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Microsoft dodaje primitive mašinskog učenja direktno u HLSL i drugi put za pokretanje većih ML grafova unutar DirectX ekosistema. To menja gde neuronsko renderovanje može da živi u budućim PC igrama.\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>DirectX postaje grafička platforma sposobna za ML.\u003C\u002Fstrong> Mali neuronski zadaci mogu se izvršavati direktno unutar šejdera kroz DX Linearnu Algebru, dok su veći modeli ciljani od strane DirectX Compute Graph Compiler-a. Cilj je da se game engine-ima omogući korišćenje GPU AI hardvera bez izgradnje posebne putanje specifične za proizvođača za svaku tehniku.\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\">Trenutni status\u003C\u002Fstrong>\u003Cdiv class=\"text-gray-700 dark:text-gray-200\">Od septembra 2026, \u003Cstrong>DX Linearna Algebra je još uvek tehnologija u pregledu\u003C\u002Fstrong> u Shader Model 6.10 \u002F Agility SDK putanji pregleda. Microsoft je najavio DirectX Compute Graph Compiler za privatni pregled, a ne kao široko dostupnu maloprodajnu funkciju. Ovaj članak objašnjava arhitekturu i smer, a ne tvrdnju da svaka trenutna igra može da ga koristi danas.\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\">Zašto neuronskom renderovanju trebaju nove DirectX primitive\u003C\u002Fa>\u003C\u002Fli>\u003Cli class=\"editorjs-toc__item\">\u003Ca href=\"#section-9\" class=\"editorjs-toc__link\">Dvonivojski DirectX ML model\u003C\u002Fa>\u003C\u002Fli>\u003Cli class=\"editorjs-toc__item\">\u003Ca href=\"#section-12\" class=\"editorjs-toc__link\">Šta DX Linearna Algebra zapravo daje šejderu\u003C\u002Fa>\u003C\u002Fli>\u003Cli class=\"editorjs-toc__item\">\u003Ca href=\"#section-17\" class=\"editorjs-toc__link\">Zašto je Cooperative Vector bio samo početak\u003C\u002Fa>\u003C\u002Fli>\u003Cli class=\"editorjs-toc__item\">\u003Ca href=\"#section-21\" class=\"editorjs-toc__link\">Šta zapravo može da se izvršava unutar šejdera?\u003C\u002Fa>\u003C\u002Fli>\u003Cli class=\"editorjs-toc__item\">\u003Ca href=\"#section-24\" class=\"editorjs-toc__link\">Zašto je ovo važno za memoriju tekstura\u003C\u002Fa>\u003C\u002Fli>\u003Cli class=\"editorjs-toc__item\">\u003Ca href=\"#section-29\" class=\"editorjs-toc__link\">Zašto puni modeli zahtevaju drugačiji put\u003C\u002Fa>\u003C\u002Fli>\u003Cli class=\"editorjs-toc__item\">\u003Ca href=\"#section-33\" class=\"editorjs-toc__link\">Granica između šejdera i grafa\u003C\u002Fa>\u003C\u002Fli>\u003Cli class=\"editorjs-toc__item\">\u003Ca href=\"#section-35\" class=\"editorjs-toc__link\">Zašto je podrška između različitih proizvođača važnija od još jedne AI funkcije\u003C\u002Fa>\u003C\u002Fli>\u003Cli class=\"editorjs-toc__item\">\u003Ca href=\"#section-40\" class=\"editorjs-toc__link\">Koji hardver podržava trenutni pregled?\u003C\u002Fa>\u003C\u002Fli>\u003Cli class=\"editorjs-toc__item\">\u003Ca href=\"#section-44\" class=\"editorjs-toc__link\">Neuronsko renderovanje postaje infrastruktura\u003C\u002Fa>\u003C\u002Fli>\u003Cli class=\"editorjs-toc__item\">\u003Ca href=\"#section-49\" class=\"editorjs-toc__link\">Neuronski renderujući stek postaje slojevit\u003C\u002Fa>\u003C\u002Fli>\u003Cli class=\"editorjs-toc__item\">\u003Ca href=\"#section-51\" class=\"editorjs-toc__link\">Zašto je objedinjeno profilisanje važno\u003C\u002Fa>\u003C\u002Fli>\u003Cli class=\"editorjs-toc__item\">\u003Ca href=\"#section-55\" class=\"editorjs-toc__link\">To ne znači da će svaki šejder postati neuronska mreža\u003C\u002Fa>\u003C\u002Fli>\u003Cli class=\"editorjs-toc__item\">\u003Ca href=\"#section-59\" class=\"editorjs-toc__link\">Test vrednosti neuronskog šejdera\u003C\u002Fa>\u003C\u002Fli>\u003Cli class=\"editorjs-toc__item\">\u003Ca href=\"#section-61\" class=\"editorjs-toc__link\">Šta ovo znači za gejmere\u003C\u002Fa>\u003C\u002Fli>\u003Cli class=\"editorjs-toc__item\">\u003Ca href=\"#section-65\" class=\"editorjs-toc__link\">Šta bi promenilo ovaj odgovor?\u003C\u002Fa>\u003C\u002Fli>\u003Cli class=\"editorjs-toc__item\">\u003Ca href=\"#section-69\" class=\"editorjs-toc__link\">Ograničenja\u003C\u002Fa>\u003C\u002Fli>\u003Cli class=\"editorjs-toc__item\">\u003Ca href=\"#section-72\" class=\"editorjs-toc__link\">Zaključak\u003C\u002Fa>\u003C\u002Fli>\u003Cli class=\"editorjs-toc__item\">\u003Ca href=\"#section-76\" class=\"editorjs-toc__link\">Često postavljana pitanja\u003C\u002Fa>\u003C\u002Fli>\u003Cli class=\"editorjs-toc__item\">\u003Ca href=\"#section-78\" class=\"editorjs-toc__link\">Pojmovnik\u003C\u002Fa>\u003C\u002Fli>\u003Cli class=\"editorjs-toc__item\">\u003Ca href=\"#section-80\" class=\"editorjs-toc__link\">Primarni izvori\u003C\u002Fa>\u003C\u002Fli>\u003C\u002Fol>\u003C\u002Fnav>\n\u003Ch2 id=\"section-5\">Zašto neuronskom renderovanju trebaju nove DirectX primitive\u003C\u002Fh2>\n\u003Cp>Moderne GPU već sadrže specijalizovani hardver za matrične operacije koje koristi mašinsko učenje. Problem za programera igara nije samo da li taj hardver postoji, već kako mu efikasno pristupiti iz real-time grafičkog pipeline-a.\u003C\u002Fp>\n\u003Cp>Microsoft je prvo istražio ovo sa podrškom za Cooperative Vector. U 2026, taj rad je evoluirao u širi DirectX Linearna Algebra dizajn koji podržava i vektor-matrične i matrično-matrične operacije.\u003C\u002Fp>\n\u003Cp>To je važno jer različiti poslovi neuronskog renderovanja imaju različite oblike. Mali model koji procenjuje ponašanje materijala po pikselu nije isti zadatak kao veliki graf za super-rezoluciju ili uklanjanje šuma.\u003C\u002Fp>\n\u003Ch2 id=\"section-9\">Dvonivojski DirectX ML model\u003C\u002Fh2>\n\u003Csection class=\"editorjs-process my-6\">\u003Ch3 class=\"mb-3 text-lg font-semibold\">Dva načina na koja ML može ući u DirectX grafički pipeline\u003C\u002Fh3>\u003Cdiv class=\"flex flex-col sm:flex-row gap-3\">\u003Cdiv class=\"editorjs-process__step min-w-0 flex-1 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. ML na nivou šejdera\u003C\u002Fdiv>\u003Cdiv class=\"mt-1 text-sm text-gray-600 dark:text-gray-300\">Mali neuronski ili linearno-algebarski zadaci izvršavaju se direktno iz HLSL-a zajedno sa tradicionalnim kodom šejdera.\u003C\u002Fdiv>\u003C\u002Fdiv>\u003Cdiv class=\"editorjs-process__arrow shrink-0 self-center text-xl text-gray-400 rotate-90 sm:rotate-0\" aria-hidden=\"true\">→\u003C\u002Fdiv>\u003Cdiv class=\"editorjs-process__step min-w-0 flex-1 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. DX Linearna Algebra\u003C\u002Fdiv>\u003Cdiv class=\"mt-1 text-sm text-gray-600 dark:text-gray-300\">Šejder može zatražiti hardverski ubrzane vektorske i matrične operacije umesto ručnog implementiranja svake ML primitive.\u003C\u002Fdiv>\u003C\u002Fdiv>\u003Cdiv class=\"editorjs-process__arrow shrink-0 self-center text-xl text-gray-400 rotate-90 sm:rotate-0\" aria-hidden=\"true\">→\u003C\u002Fdiv>\u003Cdiv class=\"editorjs-process__step min-w-0 flex-1 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. ML na nivou modela\u003C\u002Fdiv>\u003Cdiv class=\"mt-1 text-sm text-gray-600 dark:text-gray-300\">Veće neuronske mreže predstavljene su kao kompletni računski grafovi, a ne kao ručno napisani fragmenti šejdera.\u003C\u002Fdiv>\u003C\u002Fdiv>\u003Cdiv class=\"editorjs-process__arrow shrink-0 self-center text-xl text-gray-400 rotate-90 sm:rotate-0\" aria-hidden=\"true\">→\u003C\u002Fdiv>\u003Cdiv class=\"editorjs-process__step min-w-0 flex-1 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. DirectX Compute Graph Compiler\u003C\u002Fdiv>\u003Cdiv class=\"mt-1 text-sm text-gray-600 dark:text-gray-300\">Microsoft-ova planirana putanja kompajlera analizira i prevodi te grafove u optimizovane GPU zadatke integrisane sa D3D12 redovima i listama komandi.\u003C\u002Fdiv>\u003C\u002Fdiv>\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\">Jednostavna razlika\u003C\u002Fstrong>\u003Cdiv class=\"text-gray-700 dark:text-gray-200\">\u003Cstrong>DX Linearna Algebra:\u003C\u002Fstrong> stavite malu ML matematiku unutar šejdera.\u003Cbr>\u003Cstrong>Compute Graph Compiler:\u003C\u002Fstrong> unesite veći ML model u engine kao graf.\u003C\u002Fdiv>\u003C\u002Faside>\n\u003Ch2 id=\"section-12\">Šta DX Linearna Algebra zapravo daje šejderu\u003C\u002Fh2>\n\u003Cp>Tradicionalni HLSL je izgrađen oko grafičkih i računskih operacija. Neuronski zadaci se u velikoj meri oslanjaju na linearnu algebru: vektore, matrice, množenje, akumulaciju i rasporede podataka optimizovane za te operacije.\u003C\u002Fp>\n\u003Cp>Shader Model 6.10 pregled dodaje matrix API-je prvog reda kako bi programeri mogli direktnije izraziti te zadatke i pustiti drajver da ih mapira na specijalizovani hardver.\u003C\u002Fp>\n\u003Cp>Microsoft-ov pregled iz aprila 2026 eksplicitno opisuje ovo kao jedinstvenu putanju za neuronsko renderovanje, ML i zadatke obrade slika, a ne kao funkciju samo za grafiku.\u003C\u002Fp>\n\u003Csection class=\"editorjs-comparison my-6\">\u003Ch3 class=\"mb-3 text-lg font-semibold\">Tradicionalna matematika šejdera vs ML-orijentisana matematika šejdera\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 fokus šejdera\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\">ML-orijentisani fokus\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\">Tipičan rad\u003C\u002Fth>\u003Ctd class=\"border border-gray-300 px-4 py-3 dark:border-gray-700\">\u003C\u002Ftd>\u003Ctd class=\"border border-gray-300 px-4 py-3 dark:border-gray-700\">\u003C\u002Ftd>\u003C\u002Ftr>\u003Ctr>\u003Cth class=\"border border-gray-300 px-4 py-3 text-left font-semibold dark:border-gray-700\">Hardverska putanja\u003C\u002Fth>\u003Ctd class=\"border border-gray-300 px-4 py-3 dark:border-gray-700\">\u003C\u002Ftd>\u003Ctd class=\"border border-gray-300 px-4 py-3 dark:border-gray-700\">\u003C\u002Ftd>\u003C\u002Ftr>\u003Ctr>\u003Cth class=\"border border-gray-300 px-4 py-3 text-left font-semibold dark:border-gray-700\">Izražavanje programera\u003C\u002Fth>\u003Ctd class=\"border border-gray-300 px-4 py-3 dark:border-gray-700\">\u003C\u002Ftd>\u003Ctd class=\"border border-gray-300 px-4 py-3 dark:border-gray-700\">\u003C\u002Ftd>\u003C\u002Ftr>\u003C\u002Ftbody>\u003C\u002Ftable>\u003C\u002Fdiv>\u003C\u002Fsection>\n\u003Ch2 id=\"section-17\">Zašto je Cooperative Vector bio samo početak\u003C\u002Fh2>\n\u003Cp>Cooperative Vector je omogućio nitima šejdera da zatraže vektor-matrični rad koji drajveri mogu mapirati na specijalizovani hardver. To je bilo korisno za visoko paralelne zadatke po pikselu.\u003C\u002Fp>\n\u003Cp>Microsoft je kasnije zaključio da mnogi važni ML radni zadaci zahtevaju više od vektorsko-matričnih operacija. Super rezolucija, uklanjanje šuma, temporalna rekonstrukcija, veći modeli slika i opšta inferencija mogu zahtevati matrično-matrične operacije i deljeni rad kroz mnoge niti.\u003C\u002Fp>\n\u003Cp>DX Linear Algebra stoga proširuje model umesto da tretira Cooperative Vector kao konačnu apstrakciju.\u003C\u002Fp>\n\u003Ch2 id=\"section-21\">Šta zapravo može da se izvršava unutar šejdera?\u003C\u002Fh2>\n\u003Cp>Najzanimljiviji ML radni zadaci na nivou šejdera su dovoljno mali da se izvršavaju blizu grafičkih podataka na kojima rade.\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\">Radni zadatak\u003C\u002Fth>\u003Cth class=\"border border-gray-300 px-4 py-2 text-left font-semibold\">Zašto ML na nivou šejdera odgovara\u003C\u002Fth>\u003C\u002Ftr>\u003C\u002Fthead>\u003Ctbody>\u003Ctr>\u003Ctd class=\"border border-gray-300 px-4 py-2\">Neuralna kompresija tekstura\u003C\u002Ftd>\u003Ctd class=\"border border-gray-300 px-4 py-2\">Mala mreža može da rekonstruiše informacije o teksturi blizu tačke gde su šejderu potrebne\u003C\u002Ftd>\u003C\u002Ftr>\u003Ctr>\u003Ctd class=\"border border-gray-300 px-4 py-2\">Neuralna evaluacija materijala\u003C\u002Ftd>\u003Ctd class=\"border border-gray-300 px-4 py-2\">Naučena funkcija može da zameni ili dopuni skupu ručno pisanu matematiku materijala\u003C\u002Ftd>\u003C\u002Ftr>\u003Ctr>\u003Ctd class=\"border border-gray-300 px-4 py-2\">Neuralno keširanje radijanse\u003C\u002Ftd>\u003Ctd class=\"border border-gray-300 px-4 py-2\">Inferencija po pikselu ili lokalna inferencija može da proceni informacije o osvetljenju na osnovu naučenog ponašanja scene\u003C\u002Ftd>\u003C\u002Ftr>\u003Ctr>\u003Ctd class=\"border border-gray-300 px-4 py-2\">Mali kerneli za uklanjanje šuma\u002Frekonstrukciju\u003C\u002Ftd>\u003Ctd class=\"border border-gray-300 px-4 py-2\">ML operacije mogu da stoje direktno pored faze renderovanja koju poboljšavaju\u003C\u002Ftd>\u003C\u002Ftr>\u003Ctr>\u003Ctd class=\"border border-gray-300 px-4 py-2\">Inferencija za obradu slika\u003C\u002Ftd>\u003Ctd class=\"border border-gray-300 px-4 py-2\">Matrične operacije mogu da se ugrade u GPU obradu bez zasebnog eksternog runtime-a\u003C\u002Ftd>\u003C\u002Ftr>\u003C\u002Ftbody>\u003C\u002Ftable>\u003C\u002Fdiv>\n\u003Ch2 id=\"section-24\">Zašto je ovo važno za memoriju tekstura\u003C\u002Fh2>\n\u003Cp>Jedan od Microsoftovih primera koji se često ponavlja je neuralna kompresija tekstura.\u003C\u002Fp>\n\u003Cp>Umesto da čuva svaki kanal teksture pri konvencionalnoj vernosti, igra može da čuva kompaktniju reprezentaciju i koristi malu neuronsku mrežu da rekonstruiše detalje tokom renderovanja.\u003C\u002Fp>\n\u003Cp>To menja deo GPU inferencijskog rada za manji pritisak na skladištenje ili memoriju. Tačna korist zavisi od tehnike i hardvera, ali arhitektonska promena je važna: neki vizuelni detalji mogu postati računanje umesto sačuvanih podataka.\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-om: Zašto pun memorijski merač ne govori celu priču\u003C\u002Fstrong>\u003Cp class=\"mt-2 text-sm text-gray-600 dark:text-gray-300\">Praktični vodič kroz VRAM kapacitet, budžete rezidentnosti, radne skupove i zašto je memorijski pritisak komplikovaniji od jednog broja korišćenja.\u003C\u002Fp>\u003Cspan class=\"mt-3 inline-flex text-sm font-medium text-primary-600 dark:text-primary-400\">Pročitajte vodič za VRAM →\u003C\u002Fspan>\u003C\u002Fdiv>\u003C\u002Fa>\u003C\u002Faside>\n\u003Ch2 id=\"section-29\">Zašto puni modeli zahtevaju drugačiji put\u003C\u002Fh2>\n\u003Cp>Ručno pisanje nekoliko matričnih operacija u HLSL-u je praktično za male neuronske funkcije. Postaje mnogo manje praktično kada je radni zadatak kompletan moderni model sa mnogo slojeva, zavisnosti i međurezultata tenzora.\u003C\u002Fp>\n\u003Cp>Microsoftov DirectX Compute Graph Compiler je dizajniran za ovu veću klasu radnih zadataka.\u003C\u002Fp>\n\u003Cp>Umesto prepisivanja modela kao prilagođenog koda šejdera, kompajler može da prihvati graf računanja, analizira ceo graf, planira memoriju, spoji operacije i spusti rezultat u GPU rad koji se integriše sa DirectX 12.\u003C\u002Fp>\n\u003Ch2 id=\"section-33\">Granica između šejdera i grafa\u003C\u002Fh2>\n\u003Csection class=\"editorjs-comparison my-6\">\u003Ch3 class=\"mb-3 text-lg font-semibold\">Kada ML radni zadatak pripada HLSL-u, a kada kompajleru modela\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\">Put na nivou šejdera\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 na nivou modela\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\">Veličina modela\u003C\u002Fth>\u003Ctd class=\"border border-gray-300 px-4 py-3 dark:border-gray-700\">\u003C\u002Ftd>\u003Ctd class=\"border border-gray-300 px-4 py-3 dark:border-gray-700\">\u003C\u002Ftd>\u003C\u002Ftr>\u003Ctr>\u003Cth class=\"border border-gray-300 px-4 py-3 text-left font-semibold dark:border-gray-700\">Stil izvršavanja\u003C\u002Fth>\u003Ctd class=\"border border-gray-300 px-4 py-3 dark:border-gray-700\">\u003C\u002Ftd>\u003Ctd class=\"border border-gray-300 px-4 py-3 dark:border-gray-700\">\u003C\u002Ftd>\u003C\u002Ftr>\u003Ctr>\u003Cth class=\"border border-gray-300 px-4 py-3 text-left font-semibold dark:border-gray-700\">Pisanje\u003C\u002Fth>\u003Ctd class=\"border border-gray-300 px-4 py-3 dark:border-gray-700\">\u003C\u002Ftd>\u003Ctd class=\"border border-gray-300 px-4 py-3 dark:border-gray-700\">\u003C\u002Ftd>\u003C\u002Ftr>\u003Ctr>\u003Cth class=\"border border-gray-300 px-4 py-3 text-left font-semibold dark:border-gray-700\">Obim optimizacije\u003C\u002Fth>\u003Ctd class=\"border border-gray-300 px-4 py-3 dark:border-gray-700\">\u003C\u002Ftd>\u003Ctd class=\"border border-gray-300 px-4 py-3 dark:border-gray-700\">\u003C\u002Ftd>\u003C\u002Ftr>\u003Ctr>\u003Cth class=\"border border-gray-300 px-4 py-3 text-left font-semibold dark:border-gray-700\">Tipična upotreba\u003C\u002Fth>\u003Ctd class=\"border border-gray-300 px-4 py-3 dark:border-gray-700\">\u003C\u002Ftd>\u003Ctd class=\"border border-gray-300 px-4 py-3 dark:border-gray-700\">\u003C\u002Ftd>\u003C\u002Ftr>\u003C\u002Ftbody>\u003C\u002Ftable>\u003C\u002Fdiv>\u003C\u002Fsection>\n\u003Ch2 id=\"section-35\">Zašto je podrška između različitih proizvođača važnija od još jedne AI funkcije\u003C\u002Fh2>\n\u003Cp>AMD, Intel, NVIDIA i Qualcomm izlažu različite GPU arhitekture i različite oblike namenske matrične akceleracije.\u003C\u002Fp>\n\u003Cp>DirectX apstrakcija daje Microsoft-u i proizvođačima drajvera mesto da prevedu uobičajeni HLSL ili ML na nivou grafa u ispravnu hardversku putanju.\u003C\u002Fp>\n\u003Cp>To ne čini svaki GPU jednako brzim, ali može smanjiti potrebu da game engine implementira potpuno drugačiji API za neuronsko renderovanje za svakog proizvođača.\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\">Prenosivost je prava priča o platformi\u003C\u002Fstrong>\u003Cdiv class=\"text-gray-700 dark:text-gray-200\">Zanimljiv deo nije to što DirectX može da „pokreće AI“. GPU-ovi su to već mogli. Promena platforme je to što \u003Cstrong>ML postaje izražljiv kroz zajednički grafički API i toolchain\u003C\u002Fstrong>.\u003C\u002Fdiv>\u003C\u002Faside>\n\u003Ch2 id=\"section-40\">Koji hardver podržava trenutni pregled?\u003C\u002Fh2>\n\u003Cp>Odgovor zavisi od konkretne operacije linearne algebre i pregleda drajvera.\u003C\u002Fp>\n\u003Cp>Microsoft-ova tabela podrške za Agility SDK 1.721 pregled navodi LinAlg VectorAccumulate za AMD Radeon RX 9000 Series hardver, Intel Xe2 ili noviji hardver putem predstojećeg drajvera i NVIDIA RTX hardver putem podržane putanje pregleda.\u003C\u002Fp>\n\u003Cp>Ovo je podrška u eri pregleda, a ne univerzalna garancija za maloprodaju. Podrška za hardver i drajvere može se promeniti pre finalizacije.\u003C\u002Fp>\n\u003Ch2 id=\"section-44\">Neuronsko renderovanje postaje infrastruktura\u003C\u002Fh2>\n\u003Cp>O DLSS, FSR i drugim tehnologijama neuronske grafike često se govori kao o brendiranim funkcijama vidljivim u meniju podešavanja igre.\u003C\u002Fp>\n\u003Cp>DirectX Linearna algebra ukazuje na dublju promenu. Neuronska operacija može postati interni detalj implementacije unutar renderera, a ne jedna opciona funkcija naknadne obrade.\u003C\u002Fp>\n\u003Cp>Programer bi mogao da koristi ML za teksture, materijale, osvetljenje, rekonstrukciju ili druge lokalne funkcije, a da svaku od njih ne izlaže kao AI prekidač namenjen korisnicima.\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 skaliranje: šta 3D-vođeno neuronsko renderovanje zaista menja\u003C\u002Fstrong>\u003Cp class=\"mt-2 text-sm text-gray-600 dark:text-gray-300\">Kako neuronsko renderovanje prevazilazi skaliranje i generisane frejmove i ulazi u rekonstrukciju materijala i osvetljenja unutar grafičkog pipeline-a.\u003C\u002Fp>\u003Cspan class=\"mt-3 inline-flex text-sm font-medium text-primary-600 dark:text-primary-400\">Pročitajte vodič za DLSS 5 neuronsko renderovanje →\u003C\u002Fspan>\u003C\u002Fdiv>\u003C\u002Fa>\u003C\u002Faside>\n\u003Ch2 id=\"section-49\">Neuronski renderujući stek postaje slojevit\u003C\u002Fh2>\n\u003Csection class=\"editorjs-process my-6\">\u003Ch3 class=\"mb-3 text-lg font-semibold\">Mogući budući DirectX pipeline igre\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\">Tradicionalni rad engine-a\u003C\u002Fdiv>\u003Cdiv class=\"mt-1 text-sm text-gray-600 dark:text-gray-300\">Simulacija, geometrija, vidljivost i osnovno renderovanje ostaju standardne odgovornosti 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\">Ugrađeni neuronski šejderi\u003C\u002Fdiv>\u003Cdiv class=\"mt-1 text-sm text-gray-600 dark:text-gray-300\">Male naučene funkcije rekonstruišu teksture, materijale ili osvetljenje unutar HLSL-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\">3\u003C\u002Fdiv>\u003Cdiv class=\"mt-1 font-semibold text-gray-900 dark:text-gray-100\">Veći ML grafovi\u003C\u002Fdiv>\u003Cdiv class=\"mt-1 text-sm text-gray-600 dark:text-gray-300\">Modeli pune rekonstrukcije ili inferencije izvršavaju se kroz DirectX putanju na nivou grafa.\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\">Mapiranje na hardver proizvođača\u003C\u002Fdiv>\u003Cdiv class=\"mt-1 text-sm text-gray-600 dark:text-gray-300\">Drajveri mapiraju uobičajene DirectX operacije na specijalizovane AI i matrične jedinice GPU-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\">5\u003C\u002Fdiv>\u003Cdiv class=\"mt-1 font-semibold text-gray-900 dark:text-gray-100\">PIX vidljivost\u003C\u002Fdiv>\u003Cdiv class=\"mt-1 text-sm text-gray-600 dark:text-gray-300\">Grafički i ML rad mogu se profilisati zajedno umesto da postoje u odvojenim neprozirnim runtime-ovima.\u003C\u002Fdiv>\u003C\u002Fdiv>\u003C\u002Fdiv>\u003C\u002Fsection>\n\u003Ch2 id=\"section-51\">Zašto je objedinjeno profilisanje važno\u003C\u002Fh2>\n\u003Cp>Neuronski radni zadatak koji poboljšava kvalitet slike i dalje može da naškodi igri ako neočekivano troši vreme frejma, memorijski propusni opseg ili VRAM.\u003C\u002Fp>\n\u003Cp>Microsoft eksplicitno uključuje objedinjenu PIX vidljivost kao deo pravca Compute Graph Compiler-a. To je važno jer programeri moraju da vide grafički i ML rad u istom snimku frejma.\u003C\u002Fp>\n\u003Cp>Ako neuronsko renderovanje postane infrastruktura, mora biti merljivo kao i svaka druga faza renderovanja.\u003C\u002Fp>\n\u003Ch2 id=\"section-55\">To ne znači da će svaki šejder postati neuronska mreža\u003C\u002Fh2>\n\u003Cp>Tradicionalna matematika šejdera ostaje efikasna, deterministička i laka za razumevanje za mnoge radne zadatke.\u003C\u002Fp>\n\u003Cp>Neuronska funkcija ima smisla kada može efikasnije da aproksimira ili rekonstruiše nešto skupo, komprimuje podatke ili proizvede kvalitet koji bi inače zahtevao previše konvencionalne računarske snage ili memorije.\u003C\u002Fp>\n\u003Cp>Prava arhitektura će ostati hibridna.\u003C\u002Fp>\n\u003Ch2 id=\"section-59\">Test vrednosti neuronskog šejdera\u003C\u002Fh2>\n\u003Csection class=\"editorjs-process my-6\">\u003Ch3 class=\"mb-3 text-lg font-semibold\">Kada stavljanje ML u render pipeline zapravo ima smisla?\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. Identifikujte skupu tradicionalnu operaciju\u003C\u002Fdiv>\u003Cdiv class=\"mt-1 text-sm text-gray-600 dark:text-gray-300\">Koji trošak računanja, propusnog opsega, memorije ili skladištenja pokušavate da zamenite?\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. Definišite neuronsku zamenu\u003C\u002Fdiv>\u003Cdiv class=\"mt-1 text-sm text-gray-600 dark:text-gray-300\">Šta mali model može da rekonstruiše, predvidi ili komprimuje?\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. Izmerite trošak inferencije\u003C\u002Fdiv>\u003Cdiv class=\"mt-1 text-sm text-gray-600 dark:text-gray-300\">Sam ML model troši GPU vreme, memoriju i propusni opseg.\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. Izmerite stabilnost kvaliteta\u003C\u002Fdiv>\u003Cdiv class=\"mt-1 text-sm text-gray-600 dark:text-gray-300\">Tražite vremenske artefakte, greške rekonstrukcije i slučajeve neuspeha.\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. Izmerite neto uštedu\u003C\u002Fdiv>\u003Cdiv class=\"mt-1 text-sm text-gray-600 dark:text-gray-300\">Tehnika je korisna samo ako je izbegnuti tradicionalni trošak vredan dodatnog ML troška.\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. Testirajte na različitim proizvođačima\u003C\u002Fdiv>\u003Cdiv class=\"mt-1 text-sm text-gray-600 dark:text-gray-300\">DirectX apstrakcija pomaže prenosivosti, ali stvarne performanse hardvera se i dalje razlikuju.\u003C\u002Fdiv>\u003C\u002Fdiv>\u003C\u002Fdiv>\u003C\u002Fsection>\n\u003Ch2 id=\"section-61\">Šta ovo znači za gejmere\u003C\u002Fh2>\n\u003Cp>Gejmeri možda nikada neće videti prekidač „DX Linearna algebra“ u grafičkom meniju.\u003C\u002Fp>\n\u003Cp>Verovatni uticaj je indirektan: manji otisak tekstura, bolja rekonstrukcija osvetljenja, efikasnija neuronska grafika ili nove vizuelne tehnike koje postaju praktične jer engine može da pristupi matričnom ubrzanju kroz standardnu putanju.\u003C\u002Fp>\n\u003Cp>Ova funkcija je važnija kao infrastruktura nego kao brend.\u003C\u002Fp>\n\u003Ch2 id=\"section-65\">Šta bi promenilo ovaj odgovor?\u003C\u002Fh2>\n\u003Cp>Najveća neizvesnost je konačni maloprodajni oblik ovih API-ja. DX Linearna algebra je još u pregledu, a Compute Graph Compiler još nije dostigao široku maloprodajnu dostupnost.\u003C\u002Fp>\n\u003Cp>Konačna podrška hardvera, detalji API-ja, ponašanje kompajlera i usvajanje u engine-ima mogu se promeniti pre nego što ovi sistemi postanu normalna infrastruktura za igre u prodaji.\u003C\u002Fp>\n\u003Cp>Ako glavni engine-i direktno usvoje apstrakcije, neuronski šejderi bi mogli postati mnogo češći bez toga da pojedinačni timovi za igre implementiraju svaku tehniku od nule.\u003C\u002Fp>\n\u003Ch2 id=\"section-69\">Ograničenja\u003C\u002Fh2>\n\u003Cp>Ovaj članak objašnjava Microsoft-ovu dokumentovanu DirectX arhitekturu i API-je u pregledu. Ne tvrdi da DX Linearna algebra trenutno poboljšava performanse u svakoj igri niti da je Compute Graph Compiler završen maloprodajni proizvod.\u003C\u002Fp>\n\u003Cp>Microsoft-ovi primeri opisuju mogućnosti i nameravanu upotrebu. Stvarne koristi zavise od modela, integracije u engine, arhitekture GPU-a, drajvera i radnog zadatka.\u003C\u002Fp>\n\u003Ch2 id=\"section-72\">Zaključak\u003C\u002Fh2>\n\u003Cp>Važna DirectX promena nije još jedan potvrdni okvir pod nazivom AI.\u003C\u002Fp>\n\u003Cp>To je da matematika mašinskog učenja prelazi u sam model grafičkog programiranja. Male neuronske funkcije mogu da se nalaze unutar šejdera, veći modeli mogu da se kreću ka kompilaciji na nivou grafa, a isti DirectX alatni lanac može da izloži te radne zadatke kod više proizvođača GPU.\u003C\u002Fp>\n\u003Cp>Tako neuronsko renderovanje prestaje da bude jedna brendirana funkcija i počinje da postaje deo infrastrukture za renderovanje.\u003C\u002Fp>\n\u003Ch2 id=\"section-76\">Često postavljana pitanja\u003C\u002Fh2>\n\u003Csection class=\"editorjs-faq my-6 rounded-xl border border-gray-200 p-5 dark:border-gray-700\">\u003Ch3 class=\"mb-3 text-lg font-semibold\">DirectX linearna algebra i neuronski šejderi\u003C\u002Fh3>\u003Cdiv id=\"faq1\" class=\"border-t border-gray-200 py-4 first:border-t-0 dark:border-gray-700\">\u003Ch4 class=\"font-semibold text-gray-900 dark:text-gray-100\">Šta je DirectX linearna algebra?\u003C\u002Fh4>\u003Cdiv class=\"mt-2 text-gray-600 dark:text-gray-300\">To je skup DirectX\u002FHLSL funkcija za hardverski ubrzane vektorske i matrične operacije koje koriste radni zadaci mašinskog učenja, neuronskog renderovanja i obrade slika.\u003C\u002Fdiv>\u003C\u002Fdiv>\u003Cdiv id=\"faq2\" class=\"border-t border-gray-200 py-4 first:border-t-0 dark:border-gray-700\">\u003Ch4 class=\"font-semibold text-gray-900 dark:text-gray-100\">Šta je neuronski šejder?\u003C\u002Fh4>\u003Cdiv class=\"mt-2 text-gray-600 dark:text-gray-300\">Korisno objašnjenje jednostavnim jezikom je šejder koji uključuje naučenu neuronsku funkciju ili korak ML inferencije kao deo svog grafičkog rada.\u003C\u002Fdiv>\u003C\u002Fdiv>\u003Cdiv id=\"faq3\" class=\"border-t border-gray-200 py-4 first:border-t-0 dark:border-gray-700\">\u003Ch4 class=\"font-semibold text-gray-900 dark:text-gray-100\">Da li je DX linearna algebra već normalna maloprodajna DirectX funkcija?\u003C\u002Fh4>\u003Cdiv class=\"mt-2 text-gray-600 dark:text-gray-300\">Od septembra 2026. ostaje na putu pregleda Shader Model 6.10 \u002F Agility SDK.\u003C\u002Fdiv>\u003C\u002Fdiv>\u003Cdiv id=\"faq4\" class=\"border-t border-gray-200 py-4 first:border-t-0 dark:border-gray-700\">\u003Ch4 class=\"font-semibold text-gray-900 dark:text-gray-100\">Šta je DirectX Compute Graph Compiler?\u003C\u002Fh4>\u003Cdiv class=\"mt-2 text-gray-600 dark:text-gray-300\">To je Microsoftov najavljeni API kompajlera ML modela na nivou modela namenjen da uzme veće računske grafove i prevede ih u optimizovane GPU radne zadatke integrisane sa D3D12.\u003C\u002Fdiv>\u003C\u002Fdiv>\u003Cdiv id=\"faq5\" class=\"border-t border-gray-200 py-4 first:border-t-0 dark:border-gray-700\">\u003Ch4 class=\"font-semibold text-gray-900 dark:text-gray-100\">Zašto ne pokrenuti svaki ML model direktno u HLSL-u?\u003C\u002Fh4>\u003Cdiv class=\"mt-2 text-gray-600 dark:text-gray-300\">Male funkcije dobro odgovaraju autorstvu na nivou šejdera, dok veliki modeli imaju koristi od optimizacije celog grafa, planiranja memorije i fuzije operatora.\u003C\u002Fdiv>\u003C\u002Fdiv>\u003Cdiv id=\"faq6\" class=\"border-t border-gray-200 py-4 first:border-t-0 dark:border-gray-700\">\u003Ch4 class=\"font-semibold text-gray-900 dark:text-gray-100\">Da li će ovo zameniti DLSS ili FSR?\u003C\u002Fh4>\u003Cdiv class=\"mt-2 text-gray-600 dark:text-gray-300\">Ne direktno. DirectX pruža infrastrukturu nižeg nivoa koju proizvođači i programeri mogu da koriste za neuronsku grafiku. Brendirane tehnologije i dalje mogu da implementiraju sopstvene modele i strategije integracije.\u003C\u002Fdiv>\u003C\u002Fdiv>\u003C\u002Fsection>\n\u003Ch2 id=\"section-78\">Pojmovnik\u003C\u002Fh2>\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 DirectX ML pojmovi\u003C\u002Fh3>\u003Cdl>\u003Cdiv id=\"dx-linear-algebra\" 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\">DX linearna algebra\u003C\u002Fdt>\u003Cdd class=\"mt-1 text-gray-600 dark:text-gray-300\">DirectX\u002FHLSL API-ji za ubrzane vektorske i matrične operacije namenjene neuronskom renderovanju, ML i obradi slika.\u003C\u002Fdd>\u003C\u002Fdiv>\u003Cdiv id=\"cooperative-vector\" 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\">Cooperative Vector\u003C\u002Fdt>\u003Cdd class=\"mt-1 text-gray-600 dark:text-gray-300\">Raniji DirectX pristup za ubrzane vektorsko-matrične operacije unutar šejdera koji je pomogao da se uspostavi put neuronskog renderovanja na nivou šejdera.\u003C\u002Fdd>\u003C\u002Fdiv>\u003Cdiv id=\"shader-model-6-10\" 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\">Shader Model 6.10\u003C\u002Fdt>\u003Cdd class=\"mt-1 text-gray-600 dark:text-gray-300\">Generacija šejder modela u pregledu koja sadrži trenutne DirectX Linear Algebra matrične API-je.\u003C\u002Fdd>\u003C\u002Fdiv>\u003Cdiv id=\"compute-graph-compiler\" 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\">DirectX Compute Graph Compiler\u003C\u002Fdt>\u003Cdd class=\"mt-1 text-gray-600 dark:text-gray-300\">Microsoftov najavljeni API kompajlera za optimizaciju i izvršavanje većih ML računskih grafova kao native DirectX GPU radnih zadataka.\u003C\u002Fdd>\u003C\u002Fdiv>\u003Cdiv id=\"neural-shader\" 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\">Neuronski šejder\u003C\u002Fdt>\u003Cdd class=\"mt-1 text-gray-600 dark:text-gray-300\">Praktičan termin za šejder koji obavlja naučenu inferenciju ili neuronsko izračunavanje kao deo grafičke obrade.\u003C\u002Fdd>\u003C\u002Fdiv>\u003Cdiv id=\"shader-or-graph-boundary\" 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\">Granica šejdera ili grafa\u003C\u002Fdt>\u003Cdd class=\"mt-1 text-gray-600 dark:text-gray-300\">Figure Rocks model za odlučivanje da li ML radni zadatak pripada kao mala inline matematika šejdera ili kao veći računski graf na nivou modela.\u003C\u002Fdd>\u003C\u002Fdiv>\u003Cdiv id=\"neural-shader-value-test\" 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\">Test vrednosti neuronskog šejdera\u003C\u002Fdt>\u003Cdd class=\"mt-1 text-gray-600 dark:text-gray-300\">Figure Rocks radni tok za odlučivanje da li je tradicionalni računski ili memorijski trošak izbegnut neuronskom tehnikom vredan svoje cene inferencije i kompromisa u kvalitetu.\u003C\u002Fdd>\u003C\u002Fdiv>\u003C\u002Fdl>\u003C\u002Fsection>\n\u003Ch2 id=\"section-80\">Primarni izvori\u003C\u002Fh2>\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 — Evolving DirectX for the ML Era on Windows\u003C\u002Fstrong>\u003Cp class=\"text-sm text-gray-600 dark:text-gray-400\">Zvanični pregled arhitekture sa GDC 2026 koji pokriva ML na nivou šejdera, DX linearnu algebru i DirectX Compute Graph Compiler.\u003C\u002Fp>\u003C\u002Fa>\n\u003Ca href=\"https:\u002F\u002Fdevblogs.microsoft.com\u002Fdirectx\u002Fd3d12-linalg-preview\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 — D3D12 LinAlg Matrix Preview\u003C\u002Fstrong>\u003Cp class=\"text-sm text-gray-600 dark:text-gray-400\">Zvanični pregled iz aprila 2026. koji objašnjava unifikovane Linear Algebra API-je, matrične operacije i motivaciju za neuronsko renderovanje.\u003C\u002Fp>\u003C\u002Fa>\n\u003Ca href=\"https:\u002F\u002Fdevblogs.microsoft.com\u002Fdirectx\u002Fannouncing-agilitysdk-721-preview-and-more-shader-model-6-10-features\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 — Agility SDK 1.721 Preview\u003C\u002Fstrong>\u003Cp class=\"text-sm text-gray-600 dark:text-gray-400\">Zvanično izdanje iz maja 2026. koje dokumentuje Shader Model 6.10 Linear Algebra ažuriranja i podršku hardvera u pregledu.\u003C\u002Fp>\u003C\u002Fa>\n\u003Ca href=\"https:\u002F\u002Fdeveloper.microsoft.com\u002Fen-us\u002Fgames\u002Farticles\u002F2026\u002F03\u002Fgdc-2026-evolving-directx-for-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 Game Dev — GDC 2026: Evolving DirectX for the ML Era\u003C\u002Fstrong>\u003Cp class=\"text-sm text-gray-600 dark:text-gray-400\">Zvanični sažetak Microsoft Game Dev-a koji objašnjava ML na nivou šejdera i modela i ulogu Compute Graph Compiler-a.\u003C\u002Fp>\u003C\u002Fa>\n\u003Ca href=\"https:\u002F\u002Fdevblogs.microsoft.com\u002Fdirectx\u002Fcooperative-vector\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 — D3D12 Cooperative Vector\u003C\u002Fstrong>\u003Cp class=\"text-sm text-gray-600 dark:text-gray-400\">Zvanična pozadina o hardverski ubrzanim vektorskim\u002Fmatričnim operacijama i neuronskom renderovanju direktno iz niti šejdera.\u003C\u002Fp>\u003C\u002Fa>",{"time":538,"blocks":539,"version":1177},1790378095176,[540,546,554,561,568,574,579,584,589,594,614,621,626,631,636,641,668,673,678,683,688,693,698,722,727,732,737,742,751,756,761,766,771,776,809,814,819,824,829,835,840,845,850,855,860,865,870,875,883,888,909,914,919,924,929,934,939,944,949,954,978,983,988,993,998,1003,1008,1013,1018,1023,1028,1033,1038,1043,1048,1053,1058,1088,1093,1126,1131,1141,1150,1159,1168],{"id":541,"data":542,"type":544,"tunes":545},"intro",{"text":543},"DirectX više nije samo grafički API koji šalje tradicionalne šejdere na GPU. Microsoft dodaje primitive mašinskog učenja direktno u HLSL i drugi put za pokretanje većih ML grafova unutar DirectX ekosistema. To menja gde neuronsko renderovanje može da živi u budućim PC igrama.","paragraph",{},{"id":547,"data":548,"type":552,"tunes":553},"direct",{"body":549,"title":550,"variant":551},"\u003Cstrong>DirectX postaje grafička platforma sposobna za ML.\u003C\u002Fstrong> Mali neuronski zadaci mogu se izvršavati direktno unutar šejdera kroz DX Linearnu Algebru, dok su veći modeli ciljani od strane DirectX Compute Graph Compiler-a. Cilj je da se game engine-ima omogući korišćenje GPU AI hardvera bez izgradnje posebne putanje specifične za proizvođača za svaku tehniku.","Direktan odgovor","info","callout",{},{"id":555,"data":556,"type":552,"tunes":560},"status-note",{"body":557,"title":558,"variant":559},"Od septembra 2026, \u003Cstrong>DX Linearna Algebra je još uvek tehnologija u pregledu\u003C\u002Fstrong> u Shader Model 6.10 \u002F Agility SDK putanji pregleda. Microsoft je najavio DirectX Compute Graph Compiler za privatni pregled, a ne kao široko dostupnu maloprodajnu funkciju. Ovaj članak objašnjava arhitekturu i smer, a ne tvrdnju da svaka trenutna igra može da ga koristi danas.","Trenutni status","note",{},{"id":562,"data":563,"type":566,"tunes":567},"toc",{"title":564,"maxLevel":565,"minLevel":47},"Sadržaj",3,"tableOfContents",{},{"id":569,"data":570,"type":572,"tunes":573},"h-why",{"text":571,"level":47},"Zašto neuronskom renderovanju trebaju nove DirectX primitive","header",{},{"id":575,"data":576,"type":544,"tunes":578},"p-why-1",{"text":577},"Moderne GPU već sadrže specijalizovani hardver za matrične operacije koje koristi mašinsko učenje. Problem za programera igara nije samo da li taj hardver postoji, već kako mu efikasno pristupiti iz real-time grafičkog pipeline-a.",{},{"id":580,"data":581,"type":544,"tunes":583},"p-why-2",{"text":582},"Microsoft je prvo istražio ovo sa podrškom za Cooperative Vector. U 2026, taj rad je evoluirao u širi DirectX Linearna Algebra dizajn koji podržava i vektor-matrične i matrično-matrične operacije.",{},{"id":585,"data":586,"type":544,"tunes":588},"p-why-3",{"text":587},"To je važno jer različiti poslovi neuronskog renderovanja imaju različite oblike. Mali model koji procenjuje ponašanje materijala po pikselu nije isti zadatak kao veliki graf za super-rezoluciju ili uklanjanje šuma.",{},{"id":590,"data":591,"type":572,"tunes":593},"h-two-level",{"text":592,"level":47},"Dvonivojski DirectX ML model",{},{"id":595,"data":596,"type":612,"tunes":613},"two-level-flow",{"steps":597,"title":610,"orientation":611},[598,601,604,607],{"label":599,"description":600},"1. ML na nivou šejdera","Mali neuronski ili linearno-algebarski zadaci izvršavaju se direktno iz HLSL-a zajedno sa tradicionalnim kodom šejdera.",{"label":602,"description":603},"2. DX Linearna Algebra","Šejder može zatražiti hardverski ubrzane vektorske i matrične operacije umesto ručnog implementiranja svake ML primitive.",{"label":605,"description":606},"3. ML na nivou modela","Veće neuronske mreže predstavljene su kao kompletni računski grafovi, a ne kao ručno napisani fragmenti šejdera.",{"label":608,"description":609},"4. DirectX Compute Graph Compiler","Microsoft-ova planirana putanja kompajlera analizira i prevodi te grafove u optimizovane GPU zadatke integrisane sa D3D12 redovima i listama komandi.","Dva načina na koja ML može ući u DirectX grafički pipeline","auto","processFlow",{},{"id":615,"data":616,"type":552,"tunes":620},"simple-diff",{"body":617,"title":618,"variant":619},"\u003Cstrong>DX Linearna Algebra:\u003C\u002Fstrong> stavite malu ML matematiku unutar šejdera.\u003Cbr>\u003Cstrong>Compute Graph Compiler:\u003C\u002Fstrong> unesite veći ML model u engine kao graf.","Jednostavna razlika","success",{},{"id":622,"data":623,"type":572,"tunes":625},"h-linalg",{"text":624,"level":47},"Šta DX Linearna Algebra zapravo daje šejderu",{},{"id":627,"data":628,"type":544,"tunes":630},"p-linalg-1",{"text":629},"Tradicionalni HLSL je izgrađen oko grafičkih i računskih operacija. Neuronski zadaci se u velikoj meri oslanjaju na linearnu algebru: vektore, matrice, množenje, akumulaciju i rasporede podataka optimizovane za te operacije.",{},{"id":632,"data":633,"type":544,"tunes":635},"p-linalg-2",{"text":634},"Shader Model 6.10 pregled dodaje matrix API-je prvog reda kako bi programeri mogli direktnije izraziti te zadatke i pustiti drajver da ih mapira na specijalizovani hardver.",{},{"id":637,"data":638,"type":544,"tunes":640},"p-linalg-3",{"text":639},"Microsoft-ov pregled iz aprila 2026 eksplicitno opisuje ovo kao jedinstvenu putanju za neuronsko renderovanje, ML i zadatke obrade slika, a ne kao funkciju samo za grafiku.",{},{"id":642,"data":643,"type":666,"tunes":667},"math-table",{"rows":644,"title":657,"layout":658,"columns":659},[645,649,653],{"id":646,"label":647,"values":648},"work","Tipičan rad",[13,13],{"id":650,"label":651,"values":652},"hardware","Hardverska putanja",[13,13],{"id":654,"label":655,"values":656},"code","Izražavanje programera",[13,13],"Tradicionalna matematika šejdera vs ML-orijentisana matematika šejdera","table",[660,663],{"id":661,"label":662},"traditional","Tradicionalni fokus šejdera",{"id":664,"label":665},"ml","ML-orijentisani fokus","comparison",{},{"id":669,"data":670,"type":572,"tunes":672},"h-coop",{"text":671,"level":47},"Zašto je Cooperative Vector bio samo početak",{},{"id":674,"data":675,"type":544,"tunes":677},"p-coop-1",{"text":676},"Cooperative Vector je omogućio nitima šejdera da zatraže vektor-matrični rad koji drajveri mogu mapirati na specijalizovani hardver. To je bilo korisno za visoko paralelne zadatke po pikselu.",{},{"id":679,"data":680,"type":544,"tunes":682},"p-coop-2",{"text":681},"Microsoft je kasnije zaključio da mnogi važni ML radni zadaci zahtevaju više od vektorsko-matričnih operacija. Super rezolucija, uklanjanje šuma, temporalna rekonstrukcija, veći modeli slika i opšta inferencija mogu zahtevati matrično-matrične operacije i deljeni rad kroz mnoge niti.",{},{"id":684,"data":685,"type":544,"tunes":687},"p-coop-3",{"text":686},"DX Linear Algebra stoga proširuje model umesto da tretira Cooperative Vector kao konačnu apstrakciju.",{},{"id":689,"data":690,"type":572,"tunes":692},"h-usecases",{"text":691,"level":47},"Šta zapravo može da se izvršava unutar šejdera?",{},{"id":694,"data":695,"type":544,"tunes":697},"p-use-1",{"text":696},"Najzanimljiviji ML radni zadaci na nivou šejdera su dovoljno mali da se izvršavaju blizu grafičkih podataka na kojima rade.",{},{"id":699,"data":700,"type":658,"tunes":721},"usecases-table",{"content":701,"stretched":720,"withHeadings":15},[702,705,708,711,714,717],[703,704],"Radni zadatak","Zašto ML na nivou šejdera odgovara",[706,707],"Neuralna kompresija tekstura","Mala mreža može da rekonstruiše informacije o teksturi blizu tačke gde su šejderu potrebne",[709,710],"Neuralna evaluacija materijala","Naučena funkcija može da zameni ili dopuni skupu ručno pisanu matematiku materijala",[712,713],"Neuralno keširanje radijanse","Inferencija po pikselu ili lokalna inferencija može da proceni informacije o osvetljenju na osnovu naučenog ponašanja scene",[715,716],"Mali kerneli za uklanjanje šuma\u002Frekonstrukciju","ML operacije mogu da stoje direktno pored faze renderovanja koju poboljšavaju",[718,719],"Inferencija za obradu slika","Matrične operacije mogu da se ugrade u GPU obradu bez zasebnog eksternog runtime-a",false,{},{"id":723,"data":724,"type":572,"tunes":726},"h-texture",{"text":725,"level":47},"Zašto je ovo važno za memoriju tekstura",{},{"id":728,"data":729,"type":544,"tunes":731},"p-tex-1",{"text":730},"Jedan od Microsoftovih primera koji se često ponavlja je neuralna kompresija tekstura.",{},{"id":733,"data":734,"type":544,"tunes":736},"p-tex-2",{"text":735},"Umesto da čuva svaki kanal teksture pri konvencionalnoj vernosti, igra može da čuva kompaktniju reprezentaciju i koristi malu neuronsku mrežu da rekonstruiše detalje tokom renderovanja.",{},{"id":738,"data":739,"type":544,"tunes":741},"p-tex-3",{"text":740},"To menja deo GPU inferencijskog rada za manji pritisak na skladištenje ili memoriju. Tačna korist zavisi od tehnike i hardvera, ali arhitektonska promena je važna: neki vizuelni detalji mogu postati računanje umesto sačuvanih podataka.",{},{"id":743,"data":744,"type":749,"tunes":750},"ref-vram",{"url":745,"title":746,"excerpt":747,"ctaLabel":748},"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-om: Zašto pun memorijski merač ne govori celu priču","Praktični vodič kroz VRAM kapacitet, budžete rezidentnosti, radne skupove i zašto je memorijski pritisak komplikovaniji od jednog broja korišćenja.","Pročitajte vodič za VRAM","referralArticle",{},{"id":752,"data":753,"type":572,"tunes":755},"h-models",{"text":754,"level":47},"Zašto puni modeli zahtevaju drugačiji put",{},{"id":757,"data":758,"type":544,"tunes":760},"p-models-1",{"text":759},"Ručno pisanje nekoliko matričnih operacija u HLSL-u je praktično za male neuronske funkcije. Postaje mnogo manje praktično kada je radni zadatak kompletan moderni model sa mnogo slojeva, zavisnosti i međurezultata tenzora.",{},{"id":762,"data":763,"type":544,"tunes":765},"p-models-2",{"text":764},"Microsoftov DirectX Compute Graph Compiler je dizajniran za ovu veću klasu radnih zadataka.",{},{"id":767,"data":768,"type":544,"tunes":770},"p-models-3",{"text":769},"Umesto prepisivanja modela kao prilagođenog koda šejdera, kompajler može da prihvati graf računanja, analizira ceo graf, planira memoriju, spoji operacije i spusti rezultat u GPU rad koji se integriše sa DirectX 12.",{},{"id":772,"data":773,"type":572,"tunes":775},"h-boundary",{"text":774,"level":47},"Granica između šejdera i grafa",{},{"id":777,"data":778,"type":666,"tunes":808},"boundary-table",{"rows":779,"title":800,"layout":658,"columns":801},[780,784,788,792,796],{"id":781,"label":782,"values":783},"size","Veličina modela",[13,13],{"id":785,"label":786,"values":787},"location","Stil izvršavanja",[13,13],{"id":789,"label":790,"values":791},"authoring","Pisanje",[13,13],{"id":793,"label":794,"values":795},"optimization","Obim optimizacije",[13,13],{"id":797,"label":798,"values":799},"use","Tipična upotreba",[13,13],"Kada ML radni zadatak pripada HLSL-u, a kada kompajleru modela",[802,805],{"id":803,"label":804},"shader","Put na nivou šejdera",{"id":806,"label":807},"graph","Put na nivou modela",{},{"id":810,"data":811,"type":572,"tunes":813},"h-crossvendor",{"text":812,"level":47},"Zašto je podrška između različitih proizvođača važnija od još jedne AI funkcije",{},{"id":815,"data":816,"type":544,"tunes":818},"p-cross-1",{"text":817},"AMD, Intel, NVIDIA i Qualcomm izlažu različite GPU arhitekture i različite oblike namenske matrične akceleracije.",{},{"id":820,"data":821,"type":544,"tunes":823},"p-cross-2",{"text":822},"DirectX apstrakcija daje Microsoft-u i proizvođačima drajvera mesto da prevedu uobičajeni HLSL ili ML na nivou grafa u ispravnu hardversku putanju.",{},{"id":825,"data":826,"type":544,"tunes":828},"p-cross-3",{"text":827},"To ne čini svaki GPU jednako brzim, ali može smanjiti potrebu da game engine implementira potpuno drugačiji API za neuronsko renderovanje za svakog proizvođača.",{},{"id":830,"data":831,"type":552,"tunes":834},"portability-note",{"body":832,"title":833,"variant":559},"Zanimljiv deo nije to što DirectX može da „pokreće AI“. GPU-ovi su to već mogli. Promena platforme je to što \u003Cstrong>ML postaje izražljiv kroz zajednički grafički API i toolchain\u003C\u002Fstrong>.","Prenosivost je prava priča o platformi",{},{"id":836,"data":837,"type":572,"tunes":839},"h-support",{"text":838,"level":47},"Koji hardver podržava trenutni pregled?",{},{"id":841,"data":842,"type":544,"tunes":844},"p-support-1",{"text":843},"Odgovor zavisi od konkretne operacije linearne algebre i pregleda drajvera.",{},{"id":846,"data":847,"type":544,"tunes":849},"p-support-2",{"text":848},"Microsoft-ova tabela podrške za Agility SDK 1.721 pregled navodi LinAlg VectorAccumulate za AMD Radeon RX 9000 Series hardver, Intel Xe2 ili noviji hardver putem predstojećeg drajvera i NVIDIA RTX hardver putem podržane putanje pregleda.",{},{"id":851,"data":852,"type":544,"tunes":854},"p-support-3",{"text":853},"Ovo je podrška u eri pregleda, a ne univerzalna garancija za maloprodaju. Podrška za hardver i drajvere može se promeniti pre finalizacije.",{},{"id":856,"data":857,"type":572,"tunes":859},"h-infra",{"text":858,"level":47},"Neuronsko renderovanje postaje infrastruktura",{},{"id":861,"data":862,"type":544,"tunes":864},"p-infra-1",{"text":863},"O DLSS, FSR i drugim tehnologijama neuronske grafike često se govori kao o brendiranim funkcijama vidljivim u meniju podešavanja igre.",{},{"id":866,"data":867,"type":544,"tunes":869},"p-infra-2",{"text":868},"DirectX Linearna algebra ukazuje na dublju promenu. Neuronska operacija može postati interni detalj implementacije unutar renderera, a ne jedna opciona funkcija naknadne obrade.",{},{"id":871,"data":872,"type":544,"tunes":874},"p-infra-3",{"text":873},"Programer bi mogao da koristi ML za teksture, materijale, osvetljenje, rekonstrukciju ili druge lokalne funkcije, a da svaku od njih ne izlaže kao AI prekidač namenjen korisnicima.",{},{"id":876,"data":877,"type":749,"tunes":882},"ref-dlss5",{"url":878,"title":879,"excerpt":880,"ctaLabel":881},"https:\u002F\u002Ffigure.rocks\u002Fsr\u002Fblog\u002Fdlss-5-is-not-just-upscaling-what-3d-guided-neural-rendering-actually-changes","DLSS 5 nije samo skaliranje: šta 3D-vođeno neuronsko renderovanje zaista menja","Kako neuronsko renderovanje prevazilazi skaliranje i generisane frejmove i ulazi u rekonstrukciju materijala i osvetljenja unutar grafičkog pipeline-a.","Pročitajte vodič za DLSS 5 neuronsko renderovanje",{},{"id":884,"data":885,"type":572,"tunes":887},"h-stack",{"text":886,"level":47},"Neuronski renderujući stek postaje slojevit",{},{"id":889,"data":890,"type":612,"tunes":908},"stack-flow",{"steps":891,"title":907,"orientation":611},[892,895,898,901,904],{"label":893,"description":894},"Tradicionalni rad engine-a","Simulacija, geometrija, vidljivost i osnovno renderovanje ostaju standardne odgovornosti engine-a.",{"label":896,"description":897},"Ugrađeni neuronski šejderi","Male naučene funkcije rekonstruišu teksture, materijale ili osvetljenje unutar HLSL-a.",{"label":899,"description":900},"Veći ML grafovi","Modeli pune rekonstrukcije ili inferencije izvršavaju se kroz DirectX putanju na nivou grafa.",{"label":902,"description":903},"Mapiranje na hardver proizvođača","Drajveri mapiraju uobičajene DirectX operacije na specijalizovane AI i matrične jedinice GPU-a.",{"label":905,"description":906},"PIX vidljivost","Grafički i ML rad mogu se profilisati zajedno umesto da postoje u odvojenim neprozirnim runtime-ovima.","Mogući budući DirectX pipeline igre",{},{"id":910,"data":911,"type":572,"tunes":913},"h-pix",{"text":912,"level":47},"Zašto je objedinjeno profilisanje važno",{},{"id":915,"data":916,"type":544,"tunes":918},"p-pix-1",{"text":917},"Neuronski radni zadatak koji poboljšava kvalitet slike i dalje može da naškodi igri ako neočekivano troši vreme frejma, memorijski propusni opseg ili VRAM.",{},{"id":920,"data":921,"type":544,"tunes":923},"p-pix-2",{"text":922},"Microsoft eksplicitno uključuje objedinjenu PIX vidljivost kao deo pravca Compute Graph Compiler-a. To je važno jer programeri moraju da vide grafički i ML rad u istom snimku frejma.",{},{"id":925,"data":926,"type":544,"tunes":928},"p-pix-3",{"text":927},"Ako neuronsko renderovanje postane infrastruktura, mora biti merljivo kao i svaka druga faza renderovanja.",{},{"id":930,"data":931,"type":572,"tunes":933},"h-not-everything",{"text":932,"level":47},"To ne znači da će svaki šejder postati neuronska mreža",{},{"id":935,"data":936,"type":544,"tunes":938},"p-not-1",{"text":937},"Tradicionalna matematika šejdera ostaje efikasna, deterministička i laka za razumevanje za mnoge radne zadatke.",{},{"id":940,"data":941,"type":544,"tunes":943},"p-not-2",{"text":942},"Neuronska funkcija ima smisla kada može efikasnije da aproksimira ili rekonstruiše nešto skupo, komprimuje podatke ili proizvede kvalitet koji bi inače zahtevao previše konvencionalne računarske snage ili memorije.",{},{"id":945,"data":946,"type":544,"tunes":948},"p-not-3",{"text":947},"Prava arhitektura će ostati hibridna.",{},{"id":950,"data":951,"type":572,"tunes":953},"h-test",{"text":952,"level":47},"Test vrednosti neuronskog šejdera",{},{"id":955,"data":956,"type":612,"tunes":977},"value-test",{"steps":957,"title":976,"orientation":611},[958,961,964,967,970,973],{"label":959,"description":960},"1. Identifikujte skupu tradicionalnu operaciju","Koji trošak računanja, propusnog opsega, memorije ili skladištenja pokušavate da zamenite?",{"label":962,"description":963},"2. Definišite neuronsku zamenu","Šta mali model može da rekonstruiše, predvidi ili komprimuje?",{"label":965,"description":966},"3. Izmerite trošak inferencije","Sam ML model troši GPU vreme, memoriju i propusni opseg.",{"label":968,"description":969},"4. Izmerite stabilnost kvaliteta","Tražite vremenske artefakte, greške rekonstrukcije i slučajeve neuspeha.",{"label":971,"description":972},"5. Izmerite neto uštedu","Tehnika je korisna samo ako je izbegnuti tradicionalni trošak vredan dodatnog ML troška.",{"label":974,"description":975},"6. Testirajte na različitim proizvođačima","DirectX apstrakcija pomaže prenosivosti, ali stvarne performanse hardvera se i dalje razlikuju.","Kada stavljanje ML u render pipeline zapravo ima smisla?",{},{"id":979,"data":980,"type":572,"tunes":982},"h-gamers",{"text":981,"level":47},"Šta ovo znači za gejmere",{},{"id":984,"data":985,"type":544,"tunes":987},"p-gamers-1",{"text":986},"Gejmeri možda nikada neće videti prekidač „DX Linearna algebra“ u grafičkom meniju.",{},{"id":989,"data":990,"type":544,"tunes":992},"p-gamers-2",{"text":991},"Verovatni uticaj je indirektan: manji otisak tekstura, bolja rekonstrukcija osvetljenja, efikasnija neuronska grafika ili nove vizuelne tehnike koje postaju praktične jer engine može da pristupi matričnom ubrzanju kroz standardnu putanju.",{},{"id":994,"data":995,"type":544,"tunes":997},"p-gamers-3",{"text":996},"Ova funkcija je važnija kao infrastruktura nego kao brend.",{},{"id":999,"data":1000,"type":572,"tunes":1002},"h-change",{"text":1001,"level":47},"Šta bi promenilo ovaj odgovor?",{},{"id":1004,"data":1005,"type":544,"tunes":1007},"p-change-1",{"text":1006},"Najveća neizvesnost je konačni maloprodajni oblik ovih API-ja. DX Linearna algebra je još u pregledu, a Compute Graph Compiler još nije dostigao široku maloprodajnu dostupnost.",{},{"id":1009,"data":1010,"type":544,"tunes":1012},"p-change-2",{"text":1011},"Konačna podrška hardvera, detalji API-ja, ponašanje kompajlera i usvajanje u engine-ima mogu se promeniti pre nego što ovi sistemi postanu normalna infrastruktura za igre u prodaji.",{},{"id":1014,"data":1015,"type":544,"tunes":1017},"p-change-3",{"text":1016},"Ako glavni engine-i direktno usvoje apstrakcije, neuronski šejderi bi mogli postati mnogo češći bez toga da pojedinačni timovi za igre implementiraju svaku tehniku od nule.",{},{"id":1019,"data":1020,"type":572,"tunes":1022},"h-limit",{"text":1021,"level":47},"Ograničenja",{},{"id":1024,"data":1025,"type":544,"tunes":1027},"p-limit-1",{"text":1026},"Ovaj članak objašnjava Microsoft-ovu dokumentovanu DirectX arhitekturu i API-je u pregledu. Ne tvrdi da DX Linearna algebra trenutno poboljšava performanse u svakoj igri niti da je Compute Graph Compiler završen maloprodajni proizvod.",{},{"id":1029,"data":1030,"type":544,"tunes":1032},"p-limit-2",{"text":1031},"Microsoft-ovi primeri opisuju mogućnosti i nameravanu upotrebu. Stvarne koristi zavise od modela, integracije u engine, arhitekture GPU-a, drajvera i radnog zadatka.",{},{"id":1034,"data":1035,"type":572,"tunes":1037},"h-conclusion",{"text":1036,"level":47},"Zaključak",{},{"id":1039,"data":1040,"type":544,"tunes":1042},"p-conc-1",{"text":1041},"Važna DirectX promena nije još jedan potvrdni okvir pod nazivom AI.",{},{"id":1044,"data":1045,"type":544,"tunes":1047},"p-conc-2",{"text":1046},"To je da matematika mašinskog učenja prelazi u sam model grafičkog programiranja. Male neuronske funkcije mogu da se nalaze unutar šejdera, veći modeli mogu da se kreću ka kompilaciji na nivou grafa, a isti DirectX alatni lanac može da izloži te radne zadatke kod više proizvođača GPU.",{},{"id":1049,"data":1050,"type":544,"tunes":1052},"p-conc-3",{"text":1051},"Tako neuronsko renderovanje prestaje da bude jedna brendirana funkcija i počinje da postaje deo infrastrukture za renderovanje.",{},{"id":1054,"data":1055,"type":572,"tunes":1057},"h-faq",{"text":1056,"level":47},"Često postavljana pitanja",{},{"id":1059,"data":1060,"type":1059,"tunes":1087},"faq",{"items":1061,"title":1086},[1062,1066,1070,1074,1078,1082],{"id":1063,"answer":1064,"question":1065},"faq1","To je skup DirectX\u002FHLSL funkcija za hardverski ubrzane vektorske i matrične operacije koje koriste radni zadaci mašinskog učenja, neuronskog renderovanja i obrade slika.","Šta je DirectX linearna algebra?",{"id":1067,"answer":1068,"question":1069},"faq2","Korisno objašnjenje jednostavnim jezikom je šejder koji uključuje naučenu neuronsku funkciju ili korak ML inferencije kao deo svog grafičkog rada.","Šta je neuronski šejder?",{"id":1071,"answer":1072,"question":1073},"faq3","Od septembra 2026. ostaje na putu pregleda Shader Model 6.10 \u002F Agility SDK.","Da li je DX linearna algebra već normalna maloprodajna DirectX funkcija?",{"id":1075,"answer":1076,"question":1077},"faq4","To je Microsoftov najavljeni API kompajlera ML modela na nivou modela namenjen da uzme veće računske grafove i prevede ih u optimizovane GPU radne zadatke integrisane sa D3D12.","Šta je DirectX Compute Graph Compiler?",{"id":1079,"answer":1080,"question":1081},"faq5","Male funkcije dobro odgovaraju autorstvu na nivou šejdera, dok veliki modeli imaju koristi od optimizacije celog grafa, planiranja memorije i fuzije operatora.","Zašto ne pokrenuti svaki ML model direktno u HLSL-u?",{"id":1083,"answer":1084,"question":1085},"faq6","Ne direktno. DirectX pruža infrastrukturu nižeg nivoa koju proizvođači i programeri mogu da koriste za neuronsku grafiku. Brendirane tehnologije i dalje mogu da implementiraju sopstvene modele i strategije integracije.","Da li će ovo zameniti DLSS ili FSR?","DirectX linearna algebra i neuronski šejderi",{},{"id":1089,"data":1090,"type":572,"tunes":1092},"h-glossary",{"text":1091,"level":47},"Pojmovnik",{},{"id":1094,"data":1095,"type":1094,"tunes":1125},"glossary",{"title":1096,"entries":1097},"Ključni DirectX ML pojmovi",[1098,1102,1106,1110,1114,1118,1122],{"term":1099,"anchor":1100,"definition":1101},"DX linearna algebra","dx-linear-algebra","DirectX\u002FHLSL API-ji za ubrzane vektorske i matrične operacije namenjene neuronskom renderovanju, ML i obradi slika.",{"term":1103,"anchor":1104,"definition":1105},"Cooperative Vector","cooperative-vector","Raniji DirectX pristup za ubrzane vektorsko-matrične operacije unutar šejdera koji je pomogao da se uspostavi put neuronskog renderovanja na nivou šejdera.",{"term":1107,"anchor":1108,"definition":1109},"Shader Model 6.10","shader-model-6-10","Generacija šejder modela u pregledu koja sadrži trenutne DirectX Linear Algebra matrične API-je.",{"term":1111,"anchor":1112,"definition":1113},"DirectX Compute Graph Compiler","compute-graph-compiler","Microsoftov najavljeni API kompajlera za optimizaciju i izvršavanje većih ML računskih grafova kao native DirectX GPU radnih zadataka.",{"term":1115,"anchor":1116,"definition":1117},"Neuronski šejder","neural-shader","Praktičan termin za šejder koji obavlja naučenu inferenciju ili neuronsko izračunavanje kao deo grafičke obrade.",{"term":1119,"anchor":1120,"definition":1121},"Granica šejdera ili grafa","shader-or-graph-boundary","Figure Rocks model za odlučivanje da li ML radni zadatak pripada kao mala inline matematika šejdera ili kao veći računski graf na nivou modela.",{"term":952,"anchor":1123,"definition":1124},"neural-shader-value-test","Figure Rocks radni tok za odlučivanje da li je tradicionalni računski ili memorijski trošak izbegnut neuronskom tehnikom vredan svoje cene inferencije i kompromisa u kvalitetu.",{},{"id":1127,"data":1128,"type":572,"tunes":1130},"h-sources",{"text":1129,"level":47},"Primarni izvori",{},{"id":1132,"data":1133,"type":1139,"tunes":1140},"src-ms-ml-era",{"link":1134,"meta":1135},"https:\u002F\u002Fdevblogs.microsoft.com\u002Fdirectx\u002Fevolving-directx-for-the-ml-era-on-windows\u002F",{"image":1136,"title":1137,"description":1138},{"url":13},"Microsoft DirectX — Evolving DirectX for the ML Era on Windows","Zvanični pregled arhitekture sa GDC 2026 koji pokriva ML na nivou šejdera, DX linearnu algebru i DirectX Compute Graph Compiler.","linkTool",{},{"id":1142,"data":1143,"type":1139,"tunes":1149},"src-ms-linalg",{"link":1144,"meta":1145},"https:\u002F\u002Fdevblogs.microsoft.com\u002Fdirectx\u002Fd3d12-linalg-preview\u002F",{"image":1146,"title":1147,"description":1148},{"url":13},"Microsoft DirectX — D3D12 LinAlg Matrix Preview","Zvanični pregled iz aprila 2026. koji objašnjava unifikovane Linear Algebra API-je, matrične operacije i motivaciju za neuronsko renderovanje.",{},{"id":1151,"data":1152,"type":1139,"tunes":1158},"src-ms-agility721",{"link":1153,"meta":1154},"https:\u002F\u002Fdevblogs.microsoft.com\u002Fdirectx\u002Fannouncing-agilitysdk-721-preview-and-more-shader-model-6-10-features\u002F",{"image":1155,"title":1156,"description":1157},{"url":13},"Microsoft DirectX — Agility SDK 1.721 Preview","Zvanično izdanje iz maja 2026. koje dokumentuje Shader Model 6.10 Linear Algebra ažuriranja i podršku hardvera u pregledu.",{},{"id":1160,"data":1161,"type":1139,"tunes":1167},"src-ms-gdc",{"link":1162,"meta":1163},"https:\u002F\u002Fdeveloper.microsoft.com\u002Fen-us\u002Fgames\u002Farticles\u002F2026\u002F03\u002Fgdc-2026-evolving-directx-for-ml-era-on-windows\u002F",{"image":1164,"title":1165,"description":1166},{"url":13},"Microsoft Game Dev — GDC 2026: Evolving DirectX for the ML Era","Zvanični sažetak Microsoft Game Dev-a koji objašnjava ML na nivou šejdera i modela i ulogu Compute Graph Compiler-a.",{},{"id":1169,"data":1170,"type":1139,"tunes":1176},"src-ms-coop",{"link":1171,"meta":1172},"https:\u002F\u002Fdevblogs.microsoft.com\u002Fdirectx\u002Fcooperative-vector\u002F",{"image":1173,"title":1174,"description":1175},{"url":13},"Microsoft DirectX — D3D12 Cooperative Vector","Zvanična pozadina o hardverski ubrzanim vektorskim\u002Fmatričnim operacijama i neuronskom renderovanju direktno iz niti šejdera.",{},"2.31","DirectX prevazilazi tradicionalne grafičke šejdere. Microsoft dodaje hardverski ubrzanu linearnu algebru direktno u HLSL i odvojen put za veće ML modele, postavljajući temelje za neuronske teksture, naučene materijale, neuronsko osvetljenje i druge AI vođene tehnike renderovanja.","\u002Fuploads\u002F2026\u002F09\u002Fdirectx-is-becoming-an-ml-platform-what-linear-algebra-and-neural-shaders-mean-for-future-games-1790378019857-utigyk.webp","directx-is-becoming-an-ml-platform-what-linear-algebra-and-neural-shaders-mean-for-future-games-1790378019857-utigyk","PUBLISHED","2026-09-25T15:12:00.000Z","2026-09-25T23:12:37.728Z","2026-09-26T07:39:23.873Z",{"en":1186,"de":1187,"sr":1188,"es":1189,"fr":1190,"it":1191,"ru":1192,"zh":1193},"\u002Fblog\u002Fdirectx-is-becoming-an-ml-platform-what-linear-algebra-and-neural-shaders-mean-for-future-games","\u002Fde\u002Fblog\u002Fdirectx-is-becoming-an-ml-platform-what-linear-algebra-and-neural-shaders-mean-for-future-games","\u002Fsr\u002Fblog\u002Fdirectx-is-becoming-an-ml-platform-what-linear-algebra-and-neural-shaders-mean-for-future-games","\u002Fes\u002Fblog\u002Fdirectx-is-becoming-an-ml-platform-what-linear-algebra-and-neural-shaders-mean-for-future-games","\u002Ffr\u002Fblog\u002Fdirectx-is-becoming-an-ml-platform-what-linear-algebra-and-neural-shaders-mean-for-future-games","\u002Fit\u002Fblog\u002Fdirectx-is-becoming-an-ml-platform-what-linear-algebra-and-neural-shaders-mean-for-future-games","\u002Fru\u002Fblog\u002Fdirectx-is-becoming-an-ml-platform-what-linear-algebra-and-neural-shaders-mean-for-future-games","\u002Fzh\u002Fblog\u002Fdirectx-is-becoming-an-ml-platform-what-linear-algebra-and-neural-shaders-mean-for-future-games",[1195,1199,1203,1207],{"id":1196,"name":1197,"slug":1198},251,"Замућење и перзистенција","blur-and-persistence",{"id":1200,"name":1201,"slug":1202},252,"Овердрајв и замућење","overdrive-and-smearing",{"id":1204,"name":1205,"slug":1206},253,"Освежавање и јасноћа","refresh-and-clarity",{"id":1208,"name":1209,"slug":1210},220,"Дизајн у служби употребе","design-that-serves-use",{"id":283,"login":1212,"email":1213,"displayName":1214},"aleksandar","aleksandar@stajic.de","Aleksandar Stajic",[1216,1723],{"lang":8,"title":1217,"content":1218,"contentJson":1219,"excerpt":1722},"DirectX Is Becoming an ML Platform: What Linear Algebra and Neural Shaders Mean for Future Games","{\"time\":1790408311441,\"blocks\":[{\"id\":\"intro\",\"type\":\"paragraph\",\"data\":{\"text\":\"DirectX is no longer only a graphics API that sends traditional shaders to the GPU. Microsoft is adding machine-learning primitives directly to HLSL and a second path for running larger ML graphs inside the DirectX ecosystem. That changes where neural rendering can live inside future PC games.\"},\"tunes\":{}},{\"id\":\"direct\",\"type\":\"callout\",\"data\":{\"variant\":\"info\",\"title\":\"Direct answer\",\"body\":\"\u003Cstrong>DirectX is becoming an ML-capable graphics platform.\u003C\u002Fstrong> Small neural workloads can run directly inside shaders through DX Linear Algebra, while larger models are being targeted by the DirectX Compute Graph Compiler. The goal is to let game engines use GPU AI hardware without building a separate vendor-specific path for every technique.\"},\"tunes\":{}},{\"id\":\"status-note\",\"type\":\"callout\",\"data\":{\"variant\":\"note\",\"title\":\"Current status\",\"body\":\"As of September 2026, \u003Cstrong>DX Linear Algebra is still a preview technology\u003C\u002Fstrong> in the Shader Model 6.10 \u002F Agility SDK preview path. Microsoft announced the DirectX Compute Graph Compiler for private preview rather than as a broadly shipping retail feature. This article explains the architecture and direction, not a claim that every current game can use it today.\"},\"tunes\":{}},{\"id\":\"toc\",\"type\":\"tableOfContents\",\"data\":{\"title\":\"Contents\",\"minLevel\":2,\"maxLevel\":3},\"tunes\":{}},{\"id\":\"h-why\",\"type\":\"header\",\"data\":{\"text\":\"Why neural rendering needs new DirectX primitives\",\"level\":2},\"tunes\":{}},{\"id\":\"p-why-1\",\"type\":\"paragraph\",\"data\":{\"text\":\"Modern GPUs already contain specialized hardware for matrix operations used by machine learning. The problem for a game developer is not only whether that hardware exists, but how to access it efficiently from a real-time graphics pipeline.\"},\"tunes\":{}},{\"id\":\"p-why-2\",\"type\":\"paragraph\",\"data\":{\"text\":\"Microsoft first explored this with Cooperative Vector support. In 2026, that work evolved into a broader DirectX Linear Algebra design that supports both vector-matrix and matrix-matrix operations.\"},\"tunes\":{}},{\"id\":\"p-why-3\",\"type\":\"paragraph\",\"data\":{\"text\":\"That matters because different neural-rendering jobs have different shapes. A tiny model evaluating material behavior per pixel is not the same workload as a large super-resolution or denoising graph.\"},\"tunes\":{}},{\"id\":\"h-two-level\",\"type\":\"header\",\"data\":{\"text\":\"The Two-Level DirectX ML Model\",\"level\":2},\"tunes\":{}},{\"id\":\"two-level-flow\",\"type\":\"processFlow\",\"data\":{\"title\":\"Two ways ML can enter the DirectX graphics pipeline\",\"orientation\":\"auto\",\"steps\":[{\"label\":\"1. Shader-level ML\",\"description\":\"Small neural or linear-algebra workloads execute directly from HLSL alongside traditional shader code.\"},{\"label\":\"2. DX Linear Algebra\",\"description\":\"The shader can request hardware-accelerated vector and matrix operations instead of manually implementing every ML primitive.\"},{\"label\":\"3. Model-level ML\",\"description\":\"Larger neural networks are represented as complete computation graphs rather than hand-written shader fragments.\"},{\"label\":\"4. DirectX Compute Graph Compiler\",\"description\":\"Microsoft's planned compiler path analyzes and lowers those graphs into optimized GPU workloads integrated with D3D12 queues and command lists.\"}]},\"tunes\":{}},{\"id\":\"simple-diff\",\"type\":\"callout\",\"data\":{\"variant\":\"success\",\"title\":\"The simple difference\",\"body\":\"\u003Cstrong>DX Linear Algebra:\u003C\u002Fstrong> put small ML math inside the shader.\u003Cbr>\u003Cstrong>Compute Graph Compiler:\u003C\u002Fstrong> bring a larger ML model into the engine as a graph.\"},\"tunes\":{}},{\"id\":\"h-linalg\",\"type\":\"header\",\"data\":{\"text\":\"What DX Linear Algebra actually gives a shader\",\"level\":2},\"tunes\":{}},{\"id\":\"p-linalg-1\",\"type\":\"paragraph\",\"data\":{\"text\":\"Traditional HLSL is built around graphics and compute operations. Neural workloads rely heavily on linear algebra: vectors, matrices, multiplication, accumulation and data layouts optimized for those operations.\"},\"tunes\":{}},{\"id\":\"p-linalg-2\",\"type\":\"paragraph\",\"data\":{\"text\":\"The Shader Model 6.10 preview adds first-class matrix APIs so developers can express those workloads more directly and let the driver map them to specialized hardware.\"},\"tunes\":{}},{\"id\":\"p-linalg-3\",\"type\":\"paragraph\",\"data\":{\"text\":\"Microsoft's April 2026 preview explicitly describes this as a unified path for neural rendering, ML and image-processing workloads rather than a graphics-only feature.\"},\"tunes\":{}},{\"id\":\"math-table\",\"type\":\"comparison\",\"data\":{\"title\":\"Traditional shader math vs ML-oriented shader math\",\"layout\":\"table\",\"columns\":[{\"id\":\"traditional\",\"label\":\"Traditional shader focus\"},{\"id\":\"ml\",\"label\":\"ML-oriented focus\"}],\"rows\":[{\"id\":\"work\",\"label\":\"Typical work\",\"values\":[\"\",\"\"]},{\"id\":\"hardware\",\"label\":\"Hardware path\",\"values\":[\"\",\"\"]},{\"id\":\"code\",\"label\":\"Developer expression\",\"values\":[\"\",\"\"]}]},\"tunes\":{}},{\"id\":\"h-coop\",\"type\":\"header\",\"data\":{\"text\":\"Why Cooperative Vector was only the beginning\",\"level\":2},\"tunes\":{}},{\"id\":\"p-coop-1\",\"type\":\"paragraph\",\"data\":{\"text\":\"Cooperative Vector allowed shader threads to request vector-matrix work that drivers could map to specialized hardware. That was useful for highly parallel per-pixel workloads.\"},\"tunes\":{}},{\"id\":\"p-coop-2\",\"type\":\"paragraph\",\"data\":{\"text\":\"Microsoft later concluded that many important ML workloads need more than vector-matrix operations. Super resolution, denoising, temporal reconstruction, larger image models and general inference can require matrix-matrix operations and shared work across many threads.\"},\"tunes\":{}},{\"id\":\"p-coop-3\",\"type\":\"paragraph\",\"data\":{\"text\":\"DX Linear Algebra therefore broadens the model instead of treating Cooperative Vector as the final abstraction.\"},\"tunes\":{}},{\"id\":\"h-usecases\",\"type\":\"header\",\"data\":{\"text\":\"What can actually run inside a shader?\",\"level\":2},\"tunes\":{}},{\"id\":\"p-use-1\",\"type\":\"paragraph\",\"data\":{\"text\":\"The most interesting shader-level ML workloads are small enough to execute close to the graphics data they operate on.\"},\"tunes\":{}},{\"id\":\"usecases-table\",\"type\":\"table\",\"data\":{\"withHeadings\":true,\"stretched\":false,\"content\":[[\"Workload\",\"Why shader-level ML fits\"],[\"Neural texture compression\",\"A small network can reconstruct texture information near the point where the shader needs it\"],[\"Neural material evaluation\",\"A learned function can replace or augment expensive hand-authored material math\"],[\"Neural radiance caching\",\"Per-pixel or local inference can estimate lighting information from learned scene behavior\"],[\"Small denoising\u002Freconstruction kernels\",\"ML operations can sit directly beside the rendering stage they improve\"],[\"Image-processing inference\",\"Matrix operations can be embedded into GPU processing without a separate external runtime\"]]},\"tunes\":{}},{\"id\":\"h-texture\",\"type\":\"header\",\"data\":{\"text\":\"Why this matters for texture memory\",\"level\":2},\"tunes\":{}},{\"id\":\"p-tex-1\",\"type\":\"paragraph\",\"data\":{\"text\":\"One of Microsoft's recurring examples is neural texture compression.\"},\"tunes\":{}},{\"id\":\"p-tex-2\",\"type\":\"paragraph\",\"data\":{\"text\":\"Instead of storing every texture channel at conventional fidelity, a game can store a more compact representation and use a small neural network to reconstruct detail during rendering.\"},\"tunes\":{}},{\"id\":\"p-tex-3\",\"type\":\"paragraph\",\"data\":{\"text\":\"That trades some GPU inference work for lower storage or memory pressure. The exact benefit depends on the technique and hardware, but the architectural change is important: some visual detail can become computation instead of stored data.\"},\"tunes\":{}},{\"id\":\"ref-vram\",\"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 guide to VRAM capacity, residency budgets, working sets and why memory pressure is more complicated than one usage number.\",\"ctaLabel\":\"Read the VRAM guide\"},\"tunes\":{}},{\"id\":\"h-models\",\"type\":\"header\",\"data\":{\"text\":\"Why full models need a different path\",\"level\":2},\"tunes\":{}},{\"id\":\"p-models-1\",\"type\":\"paragraph\",\"data\":{\"text\":\"Hand-writing a few matrix operations in HLSL is practical for small neural functions. It becomes much less practical when the workload is a complete modern model with many layers, dependencies and intermediate tensors.\"},\"tunes\":{}},{\"id\":\"p-models-2\",\"type\":\"paragraph\",\"data\":{\"text\":\"Microsoft's DirectX Compute Graph Compiler is designed for this larger class of workload.\"},\"tunes\":{}},{\"id\":\"p-models-3\",\"type\":\"paragraph\",\"data\":{\"text\":\"Instead of rewriting the model as custom shader code, the compiler can accept a computation graph, analyze the whole graph, plan memory, fuse operations and lower the result into GPU work that integrates with DirectX 12.\"},\"tunes\":{}},{\"id\":\"h-boundary\",\"type\":\"header\",\"data\":{\"text\":\"The Shader-or-Graph Boundary\",\"level\":2},\"tunes\":{}},{\"id\":\"boundary-table\",\"type\":\"comparison\",\"data\":{\"title\":\"When the ML workload belongs in HLSL vs a model compiler\",\"layout\":\"table\",\"columns\":[{\"id\":\"shader\",\"label\":\"Shader-level path\"},{\"id\":\"graph\",\"label\":\"Model-level path\"}],\"rows\":[{\"id\":\"size\",\"label\":\"Model size\",\"values\":[\"\",\"\"]},{\"id\":\"location\",\"label\":\"Execution style\",\"values\":[\"\",\"\"]},{\"id\":\"authoring\",\"label\":\"Authoring\",\"values\":[\"\",\"\"]},{\"id\":\"optimization\",\"label\":\"Optimization scope\",\"values\":[\"\",\"\"]},{\"id\":\"use\",\"label\":\"Typical use\",\"values\":[\"\",\"\"]}]},\"tunes\":{}},{\"id\":\"h-crossvendor\",\"type\":\"header\",\"data\":{\"text\":\"Why cross-vendor support matters more than another AI feature\",\"level\":2},\"tunes\":{}},{\"id\":\"p-cross-1\",\"type\":\"paragraph\",\"data\":{\"text\":\"AMD, Intel, NVIDIA and Qualcomm expose different GPU architectures and different forms of dedicated matrix acceleration.\"},\"tunes\":{}},{\"id\":\"p-cross-2\",\"type\":\"paragraph\",\"data\":{\"text\":\"A DirectX abstraction gives Microsoft and the driver vendors a place to translate common HLSL or graph-level ML into the correct hardware path.\"},\"tunes\":{}},{\"id\":\"p-cross-3\",\"type\":\"paragraph\",\"data\":{\"text\":\"That does not make every GPU equally fast, but it can reduce the need for a game engine to implement a completely different neural-rendering API for every vendor.\"},\"tunes\":{}},{\"id\":\"portability-note\",\"type\":\"callout\",\"data\":{\"variant\":\"note\",\"title\":\"Portability is the real platform story\",\"body\":\"The interesting part is not that DirectX can “run AI.” GPUs already could. The platform change is that \u003Cstrong>ML becomes expressible through a common graphics API and toolchain\u003C\u002Fstrong>.\"},\"tunes\":{}},{\"id\":\"h-support\",\"type\":\"header\",\"data\":{\"text\":\"What hardware supports the current preview?\",\"level\":2},\"tunes\":{}},{\"id\":\"p-support-1\",\"type\":\"paragraph\",\"data\":{\"text\":\"The answer depends on the specific Linear Algebra operation and preview driver.\"},\"tunes\":{}},{\"id\":\"p-support-2\",\"type\":\"paragraph\",\"data\":{\"text\":\"Microsoft's Agility SDK 1.721 preview support table lists LinAlg VectorAccumulate for AMD Radeon RX 9000 Series hardware, Intel Xe2-or-newer hardware through an upcoming driver, and NVIDIA RTX hardware through the supported preview path.\"},\"tunes\":{}},{\"id\":\"p-support-3\",\"type\":\"paragraph\",\"data\":{\"text\":\"This is preview-era support, not a universal retail guarantee. Hardware and driver support can change before finalization.\"},\"tunes\":{}},{\"id\":\"h-infra\",\"type\":\"header\",\"data\":{\"text\":\"Neural rendering is becoming infrastructure\",\"level\":2},\"tunes\":{}},{\"id\":\"p-infra-1\",\"type\":\"paragraph\",\"data\":{\"text\":\"DLSS, FSR and other neural graphics technologies are often discussed as branded features visible in a game's settings menu.\"},\"tunes\":{}},{\"id\":\"p-infra-2\",\"type\":\"paragraph\",\"data\":{\"text\":\"DirectX Linear Algebra points to a deeper change. The neural operation can become an internal implementation detail inside the renderer rather than a single optional post-processing feature.\"},\"tunes\":{}},{\"id\":\"p-infra-3\",\"type\":\"paragraph\",\"data\":{\"text\":\"A developer could use ML for textures, materials, lighting, reconstruction or other local functions without exposing each one as a consumer-facing AI toggle.\"},\"tunes\":{}},{\"id\":\"ref-dlss5\",\"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 3D-Guided Neural Rendering Actually Changes\",\"excerpt\":\"How neural rendering is moving beyond upscaling and generated frames into material and lighting reconstruction inside the graphics pipeline.\",\"ctaLabel\":\"Read the DLSS 5 neural-rendering guide\"},\"tunes\":{}},{\"id\":\"h-stack\",\"type\":\"header\",\"data\":{\"text\":\"The Neural Rendering Stack is becoming layered\",\"level\":2},\"tunes\":{}},{\"id\":\"stack-flow\",\"type\":\"processFlow\",\"data\":{\"title\":\"A possible future DirectX game pipeline\",\"orientation\":\"auto\",\"steps\":[{\"label\":\"Traditional engine work\",\"description\":\"Simulation, geometry, visibility and base rendering remain standard engine responsibilities.\"},{\"label\":\"Inline neural shaders\",\"description\":\"Small learned functions reconstruct textures, materials or lighting inside HLSL.\"},{\"label\":\"Larger ML graphs\",\"description\":\"Full reconstruction or inference models execute through a graph-level DirectX path.\"},{\"label\":\"Vendor hardware mapping\",\"description\":\"Drivers map common DirectX operations onto the GPU's specialized AI and matrix units.\"},{\"label\":\"PIX visibility\",\"description\":\"Graphics and ML work can be profiled together instead of living in separate opaque runtimes.\"}]},\"tunes\":{}},{\"id\":\"h-pix\",\"type\":\"header\",\"data\":{\"text\":\"Why unified profiling matters\",\"level\":2},\"tunes\":{}},{\"id\":\"p-pix-1\",\"type\":\"paragraph\",\"data\":{\"text\":\"A neural workload that improves image quality can still damage a game if it unexpectedly consumes frame time, memory bandwidth or VRAM.\"},\"tunes\":{}},{\"id\":\"p-pix-2\",\"type\":\"paragraph\",\"data\":{\"text\":\"Microsoft explicitly includes unified PIX visibility as part of the Compute Graph Compiler direction. That matters because developers need to see graphics and ML work in the same frame capture.\"},\"tunes\":{}},{\"id\":\"p-pix-3\",\"type\":\"paragraph\",\"data\":{\"text\":\"If neural rendering becomes infrastructure, it must be measurable like any other rendering stage.\"},\"tunes\":{}},{\"id\":\"h-not-everything\",\"type\":\"header\",\"data\":{\"text\":\"This does not mean every shader will become a neural network\",\"level\":2},\"tunes\":{}},{\"id\":\"p-not-1\",\"type\":\"paragraph\",\"data\":{\"text\":\"Traditional shader math remains efficient, deterministic and easy to reason about for many workloads.\"},\"tunes\":{}},{\"id\":\"p-not-2\",\"type\":\"paragraph\",\"data\":{\"text\":\"A neural function makes sense when it can approximate or reconstruct something expensive more efficiently, compress data, or produce quality that would otherwise require too much conventional compute or memory.\"},\"tunes\":{}},{\"id\":\"p-not-3\",\"type\":\"paragraph\",\"data\":{\"text\":\"The right architecture will remain hybrid.\"},\"tunes\":{}},{\"id\":\"h-test\",\"type\":\"header\",\"data\":{\"text\":\"The Neural Shader Value Test\",\"level\":2},\"tunes\":{}},{\"id\":\"value-test\",\"type\":\"processFlow\",\"data\":{\"title\":\"When does putting ML into the rendering pipeline actually make sense?\",\"orientation\":\"auto\",\"steps\":[{\"label\":\"1. Identify the expensive traditional operation\",\"description\":\"What compute, bandwidth, memory or storage cost are you trying to replace?\"},{\"label\":\"2. Define the neural substitute\",\"description\":\"What can a small model reconstruct, predict or compress?\"},{\"label\":\"3. Measure inference cost\",\"description\":\"The ML model itself consumes GPU time, memory and bandwidth.\"},{\"label\":\"4. Measure quality stability\",\"description\":\"Look for temporal artifacts, reconstruction errors and failure cases.\"},{\"label\":\"5. Measure net savings\",\"description\":\"The technique is useful only if the avoided traditional cost is worth the added ML cost.\"},{\"label\":\"6. Test across vendors\",\"description\":\"A DirectX abstraction helps portability, but real hardware performance still differs.\"}]},\"tunes\":{}},{\"id\":\"h-gamers\",\"type\":\"header\",\"data\":{\"text\":\"What this means for gamers\",\"level\":2},\"tunes\":{}},{\"id\":\"p-gamers-1\",\"type\":\"paragraph\",\"data\":{\"text\":\"Gamers may never see a “DX Linear Algebra” switch in a graphics menu.\"},\"tunes\":{}},{\"id\":\"p-gamers-2\",\"type\":\"paragraph\",\"data\":{\"text\":\"The likely impact is indirect: smaller texture footprints, better lighting reconstruction, more efficient neural graphics, or new visual techniques that become practical because the engine can access matrix acceleration through a standard path.\"},\"tunes\":{}},{\"id\":\"p-gamers-3\",\"type\":\"paragraph\",\"data\":{\"text\":\"The feature is more important as infrastructure than as a brand.\"},\"tunes\":{}},{\"id\":\"h-change\",\"type\":\"header\",\"data\":{\"text\":\"What would change this answer?\",\"level\":2},\"tunes\":{}},{\"id\":\"p-change-1\",\"type\":\"paragraph\",\"data\":{\"text\":\"The biggest uncertainty is the final retail shape of these APIs. DX Linear Algebra remains in preview, and the Compute Graph Compiler has not yet reached broad retail availability.\"},\"tunes\":{}},{\"id\":\"p-change-2\",\"type\":\"paragraph\",\"data\":{\"text\":\"Final hardware support, API details, compiler behavior and engine adoption may change before these systems become normal shipping-game infrastructure.\"},\"tunes\":{}},{\"id\":\"p-change-3\",\"type\":\"paragraph\",\"data\":{\"text\":\"If major engines adopt the abstractions directly, neural shaders could become much more common without individual game teams implementing every technique from scratch.\"},\"tunes\":{}},{\"id\":\"h-limit\",\"type\":\"header\",\"data\":{\"text\":\"Limitations\",\"level\":2},\"tunes\":{}},{\"id\":\"p-limit-1\",\"type\":\"paragraph\",\"data\":{\"text\":\"This article explains Microsoft's documented DirectX architecture and preview APIs. It does not claim that DX Linear Algebra currently improves performance in every game or that the Compute Graph Compiler is a finished retail product.\"},\"tunes\":{}},{\"id\":\"p-limit-2\",\"type\":\"paragraph\",\"data\":{\"text\":\"Microsoft's examples describe capability and intended use. Real benefits depend on the model, engine integration, GPU architecture, drivers and workload.\"},\"tunes\":{}},{\"id\":\"h-conclusion\",\"type\":\"header\",\"data\":{\"text\":\"Conclusion\",\"level\":2},\"tunes\":{}},{\"id\":\"p-conc-1\",\"type\":\"paragraph\",\"data\":{\"text\":\"The important DirectX change is not another checkbox called AI.\"},\"tunes\":{}},{\"id\":\"p-conc-2\",\"type\":\"paragraph\",\"data\":{\"text\":\"It is that machine-learning math is moving into the graphics programming model itself. Small neural functions can sit inside shaders, larger models can move toward graph-level compilation, and the same DirectX toolchain can expose those workloads across multiple GPU vendors.\"},\"tunes\":{}},{\"id\":\"p-conc-3\",\"type\":\"paragraph\",\"data\":{\"text\":\"That is how neural rendering stops being one branded feature and starts becoming part of the rendering infrastructure.\"},\"tunes\":{}},{\"id\":\"h-faq\",\"type\":\"header\",\"data\":{\"text\":\"FAQ\",\"level\":2},\"tunes\":{}},{\"id\":\"faq\",\"type\":\"faq\",\"data\":{\"title\":\"DirectX Linear Algebra and neural shaders\",\"items\":[{\"id\":\"faq1\",\"question\":\"What is DirectX Linear Algebra?\",\"answer\":\"It is a DirectX\u002FHLSL feature set for hardware-accelerated vector and matrix operations used by machine learning, neural rendering and image-processing workloads.\"},{\"id\":\"faq2\",\"question\":\"What is a neural shader?\",\"answer\":\"A useful plain-English description is a shader that includes a learned neural function or ML inference step as part of its graphics work.\"},{\"id\":\"faq3\",\"question\":\"Is DX Linear Algebra already a normal retail DirectX feature?\",\"answer\":\"As of September 2026 it remains in the Shader Model 6.10 \u002F Agility SDK preview path.\"},{\"id\":\"faq4\",\"question\":\"What is the DirectX Compute Graph Compiler?\",\"answer\":\"It is Microsoft's announced model-level ML compiler API intended to take larger computation graphs and lower them into optimized GPU workloads integrated with D3D12.\"},{\"id\":\"faq5\",\"question\":\"Why not run every ML model directly in HLSL?\",\"answer\":\"Small functions fit shader-level authoring well, while large models benefit from whole-graph optimization, memory planning and operator fusion.\"},{\"id\":\"faq6\",\"question\":\"Will this replace DLSS or FSR?\",\"answer\":\"Not directly. DirectX provides lower-level infrastructure that vendors and developers can use for neural graphics. Branded technologies can still implement their own models and integration strategies.\"}]},\"tunes\":{}},{\"id\":\"h-glossary\",\"type\":\"header\",\"data\":{\"text\":\"Glossary\",\"level\":2},\"tunes\":{}},{\"id\":\"glossary\",\"type\":\"glossary\",\"data\":{\"title\":\"Key DirectX ML terms\",\"entries\":[{\"term\":\"DX Linear Algebra\",\"definition\":\"DirectX\u002FHLSL APIs for accelerated vector and matrix operations intended for neural rendering, ML and image-processing workloads.\",\"anchor\":\"dx-linear-algebra\"},{\"term\":\"Cooperative Vector\",\"definition\":\"An earlier DirectX approach for accelerated vector-matrix operations inside shaders that helped establish the shader-level neural-rendering path.\",\"anchor\":\"cooperative-vector\"},{\"term\":\"Shader Model 6.10\",\"definition\":\"The preview shader-model generation containing the current DirectX Linear Algebra matrix APIs.\",\"anchor\":\"shader-model-6-10\"},{\"term\":\"DirectX Compute Graph Compiler\",\"definition\":\"Microsoft's announced compiler API for optimizing and executing larger ML computation graphs as native DirectX GPU workloads.\",\"anchor\":\"compute-graph-compiler\"},{\"term\":\"Neural shader\",\"definition\":\"A practical term for a shader that performs learned inference or neural computation as part of graphics processing.\",\"anchor\":\"neural-shader\"},{\"term\":\"Shader-or-Graph Boundary\",\"definition\":\"A Figure Rocks model for deciding whether an ML workload belongs as small inline shader math or as a larger model-level computation graph.\",\"anchor\":\"shader-or-graph-boundary\"},{\"term\":\"Neural Shader Value Test\",\"definition\":\"A Figure Rocks workflow for deciding whether the traditional compute or memory cost avoided by a neural technique is worth its inference cost and quality trade-offs.\",\"anchor\":\"neural-shader-value-test\"}]},\"tunes\":{}},{\"id\":\"h-sources\",\"type\":\"header\",\"data\":{\"text\":\"Primary sources\",\"level\":2},\"tunes\":{}},{\"id\":\"src-ms-ml-era\",\"type\":\"linkTool\",\"data\":{\"link\":\"https:\u002F\u002Fdevblogs.microsoft.com\u002Fdirectx\u002Fevolving-directx-for-the-ml-era-on-windows\u002F\",\"meta\":{\"image\":{\"url\":\"\"},\"title\":\"Microsoft DirectX — Evolving DirectX for the ML Era on Windows\",\"description\":\"Official GDC 2026 architecture overview covering shader-level ML, DX Linear Algebra and the DirectX Compute Graph Compiler.\"}},\"tunes\":{}},{\"id\":\"src-ms-linalg\",\"type\":\"linkTool\",\"data\":{\"link\":\"https:\u002F\u002Fdevblogs.microsoft.com\u002Fdirectx\u002Fd3d12-linalg-preview\u002F\",\"meta\":{\"image\":{\"url\":\"\"},\"title\":\"Microsoft DirectX — D3D12 LinAlg Matrix Preview\",\"description\":\"Official April 2026 preview explaining the unified Linear Algebra APIs, matrix operations and neural-rendering motivation.\"}},\"tunes\":{}},{\"id\":\"src-ms-agility721\",\"type\":\"linkTool\",\"data\":{\"link\":\"https:\u002F\u002Fdevblogs.microsoft.com\u002Fdirectx\u002Fannouncing-agilitysdk-721-preview-and-more-shader-model-6-10-features\u002F\",\"meta\":{\"image\":{\"url\":\"\"},\"title\":\"Microsoft DirectX — Agility SDK 1.721 Preview\",\"description\":\"Official May 2026 release documenting Shader Model 6.10 Linear Algebra updates and preview hardware support.\"}},\"tunes\":{}},{\"id\":\"src-ms-gdc\",\"type\":\"linkTool\",\"data\":{\"link\":\"https:\u002F\u002Fdeveloper.microsoft.com\u002Fen-us\u002Fgames\u002Farticles\u002F2026\u002F03\u002Fgdc-2026-evolving-directx-for-ml-era-on-windows\u002F\",\"meta\":{\"image\":{\"url\":\"\"},\"title\":\"Microsoft Game Dev — GDC 2026: Evolving DirectX for the ML Era\",\"description\":\"Official Microsoft Game Dev summary explaining shader-level and model-level ML and the role of the Compute Graph Compiler.\"}},\"tunes\":{}},{\"id\":\"src-ms-coop\",\"type\":\"linkTool\",\"data\":{\"link\":\"https:\u002F\u002Fdevblogs.microsoft.com\u002Fdirectx\u002Fcooperative-vector\u002F\",\"meta\":{\"image\":{\"url\":\"\"},\"title\":\"Microsoft DirectX — D3D12 Cooperative Vector\",\"description\":\"Official background on hardware-accelerated vector\u002Fmatrix operations and neural rendering directly from shader threads.\"}},\"tunes\":{}}],\"version\":\"2.31.6\"}",{"time":1220,"blocks":1221,"version":1721},1790408311441,[1222,1226,1231,1236,1240,1244,1248,1252,1256,1260,1276,1281,1285,1289,1293,1297,1316,1320,1324,1328,1332,1336,1340,1362,1366,1370,1374,1378,1385,1389,1393,1397,1401,1405,1430,1434,1438,1442,1446,1451,1455,1459,1463,1467,1471,1475,1479,1483,1490,1494,1514,1518,1522,1526,1530,1534,1538,1542,1546,1550,1573,1577,1581,1585,1589,1593,1597,1601,1605,1609,1613,1617,1621,1625,1629,1633,1637,1660,1664,1687,1691,1697,1703,1709,1715],{"id":541,"data":1223,"type":544,"tunes":1225},{"text":1224},"DirectX is no longer only a graphics API that sends traditional shaders to the GPU. Microsoft is adding machine-learning primitives directly to HLSL and a second path for running larger ML graphs inside the DirectX ecosystem. That changes where neural rendering can live inside future PC games.",{},{"id":547,"data":1227,"type":552,"tunes":1230},{"body":1228,"title":1229,"variant":551},"\u003Cstrong>DirectX is becoming an ML-capable graphics platform.\u003C\u002Fstrong> Small neural workloads can run directly inside shaders through DX Linear Algebra, while larger models are being targeted by the DirectX Compute Graph Compiler. The goal is to let game engines use GPU AI hardware without building a separate vendor-specific path for every technique.","Direct answer",{},{"id":555,"data":1232,"type":552,"tunes":1235},{"body":1233,"title":1234,"variant":559},"As of September 2026, \u003Cstrong>DX Linear Algebra is still a preview technology\u003C\u002Fstrong> in the Shader Model 6.10 \u002F Agility SDK preview path. Microsoft announced the DirectX Compute Graph Compiler for private preview rather than as a broadly shipping retail feature. This article explains the architecture and direction, not a claim that every current game can use it today.","Current status",{},{"id":562,"data":1237,"type":566,"tunes":1239},{"title":1238,"maxLevel":565,"minLevel":47},"Contents",{},{"id":569,"data":1241,"type":572,"tunes":1243},{"text":1242,"level":47},"Why neural rendering needs new DirectX primitives",{},{"id":575,"data":1245,"type":544,"tunes":1247},{"text":1246},"Modern GPUs already contain specialized hardware for matrix operations used by machine learning. The problem for a game developer is not only whether that hardware exists, but how to access it efficiently from a real-time graphics pipeline.",{},{"id":580,"data":1249,"type":544,"tunes":1251},{"text":1250},"Microsoft first explored this with Cooperative Vector support. In 2026, that work evolved into a broader DirectX Linear Algebra design that supports both vector-matrix and matrix-matrix operations.",{},{"id":585,"data":1253,"type":544,"tunes":1255},{"text":1254},"That matters because different neural-rendering jobs have different shapes. A tiny model evaluating material behavior per pixel is not the same workload as a large super-resolution or denoising graph.",{},{"id":590,"data":1257,"type":572,"tunes":1259},{"text":1258,"level":47},"The Two-Level DirectX ML Model",{},{"id":595,"data":1261,"type":612,"tunes":1275},{"steps":1262,"title":1274,"orientation":611},[1263,1266,1269,1272],{"label":1264,"description":1265},"1. Shader-level ML","Small neural or linear-algebra workloads execute directly from HLSL alongside traditional shader code.",{"label":1267,"description":1268},"2. DX Linear Algebra","The shader can request hardware-accelerated vector and matrix operations instead of manually implementing every ML primitive.",{"label":1270,"description":1271},"3. Model-level ML","Larger neural networks are represented as complete computation graphs rather than hand-written shader fragments.",{"label":608,"description":1273},"Microsoft's planned compiler path analyzes and lowers those graphs into optimized GPU workloads integrated with D3D12 queues and command lists.","Two ways ML can enter the DirectX graphics pipeline",{},{"id":615,"data":1277,"type":552,"tunes":1280},{"body":1278,"title":1279,"variant":619},"\u003Cstrong>DX Linear Algebra:\u003C\u002Fstrong> put small ML math inside the shader.\u003Cbr>\u003Cstrong>Compute Graph Compiler:\u003C\u002Fstrong> bring a larger ML model into the engine as a graph.","The simple difference",{},{"id":622,"data":1282,"type":572,"tunes":1284},{"text":1283,"level":47},"What DX Linear Algebra actually gives a shader",{},{"id":627,"data":1286,"type":544,"tunes":1288},{"text":1287},"Traditional HLSL is built around graphics and compute operations. Neural workloads rely heavily on linear algebra: vectors, matrices, multiplication, accumulation and data layouts optimized for those operations.",{},{"id":632,"data":1290,"type":544,"tunes":1292},{"text":1291},"The Shader Model 6.10 preview adds first-class matrix APIs so developers can express those workloads more directly and let the driver map them to specialized hardware.",{},{"id":637,"data":1294,"type":544,"tunes":1296},{"text":1295},"Microsoft's April 2026 preview explicitly describes this as a unified path for neural rendering, ML and image-processing workloads rather than a graphics-only feature.",{},{"id":642,"data":1298,"type":666,"tunes":1315},{"rows":1299,"title":1309,"layout":658,"columns":1310},[1300,1303,1306],{"id":646,"label":1301,"values":1302},"Typical work",[13,13],{"id":650,"label":1304,"values":1305},"Hardware path",[13,13],{"id":654,"label":1307,"values":1308},"Developer expression",[13,13],"Traditional shader math vs ML-oriented shader math",[1311,1313],{"id":661,"label":1312},"Traditional shader focus",{"id":664,"label":1314},"ML-oriented focus",{},{"id":669,"data":1317,"type":572,"tunes":1319},{"text":1318,"level":47},"Why Cooperative Vector was only the beginning",{},{"id":674,"data":1321,"type":544,"tunes":1323},{"text":1322},"Cooperative Vector allowed shader threads to request vector-matrix work that drivers could map to specialized hardware. That was useful for highly parallel per-pixel workloads.",{},{"id":679,"data":1325,"type":544,"tunes":1327},{"text":1326},"Microsoft later concluded that many important ML workloads need more than vector-matrix operations. Super resolution, denoising, temporal reconstruction, larger image models and general inference can require matrix-matrix operations and shared work across many threads.",{},{"id":684,"data":1329,"type":544,"tunes":1331},{"text":1330},"DX Linear Algebra therefore broadens the model instead of treating Cooperative Vector as the final abstraction.",{},{"id":689,"data":1333,"type":572,"tunes":1335},{"text":1334,"level":47},"What can actually run inside a shader?",{},{"id":694,"data":1337,"type":544,"tunes":1339},{"text":1338},"The most interesting shader-level ML workloads are small enough to execute close to the graphics data they operate on.",{},{"id":699,"data":1341,"type":658,"tunes":1361},{"content":1342,"stretched":720,"withHeadings":15},[1343,1346,1349,1352,1355,1358],[1344,1345],"Workload","Why shader-level ML fits",[1347,1348],"Neural texture compression","A small network can reconstruct texture information near the point where the shader needs it",[1350,1351],"Neural material evaluation","A learned function can replace or augment expensive hand-authored material math",[1353,1354],"Neural radiance caching","Per-pixel or local inference can estimate lighting information from learned scene behavior",[1356,1357],"Small denoising\u002Freconstruction kernels","ML operations can sit directly beside the rendering stage they improve",[1359,1360],"Image-processing inference","Matrix operations can be embedded into GPU processing without a separate external runtime",{},{"id":723,"data":1363,"type":572,"tunes":1365},{"text":1364,"level":47},"Why this matters for texture memory",{},{"id":728,"data":1367,"type":544,"tunes":1369},{"text":1368},"One of Microsoft's recurring examples is neural texture compression.",{},{"id":733,"data":1371,"type":544,"tunes":1373},{"text":1372},"Instead of storing every texture channel at conventional fidelity, a game can store a more compact representation and use a small neural network to reconstruct detail during rendering.",{},{"id":738,"data":1375,"type":544,"tunes":1377},{"text":1376},"That trades some GPU inference work for lower storage or memory pressure. The exact benefit depends on the technique and hardware, but the architectural change is important: some visual detail can become computation instead of stored data.",{},{"id":743,"data":1379,"type":749,"tunes":1384},{"url":1380,"title":1381,"excerpt":1382,"ctaLabel":1383},"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 guide to VRAM capacity, residency budgets, working sets and why memory pressure is more complicated than one usage number.","Read the VRAM guide",{},{"id":752,"data":1386,"type":572,"tunes":1388},{"text":1387,"level":47},"Why full models need a different path",{},{"id":757,"data":1390,"type":544,"tunes":1392},{"text":1391},"Hand-writing a few matrix operations in HLSL is practical for small neural functions. It becomes much less practical when the workload is a complete modern model with many layers, dependencies and intermediate tensors.",{},{"id":762,"data":1394,"type":544,"tunes":1396},{"text":1395},"Microsoft's DirectX Compute Graph Compiler is designed for this larger class of workload.",{},{"id":767,"data":1398,"type":544,"tunes":1400},{"text":1399},"Instead of rewriting the model as custom shader code, the compiler can accept a computation graph, analyze the whole graph, plan memory, fuse operations and lower the result into GPU work that integrates with DirectX 12.",{},{"id":772,"data":1402,"type":572,"tunes":1404},{"text":1403,"level":47},"The Shader-or-Graph Boundary",{},{"id":777,"data":1406,"type":666,"tunes":1429},{"rows":1407,"title":1423,"layout":658,"columns":1424},[1408,1411,1414,1417,1420],{"id":781,"label":1409,"values":1410},"Model size",[13,13],{"id":785,"label":1412,"values":1413},"Execution style",[13,13],{"id":789,"label":1415,"values":1416},"Authoring",[13,13],{"id":793,"label":1418,"values":1419},"Optimization scope",[13,13],{"id":797,"label":1421,"values":1422},"Typical use",[13,13],"When the ML workload belongs in HLSL vs a model compiler",[1425,1427],{"id":803,"label":1426},"Shader-level path",{"id":806,"label":1428},"Model-level path",{},{"id":810,"data":1431,"type":572,"tunes":1433},{"text":1432,"level":47},"Why cross-vendor support matters more than another AI feature",{},{"id":815,"data":1435,"type":544,"tunes":1437},{"text":1436},"AMD, Intel, NVIDIA and Qualcomm expose different GPU architectures and different forms of dedicated matrix acceleration.",{},{"id":820,"data":1439,"type":544,"tunes":1441},{"text":1440},"A DirectX abstraction gives Microsoft and the driver vendors a place to translate common HLSL or graph-level ML into the correct hardware path.",{},{"id":825,"data":1443,"type":544,"tunes":1445},{"text":1444},"That does not make every GPU equally fast, but it can reduce the need for a game engine to implement a completely different neural-rendering API for every vendor.",{},{"id":830,"data":1447,"type":552,"tunes":1450},{"body":1448,"title":1449,"variant":559},"The interesting part is not that DirectX can “run AI.” GPUs already could. The platform change is that \u003Cstrong>ML becomes expressible through a common graphics API and toolchain\u003C\u002Fstrong>.","Portability is the real platform story",{},{"id":836,"data":1452,"type":572,"tunes":1454},{"text":1453,"level":47},"What hardware supports the current preview?",{},{"id":841,"data":1456,"type":544,"tunes":1458},{"text":1457},"The answer depends on the specific Linear Algebra operation and preview driver.",{},{"id":846,"data":1460,"type":544,"tunes":1462},{"text":1461},"Microsoft's Agility SDK 1.721 preview support table lists LinAlg VectorAccumulate for AMD Radeon RX 9000 Series hardware, Intel Xe2-or-newer hardware through an upcoming driver, and NVIDIA RTX hardware through the supported preview path.",{},{"id":851,"data":1464,"type":544,"tunes":1466},{"text":1465},"This is preview-era support, not a universal retail guarantee. Hardware and driver support can change before finalization.",{},{"id":856,"data":1468,"type":572,"tunes":1470},{"text":1469,"level":47},"Neural rendering is becoming infrastructure",{},{"id":861,"data":1472,"type":544,"tunes":1474},{"text":1473},"DLSS, FSR and other neural graphics technologies are often discussed as branded features visible in a game's settings menu.",{},{"id":866,"data":1476,"type":544,"tunes":1478},{"text":1477},"DirectX Linear Algebra points to a deeper change. The neural operation can become an internal implementation detail inside the renderer rather than a single optional post-processing feature.",{},{"id":871,"data":1480,"type":544,"tunes":1482},{"text":1481},"A developer could use ML for textures, materials, lighting, reconstruction or other local functions without exposing each one as a consumer-facing AI toggle.",{},{"id":876,"data":1484,"type":749,"tunes":1489},{"url":1485,"title":1486,"excerpt":1487,"ctaLabel":1488},"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 3D-Guided Neural Rendering Actually Changes","How neural rendering is moving beyond upscaling and generated frames into material and lighting reconstruction inside the graphics pipeline.","Read the DLSS 5 neural-rendering guide",{},{"id":884,"data":1491,"type":572,"tunes":1493},{"text":1492,"level":47},"The Neural Rendering Stack is becoming layered",{},{"id":889,"data":1495,"type":612,"tunes":1513},{"steps":1496,"title":1512,"orientation":611},[1497,1500,1503,1506,1509],{"label":1498,"description":1499},"Traditional engine work","Simulation, geometry, visibility and base rendering remain standard engine responsibilities.",{"label":1501,"description":1502},"Inline neural shaders","Small learned functions reconstruct textures, materials or lighting inside HLSL.",{"label":1504,"description":1505},"Larger ML graphs","Full reconstruction or inference models execute through a graph-level DirectX path.",{"label":1507,"description":1508},"Vendor hardware mapping","Drivers map common DirectX operations onto the GPU's specialized AI and matrix units.",{"label":1510,"description":1511},"PIX visibility","Graphics and ML work can be profiled together instead of living in separate opaque runtimes.","A possible future DirectX game pipeline",{},{"id":910,"data":1515,"type":572,"tunes":1517},{"text":1516,"level":47},"Why unified profiling matters",{},{"id":915,"data":1519,"type":544,"tunes":1521},{"text":1520},"A neural workload that improves image quality can still damage a game if it unexpectedly consumes frame time, memory bandwidth or VRAM.",{},{"id":920,"data":1523,"type":544,"tunes":1525},{"text":1524},"Microsoft explicitly includes unified PIX visibility as part of the Compute Graph Compiler direction. That matters because developers need to see graphics and ML work in the same frame capture.",{},{"id":925,"data":1527,"type":544,"tunes":1529},{"text":1528},"If neural rendering becomes infrastructure, it must be measurable like any other rendering stage.",{},{"id":930,"data":1531,"type":572,"tunes":1533},{"text":1532,"level":47},"This does not mean every shader will become a neural network",{},{"id":935,"data":1535,"type":544,"tunes":1537},{"text":1536},"Traditional shader math remains efficient, deterministic and easy to reason about for many workloads.",{},{"id":940,"data":1539,"type":544,"tunes":1541},{"text":1540},"A neural function makes sense when it can approximate or reconstruct something expensive more efficiently, compress data, or produce quality that would otherwise require too much conventional compute or memory.",{},{"id":945,"data":1543,"type":544,"tunes":1545},{"text":1544},"The right architecture will remain hybrid.",{},{"id":950,"data":1547,"type":572,"tunes":1549},{"text":1548,"level":47},"The Neural Shader Value Test",{},{"id":955,"data":1551,"type":612,"tunes":1572},{"steps":1552,"title":1571,"orientation":611},[1553,1556,1559,1562,1565,1568],{"label":1554,"description":1555},"1. Identify the expensive traditional operation","What compute, bandwidth, memory or storage cost are you trying to replace?",{"label":1557,"description":1558},"2. Define the neural substitute","What can a small model reconstruct, predict or compress?",{"label":1560,"description":1561},"3. Measure inference cost","The ML model itself consumes GPU time, memory and bandwidth.",{"label":1563,"description":1564},"4. Measure quality stability","Look for temporal artifacts, reconstruction errors and failure cases.",{"label":1566,"description":1567},"5. Measure net savings","The technique is useful only if the avoided traditional cost is worth the added ML cost.",{"label":1569,"description":1570},"6. Test across vendors","A DirectX abstraction helps portability, but real hardware performance still differs.","When does putting ML into the rendering pipeline actually make sense?",{},{"id":979,"data":1574,"type":572,"tunes":1576},{"text":1575,"level":47},"What this means for gamers",{},{"id":984,"data":1578,"type":544,"tunes":1580},{"text":1579},"Gamers may never see a “DX Linear Algebra” switch in a graphics menu.",{},{"id":989,"data":1582,"type":544,"tunes":1584},{"text":1583},"The likely impact is indirect: smaller texture footprints, better lighting reconstruction, more efficient neural graphics, or new visual techniques that become practical because the engine can access matrix acceleration through a standard path.",{},{"id":994,"data":1586,"type":544,"tunes":1588},{"text":1587},"The feature is more important as infrastructure than as a brand.",{},{"id":999,"data":1590,"type":572,"tunes":1592},{"text":1591,"level":47},"What would change this answer?",{},{"id":1004,"data":1594,"type":544,"tunes":1596},{"text":1595},"The biggest uncertainty is the final retail shape of these APIs. DX Linear Algebra remains in preview, and the Compute Graph Compiler has not yet reached broad retail availability.",{},{"id":1009,"data":1598,"type":544,"tunes":1600},{"text":1599},"Final hardware support, API details, compiler behavior and engine adoption may change before these systems become normal shipping-game infrastructure.",{},{"id":1014,"data":1602,"type":544,"tunes":1604},{"text":1603},"If major engines adopt the abstractions directly, neural shaders could become much more common without individual game teams implementing every technique from scratch.",{},{"id":1019,"data":1606,"type":572,"tunes":1608},{"text":1607,"level":47},"Limitations",{},{"id":1024,"data":1610,"type":544,"tunes":1612},{"text":1611},"This article explains Microsoft's documented DirectX architecture and preview APIs. It does not claim that DX Linear Algebra currently improves performance in every game or that the Compute Graph Compiler is a finished retail product.",{},{"id":1029,"data":1614,"type":544,"tunes":1616},{"text":1615},"Microsoft's examples describe capability and intended use. Real benefits depend on the model, engine integration, GPU architecture, drivers and workload.",{},{"id":1034,"data":1618,"type":572,"tunes":1620},{"text":1619,"level":47},"Conclusion",{},{"id":1039,"data":1622,"type":544,"tunes":1624},{"text":1623},"The important DirectX change is not another checkbox called AI.",{},{"id":1044,"data":1626,"type":544,"tunes":1628},{"text":1627},"It is that machine-learning math is moving into the graphics programming model itself. Small neural functions can sit inside shaders, larger models can move toward graph-level compilation, and the same DirectX toolchain can expose those workloads across multiple GPU vendors.",{},{"id":1049,"data":1630,"type":544,"tunes":1632},{"text":1631},"That is how neural rendering stops being one branded feature and starts becoming part of the rendering infrastructure.",{},{"id":1054,"data":1634,"type":572,"tunes":1636},{"text":1635,"level":47},"FAQ",{},{"id":1059,"data":1638,"type":1059,"tunes":1659},{"items":1639,"title":1658},[1640,1643,1646,1649,1652,1655],{"id":1063,"answer":1641,"question":1642},"It is a DirectX\u002FHLSL feature set for hardware-accelerated vector and matrix operations used by machine learning, neural rendering and image-processing workloads.","What is DirectX Linear Algebra?",{"id":1067,"answer":1644,"question":1645},"A useful plain-English description is a shader that includes a learned neural function or ML inference step as part of its graphics work.","What is a neural shader?",{"id":1071,"answer":1647,"question":1648},"As of September 2026 it remains in the Shader Model 6.10 \u002F Agility SDK preview path.","Is DX Linear Algebra already a normal retail DirectX feature?",{"id":1075,"answer":1650,"question":1651},"It is Microsoft's announced model-level ML compiler API intended to take larger computation graphs and lower them into optimized GPU workloads integrated with D3D12.","What is the DirectX Compute Graph Compiler?",{"id":1079,"answer":1653,"question":1654},"Small functions fit shader-level authoring well, while large models benefit from whole-graph optimization, memory planning and operator fusion.","Why not run every ML model directly in HLSL?",{"id":1083,"answer":1656,"question":1657},"Not directly. DirectX provides lower-level infrastructure that vendors and developers can use for neural graphics. Branded technologies can still implement their own models and integration strategies.","Will this replace DLSS or FSR?","DirectX Linear Algebra and neural shaders",{},{"id":1089,"data":1661,"type":572,"tunes":1663},{"text":1662,"level":47},"Glossary",{},{"id":1094,"data":1665,"type":1094,"tunes":1686},{"title":1666,"entries":1667},"Key DirectX ML terms",[1668,1671,1673,1675,1677,1680,1683],{"term":1669,"anchor":1100,"definition":1670},"DX Linear Algebra","DirectX\u002FHLSL APIs for accelerated vector and matrix operations intended for neural rendering, ML and image-processing workloads.",{"term":1103,"anchor":1104,"definition":1672},"An earlier DirectX approach for accelerated vector-matrix operations inside shaders that helped establish the shader-level neural-rendering path.",{"term":1107,"anchor":1108,"definition":1674},"The preview shader-model generation containing the current DirectX Linear Algebra matrix APIs.",{"term":1111,"anchor":1112,"definition":1676},"Microsoft's announced compiler API for optimizing and executing larger ML computation graphs as native DirectX GPU workloads.",{"term":1678,"anchor":1116,"definition":1679},"Neural shader","A practical term for a shader that performs learned inference or neural computation as part of graphics processing.",{"term":1681,"anchor":1120,"definition":1682},"Shader-or-Graph Boundary","A Figure Rocks model for deciding whether an ML workload belongs as small inline shader math or as a larger model-level computation graph.",{"term":1684,"anchor":1123,"definition":1685},"Neural Shader Value Test","A Figure Rocks workflow for deciding whether the traditional compute or memory cost avoided by a neural technique is worth its inference cost and quality trade-offs.",{},{"id":1127,"data":1688,"type":572,"tunes":1690},{"text":1689,"level":47},"Primary sources",{},{"id":1132,"data":1692,"type":1139,"tunes":1696},{"link":1134,"meta":1693},{"image":1694,"title":1137,"description":1695},{"url":13},"Official GDC 2026 architecture overview covering shader-level ML, DX Linear Algebra and the DirectX Compute Graph Compiler.",{},{"id":1142,"data":1698,"type":1139,"tunes":1702},{"link":1144,"meta":1699},{"image":1700,"title":1147,"description":1701},{"url":13},"Official April 2026 preview explaining the unified Linear Algebra APIs, matrix operations and neural-rendering motivation.",{},{"id":1151,"data":1704,"type":1139,"tunes":1708},{"link":1153,"meta":1705},{"image":1706,"title":1156,"description":1707},{"url":13},"Official May 2026 release documenting Shader Model 6.10 Linear Algebra updates and preview hardware support.",{},{"id":1160,"data":1710,"type":1139,"tunes":1714},{"link":1162,"meta":1711},{"image":1712,"title":1165,"description":1713},{"url":13},"Official Microsoft Game Dev summary explaining shader-level and model-level ML and the role of the Compute Graph Compiler.",{},{"id":1169,"data":1716,"type":1139,"tunes":1720},{"link":1171,"meta":1717},{"image":1718,"title":1174,"description":1719},{"url":13},"Official background on hardware-accelerated vector\u002Fmatrix operations and neural rendering directly from shader threads.",{},"2.31.6","DirectX is moving beyond traditional graphics shaders. Microsoft is adding hardware-accelerated linear algebra directly to HLSL and a separate path for larger ML models, laying the foundation for neural textures, learned materials, neural lighting and other AI-driven rendering 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Koristite ovaj jednostavan metod da izaberete čistu srednju postavku koja smanjuje zamućenje bez ghosting artefakata.","2026-02-21T03:30:00.000Z",{"id":2071,"slug":2072,"title":2073,"excerpt":2074,"featuredImage":2075,"publishedAt":2076},"445","amd-fsr-redstone-is-not-just-fsr-4-upscaling-frame-generation-ray-regeneration-and-radiance-caching-explained","AMD FSR Redstone nije samo FSR 4: Objašnjeni nadogradnja rezolucije, generisanje kadrova, regeneracija zraka i keširanje radijanse","AMD-ovo FSR imenovanje je promenjeno jer FSR više nije jedna funkcija. Redstone je sada paket za neuronsko renderovanje sa odvojenim tehnologijama za skaliranje, generisanje frejmova, rekonstrukciju putem praćenja zraka i naučeno globalno osvetljenje.","\u002Fuploads\u002F2026\u002F09\u002Famd-fsr-redstone-is-not-just-fsr-4-upscaling-frame-generation-ray-regeneration-and-radiance-caching-explained-1790377725715-arwmri.webp","2026-09-25T15:07:00.000Z",{"id":2078,"slug":2079,"title":2080,"excerpt":2081,"featuredImage":2082,"publishedAt":2083},"451","windows-auto-sr-is-not-dlss-how-npu-upscaling-works-without-game-integration","Windows Auto SR nije DLSS: Kako NPU skaliranje funkcioniše bez integracije u igre","Windows Auto SR može da poveća rezoluciju podržanih igara bez DLSS, FSR ili XeSS integracije. Umesto pokretanja modela rekonstrukcije unutar igre na GPU-u, Windows koristi NPU da ponovo izgradi sliku više rezolucije iz rendera niže rezolucije.","\u002Fuploads\u002F2026\u002F09\u002Fwindows-auto-sr-is-not-dlss-how-npu-upscaling-works-without-game-integration-1790406942266-77ihme.webp","2026-09-26T03:14:00.000Z",{"id":2085,"slug":2086,"title":2087,"excerpt":2088,"featuredImage":2089,"publishedAt":2090},"444","pubg-ally-shows-why-ai-teammates-need-two-brains-fast-reflexes-and-slow-reasoning","PUBG saveznik pokazuje zašto AI saigračima trebaju dva mozga: brzi refleksi i sporo rezonovanje","Jezički model može da razume taktiku i nameru igrača, ali ne bi trebalo direktno da kontroliše svaki pokret i borbenu reakciju. 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Koristite ovu kontrolnu listu da dijagnostikujete zašto 120Hz deluje lošije: pogrešan režim, pogrešna putanja osvežavanja, problemi sa VRR opsegom ili nedostajuća ograničenja.","2026-02-20T20:30:00.000Z",{"id":2098,"slug":2099,"title":2100,"excerpt":2101,"featuredImage":14,"publishedAt":2102},"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","fallback",[],[]]