004,2009,2014,2019,2024],{"id":941,"label":2005,"values":2006},"Large coherent pool",{"cost":2007,"gain":2008},"Shared clients can contend for the same bandwidth","More flexible sharing between CPU, graphics and local AI",{"id":947,"label":2010,"values":2011},"Less duplication",{"cost":2012,"gain":2013},"Coherence and scheduling add complexity","Fewer copies between separate system-RAM and VRAM pools",{"id":953,"label":2015,"values":2016},"More local AI headroom",{"cost":2017,"gain":2018},"AI can compete with graphics for compute and memory","Larger models, agents or neural assets can coexist locally",{"id":959,"label":2020,"values":2021},"System-level optimization",{"cost":2022,"gain":2023},"Tighter integration can reduce upgradeability and increase lock-in","The platform can optimize data placement and accelerators together",{"id":965,"label":2025,"values":2026},"New workload flexibility",{"cost":2027,"gain":2028},"Only if engines and runtimes actually use those capabilities","A machine may age better if future games shift toward neural workloads","Potential benefit vs real cost",[2031,2033],{"id":973,"label":2032},"Potential gain",{"id":976,"label":2034},"Cost \u002F risk",{},{"id":980,"data":2037,"type":572,"tunes":2039},{"text":2038,"level":47},"What would falsify this hypothesis?",{},{"id":985,"data":2041,"type":544,"tunes":2043},{"text":2042},"This thesis is deliberately falsifiable. The table below defines the market, software, hardware and game-adoption signals that would make the argument stronger or weaker over time.",{},{"id":990,"data":2045,"type":707,"tunes":2073},{"rows":2046,"title":2067,"layout":604,"columns":2068},[2047,2052,2057,2062],{"id":682,"label":2048,"values":2049},"Memory economics",{"weakens":2050,"strengthens":2051},"Consumer capacity expands and price pressure normalizes","Server\u002FHBM priority continues to squeeze consumer allocation",{"id":999,"label":2053,"values":2054},"Graphics stack",{"weakens":2055,"strengthens":2056},"Neural graphics remains vendor-specific optional post-processing","ML primitives become normal engine\u002FDirectX features",{"id":1005,"label":2058,"values":2059},"Hardware design",{"weakens":2060,"strengthens":2061},"Discrete GPU + separate RAM remains clearly superior on cost, flexibility and latency","More integrated heterogeneous compute and larger coherent memory pools",{"id":1011,"label":2063,"values":2064},"Game adoption",{"weakens":2065,"strengthens":2066},"Tech remains limited to demos or a few flagship games","Studios ship neural compression, generation and predictive data paths broadly","Evidence that strengthens or weakens the thesis",[2069,2071],{"id":1019,"label":2070},"Would strengthen it",{"id":1022,"label":2072},"Would weaken it",{},{"id":1026,"data":2075,"type":572,"tunes":2077},{"text":2076,"level":47},"The practical conclusion for a gamer today",{},{"id":1031,"data":2079,"type":544,"tunes":2081},{"text":2080},"Do not buy hardware because a speculative architecture might arrive. Buy for the games, resolution, latency target and workloads you actually have. But do not assume that future-proofing can still be reduced to one number such as raster performance or VRAM capacity. The direction of the software stack suggests that memory behavior, neural compute and engine support may become increasingly important parts of the value equation.",{},{"id":1036,"data":2083,"type":669,"tunes":2088},{"url":2084,"title":2085,"excerpt":2086,"ctaLabel":2087},"https:\u002F\u002Ffigure.rocks\u002Fblog\u002Fvram-usage-is-not-vram-requirement-why-a-full-memory-meter-does-not-tell-the-whole-story","VRAM Usage Is Not VRAM Requirement: Why a Full Memory Meter Does Not Tell the Whole Story","A practical explanation of working sets, residency and why an allocated VRAM number is not the same thing as a hard requirement.","Read the VRAM guide",{},{"id":1044,"data":2090,"type":572,"tunes":2092},{"text":2091,"level":47},"Primary sources",{},{"id":1049,"data":2094,"type":1056,"tunes":2099},{"link":1051,"meta":2095},{"image":2096,"title":2097,"description":2098},{"url":13},"Newzoo — 2026 Global Games Market Key Numbers","2026 games-market forecast used for the scale comparison.",{},{"id":1059,"data":2101,"type":1056,"tunes":2106},{"link":1061,"meta":2102},{"image":2103,"title":2104,"description":2105},{"url":13},"Semiconductor Industry Association — 2025 Global Sales","2025 semiconductor sales and the association's roughly $1 trillion 2026 projection.",{},{"id":1068,"data":2108,"type":1056,"tunes":2113},{"link":1070,"meta":2109},{"image":2110,"title":2111,"description":2112},{"url":13},"TrendForce — AI Server Demand Sustains Memory Price Increases in 4Q26","Late-2026 evidence for ongoing server-priority capacity allocation and consumer pressure.",{},{"id":1077,"data":2115,"type":1056,"tunes":2119},{"link":1079,"meta":2116},{"image":2117,"title":1082,"description":2118},{"url":13},"Research source for 2.2× HPC and 1.5× DL average compression ratios.",{},{"id":1086,"data":2121,"type":1056,"tunes":2126},{"link":1088,"meta":2122},{"image":2123,"title":2124,"description":2125},{"url":13},"Deep-learning based CPU–GPU UVM prefetching","Research source for learned page-prefetching results.",{},{"id":1095,"data":2128,"type":1056,"tunes":2133},{"link":1097,"meta":2129},{"image":2130,"title":2131,"description":2132},{"url":13},"AAAI 2026 — Asynchronous KV Cache Prefetching","Research source demonstrating latency hiding through proactive GPU-cache prefetching.",{},{"id":1104,"data":2135,"type":1056,"tunes":2140},{"link":1106,"meta":2136},{"image":2137,"title":2138,"description":2139},{"url":13},"TrendForce — AI Server Demand and 2Q26 Memory Prices","Current evidence for server\u002FHBM prioritization, GDDR constraints and consumer pressure.",{},{"id":1113,"data":2142,"type":1056,"tunes":2147},{"link":1115,"meta":2143},{"image":2144,"title":2145,"description":2146},{"url":13},"Microsoft DirectX — Evolving DirectX for the ML Era","Official GDC 2026 overview of shader-level and model-level ML in DirectX.",{},{"id":1122,"data":2149,"type":1056,"tunes":2154},{"link":1124,"meta":2150},{"image":2151,"title":2152,"description":2153},{"url":13},"NVIDIA Research — DLSS 5: Generative Neural Rendering","Primary research page describing DLSS 5 as a real-time generative rendering stage.",{},{"id":1131,"data":2156,"type":1056,"tunes":2161},{"link":1133,"meta":2157},{"image":2158,"title":2159,"description":2160},{"url":13},"NVIDIA — DGX Spark Specifications","Official architecture, unified memory and bandwidth specifications.",{},{"id":1140,"data":2163,"type":1056,"tunes":2167},{"link":1142,"meta":2164},{"image":2165,"title":1145,"description":2166},{"url":13},"Official current RTX 5090 product page used to distinguish confirmed product status from production-halt rumors.",{},{"id":1149,"data":2169,"type":1056,"tunes":2174},{"link":1151,"meta":2170},{"image":2171,"title":2172,"description":2173},{"url":13},"Tom’s Hardware — RTX 5090 Retail Disappearance and Production-Halt Rumors","Current market report used only as an industry signal; it explicitly notes that NVIDIA has not confirmed a production halt.",{},{"id":1158,"data":2176,"type":1056,"tunes":2181},{"link":1160,"meta":2177},{"image":2178,"title":2179,"description":2180},{"url":13},"NVIDIA Research — Random-Access Neural Compression of Material Textures","Primary source for the 4× linear resolution \u002F 16× texel comparison at 30% less memory versus BC High.",{},{"id":1167,"data":2183,"type":1056,"tunes":2187},{"link":1169,"meta":2184},{"image":2185,"title":1172,"description":2186},{"url":13},"Official source for 96 GB GDDR7 and 1,792 GB\u002Fs memory bandwidth used as a bandwidth reference point.",{},{"id":1176,"data":2189,"type":1196,"tunes":2204},{"title":2190,"entries":2191},"Key concepts",[2192,2195,2198,2201],{"term":2193,"anchor":1182,"definition":2194},"Neural rendering","Rendering methods that use trained models as part of producing, reconstructing or enhancing visual output.",{"term":2196,"anchor":1186,"definition":2197},"Heterogeneous compute","A system that assigns different workloads to different compute engines such as CPU, GPU, RT and matrix\u002FTensor hardware.",{"term":2199,"anchor":1190,"definition":2200},"Effective memory","A practical description of how much useful data a memory system can keep and feed to compute after compression, residency and movement efficiency are considered. It is not a formal replacement for physical capacity.",{"term":2202,"anchor":1194,"definition":2203},"Predictive memory","Figure Rocks shorthand for combining known game state and learned prediction to prepare likely-needed data before a stall. It is a hypothesis, not an industry 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Naučite praktične osnove ergonomije za stolicu, sto, monitor i kontrole kako biste smanjili umor i zadržali doslednost tokom dugih sesija.","2026-02-19T11:00:00.000Z",{"id":3141,"slug":3142,"title":3143,"excerpt":3144,"featuredImage":14,"publishedAt":3145},"399","beyond-scanning-creative-uses-and-modifications-of-amiibo","Iznad skeniranja – Kreativne upotrebe i modifikacije amiibo","amiibo su figurice likova opremljene NFC tehnologijom koje Nintendo izdaje od 2014. godine. Tehnički, to su mali nosači podataka unutar livenih plastičnih figura. U praksi, one se nalaze negde između igračke, kolekcionarskog predmeta i interfejs uređaja. Njihovo skeniranje u konzolu je samo jedan deo njihovog životnog ciklusa. Posmatrano tokom godina, mnogi vlasnici ih tretiraju kao objekte sa sopstvenim kreativnim potencijalom.","2026-02-27T16:36:00.000Z",{"id":3147,"slug":3148,"title":3149,"excerpt":3150,"featuredImage":3151,"publishedAt":3152},"455","what-if-a-game-could-predict-its-next-memory-access-ai-prefetching-and-the-future-of-stutter","Šta ako bi igra mogla da predvidi svoj sledeći pristup memoriji? AI prefetching i budućnost seckanja","Gejm endžini već znaju kuda idu igrač, kamera i svet. Istraživanja pokazuju da naučeno GPU prefetčovanje može da predvidi memorijske stranice. Šta se dešava ako se te ideje kombinuju?","\u002Fuploads\u002F2026\u002F10\u002Fwhat-if-a-game-could-predict-its-next-memory-access-ai-prefetching-and-the-future-of-stutter-1792339100514-lyx0yw.webp","2026-10-18T11:57:00.000Z",{"id":3154,"slug":3155,"title":3156,"excerpt":3157,"featuredImage":14,"publishedAt":3139},"65","gaming-feel-quick-checklist-fix-timing-before-you-upgrade-anything","Brza kontrolna lista za osećaj u igri: Popravite tajming pre nego što bilo šta nadogradite","Brza kontrolna lista za poboljšanje odziva, glatkoće i jasnoće bez kupovine nove opreme. Prvo dijagnostikujte tajming, zatim podesite postavke, pa onda nadogradite.",{"id":3159,"slug":3160,"title":3161,"excerpt":3162,"featuredImage":14,"publishedAt":3163},"40","experience-first-gaming-the-anti-race-handbook","Igra Fokusirana na Iskustvu: Priručnik Protiv Trke","Temeljni vodič za igrače umorne od trke za specifikacijama. Osećaj konfiguracije, stabilnost i ukus: bolja igra, smirenije nadogradnje, dugoročna vrednost.","2026-02-19T12:00:00.000Z",{"id":3165,"slug":3166,"title":3167,"excerpt":3168,"featuredImage":14,"publishedAt":3169},"295","amiibo-hub-start-here-pillars-guides-and-what-to-buy-first","amiibo centar: Počnite ovde (Stubovi, vodiči i šta prvo kupiti)","Vaša Amiibo biblioteka na jednom mestu: osnove, kompatibilnost, kupovina i sakupljanje. Koristite ovo čvorište da izaberete svoj put i izbegnete početničke greške.","2026-02-21T15:00:00.000Z",{"id":3171,"slug":3172,"title":3173,"excerpt":3174,"featuredImage":14,"publishedAt":3139},"53","smoothness-is-frame-pacing-why-stable-frametime-beats-raw-fps","Glatkoća je frame pacing: Zašto stabilan frametime pobeđuje sirovi FPS","Ako vaša igra deluje isprekidano pri visokom FPS-u, to je obično do frame pacing-a. Saznajte šta je frametime, šta uzrokuje skokove i kako ga prvo stabilizovati.",{"id":3176,"slug":3177,"title":3178,"excerpt":14,"featuredImage":3179,"publishedAt":3180},"456","what-would-a-spark-like-gaming-pc-actually-change-unified-memory-ai-compute-and-the-classic-gpu-split","Šta bi gejming PC nalik Sparku zapravo promenio? Objedinjena memorija, AI računanje i klasična podela GPU-a","\u002Fuploads\u002F2026\u002F10\u002Fwhat-would-a-spark-like-gaming-pc-actually-change-unified-memory-ai-compute-and-the-classic-gpu-split-1792340021752-bia7n6.webp","2026-10-18T12:12:00.000Z",{"id":3182,"slug":3183,"title":3184,"excerpt":3185,"featuredImage":14,"publishedAt":3139},"41","rare-games-real-value-collecting-without-the-price-race","Retke igre, prava vrednost: Kolekcionarstvo bez trke u cenama","Temeljni vodič za retke igre kroz kulturu i standarde: šta sakupljati, kako ocenjivati i kako izbeći žaljenje uzrokovano hajpom.",{"id":3187,"slug":3188,"title":3189,"excerpt":3190,"featuredImage":14,"publishedAt":3191},"20","console-vs-pc-display-setup-the-clean-stable-way","Konzola vs PC podešavanje ekrana: Čist, stabilan način","Konzolama i PC-u su potrebni različiti prioriteti prikaza. Ovaj vodič izbegava uobičajene greške i održava kretanje i latenciju doslednim.","2026-02-19T08:00:00.000Z",{"id":3193,"slug":3194,"title":3195,"excerpt":3196,"featuredImage":14,"publishedAt":3197},"245","qos-myths-for-gaming-what-helps-what-hurts-and-the-real-priority","QoS mitovi za gejming: Šta pomaže, šta odmaže i pravi prioritet","QoS nije magični ‘gejming’ prekidač. Dobro upravljanje redovima čekanja smanjuje latenciju pod opterećenjem. Loš QoS dodaje džiter ili narušava pravičnost. Evo stabilnog načina da razmišljate o tome.","2026-02-20T23:55:00.000Z",{"id":3199,"slug":3200,"title":3201,"excerpt":3202,"featuredImage":3203,"publishedAt":3204},"434","google-io-2026-gaming-ai-xr-and-the-next-wave-of-collectibles","Google I\u002FO 2026 i gejming: AI, XR i sledeći talas kolekcionarskih predmeta","Google I\u002FO 2026 nije bio klasičan gejming događaj, ali njegove najave u vezi sa AI, XR, Gemini, Google Play tehnologijama i kreativnim alatima ukazuju na jasnu promenu u gejming kulturi. Ovaj članak objašnjava kako AI otkrivanje, XR prateći slojevi, mediji koje pokreće Gemini i brža vidljivost likova mogu oblikovati igre, figure, kolekcionarske predmete i buduće ekosisteme fanova.","\u002Fuploads\u002F2026\u002F05\u002Fgoogle-io-2026-gaming-ai-xr-and-the-next-wave-of-collectibles-1779257974958-jjj07f.webp","2026-05-20T00:17:00.000Z","fallback",[],[]]