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NVIDIA RTX Neural Texture Compression menja taj model: deo podataka teksture postaje mala neuronska reprezentacija koju može da dekodira sam GPU.\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>RTX Neural Texture Compression nije nadgradnja tekstura.\u003C\u002Fstrong> On kompresuje više tekstura materijala u težine neuronske mreže plus kompaktne latentne podatke, a zatim rekonstruiše tražene vrednosti teksture pomoću male neuronske mreže. U zavisnosti od načina integracije, igra može da razmeni skladištenje i korišćenje VRAM-a za dodatni rad GPU inferencije.\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\">RTX Neural Texture Compression je još uvek \u003Cstrong>beta SDK\u003C\u002Fstrong>. Trenutna beta verzija v0.10.0 dodala je podršku za DirectX 12 Linear Algebra inferenciju, povezujući NTC direktno sa novom infrastrukturom neuronskih šejdera o kojoj se govori na drugim mestima na Figure Rocks.\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 normalna kompresija tekstura i dalje koristi mnogo memorije\u003C\u002Fa>\u003C\u002Fli>\u003Cli class=\"editorjs-toc__item\">\u003Ca href=\"#section-9\" class=\"editorjs-toc__link\">Šta RTX Neural Texture Compression zapravo čuva\u003C\u002Fa>\u003C\u002Fli>\u003Cli class=\"editorjs-toc__item\">\u003Ca href=\"#section-13\" class=\"editorjs-toc__link\">Zašto kompresija kanala zajedno može da pomogne\u003C\u002Fa>\u003C\u002Fli>\u003Cli class=\"editorjs-toc__item\">\u003Ca href=\"#section-17\" class=\"editorjs-toc__link\">Tri NTC režima izvršavanja su ključ za razumevanje tehnologije\u003C\u002Fa>\u003C\u002Fli>\u003Cli class=\"editorjs-toc__item\">\u003Ca href=\"#section-20\" class=\"editorjs-toc__link\">Zaključivanje pri učitavanju: neuronska kompresija kao format za skladištenje\u003C\u002Fa>\u003C\u002Fli>\u003Cli class=\"editorjs-toc__item\">\u003Ca href=\"#section-25\" class=\"editorjs-toc__link\">Zaključivanje na uzorku: zadržite teksturu neuronskom u VRAM-u\u003C\u002Fa>\u003C\u002Fli>\u003Cli class=\"editorjs-toc__item\">\u003Ca href=\"#section-32\" class=\"editorjs-toc__link\">Zaključivanje na povratnoj informaciji: dekodirajte samo ono što igrač zaista vidi\u003C\u002Fa>\u003C\u002Fli>\u003Cli class=\"editorjs-toc__item\">\u003Ca href=\"#section-37\" class=\"editorjs-toc__link\">Razmena između skladištenja tekstura i računanja\u003C\u002Fa>\u003C\u002Fli>\u003Cli class=\"editorjs-toc__item\">\u003Ca href=\"#section-40\" class=\"editorjs-toc__link\">Zašto je specijalizovani matrični hardver važan\u003C\u002Fa>\u003C\u002Fli>\u003Cli class=\"editorjs-toc__item\">\u003Ca href=\"#section-45\" class=\"editorjs-toc__link\">Zašto neuronska kompresija tekstura nije nadogradnja tekstura\u003C\u002Fa>\u003C\u002Fli>\u003Cli class=\"editorjs-toc__item\">\u003Ca href=\"#section-49\" class=\"editorjs-toc__link\">Kontrola kvaliteta je broj bitova po pikselu\u003C\u002Fa>\u003C\u002Fli>\u003Cli class=\"editorjs-toc__item\">\u003Ca href=\"#section-54\" class=\"editorjs-toc__link\">Zašto su korelisani kanali materijala važni\u003C\u002Fa>\u003C\u002Fli>\u003Cli class=\"editorjs-toc__item\">\u003Ca href=\"#section-58\" class=\"editorjs-toc__link\">Test vrednosti neuronske teksture\u003C\u002Fa>\u003C\u002Fli>\u003Cli class=\"editorjs-toc__item\">\u003Ca href=\"#section-60\" class=\"editorjs-toc__link\">Zašto „8× manje“ zahteva kontekst\u003C\u002Fa>\u003C\u002Fli>\u003Cli class=\"editorjs-toc__item\">\u003Ca href=\"#section-64\" class=\"editorjs-toc__link\">Veličina dekodera je još jedan kompromis između performansi i kvaliteta\u003C\u002Fa>\u003C\u002Fli>\u003Cli class=\"editorjs-toc__item\">\u003Ca href=\"#section-68\" class=\"editorjs-toc__link\">Zašto je ovo važno za buduće instalacije igara\u003C\u002Fa>\u003C\u002Fli>\u003Cli class=\"editorjs-toc__item\">\u003Ca href=\"#section-72\" class=\"editorjs-toc__link\">To takođe menja značenje pojma „memorija teksture“\u003C\u002Fa>\u003C\u002Fli>\u003Cli class=\"editorjs-toc__item\">\u003Ca href=\"#section-76\" class=\"editorjs-toc__link\">Podrška različitih proizvođača je slojevitija nego što ime RTX sugeriše\u003C\u002Fa>\u003C\u002Fli>\u003Cli class=\"editorjs-toc__item\">\u003Ca href=\"#section-80\" class=\"editorjs-toc__link\">Šta bi promenilo ovaj odgovor?\u003C\u002Fa>\u003C\u002Fli>\u003Cli class=\"editorjs-toc__item\">\u003Ca href=\"#section-83\" class=\"editorjs-toc__link\">Ograničenja\u003C\u002Fa>\u003C\u002Fli>\u003Cli class=\"editorjs-toc__item\">\u003Ca href=\"#section-86\" class=\"editorjs-toc__link\">Zaključak\u003C\u002Fa>\u003C\u002Fli>\u003Cli class=\"editorjs-toc__item\">\u003Ca href=\"#section-91\" class=\"editorjs-toc__link\">Često postavljana pitanja\u003C\u002Fa>\u003C\u002Fli>\u003Cli class=\"editorjs-toc__item\">\u003Ca href=\"#section-93\" class=\"editorjs-toc__link\">Pojmovnik\u003C\u002Fa>\u003C\u002Fli>\u003Cli class=\"editorjs-toc__item\">\u003Ca href=\"#section-95\" class=\"editorjs-toc__link\">Primarni izvori\u003C\u002Fa>\u003C\u002Fli>\u003C\u002Fol>\u003C\u002Fnav>\n\u003Ch2 id=\"section-5\">Zašto normalna kompresija tekstura i dalje koristi mnogo memorije\u003C\u002Fh2>\n\u003Cp>Moderan materijal zasnovan na fizici retko se sastoji od jedne slike. Jedna površina može da koristi albedo, normalu, hrapavost, metalnost, ambijentalnu okluziju, neprozirnost i druge kanale.\u003C\u002Fp>\n\u003Cp>Tradicionalni GPU formati blokovske kompresije kao što su BC1 do BC7 smanjuju troškove, ali GPU i dalje na kraju čuva konvencionalne blokove tekstura za materijal.\u003C\u002Fp>\n\u003Cp>Kako se rezolucija tekstura i složenost materijala povećavaju, ti kanali troše prostor na disku, propusni opseg za strimovanje i GPU memoriju.\u003C\u002Fp>\n\u003Ch2 id=\"section-9\">Šta RTX Neural Texture Compression zapravo čuva\u003C\u002Fh2>\n\u003Cp>NVIDIA RTXNTC SDK kompresuje kanale koji pripadaju jednom materijalu zajedno. Trenutni SDK podržava do 16 kanala tekstura u jednom NTC setu tekstura.\u003C\u002Fp>\n\u003Cp>Umesto da čuva samo konvencionalne kompresovane teksture, proces kompresije proizvodi dve glavne stvari: težine za mali neuronski dekoder i kompaktne latentne podatke o karakteristikama.\u003C\u002Fp>\n\u003Csection class=\"editorjs-process my-6\">\u003Ch3 class=\"mb-3 text-lg font-semibold\">Neuronski pipeline tekstura\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. Originalne teksture materijala\u003C\u002Fdiv>\u003Cdiv class=\"mt-1 text-sm text-gray-600 dark:text-gray-300\">Albedo, normala, hrapavost, metalnost i drugi kanali materijala se obezbeđuju zajedno.\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. Offline kompresija\u003C\u002Fdiv>\u003Cdiv class=\"mt-1 text-sm text-gray-600 dark:text-gray-300\">SDK uči kompaktnu reprezentaciju materijala.\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. Neuronske težine\u003C\u002Fdiv>\u003Cdiv class=\"mt-1 text-sm text-gray-600 dark:text-gray-300\">Mala mreža dekodera čuva deo onoga što je potrebno za rekonstrukciju materijala.\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. Latentni podaci\u003C\u002Fdiv>\u003Cdiv class=\"mt-1 text-sm text-gray-600 dark:text-gray-300\">Kompaktni tenzori karakteristika čuvaju informacije specifične za materijal.\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. GPU inferencija\u003C\u002Fdiv>\u003Cdiv class=\"mt-1 text-sm text-gray-600 dark:text-gray-300\">U vreme izvršavanja, dekoder kombinuje latentne podatke i neuronske težine da rekonstruiše vrednosti teksture.\u003C\u002Fdiv>\u003C\u002Fdiv>\u003C\u002Fdiv>\u003C\u002Fsection>\n\u003Ch2 id=\"section-13\">Zašto kompresija kanala zajedno može da pomogne\u003C\u002Fh2>\n\u003Cp>Kanali materijala su često povezani. Ogrebotina vidljiva u osnovnoj boji može se pojaviti i u mapi normale ili hrapavosti. Uzorak tkanine može uticati na nekoliko kanala na istoj prostornoj lokaciji.\u003C\u002Fp>\n\u003Cp>NVIDIA je dizajnirala NTC da iskoristi te korelacije umesto da kompresuje svaku teksturu nezavisno.\u003C\u002Fp>\n\u003Cp>To je jedan od razloga zašto se tehnologija opisuje kao kompresija orijentisana na materijal, a ne samo kao još jedan format slike.\u003C\u002Fp>\n\u003Ch2 id=\"section-17\">Tri NTC režima izvršavanja su ključ za razumevanje tehnologije\u003C\u002Fh2>\n\u003Cp>Najvažniji deo RTXNTC nije samo kako se materijal kompresuje. Već kada igra odluči da ga dekompresuje.\u003C\u002Fp>\n\u003Csection class=\"editorjs-comparison my-6\">\u003Ch3 class=\"mb-3 text-lg font-semibold\">Zaključivanje pri učitavanju vs Zaključivanje na uzorku vs Zaključivanje na povratnoj informaciji\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\">Kada se dešava neuronsko dekodiranje\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\">Ponašanje teksturne memorije u vreme izvršavanja\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\">Glavni kompromis\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\">Zaključivanje pri učitavanju\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>\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\">Zaključivanje na uzorku\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>\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\">Zaključivanje na povratnoj informaciji\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>\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-20\">Zaključivanje pri učitavanju: neuronska kompresija kao format za skladištenje\u003C\u002Fh2>\n\u003Cp>Zaključivanje pri učitavanju je najlakši režim za razumevanje.\u003C\u002Fp>\n\u003Cp>Igra čuva materijal u kompaktnom NTC obliku. Kada se resurs učita, GPU rekonstruiše podatke teksture i može ih transkodirati u obične BCn formate tekstura.\u003C\u002Fp>\n\u003Cp>Nakon tog koraka, renderovanje može da koristi normalno uzorkovanje tekstura. Važna ušteda je prvenstveno pre dekompresije: veličina upakovane igre, veličina preuzimanja ili propusni opseg strimovanja resursa.\u003C\u002Fp>\n\u003Cp>Ali kada se materijal u potpunosti razvije u konvencionalne teksture, njegov zauzeće VRAM-a u vreme izvršavanja ponovo približava konvencionalnoj reprezentaciji.\u003C\u002Fp>\n\u003Ch2 id=\"section-25\">Zaključivanje na uzorku: zadržite teksturu neuronskom u VRAM-u\u003C\u002Fh2>\n\u003Cp>Zaključivanje na uzorku je radikalniji režim.\u003C\u002Fp>\n\u003Cp>Umesto da se materijal razvije u konvencionalne teksture pre renderovanja, šejder čita kompaktne latentne podatke i pokreće neuronski dekoder kada su mu potrebne vrednosti teksture.\u003C\u002Fp>\n\u003Cp>NVIDIA-in sopstveni primer SDK-a upoređuje 12 MB BCn reprezentaciju materijala sa 2,5 MB NTC reprezentacijom kada se koristi Zaključivanje na uzorku.\u003C\u002Fp>\n\u003Cp>Ušteda je stvarna jer konvencionalni podaci teksture ne moraju ostati potpuno rezidentni. Ali trošak se premešta negde drugde: piksel ili hit šejder sada obavlja neuronsko zaključivanje.\u003C\u002Fp>\n\u003Caside class=\"editorjs-callout editorjs-callout--warning my-6 rounded-xl border p-5 border-amber-300 bg-amber-50 dark:border-amber-900 dark:bg-amber-950\u002F20\" role=\"note\">\u003Cstrong class=\"block mb-2 text-gray-900 dark:text-gray-100\">Kompresija ne čini da posao nestane\u003C\u002Fstrong>\u003Cdiv class=\"text-gray-700 dark:text-gray-200\">Zaključivanje na uzorku menja \u003Cstrong>memoriju i propusni opseg\u003C\u002Fstrong> za \u003Cstrong>GPU računanje\u003C\u002Fstrong>. Pravo pitanje nije „Koliko je tekstura manja?“ već „Da li je ušteda memorije vredna dodatnog troška zaključivanja na ovom radnom opterećenju?“\u003C\u002Fdiv>\u003C\u002Faside>\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 merač memorije ne govori celu priču\u003C\u002Fstrong>\u003Cp class=\"mt-2 text-sm text-gray-600 dark:text-gray-300\">Zašto su kapacitet VRAM-a, budžeti, rezidentnost i stvarni pritisak na memoriju različite stvari.\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-32\">Zaključivanje na povratnoj informaciji: dekodirajte samo ono što igrač zaista vidi\u003C\u002Fh2>\n\u003Cp>Zaključivanje na povratnoj informaciji nalazi se između dve krajnosti.\u003C\u002Fp>\n\u003Cp>Renderer prati koji se delovi teksture zaista zahtevaju. Umesto da odmah razvije ceo materijal, sistem može da dekodira zahtevane delove u serijama i održava radni keš.\u003C\u002Fp>\n\u003Cp>Konceptualno, ovo kombinuje neuronsku kompresiju sa strimovanjem tekstura: sistem plaća trošak dekodiranja samo za regione koji postanu relevantni.\u003C\u002Fp>\n\u003Cp>Trenutna implementacija primera je specijalizovanija od ostalih režima i njena ograničenja podrške se razlikuju, pa je treba tretirati kao strategiju integracije, a ne kao univerzalnu zamenu za obično strimovanje tekstura.\u003C\u002Fp>\n\u003Ch2 id=\"section-37\">Razmena između skladištenja tekstura i računanja\u003C\u002Fh2>\n\u003Cp>Najlakši način da se razume neuronska kompresija tekstura jeste kao razmena između resursa.\u003C\u002Fp>\n\u003Csection class=\"editorjs-comparison my-6\">\u003Ch3 class=\"mb-3 text-lg font-semibold\">Šta se menja kada skladištenje tekstura postane neuronsko\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\">Tradicionalna kompresovana tekstura\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\">Neuronska reprezentacija teksture\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\">Disk\u002Fskladištenje\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\">VRAM\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\">Cena uzorkovanja\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\">Kontrola kvaliteta\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-40\">Zašto je specijalizovani matrični hardver važan\u003C\u002Fh2>\n\u003Cp>Pokretanje neuronske mreže za uzorkovanje tekstura bilo bi preskupo ako bi se svako množenje moralo obrađivati kao običan skalarni rad senčera.\u003C\u002Fp>\n\u003Cp>RTXNTC stoga ima koristi od Cooperative Vector i sada DirectX 12 Linear Algebra putanja koje omogućavaju senčerima da koriste GPU hardver za matrično ubrzanje.\u003C\u002Fp>\n\u003Cp>Beta verzija v0.10.0 eksplicitno je dodala inferenciju kroz DirectX 12 Linear Algebra API predstavljen sa Shader Model 6.10.\u003C\u002Fp>\n\u003Caside class=\"editorjs-referral my-6\">\u003Ca href=\"https:\u002F\u002Ffigure.rocks\u002Fsr\u002Fblog\u002Fdirectx-is-becoming-an-ml-platform-what-linear-algebra-and-neural-shaders-mean-for-future-games\" 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\">DirectX postaje ML platforma: šta linearna algebra i neuronski senčeri znače za buduće igre\u003C\u002Fstrong>\u003Cp class=\"mt-2 text-sm text-gray-600 dark:text-gray-300\">Kako DirectX pomera matrične i neuronske operacije direktno u HLSL i grafički pipeline.\u003C\u002Fp>\u003Cspan class=\"mt-3 inline-flex text-sm font-medium text-primary-600 dark:text-primary-400\">Pročitajte vodič za DirectX neuronske senčere →\u003C\u002Fspan>\u003C\u002Fdiv>\u003C\u002Fa>\u003C\u002Faside>\n\u003Ch2 id=\"section-45\">Zašto neuronska kompresija tekstura nije nadogradnja tekstura\u003C\u002Fh2>\n\u003Cp>Obe tehnike mogu da koriste mašinsko učenje, ali rešavaju različite probleme.\u003C\u002Fp>\n\u003Csection class=\"editorjs-comparison my-6\">\u003Ch3 class=\"mb-3 text-lg font-semibold\">Neuronska kompresija tekstura naspram super rezolucije\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\">Neuronska kompresija tekstura\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\">Super rezolucija\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\">Ulaz\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\">Cilj\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\">Kada se izvršava\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\">Izlaz\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\u003Cp>Neuronska kompresija tekstura stoga može da postoji ispod DLSS, FSR, XeSS ili nativnog renderovanja. Ona menja način na koji se podaci o materijalima čuvaju i rekonstruišu, a ne konačnu rezoluciju prikaza.\u003C\u002Fp>\n\u003Ch2 id=\"section-49\">Kontrola kvaliteta je broj bitova po pikselu\u003C\u002Fh2>\n\u003Cp>NTC je kompresija sa gubicima. Količina kompresovanih informacija kontroliše se uglavnom putem podešavanja broja bitova po pikselu.\u003C\u002Fp>\n\u003Cp>Veći bitrate daje modelu više informacija i generalno poboljšava kvalitet rekonstrukcije. Manji bitrate poboljšava kompresiju, ali povećava rizik od vidljive greške.\u003C\u002Fp>\n\u003Cp>Pošto više kanala deli istu reprezentaciju, dodavanje više kanala materijala bez povećanja bitrate-a može smanjiti kvalitet dostupan svakom kanalu.\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\">Neuronsko ne znači bez gubitaka\u003C\u002Fstrong>\u003Cdiv class=\"text-gray-700 dark:text-gray-200\">Dokumentacija SDK eksplicitno napominje da je greška kompresije normalna. Praktični cilj je \u003Cstrong>prihvatljiva vizuelna greška uz korisnu cenu skladištenja i izvršavanja\u003C\u002Fstrong>.\u003C\u002Fdiv>\u003C\u002Faside>\n\u003Ch2 id=\"section-54\">Zašto su korelisani kanali materijala važni\u003C\u002Fh2>\n\u003Cp>Neuronska reprezentacija postaje vrednija kada nekoliko materijalnih kanala opisuje povezanu strukturu.\u003C\u002Fp>\n\u003Cp>Ako albedo, normala i hrapavost sadrže iste ogrebotine, šavove ili teksturu tkanja, dekoder može da iskoristi deljene prostorne informacije.\u003C\u002Fp>\n\u003Cp>Ako su kanali nepovezani šum, postoji manje zajedničke strukture za iskorišćavanje i problem kompresije postaje teži.\u003C\u002Fp>\n\u003Ch2 id=\"section-58\">Test vrednosti neuronske teksture\u003C\u002Fh2>\n\u003Csection class=\"editorjs-process my-6\">\u003Ch3 class=\"mb-3 text-lg font-semibold\">Kada je neuronska kompresija tekstura zapravo korisna?\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. Izmerite konvencionalnu cenu teksture\u003C\u002Fdiv>\u003Cdiv class=\"mt-1 text-sm text-gray-600 dark:text-gray-300\">Koliko prostora na disku, propusnog opsega za strimovanje i VRAM-a troše postojeće teksture materijala?\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. Izaberite režim rada\u003C\u002Fdiv>\u003Cdiv class=\"mt-1 text-sm text-gray-600 dark:text-gray-300\">Da li želite samo uštedu u skladištenju, trajnu uštedu VRAM-a ili rekonstrukciju strimovanih pločica?\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. Postavite prihvatljiv cilj kvaliteta\u003C\u002Fdiv>\u003Cdiv class=\"mt-1 text-sm text-gray-600 dark:text-gray-300\">Uporedite rekonstruisane kanale sa originalnim materijalom, ne samo sa finalnom slikom.\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 cenu inferencije\u003C\u002Fdiv>\u003Cdiv class=\"mt-1 text-sm text-gray-600 dark:text-gray-300\">Zabeležite dodatno vreme šejdera ili dekompresije na stvarnom ciljnom GPU-u.\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 uštedu memorije\u003C\u002Fdiv>\u003Cdiv class=\"mt-1 text-sm text-gray-600 dark:text-gray-300\">Proverite stvarni radni set u vreme izvršavanja, a ne samo veličinu kompresovanog fajla.\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 teške materijale\u003C\u002Fdiv>\u003Cdiv class=\"mt-1 text-sm text-gray-600 dark:text-gray-300\">Fine normale, oštre maske, neprozirnost, tekst i nepovezani kanali mogu da otkriju neuspehe kompresije.\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\">7\u003C\u002Fdiv>\u003Cdiv class=\"mt-1 font-semibold text-gray-900 dark:text-gray-100\">7. Odlučite na osnovu neto koristi\u003C\u002Fdiv>\u003Cdiv class=\"mt-1 text-sm text-gray-600 dark:text-gray-300\">Koristite NTC samo tamo gde su uštede memorije\u002Fpropusnog opsega vredne dodatne složenosti inferencije i integracije.\u003C\u002Fdiv>\u003C\u002Fdiv>\u003C\u002Fdiv>\u003C\u002Fsection>\n\u003Ch2 id=\"section-60\">Zašto „8× manje“ zahteva kontekst\u003C\u002Fh2>\n\u003Cp>NVIDIA RTX Kit opisuje RTX neuronsku kompresiju tekstura kao rešenje koje nudi do 8× poboljšanje disk-memorije uz vizuelnu vernost sličnu tradicionalnoj blokovskoj kompresiji.\u003C\u002Fp>\n\u003Cp>Fraza „do“ je važna. Odnos kompresije zavisi od materijala, broja kanala, ciljnog bitrate-a, konfiguracije dekodera i praga kvaliteta.\u003C\u002Fp>\n\u003Cp>Isti odnos takođe ne opisuje automatski uštedu VRAM-a. Inferencija pri učitavanju može da počne od kompaktnog fajla i da se ipak proširi u konvencionalne GPU teksture. Inferencija pri uzorkovanju čuva kompaktnu reprezentaciju u GPU memoriji, ali troši više računanja tokom senčenja.\u003C\u002Fp>\n\u003Ch2 id=\"section-64\">Veličina dekodera je još jedan kompromis između performansi i kvaliteta\u003C\u002Fh2>\n\u003Cp>NTC runtime koristi mali višeslojni perceptron za dekodiranje vrednosti teksture.\u003C\u002Fp>\n\u003Cp>NVIDIA-ina trenutna biblioteka koristi konfigurabilnu arhitekturu dekodera. Veće mreže mogu da poboljšaju kvalitet kompresije, ali koštaju više za izvršavanje; manje mreže mogu da rade brže uz određeni gubitak u kvalitetu rekonstrukcije.\u003C\u002Fp>\n\u003Cp>To daje programerima engine-a još jednu dimenziju za podešavanje pored rezolucije teksture i bitrate-a.\u003C\u002Fp>\n\u003Ch2 id=\"section-68\">Zašto je ovo važno za buduće instalacije igara\u003C\u002Fh2>\n\u003Cp>Moderne igre sve više isporučuju skupove materijala visoke rezolucije koji utiču na veličinu preuzimanja, kao i na memoriju u vreme izvršavanja.\u003C\u002Fp>\n\u003Cp>Neuronska kompresija tekstura stvara novu opciju: isporučiti kompaktnu naučenu reprezentaciju i kasnije odlučiti da li da se ona proširi pri učitavanju, rekonstruiše direktno tokom senčenja ili dekodiraju samo tražene pločice.\u003C\u002Fp>\n\u003Cp>To znači da jedna kompresovana reprezentacija sredstva može da učestvuje u nekoliko različitih strategija memorije u vreme izvršavanja.\u003C\u002Fp>\n\u003Ch2 id=\"section-72\">To takođe menja značenje pojma „memorija teksture“\u003C\u002Fh2>\n\u003Cp>Sa tradicionalnim renderovanjem, budžet za memoriju tekstura se uglavnom odnosi na formate tekstura, mip nivoe, rezoluciju i rezidentnost.\u003C\u002Fp>\n\u003Cp>Sa neuronskim teksturama, programeri mogu takođe da planiraju budžet za latentne podatke, težine dekodera, bafere za inferenciju, transkodirane keševe i računarske resurse potrebne za rekonstrukciju traženih vrednosti.\u003C\u002Fp>\n\u003Cp>Tako da sredstvo više nema jedan jednostavan fiksni memorijski identitet.\u003C\u002Fp>\n\u003Ch2 id=\"section-76\">Podrška različitih proizvođača je slojevitija nego što ime RTX sugeriše\u003C\u002Fh2>\n\u003Cp>RTXNTC je NVIDIA SDK, a sama kompresija trenutno zahteva NVIDIA GPU prema zahtevima SDK-a.\u003C\u002Fp>\n\u003Cp>Dekompresija u vreme izvršavanja je šira. NVIDIA dokumentuje funkcionalne putanje na Shader Model 6 hardveru i beleži validaciju na NVIDIA, AMD i Intel GPU-ovima, dok napredne Cooperative Vector \u002F Linear Algebra putanje zavise od podrške API-ja i drajvera.\u003C\u002Fp>\n\u003Cp>Performanse i paritet funkcija stoga ne treba pretpostavljati kod različitih proizvođača samo zato što osnovni dekoder može da se pokrene.\u003C\u002Fp>\n\u003Ch2 id=\"section-80\">Šta bi promenilo ovaj odgovor?\u003C\u002Fh2>\n\u003Cp>NTC je još uvek beta. Režimi izvršavanja, arhitekture dekodera, podrška drajvera i putevi integracije mogu se promeniti pre stabilnog izdanja za produkciju.\u003C\u002Fp>\n\u003Cp>Najveća dugoročna promena bila bi široko usvajanje standardizovanih neuronskih šejderskih primitiva kroz DirectX i Vulkan. To bi učinilo dekodiranje neuronskih tekstura manje zavisnim od prilagođenih izvršnih putanja specifičnih za proizvođača.\u003C\u002Fp>\n\u003Ch2 id=\"section-83\">Ograničenja\u003C\u002Fh2>\n\u003Cp>Ovaj članak opisuje arhitekturu i trenutno javno ponašanje RTXNTC SDK-a. NVIDIA-ine navedene tvrdnje o kompresiji potiču od proizvođača i ne treba ih smatrati garantovanim rezultatima za svaki materijal.\u003C\u002Fp>\n\u003Cp>Trenutni SDK je beta softver, a neke pregledne putanje imaju dokumentovana ograničenja drajvera i platforme.\u003C\u002Fp>\n\u003Ch2 id=\"section-86\">Zaključak\u003C\u002Fh2>\n\u003Cp>RTX Neural Texture Compression je zanimljiv jer menja jednu veoma staru pretpostavku: detalji teksture ne moraju uvek da postoje u memoriji kao konvencionalni tekseli.\u003C\u002Fp>\n\u003Cp>Materijal može umesto toga da se čuva delimično kao kompaktna naučena reprezentacija i rekonstruiše kada je potrebno.\u003C\u002Fp>\n\u003Cp>To ne daje besplatan kvalitet niti besplatnu memoriju. Stvara novu razmenu: manje skladištenja, propusnog opsega i potencijalno VRAM-a u zamenu za rad neuronske inferencije.\u003C\u002Fp>\n\u003Cp>Prava inovacija nije „AI čini teksture oštrijim“. Već to što deo podataka o materijalima u igri može postati računanje.\u003C\u002Fp>\n\u003Ch2 id=\"section-91\">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\">RTX Neural Texture Compression jednostavnim jezikom\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\">Da li je RTX Neural Texture Compression alat za povećanje rezolucije?\u003C\u002Fh4>\u003Cdiv class=\"mt-2 text-gray-600 dark:text-gray-300\">Ne. On kompresuje i rekonstruiše podatke o teksturama materijala. Tehnologije super-rezolucije rekonstruišu finalnu renderovanu sliku na višoj rezoluciji prikaza.\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\">Može li NTC da smanji upotrebu VRAM-a?\u003C\u002Fh4>\u003Cdiv class=\"mt-2 text-gray-600 dark:text-gray-300\">Da, posebno kod Inference on Sample jer kompaktna neuronska reprezentacija može da ostane u GPU memoriji umesto potpuno proširenih konvencionalnih tekstura. Inference on Load uglavnom čuva uštedu u skladištenju pre proširenja.\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\">Šta neuronska mreža čuva?\u003C\u002Fh4>\u003Cdiv class=\"mt-2 text-gray-600 dark:text-gray-300\">Kompresovani materijal sadrži kompaktne latentne podatke o karakteristikama plus težine za malu dekodersku mrežu koja rekonstruiše vrednosti tekstura.\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\">Da li NTC dekodira celu teksturu pre renderovanja?\u003C\u002Fh4>\u003Cdiv class=\"mt-2 text-gray-600 dark:text-gray-300\">Ne nužno. Inference on Load to radi, Inference on Sample rekonstruiše vrednosti tokom šejderskog semplovanja, a Inference on Feedback može da dekodira zahtevane delove teksture.\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\">Da li je neuronska kompresija tekstura bez gubitaka?\u003C\u002Fh4>\u003Cdiv class=\"mt-2 text-gray-600 dark:text-gray-300\">Ne. RTXNTC je kompresija sa gubicima i kvalitet zavisi od bitrate-a, broja kanala, konfiguracije dekodera i samog materijala.\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 je RTXNTC spreman za produkciju?\u003C\u002Fh4>\u003Cdiv class=\"mt-2 text-gray-600 dark:text-gray-300\">Trenutni javni SDK je još uvek označen kao beta, tako da se API-ji, podrška i karakteristike performansi mogu i dalje menjati.\u003C\u002Fdiv>\u003C\u002Fdiv>\u003C\u002Fsection>\n\u003Ch2 id=\"section-93\">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 pojmovi neuronskih tekstura\u003C\u002Fh3>\u003Cdl>\u003Cdiv id=\"neural-texture-compression\" 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\">Neural Texture Compression\u003C\u002Fdt>\u003Cdd class=\"mt-1 text-gray-600 dark:text-gray-300\">Tehnika koja čuva informacije o teksturi kao kompaktne latentne podatke plus težine neuronskog dekodera umesto samo konvencionalnih blokova teksela.\u003C\u002Fdd>\u003C\u002Fdiv>\u003Cdiv id=\"latent-data\" 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\">Latentni podaci\u003C\u002Fdt>\u003Cdd class=\"mt-1 text-gray-600 dark:text-gray-300\">Kompaktne naučene karakteristike koje neuronski dekoder koristi za rekonstrukciju vrednosti tekstura.\u003C\u002Fdd>\u003C\u002Fdiv>\u003Cdiv id=\"decoder\" 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\">Dekoder\u003C\u002Fdt>\u003Cdd class=\"mt-1 text-gray-600 dark:text-gray-300\">Mala neuronska mreža koja pretvara latentne karakteristike u rekonstruisane kanale teksture.\u003C\u002Fdd>\u003C\u002Fdiv>\u003Cdiv id=\"inference-on-load\" 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\">Inference on Load\u003C\u002Fdt>\u003Cdd class=\"mt-1 text-gray-600 dark:text-gray-300\">Režim rada koji dekodira neuronsku teksturu kada se resurs učita, obično u konvencionalne formate tekstura.\u003C\u002Fdd>\u003C\u002Fdiv>\u003Cdiv id=\"inference-on-sample\" 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\">Inference on Sample\u003C\u002Fdt>\u003Cdd class=\"mt-1 text-gray-600 dark:text-gray-300\">Režim rada koji obavlja neuronsko dekodiranje direktno tokom semplovanja teksture tako da kompresovana reprezentacija može da ostane rezidentna.\u003C\u002Fdd>\u003C\u002Fdiv>\u003Cdiv id=\"inference-on-feedback\" 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\">Inference on Feedback\u003C\u002Fdt>\u003Cdd class=\"mt-1 text-gray-600 dark:text-gray-300\">Strategija rada koja koristi povratne informacije o teksturi da dekodira i kešira samo zahtevane delove.\u003C\u002Fdd>\u003C\u002Fdiv>\u003Cdiv id=\"texture-storage-compute-exchange\" 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\">Texture Storage–Compute Exchange\u003C\u002Fdt>\u003Cdd class=\"mt-1 text-gray-600 dark:text-gray-300\">Model Figure Rocks koji opisuje razmenu skladištenja tekstura, propusnog opsega i VRAM-a za dodatni rad GPU neuronske inferencije.\u003C\u002Fdd>\u003C\u002Fdiv>\u003Cdiv id=\"neural-texture-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\">Neural Texture Value Test\u003C\u002Fdt>\u003Cdd class=\"mt-1 text-gray-600 dark:text-gray-300\">Radni tok Figure Rocks za odlučivanje da li neuronska kompresija tekstura stvara neto korist za određeni materijal i ciljni GPU.\u003C\u002Fdd>\u003C\u002Fdiv>\u003C\u002Fdl>\u003C\u002Fsection>\n\u003Ch2 id=\"section-95\">Primarni izvori\u003C\u002Fh2>\n\u003Ca href=\"https:\u002F\u002Fdeveloper.nvidia.com\u002Frtx-kit\" target=\"_blank\" rel=\"noopener noreferrer\" class=\"editorjs-link-tool block border border-gray-200 dark:border-gray-700 rounded-lg p-4 transition text-gray-900 dark:text-gray-100 hover:border-primary-500 hover:bg-primary-50 dark:hover:bg-gray-900 hover:text-gray-900 dark:hover:text-gray-100\">\u003Cstrong class=\"block font-semibold\">NVIDIA Developer — RTX Kit\u003C\u002Fstrong>\u003Cp class=\"text-sm text-gray-600 dark:text-gray-400\">Zvanični pregled RTX Neural Texture Compression i NVIDIA pozicioniranja objavljenog smanjenja skladištenja.\u003C\u002Fp>\u003C\u002Fa>\n\u003Ca href=\"https:\u002F\u002Fgithub.com\u002FNVIDIA-RTX\u002FRTXNTC\u002Fblob\u002Fmain\u002FREADME.md\" target=\"_blank\" rel=\"noopener noreferrer\" class=\"editorjs-link-tool block border border-gray-200 dark:border-gray-700 rounded-lg p-4 transition text-gray-900 dark:text-gray-100 hover:border-primary-500 hover:bg-primary-50 dark:hover:bg-gray-900 hover:text-gray-900 dark:hover:text-gray-100\">\u003Cstrong class=\"block font-semibold\">NVIDIA RTXNTC — SDK README\u003C\u002Fstrong>\u003Cp class=\"text-sm text-gray-600 dark:text-gray-400\">Zvanična SDK dokumentacija koja opisuje kompresiju kanala materijala, reprezentaciju dekodera\u002Flatenta, režime rada, primere memorije, sistemske zahteve i podršku za Cooperative Vector.\u003C\u002Fp>\u003C\u002Fa>\n\u003Ca href=\"https:\u002F\u002Fgithub.com\u002FNVIDIA-RTX\u002FRTXNTC\u002Freleases\" target=\"_blank\" rel=\"noopener noreferrer\" class=\"editorjs-link-tool block border border-gray-200 dark:border-gray-700 rounded-lg p-4 transition text-gray-900 dark:text-gray-100 hover:border-primary-500 hover:bg-primary-50 dark:hover:bg-gray-900 hover:text-gray-900 dark:hover:text-gray-100\">\u003Cstrong class=\"block font-semibold\">NVIDIA RTXNTC — Izdanja\u003C\u002Fstrong>\u003Cp class=\"text-sm text-gray-600 dark:text-gray-400\">Zvanična istorija izdanja, uključujući v0.10.0 beta i podršku za DirectX 12 Linear Algebra inferenciju.\u003C\u002Fp>\u003C\u002Fa>\n\u003Ca href=\"https:\u002F\u002Fgithub.com\u002FNVIDIA-RTX\u002FRTXNTC\u002Fblob\u002Fmain\u002Fdocs\u002FSettingsAndQuality.md\" target=\"_blank\" rel=\"noopener noreferrer\" class=\"editorjs-link-tool block border border-gray-200 dark:border-gray-700 rounded-lg p-4 transition text-gray-900 dark:text-gray-100 hover:border-primary-500 hover:bg-primary-50 dark:hover:bg-gray-900 hover:text-gray-900 dark:hover:text-gray-100\">\u003Cstrong class=\"block font-semibold\">NVIDIA RTXNTC — Podešavanja kompresije i kvalitet slike\u003C\u002Fstrong>\u003Cp class=\"text-sm text-gray-600 dark:text-gray-400\">Zvanična dokumentacija koja pokriva bitrate, interakcije kanala, merenje kvaliteta i ponašanje kompresije sa gubicima.\u003C\u002Fp>\u003C\u002Fa>\n\u003Ca href=\"https:\u002F\u002Fgithub.com\u002FNVIDIA-RTX\u002FRTXNTC-Library\" target=\"_blank\" rel=\"noopener noreferrer\" class=\"editorjs-link-tool block border border-gray-200 dark:border-gray-700 rounded-lg p-4 transition text-gray-900 dark:text-gray-100 hover:border-primary-500 hover:bg-primary-50 dark:hover:bg-gray-900 hover:text-gray-900 dark:hover:text-gray-100\">\u003Cstrong class=\"block font-semibold\">NVIDIA — LibNTC\u003C\u002Fstrong>\u003Cp class=\"text-sm text-gray-600 dark:text-gray-400\">Zvanična dokumentacija biblioteke za rad koja opisuje konfiguraciju dekodera i kompromis između kvaliteta i performansi različitih veličina neuronskih mreža.\u003C\u002Fp>\u003C\u002Fa>",{"time":538,"blocks":539,"version":1248},1790378999963,[540,546,554,561,568,574,579,584,589,594,599,604,627,632,637,642,647,652,657,687,692,697,702,707,712,717,722,727,732,737,743,752,757,762,767,772,777,782,787,816,821,826,831,836,844,849,854,881,886,891,896,901,906,912,917,922,927,932,937,964,969,974,979,984,989,994,999,1004,1009,1014,1019,1024,1029,1034,1039,1044,1049,1054,1059,1064,1069,1074,1079,1084,1089,1094,1099,1104,1109,1114,1119,1124,1154,1159,1197,1202,1212,1221,1230,1239],{"id":541,"data":542,"type":544,"tunes":545},"intro",{"text":543},"Kompresija tekstura obično podrazumeva čuvanje manje verzije podataka teksture i njeno proširivanje u konvencionalni GPU format pre ili tokom upotrebe. NVIDIA RTX Neural Texture Compression menja taj model: deo podataka teksture postaje mala neuronska reprezentacija koju može da dekodira sam GPU.","paragraph",{},{"id":547,"data":548,"type":552,"tunes":553},"direct",{"body":549,"title":550,"variant":551},"\u003Cstrong>RTX Neural Texture Compression nije nadgradnja tekstura.\u003C\u002Fstrong> On kompresuje više tekstura materijala u težine neuronske mreže plus kompaktne latentne podatke, a zatim rekonstruiše tražene vrednosti teksture pomoću male neuronske mreže. U zavisnosti od načina integracije, igra može da razmeni skladištenje i korišćenje VRAM-a za dodatni rad GPU inferencije.","Direktan odgovor","info","callout",{},{"id":555,"data":556,"type":552,"tunes":560},"status",{"body":557,"title":558,"variant":559},"RTX Neural Texture Compression je još uvek \u003Cstrong>beta SDK\u003C\u002Fstrong>. Trenutna beta verzija v0.10.0 dodala je podršku za DirectX 12 Linear Algebra inferenciju, povezujući NTC direktno sa novom infrastrukturom neuronskih šejdera o kojoj se govori na drugim mestima na Figure Rocks.","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-normal",{"text":571,"level":47},"Zašto normalna kompresija tekstura i dalje koristi mnogo memorije","header",{},{"id":575,"data":576,"type":544,"tunes":578},"p-normal-1",{"text":577},"Moderan materijal zasnovan na fizici retko se sastoji od jedne slike. Jedna površina može da koristi albedo, normalu, hrapavost, metalnost, ambijentalnu okluziju, neprozirnost i druge kanale.",{},{"id":580,"data":581,"type":544,"tunes":583},"p-normal-2",{"text":582},"Tradicionalni GPU formati blokovske kompresije kao što su BC1 do BC7 smanjuju troškove, ali GPU i dalje na kraju čuva konvencionalne blokove tekstura za materijal.",{},{"id":585,"data":586,"type":544,"tunes":588},"p-normal-3",{"text":587},"Kako se rezolucija tekstura i složenost materijala povećavaju, ti kanali troše prostor na disku, propusni opseg za strimovanje i GPU memoriju.",{},{"id":590,"data":591,"type":572,"tunes":593},"h-store",{"text":592,"level":47},"Šta RTX Neural Texture Compression zapravo čuva",{},{"id":595,"data":596,"type":544,"tunes":598},"p-store-1",{"text":597},"NVIDIA RTXNTC SDK kompresuje kanale koji pripadaju jednom materijalu zajedno. Trenutni SDK podržava do 16 kanala tekstura u jednom NTC setu tekstura.",{},{"id":600,"data":601,"type":544,"tunes":603},"p-store-2",{"text":602},"Umesto da čuva samo konvencionalne kompresovane teksture, proces kompresije proizvodi dve glavne stvari: težine za mali neuronski dekoder i kompaktne latentne podatke o karakteristikama.",{},{"id":605,"data":606,"type":625,"tunes":626},"pipeline",{"steps":607,"title":623,"orientation":624},[608,611,614,617,620],{"label":609,"description":610},"1. Originalne teksture materijala","Albedo, normala, hrapavost, metalnost i drugi kanali materijala se obezbeđuju zajedno.",{"label":612,"description":613},"2. Offline kompresija","SDK uči kompaktnu reprezentaciju materijala.",{"label":615,"description":616},"3. Neuronske težine","Mala mreža dekodera čuva deo onoga što je potrebno za rekonstrukciju materijala.",{"label":618,"description":619},"4. Latentni podaci","Kompaktni tenzori karakteristika čuvaju informacije specifične za materijal.",{"label":621,"description":622},"5. GPU inferencija","U vreme izvršavanja, dekoder kombinuje latentne podatke i neuronske težine da rekonstruiše vrednosti teksture.","Neuronski pipeline tekstura","auto","processFlow",{},{"id":628,"data":629,"type":572,"tunes":631},"h-correlation",{"text":630,"level":47},"Zašto kompresija kanala zajedno može da pomogne",{},{"id":633,"data":634,"type":544,"tunes":636},"p-cor-1",{"text":635},"Kanali materijala su često povezani. Ogrebotina vidljiva u osnovnoj boji može se pojaviti i u mapi normale ili hrapavosti. Uzorak tkanine može uticati na nekoliko kanala na istoj prostornoj lokaciji.",{},{"id":638,"data":639,"type":544,"tunes":641},"p-cor-2",{"text":640},"NVIDIA je dizajnirala NTC da iskoristi te korelacije umesto da kompresuje svaku teksturu nezavisno.",{},{"id":643,"data":644,"type":544,"tunes":646},"p-cor-3",{"text":645},"To je jedan od razloga zašto se tehnologija opisuje kao kompresija orijentisana na materijal, a ne samo kao još jedan format slike.",{},{"id":648,"data":649,"type":572,"tunes":651},"h-modes",{"text":650,"level":47},"Tri NTC režima izvršavanja su ključ za razumevanje tehnologije",{},{"id":653,"data":654,"type":544,"tunes":656},"p-modes-1",{"text":655},"Najvažniji deo RTXNTC nije samo kako se materijal kompresuje. Već kada igra odluči da ga dekompresuje.",{},{"id":658,"data":659,"type":685,"tunes":686},"modes-table",{"rows":660,"title":673,"layout":674,"columns":675},[661,665,669],{"id":662,"label":663,"values":664},"load","Zaključivanje pri učitavanju",[13,13,13],{"id":666,"label":667,"values":668},"sample","Zaključivanje na uzorku",[13,13,13],{"id":670,"label":671,"values":672},"feedback","Zaključivanje na povratnoj informaciji",[13,13,13],"Zaključivanje pri učitavanju vs Zaključivanje na uzorku vs Zaključivanje na povratnoj informaciji","table",[676,679,682],{"id":677,"label":678},"when","Kada se dešava neuronsko dekodiranje",{"id":680,"label":681},"memory","Ponašanje teksturne memorije u vreme izvršavanja",{"id":683,"label":684},"tradeoff","Glavni kompromis","comparison",{},{"id":688,"data":689,"type":572,"tunes":691},"h-load",{"text":690,"level":47},"Zaključivanje pri učitavanju: neuronska kompresija kao format za skladištenje",{},{"id":693,"data":694,"type":544,"tunes":696},"p-load-1",{"text":695},"Zaključivanje pri učitavanju je najlakši režim za razumevanje.",{},{"id":698,"data":699,"type":544,"tunes":701},"p-load-2",{"text":700},"Igra čuva materijal u kompaktnom NTC obliku. Kada se resurs učita, GPU rekonstruiše podatke teksture i može ih transkodirati u obične BCn formate tekstura.",{},{"id":703,"data":704,"type":544,"tunes":706},"p-load-3",{"text":705},"Nakon tog koraka, renderovanje može da koristi normalno uzorkovanje tekstura. Važna ušteda je prvenstveno pre dekompresije: veličina upakovane igre, veličina preuzimanja ili propusni opseg strimovanja resursa.",{},{"id":708,"data":709,"type":544,"tunes":711},"p-load-4",{"text":710},"Ali kada se materijal u potpunosti razvije u konvencionalne teksture, njegov zauzeće VRAM-a u vreme izvršavanja ponovo približava konvencionalnoj reprezentaciji.",{},{"id":713,"data":714,"type":572,"tunes":716},"h-sample",{"text":715,"level":47},"Zaključivanje na uzorku: zadržite teksturu neuronskom u VRAM-u",{},{"id":718,"data":719,"type":544,"tunes":721},"p-sample-1",{"text":720},"Zaključivanje na uzorku je radikalniji režim.",{},{"id":723,"data":724,"type":544,"tunes":726},"p-sample-2",{"text":725},"Umesto da se materijal razvije u konvencionalne teksture pre renderovanja, šejder čita kompaktne latentne podatke i pokreće neuronski dekoder kada su mu potrebne vrednosti teksture.",{},{"id":728,"data":729,"type":544,"tunes":731},"p-sample-3",{"text":730},"NVIDIA-in sopstveni primer SDK-a upoređuje 12 MB BCn reprezentaciju materijala sa 2,5 MB NTC reprezentacijom kada se koristi Zaključivanje na uzorku.",{},{"id":733,"data":734,"type":544,"tunes":736},"p-sample-4",{"text":735},"Ušteda je stvarna jer konvencionalni podaci teksture ne moraju ostati potpuno rezidentni. Ali trošak se premešta negde drugde: piksel ili hit šejder sada obavlja neuronsko zaključivanje.",{},{"id":683,"data":738,"type":552,"tunes":742},{"body":739,"title":740,"variant":741},"Zaključivanje na uzorku menja \u003Cstrong>memoriju i propusni opseg\u003C\u002Fstrong> za \u003Cstrong>GPU računanje\u003C\u002Fstrong>. Pravo pitanje nije „Koliko je tekstura manja?“ već „Da li je ušteda memorije vredna dodatnog troška zaključivanja na ovom radnom opterećenju?“","Kompresija ne čini da posao nestane","warning",{},{"id":744,"data":745,"type":750,"tunes":751},"ref-vram",{"url":746,"title":747,"excerpt":748,"ctaLabel":749},"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 merač memorije ne govori celu priču","Zašto su kapacitet VRAM-a, budžeti, rezidentnost i stvarni pritisak na memoriju različite stvari.","Pročitajte vodič za VRAM","referralArticle",{},{"id":753,"data":754,"type":572,"tunes":756},"h-feedback",{"text":755,"level":47},"Zaključivanje na povratnoj informaciji: dekodirajte samo ono što igrač zaista vidi",{},{"id":758,"data":759,"type":544,"tunes":761},"p-feed-1",{"text":760},"Zaključivanje na povratnoj informaciji nalazi se između dve krajnosti.",{},{"id":763,"data":764,"type":544,"tunes":766},"p-feed-2",{"text":765},"Renderer prati koji se delovi teksture zaista zahtevaju. Umesto da odmah razvije ceo materijal, sistem može da dekodira zahtevane delove u serijama i održava radni keš.",{},{"id":768,"data":769,"type":544,"tunes":771},"p-feed-3",{"text":770},"Konceptualno, ovo kombinuje neuronsku kompresiju sa strimovanjem tekstura: sistem plaća trošak dekodiranja samo za regione koji postanu relevantni.",{},{"id":773,"data":774,"type":544,"tunes":776},"p-feed-4",{"text":775},"Trenutna implementacija primera je specijalizovanija od ostalih režima i njena ograničenja podrške se razlikuju, pa je treba tretirati kao strategiju integracije, a ne kao univerzalnu zamenu za obično strimovanje tekstura.",{},{"id":778,"data":779,"type":572,"tunes":781},"h-exchange",{"text":780,"level":47},"Razmena između skladištenja tekstura i računanja",{},{"id":783,"data":784,"type":544,"tunes":786},"p-exchange-1",{"text":785},"Najlakši način da se razume neuronska kompresija tekstura jeste kao razmena između resursa.",{},{"id":788,"data":789,"type":685,"tunes":815},"exchange-table",{"rows":790,"title":807,"layout":674,"columns":808},[791,795,799,803],{"id":792,"label":793,"values":794},"storage","Disk\u002Fskladištenje",[13,13],{"id":796,"label":797,"values":798},"vram","VRAM",[13,13],{"id":800,"label":801,"values":802},"sampling","Cena uzorkovanja",[13,13],{"id":804,"label":805,"values":806},"quality","Kontrola kvaliteta",[13,13],"Šta se menja kada skladištenje tekstura postane neuronsko",[809,812],{"id":810,"label":811},"traditional","Tradicionalna kompresovana tekstura",{"id":813,"label":814},"neural","Neuronska reprezentacija teksture",{},{"id":817,"data":818,"type":572,"tunes":820},"h-matrix",{"text":819,"level":47},"Zašto je specijalizovani matrični hardver važan",{},{"id":822,"data":823,"type":544,"tunes":825},"p-matrix-1",{"text":824},"Pokretanje neuronske mreže za uzorkovanje tekstura bilo bi preskupo ako bi se svako množenje moralo obrađivati kao običan skalarni rad senčera.",{},{"id":827,"data":828,"type":544,"tunes":830},"p-matrix-2",{"text":829},"RTXNTC stoga ima koristi od Cooperative Vector i sada DirectX 12 Linear Algebra putanja koje omogućavaju senčerima da koriste GPU hardver za matrično ubrzanje.",{},{"id":832,"data":833,"type":544,"tunes":835},"p-matrix-3",{"text":834},"Beta verzija v0.10.0 eksplicitno je dodala inferenciju kroz DirectX 12 Linear Algebra API predstavljen sa Shader Model 6.10.",{},{"id":837,"data":838,"type":750,"tunes":843},"ref-directx",{"url":839,"title":840,"excerpt":841,"ctaLabel":842},"https:\u002F\u002Ffigure.rocks\u002Fsr\u002Fblog\u002Fdirectx-is-becoming-an-ml-platform-what-linear-algebra-and-neural-shaders-mean-for-future-games","DirectX postaje ML platforma: šta linearna algebra i neuronski senčeri znače za buduće igre","Kako DirectX pomera matrične i neuronske operacije direktno u HLSL i grafički pipeline.","Pročitajte vodič za DirectX neuronske senčere",{},{"id":845,"data":846,"type":572,"tunes":848},"h-not-upscale",{"text":847,"level":47},"Zašto neuronska kompresija tekstura nije nadogradnja tekstura",{},{"id":850,"data":851,"type":544,"tunes":853},"p-notup-1",{"text":852},"Obe tehnike mogu da koriste mašinsko učenje, ali rešavaju različite probleme.",{},{"id":855,"data":856,"type":685,"tunes":880},"ntc-vs-sr",{"rows":857,"title":873,"layout":674,"columns":874},[858,862,866,869],{"id":859,"label":860,"values":861},"input","Ulaz",[13,13],{"id":863,"label":864,"values":865},"goal","Cilj",[13,13],{"id":677,"label":867,"values":868},"Kada se izvršava",[13,13],{"id":870,"label":871,"values":872},"output","Izlaz",[13,13],"Neuronska kompresija tekstura naspram super rezolucije",[875,878],{"id":876,"label":877},"ntc","Neuronska kompresija tekstura",{"id":7,"label":879},"Super rezolucija",{},{"id":882,"data":883,"type":544,"tunes":885},"p-notup-2",{"text":884},"Neuronska kompresija tekstura stoga može da postoji ispod DLSS, FSR, XeSS ili nativnog renderovanja. Ona menja način na koji se podaci o materijalima čuvaju i rekonstruišu, a ne konačnu rezoluciju prikaza.",{},{"id":887,"data":888,"type":572,"tunes":890},"h-bpp",{"text":889,"level":47},"Kontrola kvaliteta je broj bitova po pikselu",{},{"id":892,"data":893,"type":544,"tunes":895},"p-bpp-1",{"text":894},"NTC je kompresija sa gubicima. Količina kompresovanih informacija kontroliše se uglavnom putem podešavanja broja bitova po pikselu.",{},{"id":897,"data":898,"type":544,"tunes":900},"p-bpp-2",{"text":899},"Veći bitrate daje modelu više informacija i generalno poboljšava kvalitet rekonstrukcije. Manji bitrate poboljšava kompresiju, ali povećava rizik od vidljive greške.",{},{"id":902,"data":903,"type":544,"tunes":905},"p-bpp-3",{"text":904},"Pošto više kanala deli istu reprezentaciju, dodavanje više kanala materijala bez povećanja bitrate-a može smanjiti kvalitet dostupan svakom kanalu.",{},{"id":907,"data":908,"type":552,"tunes":911},"lossy-note",{"body":909,"title":910,"variant":559},"Dokumentacija SDK eksplicitno napominje da je greška kompresije normalna. Praktični cilj je \u003Cstrong>prihvatljiva vizuelna greška uz korisnu cenu skladištenja i izvršavanja\u003C\u002Fstrong>.","Neuronsko ne znači bez gubitaka",{},{"id":913,"data":914,"type":572,"tunes":916},"h-channels",{"text":915,"level":47},"Zašto su korelisani kanali materijala važni",{},{"id":918,"data":919,"type":544,"tunes":921},"p-channels-1",{"text":920},"Neuronska reprezentacija postaje vrednija kada nekoliko materijalnih kanala opisuje povezanu strukturu.",{},{"id":923,"data":924,"type":544,"tunes":926},"p-channels-2",{"text":925},"Ako albedo, normala i hrapavost sadrže iste ogrebotine, šavove ili teksturu tkanja, dekoder može da iskoristi deljene prostorne informacije.",{},{"id":928,"data":929,"type":544,"tunes":931},"p-channels-3",{"text":930},"Ako su kanali nepovezani šum, postoji manje zajedničke strukture za iskorišćavanje i problem kompresije postaje teži.",{},{"id":933,"data":934,"type":572,"tunes":936},"h-test",{"text":935,"level":47},"Test vrednosti neuronske teksture",{},{"id":938,"data":939,"type":625,"tunes":963},"value-test",{"steps":940,"title":962,"orientation":624},[941,944,947,950,953,956,959],{"label":942,"description":943},"1. Izmerite konvencionalnu cenu teksture","Koliko prostora na disku, propusnog opsega za strimovanje i VRAM-a troše postojeće teksture materijala?",{"label":945,"description":946},"2. Izaberite režim rada","Da li želite samo uštedu u skladištenju, trajnu uštedu VRAM-a ili rekonstrukciju strimovanih pločica?",{"label":948,"description":949},"3. Postavite prihvatljiv cilj kvaliteta","Uporedite rekonstruisane kanale sa originalnim materijalom, ne samo sa finalnom slikom.",{"label":951,"description":952},"4. Izmerite cenu inferencije","Zabeležite dodatno vreme šejdera ili dekompresije na stvarnom ciljnom GPU-u.",{"label":954,"description":955},"5. Izmerite uštedu memorije","Proverite stvarni radni set u vreme izvršavanja, a ne samo veličinu kompresovanog fajla.",{"label":957,"description":958},"6. Testirajte teške materijale","Fine normale, oštre maske, neprozirnost, tekst i nepovezani kanali mogu da otkriju neuspehe kompresije.",{"label":960,"description":961},"7. Odlučite na osnovu neto koristi","Koristite NTC samo tamo gde su uštede memorije\u002Fpropusnog opsega vredne dodatne složenosti inferencije i integracije.","Kada je neuronska kompresija tekstura zapravo korisna?",{},{"id":965,"data":966,"type":572,"tunes":968},"h-8x",{"text":967,"level":47},"Zašto „8× manje“ zahteva kontekst",{},{"id":970,"data":971,"type":544,"tunes":973},"p-8x-1",{"text":972},"NVIDIA RTX Kit opisuje RTX neuronsku kompresiju tekstura kao rešenje koje nudi do 8× poboljšanje disk-memorije uz vizuelnu vernost sličnu tradicionalnoj blokovskoj kompresiji.",{},{"id":975,"data":976,"type":544,"tunes":978},"p-8x-2",{"text":977},"Fraza „do“ je važna. Odnos kompresije zavisi od materijala, broja kanala, ciljnog bitrate-a, konfiguracije dekodera i praga kvaliteta.",{},{"id":980,"data":981,"type":544,"tunes":983},"p-8x-3",{"text":982},"Isti odnos takođe ne opisuje automatski uštedu VRAM-a. Inferencija pri učitavanju može da počne od kompaktnog fajla i da se ipak proširi u konvencionalne GPU teksture. Inferencija pri uzorkovanju čuva kompaktnu reprezentaciju u GPU memoriji, ali troši više računanja tokom senčenja.",{},{"id":985,"data":986,"type":572,"tunes":988},"h-decoder",{"text":987,"level":47},"Veličina dekodera je još jedan kompromis između performansi i kvaliteta",{},{"id":990,"data":991,"type":544,"tunes":993},"p-dec-1",{"text":992},"NTC runtime koristi mali višeslojni perceptron za dekodiranje vrednosti teksture.",{},{"id":995,"data":996,"type":544,"tunes":998},"p-dec-2",{"text":997},"NVIDIA-ina trenutna biblioteka koristi konfigurabilnu arhitekturu dekodera. Veće mreže mogu da poboljšaju kvalitet kompresije, ali koštaju više za izvršavanje; manje mreže mogu da rade brže uz određeni gubitak u kvalitetu rekonstrukcije.",{},{"id":1000,"data":1001,"type":544,"tunes":1003},"p-dec-3",{"text":1002},"To daje programerima engine-a još jednu dimenziju za podešavanje pored rezolucije teksture i bitrate-a.",{},{"id":1005,"data":1006,"type":572,"tunes":1008},"h-install",{"text":1007,"level":47},"Zašto je ovo važno za buduće instalacije igara",{},{"id":1010,"data":1011,"type":544,"tunes":1013},"p-install-1",{"text":1012},"Moderne igre sve više isporučuju skupove materijala visoke rezolucije koji utiču na veličinu preuzimanja, kao i na memoriju u vreme izvršavanja.",{},{"id":1015,"data":1016,"type":544,"tunes":1018},"p-install-2",{"text":1017},"Neuronska kompresija tekstura stvara novu opciju: isporučiti kompaktnu naučenu reprezentaciju i kasnije odlučiti da li da se ona proširi pri učitavanju, rekonstruiše direktno tokom senčenja ili dekodiraju samo tražene pločice.",{},{"id":1020,"data":1021,"type":544,"tunes":1023},"p-install-3",{"text":1022},"To znači da jedna kompresovana reprezentacija sredstva može da učestvuje u nekoliko različitih strategija memorije u vreme izvršavanja.",{},{"id":1025,"data":1026,"type":572,"tunes":1028},"h-meaning",{"text":1027,"level":47},"To takođe menja značenje pojma „memorija teksture“",{},{"id":1030,"data":1031,"type":544,"tunes":1033},"p-meaning-1",{"text":1032},"Sa tradicionalnim renderovanjem, budžet za memoriju tekstura se uglavnom odnosi na formate tekstura, mip nivoe, rezoluciju i rezidentnost.",{},{"id":1035,"data":1036,"type":544,"tunes":1038},"p-meaning-2",{"text":1037},"Sa neuronskim teksturama, programeri mogu takođe da planiraju budžet za latentne podatke, težine dekodera, bafere za inferenciju, transkodirane keševe i računarske resurse potrebne za rekonstrukciju traženih vrednosti.",{},{"id":1040,"data":1041,"type":544,"tunes":1043},"p-meaning-3",{"text":1042},"Tako da sredstvo više nema jedan jednostavan fiksni memorijski identitet.",{},{"id":1045,"data":1046,"type":572,"tunes":1048},"h-cross",{"text":1047,"level":47},"Podrška različitih proizvođača je slojevitija nego što ime RTX sugeriše",{},{"id":1050,"data":1051,"type":544,"tunes":1053},"p-cross-1",{"text":1052},"RTXNTC je NVIDIA SDK, a sama kompresija trenutno zahteva NVIDIA GPU prema zahtevima SDK-a.",{},{"id":1055,"data":1056,"type":544,"tunes":1058},"p-cross-2",{"text":1057},"Dekompresija u vreme izvršavanja je šira. NVIDIA dokumentuje funkcionalne putanje na Shader Model 6 hardveru i beleži validaciju na NVIDIA, AMD i Intel GPU-ovima, dok napredne Cooperative Vector \u002F Linear Algebra putanje zavise od podrške API-ja i drajvera.",{},{"id":1060,"data":1061,"type":544,"tunes":1063},"p-cross-3",{"text":1062},"Performanse i paritet funkcija stoga ne treba pretpostavljati kod različitih proizvođača samo zato što osnovni dekoder može da se pokrene.",{},{"id":1065,"data":1066,"type":572,"tunes":1068},"h-change",{"text":1067,"level":47},"Šta bi promenilo ovaj odgovor?",{},{"id":1070,"data":1071,"type":544,"tunes":1073},"p-change-1",{"text":1072},"NTC je još uvek beta. Režimi izvršavanja, arhitekture dekodera, podrška drajvera i putevi integracije mogu se promeniti pre stabilnog izdanja za produkciju.",{},{"id":1075,"data":1076,"type":544,"tunes":1078},"p-change-2",{"text":1077},"Najveća dugoročna promena bila bi široko usvajanje standardizovanih neuronskih šejderskih primitiva kroz DirectX i Vulkan. To bi učinilo dekodiranje neuronskih tekstura manje zavisnim od prilagođenih izvršnih putanja specifičnih za proizvođača.",{},{"id":1080,"data":1081,"type":572,"tunes":1083},"h-limit",{"text":1082,"level":47},"Ograničenja",{},{"id":1085,"data":1086,"type":544,"tunes":1088},"p-limit-1",{"text":1087},"Ovaj članak opisuje arhitekturu i trenutno javno ponašanje RTXNTC SDK-a. NVIDIA-ine navedene tvrdnje o kompresiji potiču od proizvođača i ne treba ih smatrati garantovanim rezultatima za svaki materijal.",{},{"id":1090,"data":1091,"type":544,"tunes":1093},"p-limit-2",{"text":1092},"Trenutni SDK je beta softver, a neke pregledne putanje imaju dokumentovana ograničenja drajvera i platforme.",{},{"id":1095,"data":1096,"type":572,"tunes":1098},"h-conclusion",{"text":1097,"level":47},"Zaključak",{},{"id":1100,"data":1101,"type":544,"tunes":1103},"p-conc-1",{"text":1102},"RTX Neural Texture Compression je zanimljiv jer menja jednu veoma staru pretpostavku: detalji teksture ne moraju uvek da postoje u memoriji kao konvencionalni tekseli.",{},{"id":1105,"data":1106,"type":544,"tunes":1108},"p-conc-2",{"text":1107},"Materijal može umesto toga da se čuva delimično kao kompaktna naučena reprezentacija i rekonstruiše kada je potrebno.",{},{"id":1110,"data":1111,"type":544,"tunes":1113},"p-conc-3",{"text":1112},"To ne daje besplatan kvalitet niti besplatnu memoriju. Stvara novu razmenu: manje skladištenja, propusnog opsega i potencijalno VRAM-a u zamenu za rad neuronske inferencije.",{},{"id":1115,"data":1116,"type":544,"tunes":1118},"p-conc-4",{"text":1117},"Prava inovacija nije „AI čini teksture oštrijim“. Već to što deo podataka o materijalima u igri može postati računanje.",{},{"id":1120,"data":1121,"type":572,"tunes":1123},"h-faq",{"text":1122,"level":47},"Često postavljana pitanja",{},{"id":1125,"data":1126,"type":1125,"tunes":1153},"faq",{"items":1127,"title":1152},[1128,1132,1136,1140,1144,1148],{"id":1129,"answer":1130,"question":1131},"faq1","Ne. On kompresuje i rekonstruiše podatke o teksturama materijala. Tehnologije super-rezolucije rekonstruišu finalnu renderovanu sliku na višoj rezoluciji prikaza.","Da li je RTX Neural Texture Compression alat za povećanje rezolucije?",{"id":1133,"answer":1134,"question":1135},"faq2","Da, posebno kod Inference on Sample jer kompaktna neuronska reprezentacija može da ostane u GPU memoriji umesto potpuno proširenih konvencionalnih tekstura. Inference on Load uglavnom čuva uštedu u skladištenju pre proširenja.","Može li NTC da smanji upotrebu VRAM-a?",{"id":1137,"answer":1138,"question":1139},"faq3","Kompresovani materijal sadrži kompaktne latentne podatke o karakteristikama plus težine za malu dekodersku mrežu koja rekonstruiše vrednosti tekstura.","Šta neuronska mreža čuva?",{"id":1141,"answer":1142,"question":1143},"faq4","Ne nužno. Inference on Load to radi, Inference on Sample rekonstruiše vrednosti tokom šejderskog semplovanja, a Inference on Feedback može da dekodira zahtevane delove teksture.","Da li NTC dekodira celu teksturu pre renderovanja?",{"id":1145,"answer":1146,"question":1147},"faq5","Ne. RTXNTC je kompresija sa gubicima i kvalitet zavisi od bitrate-a, broja kanala, konfiguracije dekodera i samog materijala.","Da li je neuronska kompresija tekstura bez gubitaka?",{"id":1149,"answer":1150,"question":1151},"faq6","Trenutni javni SDK je još uvek označen kao beta, tako da se API-ji, podrška i karakteristike performansi mogu i dalje menjati.","Da li je RTXNTC spreman za produkciju?","RTX Neural Texture Compression jednostavnim jezikom",{},{"id":1155,"data":1156,"type":572,"tunes":1158},"h-glossary",{"text":1157,"level":47},"Pojmovnik",{},{"id":1160,"data":1161,"type":1160,"tunes":1196},"glossary",{"title":1162,"entries":1163},"Ključni pojmovi neuronskih tekstura",[1164,1168,1172,1176,1180,1184,1188,1192],{"term":1165,"anchor":1166,"definition":1167},"Neural Texture Compression","neural-texture-compression","Tehnika koja čuva informacije o teksturi kao kompaktne latentne podatke plus težine neuronskog dekodera umesto samo konvencionalnih blokova teksela.",{"term":1169,"anchor":1170,"definition":1171},"Latentni podaci","latent-data","Kompaktne naučene karakteristike koje neuronski dekoder koristi za rekonstrukciju vrednosti tekstura.",{"term":1173,"anchor":1174,"definition":1175},"Dekoder","decoder","Mala neuronska mreža koja pretvara latentne karakteristike u rekonstruisane kanale teksture.",{"term":1177,"anchor":1178,"definition":1179},"Inference on Load","inference-on-load","Režim rada koji dekodira neuronsku teksturu kada se resurs učita, obično u konvencionalne formate tekstura.",{"term":1181,"anchor":1182,"definition":1183},"Inference on Sample","inference-on-sample","Režim rada koji obavlja neuronsko dekodiranje direktno tokom semplovanja teksture tako da kompresovana reprezentacija može da ostane rezidentna.",{"term":1185,"anchor":1186,"definition":1187},"Inference on Feedback","inference-on-feedback","Strategija rada koja koristi povratne informacije o teksturi da dekodira i kešira samo zahtevane delove.",{"term":1189,"anchor":1190,"definition":1191},"Texture Storage–Compute Exchange","texture-storage-compute-exchange","Model Figure Rocks koji opisuje razmenu skladištenja tekstura, propusnog opsega i VRAM-a za dodatni rad GPU neuronske inferencije.",{"term":1193,"anchor":1194,"definition":1195},"Neural Texture Value Test","neural-texture-value-test","Radni tok Figure Rocks za odlučivanje da li neuronska kompresija tekstura stvara neto korist za određeni materijal i ciljni GPU.",{},{"id":1198,"data":1199,"type":572,"tunes":1201},"h-sources",{"text":1200,"level":47},"Primarni izvori",{},{"id":1203,"data":1204,"type":1210,"tunes":1211},"src-rtxkit",{"link":1205,"meta":1206},"https:\u002F\u002Fdeveloper.nvidia.com\u002Frtx-kit",{"image":1207,"title":1208,"description":1209},{"url":13},"NVIDIA Developer — RTX Kit","Zvanični pregled RTX Neural Texture Compression i NVIDIA pozicioniranja objavljenog smanjenja skladištenja.","linkTool",{},{"id":1213,"data":1214,"type":1210,"tunes":1220},"src-rtxntc-readme",{"link":1215,"meta":1216},"https:\u002F\u002Fgithub.com\u002FNVIDIA-RTX\u002FRTXNTC\u002Fblob\u002Fmain\u002FREADME.md",{"image":1217,"title":1218,"description":1219},{"url":13},"NVIDIA RTXNTC — SDK README","Zvanična SDK dokumentacija koja opisuje kompresiju kanala materijala, reprezentaciju dekodera\u002Flatenta, režime rada, primere memorije, sistemske zahteve i podršku za Cooperative Vector.",{},{"id":1222,"data":1223,"type":1210,"tunes":1229},"src-rtxntc-release",{"link":1224,"meta":1225},"https:\u002F\u002Fgithub.com\u002FNVIDIA-RTX\u002FRTXNTC\u002Freleases",{"image":1226,"title":1227,"description":1228},{"url":13},"NVIDIA RTXNTC — Izdanja","Zvanična istorija izdanja, uključujući v0.10.0 beta i podršku za DirectX 12 Linear Algebra inferenciju.",{},{"id":1231,"data":1232,"type":1210,"tunes":1238},"src-rtxntc-quality",{"link":1233,"meta":1234},"https:\u002F\u002Fgithub.com\u002FNVIDIA-RTX\u002FRTXNTC\u002Fblob\u002Fmain\u002Fdocs\u002FSettingsAndQuality.md",{"image":1235,"title":1236,"description":1237},{"url":13},"NVIDIA RTXNTC — Podešavanja kompresije i kvalitet slike","Zvanična dokumentacija koja pokriva bitrate, interakcije kanala, merenje kvaliteta i ponašanje kompresije sa gubicima.",{},{"id":1240,"data":1241,"type":1210,"tunes":1247},"src-libntc",{"link":1242,"meta":1243},"https:\u002F\u002Fgithub.com\u002FNVIDIA-RTX\u002FRTXNTC-Library",{"image":1244,"title":1245,"description":1246},{"url":13},"NVIDIA — LibNTC","Zvanična dokumentacija biblioteke za rad koja opisuje konfiguraciju dekodera i kompromis između kvaliteta i performansi različitih veličina neuronskih mreža.",{},"2.31","NVIDIA RTX Neural Texture Compression menja način na koji se materijali u igrama mogu čuvati. Umesto da se svaki kanal teksture čuva samo kao konvencionalni tekseli, materijal može biti komprimovan u kompaktne latentne podatke i mali neuronski dekoder, a zatim rekonstruisan od strane GPU-a kada je potrebno.","\u002Fuploads\u002F2026\u002F09\u002Frtx-neural-texture-compression-is-not-upscaling-how-ai-can-trade-texture-memory-for-gpu-compute-1790378933528-ul75hf.webp","rtx-neural-texture-compression-is-not-upscaling-how-ai-can-trade-texture-memory-for-gpu-compute-1790378933528-ul75hf",false,"PUBLISHED","2026-09-25T21:27:00.000Z","2026-09-25T23:27:34.869Z","2026-09-25T23:32:35.051Z",{"en":1258,"de":1259,"sr":1260,"es":1261,"fr":1262,"it":1263,"ru":1264,"zh":1265},"\u002Fblog\u002Frtx-neural-texture-compression-is-not-upscaling-how-ai-can-trade-texture-memory-for-gpu-compute","\u002Fde\u002Fblog\u002Frtx-neural-texture-compression-is-not-upscaling-how-ai-can-trade-texture-memory-for-gpu-compute","\u002Fsr\u002Fblog\u002Frtx-neural-texture-compression-is-not-upscaling-how-ai-can-trade-texture-memory-for-gpu-compute","\u002Fes\u002Fblog\u002Frtx-neural-texture-compression-is-not-upscaling-how-ai-can-trade-texture-memory-for-gpu-compute","\u002Ffr\u002Fblog\u002Frtx-neural-texture-compression-is-not-upscaling-how-ai-can-trade-texture-memory-for-gpu-compute","\u002Fit\u002Fblog\u002Frtx-neural-texture-compression-is-not-upscaling-how-ai-can-trade-texture-memory-for-gpu-compute","\u002Fru\u002Fblog\u002Frtx-neural-texture-compression-is-not-upscaling-how-ai-can-trade-texture-memory-for-gpu-compute","\u002Fzh\u002Fblog\u002Frtx-neural-texture-compression-is-not-upscaling-how-ai-can-trade-texture-memory-for-gpu-compute",[1267,1271,1275,1279,1283],{"id":1268,"name":1269,"slug":1270},152,"VRAM и стримовање","vram-and-streaming",{"id":1272,"name":1273,"slug":1274},330,"Исправке за стриминг и IO","streaming-and-io-fixes",{"id":1276,"name":1277,"slug":1278},173,"Процесори, меморија, складиштење","cpus-memory-storage",{"id":1280,"name":1281,"slug":1282},210,"Шта квалитет значи","what-quality-means",{"id":1284,"name":1285,"slug":1286},214,"Маркетинг против стварности","marketing-vs-reality",{"id":283,"login":1288,"email":1289,"displayName":1290},"aleksandar","aleksandar@stajic.de","Aleksandar Stajic",[1292,1846],{"lang":8,"title":1293,"content":1294,"contentJson":1295,"excerpt":1845},"RTX Neural Texture Compression Is Not Upscaling: How AI Can Trade Texture Memory for GPU Compute","{\"time\":1790378899383,\"blocks\":[{\"id\":\"intro\",\"type\":\"paragraph\",\"data\":{\"text\":\"Texture compression normally means storing a smaller version of texture data and expanding it into a conventional GPU format before or during use. NVIDIA RTX Neural Texture Compression changes that model: part of the texture data becomes a small neural representation that can be decoded by the GPU itself.\"},\"tunes\":{}},{\"id\":\"direct\",\"type\":\"callout\",\"data\":{\"variant\":\"info\",\"title\":\"Direct answer\",\"body\":\"\u003Cstrong>RTX Neural Texture Compression is not texture upscaling.\u003C\u002Fstrong> It compresses multiple material textures into neural-network weights plus compact latent data, then reconstructs the requested texture values with a small neural network. Depending on the integration mode, the game can trade storage and VRAM usage for additional GPU inference work.\"},\"tunes\":{}},{\"id\":\"status\",\"type\":\"callout\",\"data\":{\"variant\":\"note\",\"title\":\"Current status\",\"body\":\"RTX Neural Texture Compression is still a \u003Cstrong>beta SDK\u003C\u002Fstrong>. The current v0.10.0 beta added DirectX 12 Linear Algebra inference support, connecting NTC directly to the new neural-shader infrastructure discussed elsewhere on Figure Rocks.\"},\"tunes\":{}},{\"id\":\"toc\",\"type\":\"tableOfContents\",\"data\":{\"title\":\"Contents\",\"minLevel\":2,\"maxLevel\":3},\"tunes\":{}},{\"id\":\"h-normal\",\"type\":\"header\",\"data\":{\"text\":\"Why normal texture compression still uses a lot of memory\",\"level\":2},\"tunes\":{}},{\"id\":\"p-normal-1\",\"type\":\"paragraph\",\"data\":{\"text\":\"A modern physically based material rarely consists of one image. A single surface may use albedo, normal, roughness, metalness, ambient occlusion, opacity and other channels.\"},\"tunes\":{}},{\"id\":\"p-normal-2\",\"type\":\"paragraph\",\"data\":{\"text\":\"Traditional GPU block-compression formats such as BC1 through BC7 reduce the cost, but the GPU still ends up storing conventional texture blocks for the material.\"},\"tunes\":{}},{\"id\":\"p-normal-3\",\"type\":\"paragraph\",\"data\":{\"text\":\"As texture resolution and material complexity increase, those channels consume disk space, streaming bandwidth and GPU memory.\"},\"tunes\":{}},{\"id\":\"h-store\",\"type\":\"header\",\"data\":{\"text\":\"What RTX Neural Texture Compression actually stores\",\"level\":2},\"tunes\":{}},{\"id\":\"p-store-1\",\"type\":\"paragraph\",\"data\":{\"text\":\"NVIDIA's RTXNTC SDK compresses the channels belonging to one material together. The current SDK supports up to 16 texture channels in one NTC texture set.\"},\"tunes\":{}},{\"id\":\"p-store-2\",\"type\":\"paragraph\",\"data\":{\"text\":\"Instead of keeping only conventional compressed texels, the compression process produces two main things: weights for a small neural decoder and compact latent feature data.\"},\"tunes\":{}},{\"id\":\"pipeline\",\"type\":\"processFlow\",\"data\":{\"title\":\"The neural texture pipeline\",\"orientation\":\"auto\",\"steps\":[{\"label\":\"1. Original material textures\",\"description\":\"Albedo, normal, roughness, metalness and other material channels are provided together.\"},{\"label\":\"2. Offline compression\",\"description\":\"The SDK learns a compact representation of the material.\"},{\"label\":\"3. Neural weights\",\"description\":\"A small decoder network stores part of what is needed to reconstruct the material.\"},{\"label\":\"4. Latent data\",\"description\":\"Compact feature tensors store material-specific information.\"},{\"label\":\"5. GPU inference\",\"description\":\"At runtime, the decoder combines the latent data and neural weights to reconstruct texture values.\"}]},\"tunes\":{}},{\"id\":\"h-correlation\",\"type\":\"header\",\"data\":{\"text\":\"Why compressing channels together can help\",\"level\":2},\"tunes\":{}},{\"id\":\"p-cor-1\",\"type\":\"paragraph\",\"data\":{\"text\":\"Material channels are often related. A scratch visible in the base color may also appear in the normal or roughness map. A fabric pattern can influence several channels at the same spatial location.\"},\"tunes\":{}},{\"id\":\"p-cor-2\",\"type\":\"paragraph\",\"data\":{\"text\":\"NVIDIA designed NTC to exploit those correlations instead of compressing every texture independently.\"},\"tunes\":{}},{\"id\":\"p-cor-3\",\"type\":\"paragraph\",\"data\":{\"text\":\"That is one reason the technology is described as material-oriented compression rather than merely another image format.\"},\"tunes\":{}},{\"id\":\"h-modes\",\"type\":\"header\",\"data\":{\"text\":\"The three NTC runtime modes are the key to understanding the technology\",\"level\":2},\"tunes\":{}},{\"id\":\"p-modes-1\",\"type\":\"paragraph\",\"data\":{\"text\":\"The most important part of RTXNTC is not only how the material is compressed. It is when the game chooses to decompress it.\"},\"tunes\":{}},{\"id\":\"modes-table\",\"type\":\"comparison\",\"data\":{\"title\":\"Inference on Load vs Inference on Sample vs Inference on Feedback\",\"layout\":\"table\",\"columns\":[{\"id\":\"when\",\"label\":\"When neural decoding happens\"},{\"id\":\"memory\",\"label\":\"Runtime texture-memory behavior\"},{\"id\":\"tradeoff\",\"label\":\"Main trade-off\"}],\"rows\":[{\"id\":\"load\",\"label\":\"Inference on Load\",\"values\":[\"\",\"\",\"\"]},{\"id\":\"sample\",\"label\":\"Inference on Sample\",\"values\":[\"\",\"\",\"\"]},{\"id\":\"feedback\",\"label\":\"Inference on Feedback\",\"values\":[\"\",\"\",\"\"]}]},\"tunes\":{}},{\"id\":\"h-load\",\"type\":\"header\",\"data\":{\"text\":\"Inference on Load: neural compression as a storage format\",\"level\":2},\"tunes\":{}},{\"id\":\"p-load-1\",\"type\":\"paragraph\",\"data\":{\"text\":\"Inference on Load is the easiest mode to understand.\"},\"tunes\":{}},{\"id\":\"p-load-2\",\"type\":\"paragraph\",\"data\":{\"text\":\"The game stores the material in compact NTC form. When the asset is loaded, the GPU reconstructs the texture data and can transcode it into ordinary BCn texture formats.\"},\"tunes\":{}},{\"id\":\"p-load-3\",\"type\":\"paragraph\",\"data\":{\"text\":\"After that step, rendering can use normal texture sampling. The important saving is primarily before decompression: packaged game size, download size or asset-streaming bandwidth.\"},\"tunes\":{}},{\"id\":\"p-load-4\",\"type\":\"paragraph\",\"data\":{\"text\":\"But once the material is fully expanded into conventional textures, its runtime VRAM footprint approaches the conventional representation again.\"},\"tunes\":{}},{\"id\":\"h-sample\",\"type\":\"header\",\"data\":{\"text\":\"Inference on Sample: keep the texture neural in VRAM\",\"level\":2},\"tunes\":{}},{\"id\":\"p-sample-1\",\"type\":\"paragraph\",\"data\":{\"text\":\"Inference on Sample is the more radical mode.\"},\"tunes\":{}},{\"id\":\"p-sample-2\",\"type\":\"paragraph\",\"data\":{\"text\":\"Instead of expanding the material into conventional textures before rendering, the shader reads compact latent data and runs the neural decoder when it needs texture values.\"},\"tunes\":{}},{\"id\":\"p-sample-3\",\"type\":\"paragraph\",\"data\":{\"text\":\"NVIDIA's own SDK example compares a 12 MB BCn material representation with a 2.5 MB NTC representation when using Inference on Sample.\"},\"tunes\":{}},{\"id\":\"p-sample-4\",\"type\":\"paragraph\",\"data\":{\"text\":\"The saving is real because the conventional texture data does not need to remain fully resident. But the cost moves somewhere else: the pixel or hit shader now performs neural inference.\"},\"tunes\":{}},{\"id\":\"tradeoff\",\"type\":\"callout\",\"data\":{\"variant\":\"warning\",\"title\":\"Compression does not make the work disappear\",\"body\":\"Inference on Sample trades \u003Cstrong>memory and bandwidth\u003C\u002Fstrong> for \u003Cstrong>GPU computation\u003C\u002Fstrong>. The right question is not “How much smaller is the texture?” but “Is the memory saving worth the added inference cost on this workload?”\"},\"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\":\"Why VRAM capacity, budgets, residency and actual memory pressure are different things.\",\"ctaLabel\":\"Read the VRAM guide\"},\"tunes\":{}},{\"id\":\"h-feedback\",\"type\":\"header\",\"data\":{\"text\":\"Inference on Feedback: decode only what the player actually sees\",\"level\":2},\"tunes\":{}},{\"id\":\"p-feed-1\",\"type\":\"paragraph\",\"data\":{\"text\":\"Inference on Feedback sits between the two extremes.\"},\"tunes\":{}},{\"id\":\"p-feed-2\",\"type\":\"paragraph\",\"data\":{\"text\":\"The renderer tracks which texture tiles are actually requested. Instead of expanding the full material immediately, the system can decode requested tiles in batches and keep a working cache.\"},\"tunes\":{}},{\"id\":\"p-feed-3\",\"type\":\"paragraph\",\"data\":{\"text\":\"Conceptually, this combines neural compression with texture streaming: the system pays the decoding cost only for regions that become relevant.\"},\"tunes\":{}},{\"id\":\"p-feed-4\",\"type\":\"paragraph\",\"data\":{\"text\":\"The current sample implementation is more specialized than the other modes and its support constraints differ, so it should be treated as an integration strategy rather than a universal replacement for ordinary texture streaming.\"},\"tunes\":{}},{\"id\":\"h-exchange\",\"type\":\"header\",\"data\":{\"text\":\"The Texture Storage–Compute Exchange\",\"level\":2},\"tunes\":{}},{\"id\":\"p-exchange-1\",\"type\":\"paragraph\",\"data\":{\"text\":\"The easiest way to understand neural texture compression is as an exchange between resources.\"},\"tunes\":{}},{\"id\":\"exchange-table\",\"type\":\"comparison\",\"data\":{\"title\":\"What moves when texture storage becomes neural\",\"layout\":\"table\",\"columns\":[{\"id\":\"traditional\",\"label\":\"Traditional compressed texture\"},{\"id\":\"neural\",\"label\":\"Neural texture representation\"}],\"rows\":[{\"id\":\"storage\",\"label\":\"Disk\u002Fstorage\",\"values\":[\"\",\"\"]},{\"id\":\"vram\",\"label\":\"VRAM\",\"values\":[\"\",\"\"]},{\"id\":\"sampling\",\"label\":\"Sampling cost\",\"values\":[\"\",\"\"]},{\"id\":\"quality\",\"label\":\"Quality control\",\"values\":[\"\",\"\"]}]},\"tunes\":{}},{\"id\":\"h-matrix\",\"type\":\"header\",\"data\":{\"text\":\"Why special matrix hardware matters\",\"level\":2},\"tunes\":{}},{\"id\":\"p-matrix-1\",\"type\":\"paragraph\",\"data\":{\"text\":\"Running a neural network for texture sampling would be too expensive if every multiply had to be handled like ordinary scalar shader work.\"},\"tunes\":{}},{\"id\":\"p-matrix-2\",\"type\":\"paragraph\",\"data\":{\"text\":\"RTXNTC therefore benefits from Cooperative Vector and now DirectX 12 Linear Algebra paths that let shaders use GPU matrix-acceleration hardware.\"},\"tunes\":{}},{\"id\":\"p-matrix-3\",\"type\":\"paragraph\",\"data\":{\"text\":\"The v0.10.0 beta explicitly added inference through the DirectX 12 Linear Algebra API introduced with Shader Model 6.10.\"},\"tunes\":{}},{\"id\":\"ref-directx\",\"type\":\"referralArticle\",\"data\":{\"url\":\"https:\u002F\u002Ffigure.rocks\u002Fblog\u002Fdirectx-is-becoming-an-ml-platform-what-linear-algebra-and-neural-shaders-mean-for-future-games\",\"title\":\"DirectX Is Becoming an ML Platform: What Linear Algebra and Neural Shaders Mean for Future Games\",\"excerpt\":\"How DirectX is moving matrix and neural operations directly into HLSL and the graphics pipeline.\",\"ctaLabel\":\"Read the DirectX neural-shader guide\"},\"tunes\":{}},{\"id\":\"h-not-upscale\",\"type\":\"header\",\"data\":{\"text\":\"Why neural texture compression is not texture upscaling\",\"level\":2},\"tunes\":{}},{\"id\":\"p-notup-1\",\"type\":\"paragraph\",\"data\":{\"text\":\"The two techniques can both use machine learning, but they solve different problems.\"},\"tunes\":{}},{\"id\":\"ntc-vs-sr\",\"type\":\"comparison\",\"data\":{\"title\":\"Neural Texture Compression vs Super Resolution\",\"layout\":\"table\",\"columns\":[{\"id\":\"ntc\",\"label\":\"Neural Texture Compression\"},{\"id\":\"sr\",\"label\":\"Super Resolution\"}],\"rows\":[{\"id\":\"input\",\"label\":\"Input\",\"values\":[\"\",\"\"]},{\"id\":\"goal\",\"label\":\"Goal\",\"values\":[\"\",\"\"]},{\"id\":\"when\",\"label\":\"When it runs\",\"values\":[\"\",\"\"]},{\"id\":\"output\",\"label\":\"Output\",\"values\":[\"\",\"\"]}]},\"tunes\":{}},{\"id\":\"p-notup-2\",\"type\":\"paragraph\",\"data\":{\"text\":\"Neural Texture Compression can therefore exist underneath DLSS, FSR, XeSS or native rendering. It changes how material data is stored and reconstructed, not the final display resolution.\"},\"tunes\":{}},{\"id\":\"h-bpp\",\"type\":\"header\",\"data\":{\"text\":\"The quality knob is bits per pixel\",\"level\":2},\"tunes\":{}},{\"id\":\"p-bpp-1\",\"type\":\"paragraph\",\"data\":{\"text\":\"NTC is lossy compression. The amount of compressed information is controlled largely through the bits-per-pixel setting.\"},\"tunes\":{}},{\"id\":\"p-bpp-2\",\"type\":\"paragraph\",\"data\":{\"text\":\"Higher bitrate gives the model more information and generally improves reconstruction quality. Lower bitrate improves compression but increases the risk of visible error.\"},\"tunes\":{}},{\"id\":\"p-bpp-3\",\"type\":\"paragraph\",\"data\":{\"text\":\"Because multiple channels share the same representation, adding more material channels without increasing the bitrate can reduce the quality available to each channel.\"},\"tunes\":{}},{\"id\":\"lossy-note\",\"type\":\"callout\",\"data\":{\"variant\":\"note\",\"title\":\"Neural does not mean lossless\",\"body\":\"The SDK documentation explicitly notes that compression error is normal. The practical target is \u003Cstrong>acceptable visual error at a useful storage and runtime cost\u003C\u002Fstrong>.\"},\"tunes\":{}},{\"id\":\"h-channels\",\"type\":\"header\",\"data\":{\"text\":\"Why correlated material channels are important\",\"level\":2},\"tunes\":{}},{\"id\":\"p-channels-1\",\"type\":\"paragraph\",\"data\":{\"text\":\"A neural representation becomes more valuable when several material channels describe related structure.\"},\"tunes\":{}},{\"id\":\"p-channels-2\",\"type\":\"paragraph\",\"data\":{\"text\":\"If albedo, normal and roughness all contain the same scratches, seams or fabric weave, the decoder can exploit shared spatial information.\"},\"tunes\":{}},{\"id\":\"p-channels-3\",\"type\":\"paragraph\",\"data\":{\"text\":\"If the channels are unrelated noise, there is less common structure to exploit and the compression problem becomes harder.\"},\"tunes\":{}},{\"id\":\"h-test\",\"type\":\"header\",\"data\":{\"text\":\"The Neural Texture Value Test\",\"level\":2},\"tunes\":{}},{\"id\":\"value-test\",\"type\":\"processFlow\",\"data\":{\"title\":\"When is neural texture compression actually useful?\",\"orientation\":\"auto\",\"steps\":[{\"label\":\"1. Measure conventional texture cost\",\"description\":\"How much disk space, streaming bandwidth and VRAM do the existing material textures consume?\"},{\"label\":\"2. Choose the runtime mode\",\"description\":\"Do you want storage savings only, persistent VRAM savings or streamed tile reconstruction?\"},{\"label\":\"3. Set an acceptable quality target\",\"description\":\"Compare reconstructed channels against the original material, not only the final beauty image.\"},{\"label\":\"4. Measure inference cost\",\"description\":\"Record added shader or decompression time on the actual target GPU.\"},{\"label\":\"5. Measure memory savings\",\"description\":\"Check the real runtime working set rather than only the compressed file size.\"},{\"label\":\"6. Test difficult materials\",\"description\":\"Fine normals, sharp masks, opacity, text and unrelated channels can expose compression failures.\"},{\"label\":\"7. Decide by net benefit\",\"description\":\"Use NTC only where the saved memory\u002Fbandwidth is worth the extra inference and integration complexity.\"}]},\"tunes\":{}},{\"id\":\"h-8x\",\"type\":\"header\",\"data\":{\"text\":\"Why “8× smaller” needs context\",\"level\":2},\"tunes\":{}},{\"id\":\"p-8x-1\",\"type\":\"paragraph\",\"data\":{\"text\":\"NVIDIA's RTX Kit describes RTX Neural Texture Compression as offering up to 8× disk-memory improvement at similar visual fidelity to traditional block compression.\"},\"tunes\":{}},{\"id\":\"p-8x-2\",\"type\":\"paragraph\",\"data\":{\"text\":\"The phrase “up to” matters. Compression ratio depends on the material, number of channels, target bitrate, decoder configuration and quality threshold.\"},\"tunes\":{}},{\"id\":\"p-8x-3\",\"type\":\"paragraph\",\"data\":{\"text\":\"The same ratio also does not automatically describe VRAM savings. Inference on Load can start from a compact file and still expand into conventional GPU textures. Inference on Sample preserves the compact representation in GPU memory but spends more compute during shading.\"},\"tunes\":{}},{\"id\":\"h-decoder\",\"type\":\"header\",\"data\":{\"text\":\"Decoder size is another performance-quality trade-off\",\"level\":2},\"tunes\":{}},{\"id\":\"p-dec-1\",\"type\":\"paragraph\",\"data\":{\"text\":\"The NTC runtime uses a small multilayer perceptron to decode texture values.\"},\"tunes\":{}},{\"id\":\"p-dec-2\",\"type\":\"paragraph\",\"data\":{\"text\":\"NVIDIA's current library uses a configurable decoder architecture. Larger networks can improve compression quality but cost more to execute; smaller networks can run faster with some loss in reconstruction quality.\"},\"tunes\":{}},{\"id\":\"p-dec-3\",\"type\":\"paragraph\",\"data\":{\"text\":\"That gives engine developers another tuning dimension beyond texture resolution and bitrate.\"},\"tunes\":{}},{\"id\":\"h-install\",\"type\":\"header\",\"data\":{\"text\":\"Why this matters for future game installations\",\"level\":2},\"tunes\":{}},{\"id\":\"p-install-1\",\"type\":\"paragraph\",\"data\":{\"text\":\"Modern games increasingly ship high-resolution material sets that affect download size as well as runtime memory.\"},\"tunes\":{}},{\"id\":\"p-install-2\",\"type\":\"paragraph\",\"data\":{\"text\":\"Neural texture compression creates a new option: ship a compact learned representation and decide later whether to expand it on load, reconstruct it directly during shading or decode only requested tiles.\"},\"tunes\":{}},{\"id\":\"p-install-3\",\"type\":\"paragraph\",\"data\":{\"text\":\"That means one compressed asset representation can participate in several different runtime memory strategies.\"},\"tunes\":{}},{\"id\":\"h-meaning\",\"type\":\"header\",\"data\":{\"text\":\"It also changes what 'texture memory' means\",\"level\":2},\"tunes\":{}},{\"id\":\"p-meaning-1\",\"type\":\"paragraph\",\"data\":{\"text\":\"With traditional rendering, a texture-memory budget is mostly about texture formats, mip levels, resolution and residency.\"},\"tunes\":{}},{\"id\":\"p-meaning-2\",\"type\":\"paragraph\",\"data\":{\"text\":\"With neural textures, developers may also budget latent data, decoder weights, inference buffers, transcoded caches and the compute needed to reconstruct requested values.\"},\"tunes\":{}},{\"id\":\"p-meaning-3\",\"type\":\"paragraph\",\"data\":{\"text\":\"So the asset no longer has one simple fixed memory identity.\"},\"tunes\":{}},{\"id\":\"h-cross\",\"type\":\"header\",\"data\":{\"text\":\"Cross-vendor support is more nuanced than the RTX name suggests\",\"level\":2},\"tunes\":{}},{\"id\":\"p-cross-1\",\"type\":\"paragraph\",\"data\":{\"text\":\"RTXNTC is an NVIDIA SDK, and compression itself currently requires an NVIDIA GPU according to the SDK requirements.\"},\"tunes\":{}},{\"id\":\"p-cross-2\",\"type\":\"paragraph\",\"data\":{\"text\":\"Runtime decompression is broader. NVIDIA documents functional paths on Shader Model 6 hardware and notes validation on NVIDIA, AMD and Intel GPUs, while advanced Cooperative Vector \u002F Linear Algebra paths depend on API and driver support.\"},\"tunes\":{}},{\"id\":\"p-cross-3\",\"type\":\"paragraph\",\"data\":{\"text\":\"Performance and feature parity therefore should not be assumed across vendors merely because the basic decoder can run.\"},\"tunes\":{}},{\"id\":\"h-change\",\"type\":\"header\",\"data\":{\"text\":\"What would change this answer?\",\"level\":2},\"tunes\":{}},{\"id\":\"p-change-1\",\"type\":\"paragraph\",\"data\":{\"text\":\"NTC is still beta. Runtime modes, decoder architectures, driver support and integration paths can change before a stable production release.\"},\"tunes\":{}},{\"id\":\"p-change-2\",\"type\":\"paragraph\",\"data\":{\"text\":\"The biggest long-term change would be broad adoption of standardized neural-shader primitives across DirectX and Vulkan. That would make neural texture decoding less dependent on custom vendor-specific execution paths.\"},\"tunes\":{}},{\"id\":\"h-limit\",\"type\":\"header\",\"data\":{\"text\":\"Limitations\",\"level\":2},\"tunes\":{}},{\"id\":\"p-limit-1\",\"type\":\"paragraph\",\"data\":{\"text\":\"This article describes the architecture and current public RTXNTC SDK behavior. NVIDIA's quoted compression claims are vendor-provided and should not be treated as guaranteed results for every material.\"},\"tunes\":{}},{\"id\":\"p-limit-2\",\"type\":\"paragraph\",\"data\":{\"text\":\"The current SDK is beta software, and some preview paths have documented driver and platform limitations.\"},\"tunes\":{}},{\"id\":\"h-conclusion\",\"type\":\"header\",\"data\":{\"text\":\"Conclusion\",\"level\":2},\"tunes\":{}},{\"id\":\"p-conc-1\",\"type\":\"paragraph\",\"data\":{\"text\":\"RTX Neural Texture Compression is interesting because it changes a very old assumption: texture detail does not always have to exist in memory as conventional texels.\"},\"tunes\":{}},{\"id\":\"p-conc-2\",\"type\":\"paragraph\",\"data\":{\"text\":\"A material can instead be stored partly as a compact learned representation and reconstructed when needed.\"},\"tunes\":{}},{\"id\":\"p-conc-3\",\"type\":\"paragraph\",\"data\":{\"text\":\"That does not give free quality or free memory. It creates a new exchange: less storage, bandwidth and potentially VRAM in return for neural inference work.\"},\"tunes\":{}},{\"id\":\"p-conc-4\",\"type\":\"paragraph\",\"data\":{\"text\":\"The real innovation is not “AI makes textures sharper.” It is that part of a game's material data can become computation.\"},\"tunes\":{}},{\"id\":\"h-faq\",\"type\":\"header\",\"data\":{\"text\":\"FAQ\",\"level\":2},\"tunes\":{}},{\"id\":\"faq\",\"type\":\"faq\",\"data\":{\"title\":\"RTX Neural Texture Compression in plain English\",\"items\":[{\"id\":\"faq1\",\"question\":\"Is RTX Neural Texture Compression an upscaler?\",\"answer\":\"No. It compresses and reconstructs material texture data. Super-resolution technologies reconstruct the final rendered image at a higher display resolution.\"},{\"id\":\"faq2\",\"question\":\"Can NTC reduce VRAM usage?\",\"answer\":\"Yes, particularly with Inference on Sample because the compact neural representation can remain in GPU memory instead of fully expanded conventional textures. Inference on Load mainly preserves storage savings before expansion.\"},{\"id\":\"faq3\",\"question\":\"What does the neural network store?\",\"answer\":\"The compressed material contains compact latent feature data plus weights for a small decoder network that reconstructs texture values.\"},{\"id\":\"faq4\",\"question\":\"Does NTC decode the whole texture before rendering?\",\"answer\":\"Not necessarily. Inference on Load does, Inference on Sample reconstructs values during shader sampling, and Inference on Feedback can decode requested texture tiles.\"},{\"id\":\"faq5\",\"question\":\"Is neural texture compression lossless?\",\"answer\":\"No. RTXNTC is lossy compression and quality depends on bitrate, channel count, decoder configuration and the material itself.\"},{\"id\":\"faq6\",\"question\":\"Is RTXNTC production-ready?\",\"answer\":\"The current public SDK is still labeled beta, so APIs, support and performance characteristics may continue to change.\"}]},\"tunes\":{}},{\"id\":\"h-glossary\",\"type\":\"header\",\"data\":{\"text\":\"Glossary\",\"level\":2},\"tunes\":{}},{\"id\":\"glossary\",\"type\":\"glossary\",\"data\":{\"title\":\"Key neural texture terms\",\"entries\":[{\"term\":\"Neural Texture Compression\",\"definition\":\"A technique that stores texture information as compact latent data plus neural decoder weights instead of only conventional texel blocks.\",\"anchor\":\"neural-texture-compression\"},{\"term\":\"Latent data\",\"definition\":\"Compact learned features that the neural decoder uses to reconstruct texture values.\",\"anchor\":\"latent-data\"},{\"term\":\"Decoder\",\"definition\":\"A small neural network that converts latent features into reconstructed texture channels.\",\"anchor\":\"decoder\"},{\"term\":\"Inference on Load\",\"definition\":\"Runtime mode that decodes the neural texture when an asset is loaded, usually into conventional texture formats.\",\"anchor\":\"inference-on-load\"},{\"term\":\"Inference on Sample\",\"definition\":\"Runtime mode that performs neural decoding directly during texture sampling so the compressed representation can remain resident.\",\"anchor\":\"inference-on-sample\"},{\"term\":\"Inference on Feedback\",\"definition\":\"Runtime strategy that uses texture feedback to decode and cache only requested tiles.\",\"anchor\":\"inference-on-feedback\"},{\"term\":\"Texture Storage–Compute Exchange\",\"definition\":\"A Figure Rocks model describing the trade of texture storage, bandwidth and VRAM for additional GPU neural-inference work.\",\"anchor\":\"texture-storage-compute-exchange\"},{\"term\":\"Neural Texture Value Test\",\"definition\":\"A Figure Rocks workflow for deciding whether neural texture compression creates a net benefit for a particular material and target GPU.\",\"anchor\":\"neural-texture-value-test\"}]},\"tunes\":{}},{\"id\":\"h-sources\",\"type\":\"header\",\"data\":{\"text\":\"Primary sources\",\"level\":2},\"tunes\":{}},{\"id\":\"src-rtxkit\",\"type\":\"linkTool\",\"data\":{\"link\":\"https:\u002F\u002Fdeveloper.nvidia.com\u002Frtx-kit\",\"meta\":{\"title\":\"NVIDIA Developer — RTX Kit\",\"description\":\"Official overview of RTX Neural Texture Compression and NVIDIA's published storage-reduction positioning.\",\"image\":{\"url\":\"\"}}},\"tunes\":{}},{\"id\":\"src-rtxntc-readme\",\"type\":\"linkTool\",\"data\":{\"link\":\"https:\u002F\u002Fgithub.com\u002FNVIDIA-RTX\u002FRTXNTC\u002Fblob\u002Fmain\u002FREADME.md\",\"meta\":{\"title\":\"NVIDIA RTXNTC — SDK README\",\"description\":\"Official SDK documentation describing material-channel compression, decoder\u002Flatent representation, runtime modes, memory examples, system requirements and Cooperative Vector support.\",\"image\":{\"url\":\"\"}}},\"tunes\":{}},{\"id\":\"src-rtxntc-release\",\"type\":\"linkTool\",\"data\":{\"link\":\"https:\u002F\u002Fgithub.com\u002FNVIDIA-RTX\u002FRTXNTC\u002Freleases\",\"meta\":{\"title\":\"NVIDIA RTXNTC — Releases\",\"description\":\"Official release history, including v0.10.0 beta and DirectX 12 Linear Algebra inference support.\",\"image\":{\"url\":\"\"}}},\"tunes\":{}},{\"id\":\"src-rtxntc-quality\",\"type\":\"linkTool\",\"data\":{\"link\":\"https:\u002F\u002Fgithub.com\u002FNVIDIA-RTX\u002FRTXNTC\u002Fblob\u002Fmain\u002Fdocs\u002FSettingsAndQuality.md\",\"meta\":{\"title\":\"NVIDIA RTXNTC — Compression Settings and Image Quality\",\"description\":\"Official documentation covering bitrate, channel interactions, quality measurement and lossy compression behavior.\",\"image\":{\"url\":\"\"}}},\"tunes\":{}},{\"id\":\"src-libntc\",\"type\":\"linkTool\",\"data\":{\"link\":\"https:\u002F\u002Fgithub.com\u002FNVIDIA-RTX\u002FRTXNTC-Library\",\"meta\":{\"title\":\"NVIDIA — LibNTC\",\"description\":\"Official runtime library documentation describing decoder configuration and the quality\u002Fperformance trade-off of different neural-network sizes.\",\"image\":{\"url\":\"\"}}},\"tunes\":{}}],\"version\":\"2.31.6\"}",{"time":1296,"blocks":1297,"version":1844},1790378899383,[1298,1302,1307,1312,1316,1320,1324,1328,1332,1336,1340,1344,1364,1368,1372,1376,1380,1384,1388,1406,1410,1414,1418,1422,1426,1430,1434,1438,1442,1446,1451,1458,1462,1466,1470,1474,1478,1482,1486,1507,1511,1515,1519,1523,1530,1534,1538,1559,1563,1567,1571,1575,1579,1584,1588,1592,1596,1600,1604,1630,1634,1638,1642,1646,1650,1654,1658,1662,1666,1670,1674,1678,1682,1686,1690,1694,1698,1702,1706,1710,1714,1718,1722,1726,1730,1734,1738,1742,1746,1750,1754,1758,1781,1785,1808,1812,1818,1824,1831,1838],{"id":541,"data":1299,"type":544,"tunes":1301},{"text":1300},"Texture compression normally means storing a smaller version of texture data and expanding it into a conventional GPU format before or during use. NVIDIA RTX Neural Texture Compression changes that model: part of the texture data becomes a small neural representation that can be decoded by the GPU itself.",{},{"id":547,"data":1303,"type":552,"tunes":1306},{"body":1304,"title":1305,"variant":551},"\u003Cstrong>RTX Neural Texture Compression is not texture upscaling.\u003C\u002Fstrong> It compresses multiple material textures into neural-network weights plus compact latent data, then reconstructs the requested texture values with a small neural network. Depending on the integration mode, the game can trade storage and VRAM usage for additional GPU inference work.","Direct answer",{},{"id":555,"data":1308,"type":552,"tunes":1311},{"body":1309,"title":1310,"variant":559},"RTX Neural Texture Compression is still a \u003Cstrong>beta SDK\u003C\u002Fstrong>. The current v0.10.0 beta added DirectX 12 Linear Algebra inference support, connecting NTC directly to the new neural-shader infrastructure discussed elsewhere on Figure Rocks.","Current status",{},{"id":562,"data":1313,"type":566,"tunes":1315},{"title":1314,"maxLevel":565,"minLevel":47},"Contents",{},{"id":569,"data":1317,"type":572,"tunes":1319},{"text":1318,"level":47},"Why normal texture compression still uses a lot of memory",{},{"id":575,"data":1321,"type":544,"tunes":1323},{"text":1322},"A modern physically based material rarely consists of one image. A single surface may use albedo, normal, roughness, metalness, ambient occlusion, opacity and other channels.",{},{"id":580,"data":1325,"type":544,"tunes":1327},{"text":1326},"Traditional GPU block-compression formats such as BC1 through BC7 reduce the cost, but the GPU still ends up storing conventional texture blocks for the material.",{},{"id":585,"data":1329,"type":544,"tunes":1331},{"text":1330},"As texture resolution and material complexity increase, those channels consume disk space, streaming bandwidth and GPU memory.",{},{"id":590,"data":1333,"type":572,"tunes":1335},{"text":1334,"level":47},"What RTX Neural Texture Compression actually stores",{},{"id":595,"data":1337,"type":544,"tunes":1339},{"text":1338},"NVIDIA's RTXNTC SDK compresses the channels belonging to one material together. The current SDK supports up to 16 texture channels in one NTC texture set.",{},{"id":600,"data":1341,"type":544,"tunes":1343},{"text":1342},"Instead of keeping only conventional compressed texels, the compression process produces two main things: weights for a small neural decoder and compact latent feature data.",{},{"id":605,"data":1345,"type":625,"tunes":1363},{"steps":1346,"title":1362,"orientation":624},[1347,1350,1353,1356,1359],{"label":1348,"description":1349},"1. Original material textures","Albedo, normal, roughness, metalness and other material channels are provided together.",{"label":1351,"description":1352},"2. Offline compression","The SDK learns a compact representation of the material.",{"label":1354,"description":1355},"3. Neural weights","A small decoder network stores part of what is needed to reconstruct the material.",{"label":1357,"description":1358},"4. Latent data","Compact feature tensors store material-specific information.",{"label":1360,"description":1361},"5. GPU inference","At runtime, the decoder combines the latent data and neural weights to reconstruct texture values.","The neural texture pipeline",{},{"id":628,"data":1365,"type":572,"tunes":1367},{"text":1366,"level":47},"Why compressing channels together can help",{},{"id":633,"data":1369,"type":544,"tunes":1371},{"text":1370},"Material channels are often related. A scratch visible in the base color may also appear in the normal or roughness map. A fabric pattern can influence several channels at the same spatial location.",{},{"id":638,"data":1373,"type":544,"tunes":1375},{"text":1374},"NVIDIA designed NTC to exploit those correlations instead of compressing every texture independently.",{},{"id":643,"data":1377,"type":544,"tunes":1379},{"text":1378},"That is one reason the technology is described as material-oriented compression rather than merely another image format.",{},{"id":648,"data":1381,"type":572,"tunes":1383},{"text":1382,"level":47},"The three NTC runtime modes are the key to understanding the technology",{},{"id":653,"data":1385,"type":544,"tunes":1387},{"text":1386},"The most important part of RTXNTC is not only how the material is compressed. It is when the game chooses to decompress it.",{},{"id":658,"data":1389,"type":685,"tunes":1405},{"rows":1390,"title":1397,"layout":674,"columns":1398},[1391,1393,1395],{"id":662,"label":1177,"values":1392},[13,13,13],{"id":666,"label":1181,"values":1394},[13,13,13],{"id":670,"label":1185,"values":1396},[13,13,13],"Inference on Load vs Inference on Sample vs Inference on Feedback",[1399,1401,1403],{"id":677,"label":1400},"When neural decoding happens",{"id":680,"label":1402},"Runtime texture-memory behavior",{"id":683,"label":1404},"Main trade-off",{},{"id":688,"data":1407,"type":572,"tunes":1409},{"text":1408,"level":47},"Inference on Load: neural compression as a storage format",{},{"id":693,"data":1411,"type":544,"tunes":1413},{"text":1412},"Inference on Load is the easiest mode to understand.",{},{"id":698,"data":1415,"type":544,"tunes":1417},{"text":1416},"The game stores the material in compact NTC form. When the asset is loaded, the GPU reconstructs the texture data and can transcode it into ordinary BCn texture formats.",{},{"id":703,"data":1419,"type":544,"tunes":1421},{"text":1420},"After that step, rendering can use normal texture sampling. The important saving is primarily before decompression: packaged game size, download size or asset-streaming bandwidth.",{},{"id":708,"data":1423,"type":544,"tunes":1425},{"text":1424},"But once the material is fully expanded into conventional textures, its runtime VRAM footprint approaches the conventional representation again.",{},{"id":713,"data":1427,"type":572,"tunes":1429},{"text":1428,"level":47},"Inference on Sample: keep the texture neural in VRAM",{},{"id":718,"data":1431,"type":544,"tunes":1433},{"text":1432},"Inference on Sample is the more radical mode.",{},{"id":723,"data":1435,"type":544,"tunes":1437},{"text":1436},"Instead of expanding the material into conventional textures before rendering, the shader reads compact latent data and runs the neural decoder when it needs texture values.",{},{"id":728,"data":1439,"type":544,"tunes":1441},{"text":1440},"NVIDIA's own SDK example compares a 12 MB BCn material representation with a 2.5 MB NTC representation when using Inference on Sample.",{},{"id":733,"data":1443,"type":544,"tunes":1445},{"text":1444},"The saving is real because the conventional texture data does not need to remain fully resident. But the cost moves somewhere else: the pixel or hit shader now performs neural inference.",{},{"id":683,"data":1447,"type":552,"tunes":1450},{"body":1448,"title":1449,"variant":741},"Inference on Sample trades \u003Cstrong>memory and bandwidth\u003C\u002Fstrong> for \u003Cstrong>GPU computation\u003C\u002Fstrong>. The right question is not “How much smaller is the texture?” but “Is the memory saving worth the added inference cost on this workload?”","Compression does not make the work disappear",{},{"id":744,"data":1452,"type":750,"tunes":1457},{"url":1453,"title":1454,"excerpt":1455,"ctaLabel":1456},"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","Why VRAM capacity, budgets, residency and actual memory pressure are different things.","Read the VRAM guide",{},{"id":753,"data":1459,"type":572,"tunes":1461},{"text":1460,"level":47},"Inference on Feedback: decode only what the player actually sees",{},{"id":758,"data":1463,"type":544,"tunes":1465},{"text":1464},"Inference on Feedback sits between the two extremes.",{},{"id":763,"data":1467,"type":544,"tunes":1469},{"text":1468},"The renderer tracks which texture tiles are actually requested. Instead of expanding the full material immediately, the system can decode requested tiles in batches and keep a working cache.",{},{"id":768,"data":1471,"type":544,"tunes":1473},{"text":1472},"Conceptually, this combines neural compression with texture streaming: the system pays the decoding cost only for regions that become relevant.",{},{"id":773,"data":1475,"type":544,"tunes":1477},{"text":1476},"The current sample implementation is more specialized than the other modes and its support constraints differ, so it should be treated as an integration strategy rather than a universal replacement for ordinary texture streaming.",{},{"id":778,"data":1479,"type":572,"tunes":1481},{"text":1480,"level":47},"The Texture Storage–Compute Exchange",{},{"id":783,"data":1483,"type":544,"tunes":1485},{"text":1484},"The easiest way to understand neural texture compression is as an exchange between resources.",{},{"id":788,"data":1487,"type":685,"tunes":1506},{"rows":1488,"title":1500,"layout":674,"columns":1501},[1489,1492,1494,1497],{"id":792,"label":1490,"values":1491},"Disk\u002Fstorage",[13,13],{"id":796,"label":797,"values":1493},[13,13],{"id":800,"label":1495,"values":1496},"Sampling cost",[13,13],{"id":804,"label":1498,"values":1499},"Quality control",[13,13],"What moves when texture storage becomes neural",[1502,1504],{"id":810,"label":1503},"Traditional compressed texture",{"id":813,"label":1505},"Neural texture representation",{},{"id":817,"data":1508,"type":572,"tunes":1510},{"text":1509,"level":47},"Why special matrix hardware matters",{},{"id":822,"data":1512,"type":544,"tunes":1514},{"text":1513},"Running a neural network for texture sampling would be too expensive if every multiply had to be handled like ordinary scalar shader work.",{},{"id":827,"data":1516,"type":544,"tunes":1518},{"text":1517},"RTXNTC therefore benefits from Cooperative Vector and now DirectX 12 Linear Algebra paths that let shaders use GPU matrix-acceleration hardware.",{},{"id":832,"data":1520,"type":544,"tunes":1522},{"text":1521},"The v0.10.0 beta explicitly added inference through the DirectX 12 Linear Algebra API introduced with Shader Model 6.10.",{},{"id":837,"data":1524,"type":750,"tunes":1529},{"url":1525,"title":1526,"excerpt":1527,"ctaLabel":1528},"https:\u002F\u002Ffigure.rocks\u002Fblog\u002Fdirectx-is-becoming-an-ml-platform-what-linear-algebra-and-neural-shaders-mean-for-future-games","DirectX Is Becoming an ML Platform: What Linear Algebra and Neural Shaders Mean for Future Games","How DirectX is moving matrix and neural operations directly into HLSL and the graphics pipeline.","Read the DirectX neural-shader guide",{},{"id":845,"data":1531,"type":572,"tunes":1533},{"text":1532,"level":47},"Why neural texture compression is not texture upscaling",{},{"id":850,"data":1535,"type":544,"tunes":1537},{"text":1536},"The two techniques can both use machine learning, but they solve different problems.",{},{"id":855,"data":1539,"type":685,"tunes":1558},{"rows":1540,"title":1553,"layout":674,"columns":1554},[1541,1544,1547,1550],{"id":859,"label":1542,"values":1543},"Input",[13,13],{"id":863,"label":1545,"values":1546},"Goal",[13,13],{"id":677,"label":1548,"values":1549},"When it runs",[13,13],{"id":870,"label":1551,"values":1552},"Output",[13,13],"Neural Texture Compression vs Super Resolution",[1555,1556],{"id":876,"label":1165},{"id":7,"label":1557},"Super Resolution",{},{"id":882,"data":1560,"type":544,"tunes":1562},{"text":1561},"Neural Texture Compression can therefore exist underneath DLSS, FSR, XeSS or native rendering. It changes how material data is stored and reconstructed, not the final display resolution.",{},{"id":887,"data":1564,"type":572,"tunes":1566},{"text":1565,"level":47},"The quality knob is bits per pixel",{},{"id":892,"data":1568,"type":544,"tunes":1570},{"text":1569},"NTC is lossy compression. The amount of compressed information is controlled largely through the bits-per-pixel setting.",{},{"id":897,"data":1572,"type":544,"tunes":1574},{"text":1573},"Higher bitrate gives the model more information and generally improves reconstruction quality. Lower bitrate improves compression but increases the risk of visible error.",{},{"id":902,"data":1576,"type":544,"tunes":1578},{"text":1577},"Because multiple channels share the same representation, adding more material channels without increasing the bitrate can reduce the quality available to each channel.",{},{"id":907,"data":1580,"type":552,"tunes":1583},{"body":1581,"title":1582,"variant":559},"The SDK documentation explicitly notes that compression error is normal. The practical target is \u003Cstrong>acceptable visual error at a useful storage and runtime cost\u003C\u002Fstrong>.","Neural does not mean lossless",{},{"id":913,"data":1585,"type":572,"tunes":1587},{"text":1586,"level":47},"Why correlated material channels are important",{},{"id":918,"data":1589,"type":544,"tunes":1591},{"text":1590},"A neural representation becomes more valuable when several material channels describe related structure.",{},{"id":923,"data":1593,"type":544,"tunes":1595},{"text":1594},"If albedo, normal and roughness all contain the same scratches, seams or fabric weave, the decoder can exploit shared spatial information.",{},{"id":928,"data":1597,"type":544,"tunes":1599},{"text":1598},"If the channels are unrelated noise, there is less common structure to exploit and the compression problem becomes harder.",{},{"id":933,"data":1601,"type":572,"tunes":1603},{"text":1602,"level":47},"The Neural Texture Value Test",{},{"id":938,"data":1605,"type":625,"tunes":1629},{"steps":1606,"title":1628,"orientation":624},[1607,1610,1613,1616,1619,1622,1625],{"label":1608,"description":1609},"1. Measure conventional texture cost","How much disk space, streaming bandwidth and VRAM do the existing material textures consume?",{"label":1611,"description":1612},"2. Choose the runtime mode","Do you want storage savings only, persistent VRAM savings or streamed tile reconstruction?",{"label":1614,"description":1615},"3. Set an acceptable quality target","Compare reconstructed channels against the original material, not only the final beauty image.",{"label":1617,"description":1618},"4. Measure inference cost","Record added shader or decompression time on the actual target GPU.",{"label":1620,"description":1621},"5. Measure memory savings","Check the real runtime working set rather than only the compressed file size.",{"label":1623,"description":1624},"6. Test difficult materials","Fine normals, sharp masks, opacity, text and unrelated channels can expose compression failures.",{"label":1626,"description":1627},"7. Decide by net benefit","Use NTC only where the saved memory\u002Fbandwidth is worth the extra inference and integration complexity.","When is neural texture compression actually useful?",{},{"id":965,"data":1631,"type":572,"tunes":1633},{"text":1632,"level":47},"Why “8× smaller” needs context",{},{"id":970,"data":1635,"type":544,"tunes":1637},{"text":1636},"NVIDIA's RTX Kit describes RTX Neural Texture Compression as offering up to 8× disk-memory improvement at similar visual fidelity to traditional block compression.",{},{"id":975,"data":1639,"type":544,"tunes":1641},{"text":1640},"The phrase “up to” matters. Compression ratio depends on the material, number of channels, target bitrate, decoder configuration and quality threshold.",{},{"id":980,"data":1643,"type":544,"tunes":1645},{"text":1644},"The same ratio also does not automatically describe VRAM savings. Inference on Load can start from a compact file and still expand into conventional GPU textures. Inference on Sample preserves the compact representation in GPU memory but spends more compute during shading.",{},{"id":985,"data":1647,"type":572,"tunes":1649},{"text":1648,"level":47},"Decoder size is another performance-quality trade-off",{},{"id":990,"data":1651,"type":544,"tunes":1653},{"text":1652},"The NTC runtime uses a small multilayer perceptron to decode texture values.",{},{"id":995,"data":1655,"type":544,"tunes":1657},{"text":1656},"NVIDIA's current library uses a configurable decoder architecture. Larger networks can improve compression quality but cost more to execute; smaller networks can run faster with some loss in reconstruction quality.",{},{"id":1000,"data":1659,"type":544,"tunes":1661},{"text":1660},"That gives engine developers another tuning dimension beyond texture resolution and bitrate.",{},{"id":1005,"data":1663,"type":572,"tunes":1665},{"text":1664,"level":47},"Why this matters for future game installations",{},{"id":1010,"data":1667,"type":544,"tunes":1669},{"text":1668},"Modern games increasingly ship high-resolution material sets that affect download size as well as runtime memory.",{},{"id":1015,"data":1671,"type":544,"tunes":1673},{"text":1672},"Neural texture compression creates a new option: ship a compact learned representation and decide later whether to expand it on load, reconstruct it directly during shading or decode only requested tiles.",{},{"id":1020,"data":1675,"type":544,"tunes":1677},{"text":1676},"That means one compressed asset representation can participate in several different runtime memory strategies.",{},{"id":1025,"data":1679,"type":572,"tunes":1681},{"text":1680,"level":47},"It also changes what 'texture memory' means",{},{"id":1030,"data":1683,"type":544,"tunes":1685},{"text":1684},"With traditional rendering, a texture-memory budget is mostly about texture formats, mip levels, resolution and residency.",{},{"id":1035,"data":1687,"type":544,"tunes":1689},{"text":1688},"With neural textures, developers may also budget latent data, decoder weights, inference buffers, transcoded caches and the compute needed to reconstruct requested values.",{},{"id":1040,"data":1691,"type":544,"tunes":1693},{"text":1692},"So the asset no longer has one simple fixed memory identity.",{},{"id":1045,"data":1695,"type":572,"tunes":1697},{"text":1696,"level":47},"Cross-vendor support is more nuanced than the RTX name suggests",{},{"id":1050,"data":1699,"type":544,"tunes":1701},{"text":1700},"RTXNTC is an NVIDIA SDK, and compression itself currently requires an NVIDIA GPU according to the SDK requirements.",{},{"id":1055,"data":1703,"type":544,"tunes":1705},{"text":1704},"Runtime decompression is broader. NVIDIA documents functional paths on Shader Model 6 hardware and notes validation on NVIDIA, AMD and Intel GPUs, while advanced Cooperative Vector \u002F Linear Algebra paths depend on API and driver support.",{},{"id":1060,"data":1707,"type":544,"tunes":1709},{"text":1708},"Performance and feature parity therefore should not be assumed across vendors merely because the basic decoder can run.",{},{"id":1065,"data":1711,"type":572,"tunes":1713},{"text":1712,"level":47},"What would change this answer?",{},{"id":1070,"data":1715,"type":544,"tunes":1717},{"text":1716},"NTC is still beta. Runtime modes, decoder architectures, driver support and integration paths can change before a stable production release.",{},{"id":1075,"data":1719,"type":544,"tunes":1721},{"text":1720},"The biggest long-term change would be broad adoption of standardized neural-shader primitives across DirectX and Vulkan. That would make neural texture decoding less dependent on custom vendor-specific execution paths.",{},{"id":1080,"data":1723,"type":572,"tunes":1725},{"text":1724,"level":47},"Limitations",{},{"id":1085,"data":1727,"type":544,"tunes":1729},{"text":1728},"This article describes the architecture and current public RTXNTC SDK behavior. NVIDIA's quoted compression claims are vendor-provided and should not be treated as guaranteed results for every material.",{},{"id":1090,"data":1731,"type":544,"tunes":1733},{"text":1732},"The current SDK is beta software, and some preview paths have documented driver and platform limitations.",{},{"id":1095,"data":1735,"type":572,"tunes":1737},{"text":1736,"level":47},"Conclusion",{},{"id":1100,"data":1739,"type":544,"tunes":1741},{"text":1740},"RTX Neural Texture Compression is interesting because it changes a very old assumption: texture detail does not always have to exist in memory as conventional texels.",{},{"id":1105,"data":1743,"type":544,"tunes":1745},{"text":1744},"A material can instead be stored partly as a compact learned representation and reconstructed when needed.",{},{"id":1110,"data":1747,"type":544,"tunes":1749},{"text":1748},"That does not give free quality or free memory. It creates a new exchange: less storage, bandwidth and potentially VRAM in return for neural inference work.",{},{"id":1115,"data":1751,"type":544,"tunes":1753},{"text":1752},"The real innovation is not “AI makes textures sharper.” It is that part of a game's material data can become computation.",{},{"id":1120,"data":1755,"type":572,"tunes":1757},{"text":1756,"level":47},"FAQ",{},{"id":1125,"data":1759,"type":1125,"tunes":1780},{"items":1760,"title":1779},[1761,1764,1767,1770,1773,1776],{"id":1129,"answer":1762,"question":1763},"No. It compresses and reconstructs material texture data. Super-resolution technologies reconstruct the final rendered image at a higher display resolution.","Is RTX Neural Texture Compression an upscaler?",{"id":1133,"answer":1765,"question":1766},"Yes, particularly with Inference on Sample because the compact neural representation can remain in GPU memory instead of fully expanded conventional textures. Inference on Load mainly preserves storage savings before expansion.","Can NTC reduce VRAM usage?",{"id":1137,"answer":1768,"question":1769},"The compressed material contains compact latent feature data plus weights for a small decoder network that reconstructs texture values.","What does the neural network store?",{"id":1141,"answer":1771,"question":1772},"Not necessarily. Inference on Load does, Inference on Sample reconstructs values during shader sampling, and Inference on Feedback can decode requested texture tiles.","Does NTC decode the whole texture before rendering?",{"id":1145,"answer":1774,"question":1775},"No. RTXNTC is lossy compression and quality depends on bitrate, channel count, decoder configuration and the material itself.","Is neural texture compression lossless?",{"id":1149,"answer":1777,"question":1778},"The current public SDK is still labeled beta, so APIs, support and performance characteristics may continue to change.","Is RTXNTC production-ready?","RTX Neural Texture Compression in plain English",{},{"id":1155,"data":1782,"type":572,"tunes":1784},{"text":1783,"level":47},"Glossary",{},{"id":1160,"data":1786,"type":1160,"tunes":1807},{"title":1787,"entries":1788},"Key neural texture terms",[1789,1791,1794,1797,1799,1801,1803,1805],{"term":1165,"anchor":1166,"definition":1790},"A technique that stores texture information as compact latent data plus neural decoder weights instead of only conventional texel blocks.",{"term":1792,"anchor":1170,"definition":1793},"Latent data","Compact learned features that the neural decoder uses to reconstruct texture values.",{"term":1795,"anchor":1174,"definition":1796},"Decoder","A small neural network that converts latent features into reconstructed texture channels.",{"term":1177,"anchor":1178,"definition":1798},"Runtime mode that decodes the neural texture when an asset is loaded, usually into conventional texture formats.",{"term":1181,"anchor":1182,"definition":1800},"Runtime mode that performs neural decoding directly during texture sampling so the compressed representation can remain resident.",{"term":1185,"anchor":1186,"definition":1802},"Runtime strategy that uses texture feedback to decode and cache only requested tiles.",{"term":1189,"anchor":1190,"definition":1804},"A Figure Rocks model describing the trade of texture storage, bandwidth and VRAM for additional GPU neural-inference work.",{"term":1193,"anchor":1194,"definition":1806},"A Figure Rocks workflow for deciding whether neural texture compression creates a net benefit for a particular material and target GPU.",{},{"id":1198,"data":1809,"type":572,"tunes":1811},{"text":1810,"level":47},"Primary sources",{},{"id":1203,"data":1813,"type":1210,"tunes":1817},{"link":1205,"meta":1814},{"image":1815,"title":1208,"description":1816},{"url":13},"Official overview of RTX Neural Texture Compression and NVIDIA's published storage-reduction positioning.",{},{"id":1213,"data":1819,"type":1210,"tunes":1823},{"link":1215,"meta":1820},{"image":1821,"title":1218,"description":1822},{"url":13},"Official SDK documentation describing material-channel compression, decoder\u002Flatent representation, runtime modes, memory examples, system requirements and Cooperative Vector support.",{},{"id":1222,"data":1825,"type":1210,"tunes":1830},{"link":1224,"meta":1826},{"image":1827,"title":1828,"description":1829},{"url":13},"NVIDIA RTXNTC — Releases","Official release history, including v0.10.0 beta and DirectX 12 Linear Algebra inference support.",{},{"id":1231,"data":1832,"type":1210,"tunes":1837},{"link":1233,"meta":1833},{"image":1834,"title":1835,"description":1836},{"url":13},"NVIDIA RTXNTC — Compression Settings and Image Quality","Official documentation covering bitrate, channel interactions, quality measurement and lossy compression behavior.",{},{"id":1240,"data":1839,"type":1210,"tunes":1843},{"link":1242,"meta":1840},{"image":1841,"title":1245,"description":1842},{"url":13},"Official runtime library documentation describing decoder configuration and the quality\u002Fperformance trade-off of different neural-network sizes.",{},"2.31.6","NVIDIA RTX Neural Texture Compression changes how game materials can be stored. 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