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NVIDIA RTX Neural Texture Compression cambia questo modello: parte dei dati della texture diventa una piccola rappresentazione neurale che può essere decodificata dalla GPU stessa.\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\">Risposta diretta\u003C\u002Fstrong>\u003Cdiv class=\"text-gray-700 dark:text-gray-200\">\u003Cstrong>RTX Neural Texture Compression non è upscaling delle texture.\u003C\u002Fstrong> Comprime più texture di materiale in pesi di rete neurale più dati latenti compatti, poi ricostruisce i valori delle texture richiesti con una piccola rete neurale. A seconda della modalità di integrazione, il gioco può scambiare spazio di archiviazione e utilizzo della VRAM con lavoro di inferenza GPU aggiuntivo.\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\">Stato attuale\u003C\u002Fstrong>\u003Cdiv class=\"text-gray-700 dark:text-gray-200\">RTX Neural Texture Compression è ancora un \u003Cstrong>SDK beta\u003C\u002Fstrong>. L'attuale beta v0.10.0 ha aggiunto il supporto all'inferenza DirectX 12 Linear Algebra, collegando NTC direttamente alla nuova infrastruttura di shader neurali discussa altrove su Figure Rocks.\u003C\u002Fdiv>\u003C\u002Faside>\n\u003Cnav class=\"editorjs-toc\" data-editorjs-toc=\"true\" aria-label=\"Contenuti\">\u003Cstrong class=\"editorjs-toc__title\">Contenuti\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\">Perché la normale compressione delle texture usa ancora molta memoria\u003C\u002Fa>\u003C\u002Fli>\u003Cli class=\"editorjs-toc__item\">\u003Ca href=\"#section-9\" class=\"editorjs-toc__link\">Cosa memorizza effettivamente RTX Neural Texture Compression\u003C\u002Fa>\u003C\u002Fli>\u003Cli class=\"editorjs-toc__item\">\u003Ca href=\"#section-13\" class=\"editorjs-toc__link\">Perché comprimere i canali insieme può aiutare\u003C\u002Fa>\u003C\u002Fli>\u003Cli class=\"editorjs-toc__item\">\u003Ca href=\"#section-17\" class=\"editorjs-toc__link\">Le tre modalità runtime NTC sono la chiave per comprendere la tecnologia\u003C\u002Fa>\u003C\u002Fli>\u003Cli class=\"editorjs-toc__item\">\u003Ca href=\"#section-20\" class=\"editorjs-toc__link\">Inferenza al caricamento: compressione neurale come formato di archiviazione\u003C\u002Fa>\u003C\u002Fli>\u003Cli class=\"editorjs-toc__item\">\u003Ca href=\"#section-25\" class=\"editorjs-toc__link\">Inferenza al campionamento: mantenere la texture neurale in VRAM\u003C\u002Fa>\u003C\u002Fli>\u003Cli class=\"editorjs-toc__item\">\u003Ca href=\"#section-32\" class=\"editorjs-toc__link\">Inferenza al feedback: decodificare solo ciò che il giocatore vede effettivamente\u003C\u002Fa>\u003C\u002Fli>\u003Cli class=\"editorjs-toc__item\">\u003Ca href=\"#section-37\" class=\"editorjs-toc__link\">Lo scambio tra archiviazione delle texture e calcolo\u003C\u002Fa>\u003C\u002Fli>\u003Cli class=\"editorjs-toc__item\">\u003Ca href=\"#section-40\" class=\"editorjs-toc__link\">Perché l'hardware specializzato per matrici è importante\u003C\u002Fa>\u003C\u002Fli>\u003Cli class=\"editorjs-toc__item\">\u003Ca href=\"#section-45\" class=\"editorjs-toc__link\">Perché la compressione neurale delle texture non è upscaling delle texture\u003C\u002Fa>\u003C\u002Fli>\u003Cli class=\"editorjs-toc__item\">\u003Ca href=\"#section-49\" class=\"editorjs-toc__link\">Il parametro di qualità è i bit per pixel\u003C\u002Fa>\u003C\u002Fli>\u003Cli class=\"editorjs-toc__item\">\u003Ca href=\"#section-54\" class=\"editorjs-toc__link\">Perché i canali del materiale correlati sono importanti\u003C\u002Fa>\u003C\u002Fli>\u003Cli class=\"editorjs-toc__item\">\u003Ca href=\"#section-58\" class=\"editorjs-toc__link\">Il test del valore della texture neurale\u003C\u002Fa>\u003C\u002Fli>\u003Cli class=\"editorjs-toc__item\">\u003Ca href=\"#section-60\" class=\"editorjs-toc__link\">Perché “8× più piccolo” necessita di contesto\u003C\u002Fa>\u003C\u002Fli>\u003Cli class=\"editorjs-toc__item\">\u003Ca href=\"#section-64\" class=\"editorjs-toc__link\">La dimensione del decoder è un altro compromesso prestazioni-qualità\u003C\u002Fa>\u003C\u002Fli>\u003Cli class=\"editorjs-toc__item\">\u003Ca href=\"#section-68\" class=\"editorjs-toc__link\">Perché questo è importante per le future installazioni di giochi\u003C\u002Fa>\u003C\u002Fli>\u003Cli class=\"editorjs-toc__item\">\u003Ca href=\"#section-72\" class=\"editorjs-toc__link\">Cambia anche cosa significa 'memoria delle texture'\u003C\u002Fa>\u003C\u002Fli>\u003Cli class=\"editorjs-toc__item\">\u003Ca href=\"#section-76\" class=\"editorjs-toc__link\">Il supporto cross-vendor è più sfumato di quanto suggerisca il nome RTX\u003C\u002Fa>\u003C\u002Fli>\u003Cli class=\"editorjs-toc__item\">\u003Ca href=\"#section-80\" class=\"editorjs-toc__link\">Cosa cambierebbe questa risposta?\u003C\u002Fa>\u003C\u002Fli>\u003Cli class=\"editorjs-toc__item\">\u003Ca href=\"#section-83\" class=\"editorjs-toc__link\">Limitazioni\u003C\u002Fa>\u003C\u002Fli>\u003Cli class=\"editorjs-toc__item\">\u003Ca href=\"#section-86\" class=\"editorjs-toc__link\">Conclusione\u003C\u002Fa>\u003C\u002Fli>\u003Cli class=\"editorjs-toc__item\">\u003Ca href=\"#section-91\" class=\"editorjs-toc__link\">FAQ\u003C\u002Fa>\u003C\u002Fli>\u003Cli class=\"editorjs-toc__item\">\u003Ca href=\"#section-93\" class=\"editorjs-toc__link\">Glossario\u003C\u002Fa>\u003C\u002Fli>\u003Cli class=\"editorjs-toc__item\">\u003Ca href=\"#section-95\" class=\"editorjs-toc__link\">Fonti primarie\u003C\u002Fa>\u003C\u002Fli>\u003C\u002Fol>\u003C\u002Fnav>\n\u003Ch2 id=\"section-5\">Perché la normale compressione delle texture usa ancora molta memoria\u003C\u002Fh2>\n\u003Cp>Un moderno materiale basato sulla fisica raramente consiste in una sola immagine. Una singola superficie può utilizzare albedo, normal, roughness, metalness, occlusione ambientale, opacità e altri canali.\u003C\u002Fp>\n\u003Cp>I formati tradizionali di compressione a blocchi GPU come BC1 fino a BC7 riducono il costo, ma la GPU finisce comunque per memorizzare blocchi di texture convenzionali per il materiale.\u003C\u002Fp>\n\u003Cp>Man mano che la risoluzione delle texture e la complessità dei materiali aumentano, quei canali consumano spazio su disco, larghezza di banda in streaming e memoria GPU.\u003C\u002Fp>\n\u003Ch2 id=\"section-9\">Cosa memorizza effettivamente RTX Neural Texture Compression\u003C\u002Fh2>\n\u003Cp>L'SDK RTXNTC di NVIDIA comprime insieme i canali appartenenti a un materiale. L'SDK attuale supporta fino a 16 canali di texture in un set di texture NTC.\u003C\u002Fp>\n\u003Cp>Invece di mantenere solo texel compressi convenzionali, il processo di compressione produce due cose principali: pesi per un piccolo decoder neurale e dati di feature latenti compatti.\u003C\u002Fp>\n\u003Csection class=\"editorjs-process my-6\">\u003Ch3 class=\"mb-3 text-lg font-semibold\">La pipeline delle texture neurali\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. Texture originali del materiale\u003C\u002Fdiv>\u003Cdiv class=\"mt-1 text-sm text-gray-600 dark:text-gray-300\">Albedo, normal, roughness, metalness e altri canali del materiale sono forniti insieme.\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. Compressione offline\u003C\u002Fdiv>\u003Cdiv class=\"mt-1 text-sm text-gray-600 dark:text-gray-300\">L'SDK apprende una rappresentazione compatta del materiale.\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. Pesi neurali\u003C\u002Fdiv>\u003Cdiv class=\"mt-1 text-sm text-gray-600 dark:text-gray-300\">Una piccola rete decoder memorizza parte di ciò che serve per ricostruire il materiale.\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. Dati latenti\u003C\u002Fdiv>\u003Cdiv class=\"mt-1 text-sm text-gray-600 dark:text-gray-300\">Tensori di feature compatti memorizzano informazioni specifiche del materiale.\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. Inferenza GPU\u003C\u002Fdiv>\u003Cdiv class=\"mt-1 text-sm text-gray-600 dark:text-gray-300\">A runtime, il decoder combina i dati latenti e i pesi neurali per ricostruire i valori delle texture.\u003C\u002Fdiv>\u003C\u002Fdiv>\u003C\u002Fdiv>\u003C\u002Fsection>\n\u003Ch2 id=\"section-13\">Perché comprimere i canali insieme può aiutare\u003C\u002Fh2>\n\u003Cp>I canali del materiale sono spesso correlati. Un graffio visibile nel colore di base può apparire anche nella mappa normal o roughness. Un motivo di tessuto può influenzare diversi canali nella stessa posizione spaziale.\u003C\u002Fp>\n\u003Cp>NVIDIA ha progettato NTC per sfruttare queste correlazioni invece di comprimere ogni texture in modo indipendente.\u003C\u002Fp>\n\u003Cp>Questo è uno dei motivi per cui la tecnologia è descritta come compressione orientata al materiale piuttosto che semplicemente un altro formato immagine.\u003C\u002Fp>\n\u003Ch2 id=\"section-17\">Le tre modalità runtime NTC sono la chiave per comprendere la tecnologia\u003C\u002Fh2>\n\u003Cp>La parte più importante di RTXNTC non è solo come viene compresso il materiale. È quando il gioco sceglie di decomprimerlo.\u003C\u002Fp>\n\u003Csection class=\"editorjs-comparison my-6\">\u003Ch3 class=\"mb-3 text-lg font-semibold\">Inferenza al caricamento vs Inferenza al campionamento vs Inferenza al feedback\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\">Quando avviene la decodifica neurale\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\">Comportamento della memoria texture a runtime\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\">Compromesso principale\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\">Inferenza al caricamento\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\">Inferenza al campionamento\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\">Inferenza al feedback\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\">Inferenza al caricamento: compressione neurale come formato di archiviazione\u003C\u002Fh2>\n\u003Cp>L'Inferenza al caricamento è la modalità più semplice da comprendere.\u003C\u002Fp>\n\u003Cp>Il gioco memorizza il materiale in forma NTC compatta. Quando l'asset viene caricato, la GPU ricostruisce i dati della texture e può transcodificarli nei formati di texture BCn ordinari.\u003C\u002Fp>\n\u003Cp>Dopo questo passaggio, il rendering può utilizzare il campionamento texture normale. Il risparmio importante avviene principalmente prima della decompressione: dimensione del gioco pacchettizzato, dimensione del download o larghezza di banda dello streaming degli asset.\u003C\u002Fp>\n\u003Cp>Ma una volta che il materiale è completamente espanso in texture convenzionali, il suo ingombro in VRAM a runtime si avvicina di nuovo alla rappresentazione convenzionale.\u003C\u002Fp>\n\u003Ch2 id=\"section-25\">Inferenza al campionamento: mantenere la texture neurale in VRAM\u003C\u002Fh2>\n\u003Cp>L'Inferenza al campionamento è la modalità più radicale.\u003C\u002Fp>\n\u003Cp>Invece di espandere il materiale in texture convenzionali prima del rendering, lo shader legge dati latenti compatti ed esegue il decodificatore neurale quando ha bisogno dei valori della texture.\u003C\u002Fp>\n\u003Cp>L'esempio dell'SDK di NVIDIA confronta una rappresentazione del materiale BCn da 12 MB con una rappresentazione NTC da 2,5 MB quando si utilizza l'Inferenza al campionamento.\u003C\u002Fp>\n\u003Cp>Il risparmio è reale perché i dati della texture convenzionale non devono rimanere completamente residenti. Ma il costo si sposta altrove: il pixel shader o l'hit shader ora esegue l'inferenza neurale.\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\">La compressione non fa sparire il lavoro\u003C\u002Fstrong>\u003Cdiv class=\"text-gray-700 dark:text-gray-200\">L'Inferenza al campionamento scambia \u003Cstrong>memoria e larghezza di banda\u003C\u002Fstrong> con \u003Cstrong>calcolo GPU\u003C\u002Fstrong>. La domanda giusta non è "Quanto è più piccola la texture?" ma "Il risparmio di memoria vale il costo di inferenza aggiuntivo su questo carico di lavoro?"\u003C\u002Fdiv>\u003C\u002Faside>\n\u003Caside class=\"editorjs-referral my-6\">\u003Ca href=\"https:\u002F\u002Ffigure.rocks\u002Fit\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\">L'uso della VRAM non è il fabbisogno di VRAM: perché un misuratore di memoria pieno non racconta tutta la storia\u003C\u002Fstrong>\u003Cp class=\"mt-2 text-sm text-gray-600 dark:text-gray-300\">Perché capacità VRAM, budget, residenza e pressione di memoria effettiva sono cose diverse.\u003C\u002Fp>\u003Cspan class=\"mt-3 inline-flex text-sm font-medium text-primary-600 dark:text-primary-400\">Leggi la guida sulla VRAM →\u003C\u002Fspan>\u003C\u002Fdiv>\u003C\u002Fa>\u003C\u002Faside>\n\u003Ch2 id=\"section-32\">Inferenza al feedback: decodificare solo ciò che il giocatore vede effettivamente\u003C\u002Fh2>\n\u003Cp>L'Inferenza al feedback si colloca tra i due estremi.\u003C\u002Fp>\n\u003Cp>Il renderer tiene traccia di quali tile di texture sono effettivamente richiesti. Invece di espandere immediatamente l'intero materiale, il sistema può decodificare i tile richiesti in batch e mantenere una cache di lavoro.\u003C\u002Fp>\n\u003Cp>Concettualmente, questo combina la compressione neurale con lo streaming delle texture: il sistema paga il costo di decodifica solo per le regioni che diventano rilevanti.\u003C\u002Fp>\n\u003Cp>L'attuale implementazione di esempio è più specializzata rispetto alle altre modalità e i suoi vincoli di supporto differiscono, quindi dovrebbe essere trattata come una strategia di integrazione piuttosto che come un sostituto universale dello streaming di texture ordinario.\u003C\u002Fp>\n\u003Ch2 id=\"section-37\">Lo scambio tra archiviazione delle texture e calcolo\u003C\u002Fh2>\n\u003Cp>Il modo più semplice per comprendere la compressione neurale delle texture è considerarla uno scambio tra risorse.\u003C\u002Fp>\n\u003Csection class=\"editorjs-comparison my-6\">\u003Ch3 class=\"mb-3 text-lg font-semibold\">Cosa cambia quando l'archiviazione delle texture diventa neurale\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\">Texture compressa tradizionale\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\">Rappresentazione neurale della texture\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\">Disco\u002Farchiviazione\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\">Costo di campionamento\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\">Controllo qualità\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\">Perché l'hardware specializzato per matrici è importante\u003C\u002Fh2>\n\u003Cp>Eseguire una rete neurale per il campionamento delle texture sarebbe troppo costoso se ogni moltiplicazione dovesse essere gestita come un normale lavoro scalare dello shader.\u003C\u002Fp>\n\u003Cp>RTXNTC trae quindi vantaggio da Cooperative Vector e ora dai percorsi DirectX 12 Linear Algebra che consentono agli shader di utilizzare l'hardware di accelerazione matriciale della GPU.\u003C\u002Fp>\n\u003Cp>La beta v0.10.0 ha aggiunto esplicitamente l'inferenza tramite l'API DirectX 12 Linear Algebra introdotta con Shader Model 6.10.\u003C\u002Fp>\n\u003Caside class=\"editorjs-referral my-6\">\u003Ca href=\"https:\u002F\u002Ffigure.rocks\u002Fit\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 sta diventando una piattaforma ML: cosa significano Linear Algebra e Neural Shaders per i giochi futuri\u003C\u002Fstrong>\u003Cp class=\"mt-2 text-sm text-gray-600 dark:text-gray-300\">Come DirectX sta spostando le operazioni matriciali e neurali direttamente in HLSL e nella pipeline grafica.\u003C\u002Fp>\u003Cspan class=\"mt-3 inline-flex text-sm font-medium text-primary-600 dark:text-primary-400\">Leggi la guida ai neural shader di DirectX →\u003C\u002Fspan>\u003C\u002Fdiv>\u003C\u002Fa>\u003C\u002Faside>\n\u003Ch2 id=\"section-45\">Perché la compressione neurale delle texture non è upscaling delle texture\u003C\u002Fh2>\n\u003Cp>Entrambe le tecniche possono utilizzare il machine learning, ma risolvono problemi diversi.\u003C\u002Fp>\n\u003Csection class=\"editorjs-comparison my-6\">\u003Ch3 class=\"mb-3 text-lg font-semibold\">Compressione neurale delle texture vs Super Resolution\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\">Compressione neurale delle texture\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 Resolution\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\">Input\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\">Obiettivo\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\">Quando viene eseguita\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\">Output\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>La compressione neurale delle texture può quindi esistere al di sotto di DLSS, FSR, XeSS o rendering nativo. Cambia il modo in cui i dati dei materiali vengono archiviati e ricostruiti, non la risoluzione finale di visualizzazione.\u003C\u002Fp>\n\u003Ch2 id=\"section-49\">Il parametro di qualità è i bit per pixel\u003C\u002Fh2>\n\u003Cp>NTC è una compressione con perdita. La quantità di informazioni compresse è controllata in gran parte tramite l'impostazione dei bit per pixel.\u003C\u002Fp>\n\u003Cp>Un bitrate più elevato fornisce al modello più informazioni e generalmente migliora la qualità della ricostruzione. Un bitrate più basso migliora la compressione ma aumenta il rischio di errori visibili.\u003C\u002Fp>\n\u003Cp>Poiché più canali condividono la stessa rappresentazione, aggiungere più canali del materiale senza aumentare il bitrate può ridurre la qualità disponibile per ciascun canale.\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\">Neurale non significa senza perdita\u003C\u002Fstrong>\u003Cdiv class=\"text-gray-700 dark:text-gray-200\">La documentazione dell'SDK osserva esplicitamente che l'errore di compressione è normale. L'obiettivo pratico è \u003Cstrong>un errore visivo accettabile a un costo utile di archiviazione e runtime\u003C\u002Fstrong>.\u003C\u002Fdiv>\u003C\u002Faside>\n\u003Ch2 id=\"section-54\">Perché i canali del materiale correlati sono importanti\u003C\u002Fh2>\n\u003Cp>Una rappresentazione neurale diventa più preziosa quando diversi canali di materiale descrivono una struttura correlata.\u003C\u002Fp>\n\u003Cp>Se albedo, normal e roughness contengono tutti gli stessi graffi, cuciture o trama del tessuto, il decoder può sfruttare le informazioni spaziali condivise.\u003C\u002Fp>\n\u003Cp>Se i canali sono rumore non correlato, c'è meno struttura comune da sfruttare e il problema di compressione diventa più difficile.\u003C\u002Fp>\n\u003Ch2 id=\"section-58\">Il test del valore della texture neurale\u003C\u002Fh2>\n\u003Csection class=\"editorjs-process my-6\">\u003Ch3 class=\"mb-3 text-lg font-semibold\">Quando la compressione neurale delle texture è realmente utile?\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. Misurare il costo convenzionale della texture\u003C\u002Fdiv>\u003Cdiv class=\"mt-1 text-sm text-gray-600 dark:text-gray-300\">Quanto spazio su disco, larghezza di banda in streaming e VRAM consumano le texture dei materiali esistenti?\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. Scegliere la modalità di runtime\u003C\u002Fdiv>\u003Cdiv class=\"mt-1 text-sm text-gray-600 dark:text-gray-300\">Vuoi solo risparmiare spazio di archiviazione, risparmiare VRAM in modo persistente o ricostruire tile in streaming?\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. Impostare un obiettivo di qualità accettabile\u003C\u002Fdiv>\u003Cdiv class=\"mt-1 text-sm text-gray-600 dark:text-gray-300\">Confronta i canali ricostruiti con il materiale originale, non solo con l'immagine finale.\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. Misurare il costo di inferenza\u003C\u002Fdiv>\u003Cdiv class=\"mt-1 text-sm text-gray-600 dark:text-gray-300\">Registra il tempo aggiuntivo dello shader o di decompressione sulla GPU di destinazione effettiva.\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. Misurare il risparmio di memoria\u003C\u002Fdiv>\u003Cdiv class=\"mt-1 text-sm text-gray-600 dark:text-gray-300\">Controlla il working set reale a runtime, non solo la dimensione del file compresso.\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. Testare materiali difficili\u003C\u002Fdiv>\u003Cdiv class=\"mt-1 text-sm text-gray-600 dark:text-gray-300\">Normali fini, maschere nitide, opacità, testo e canali non correlati possono esporre fallimenti di compressione.\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. Decidere in base al beneficio netto\u003C\u002Fdiv>\u003Cdiv class=\"mt-1 text-sm text-gray-600 dark:text-gray-300\">Usa NTC solo dove la memoria\u002Flarghezza di banda risparmiata vale l'inferenza extra e la complessità di integrazione.\u003C\u002Fdiv>\u003C\u002Fdiv>\u003C\u002Fdiv>\u003C\u002Fsection>\n\u003Ch2 id=\"section-60\">Perché “8× più piccolo” necessita di contesto\u003C\u002Fh2>\n\u003Cp>RTX Kit di NVIDIA descrive RTX Neural Texture Compression come in grado di offrire fino a 8× di miglioramento della memoria su disco a una fedeltà visiva simile alla compressione a blocchi tradizionale.\u003C\u002Fp>\n\u003Cp>La frase “fino a” è importante. Il rapporto di compressione dipende dal materiale, dal numero di canali, dal bitrate target, dalla configurazione del decoder e dalla soglia di qualità.\u003C\u002Fp>\n\u003Cp>Lo stesso rapporto non descrive automaticamente il risparmio di VRAM. L'inferenza al caricamento può partire da un file compatto e comunque espandersi in texture GPU convenzionali. L'inferenza al campionamento preserva la rappresentazione compatta nella memoria GPU ma spende più calcolo durante lo shading.\u003C\u002Fp>\n\u003Ch2 id=\"section-64\">La dimensione del decoder è un altro compromesso prestazioni-qualità\u003C\u002Fh2>\n\u003Cp>Il runtime NTC utilizza un piccolo percettrone multistrato per decodificare i valori delle texture.\u003C\u002Fp>\n\u003Cp>L'attuale libreria di NVIDIA utilizza un'architettura decoder configurabile. Reti più grandi possono migliorare la qualità di compressione ma costano di più da eseguire; reti più piccole possono funzionare più velocemente con qualche perdita di qualità di ricostruzione.\u003C\u002Fp>\n\u003Cp>Ciò offre agli sviluppatori di engine un'altra dimensione di tuning oltre alla risoluzione delle texture e al bitrate.\u003C\u002Fp>\n\u003Ch2 id=\"section-68\">Perché questo è importante per le future installazioni di giochi\u003C\u002Fh2>\n\u003Cp>I giochi moderni distribuiscono sempre più set di materiali ad alta risoluzione che influenzano sia la dimensione del download che la memoria a runtime.\u003C\u002Fp>\n\u003Cp>La compressione neurale delle texture crea una nuova opzione: distribuire una rappresentazione compatta appresa e decidere in seguito se espanderla al caricamento, ricostruirla direttamente durante lo shading o decodificare solo le tile richieste.\u003C\u002Fp>\n\u003Cp>Ciò significa che una singola rappresentazione compressa di asset può partecipare a diverse strategie di memoria a runtime.\u003C\u002Fp>\n\u003Ch2 id=\"section-72\">Cambia anche cosa significa 'memoria delle texture'\u003C\u002Fh2>\n\u003Cp>Con il rendering tradizionale, un budget di memoria delle texture riguarda principalmente formati delle texture, livelli mip, risoluzione e residenza.\u003C\u002Fp>\n\u003Cp>Con le texture neurali, gli sviluppatori possono anche preventivare dati latenti, pesi del decoder, buffer di inferenza, cache transcodificate e la potenza di calcolo necessaria per ricostruire i valori richiesti.\u003C\u002Fp>\n\u003Cp>Quindi l'asset non ha più una sola semplice identità di memoria fissa.\u003C\u002Fp>\n\u003Ch2 id=\"section-76\">Il supporto cross-vendor è più sfumato di quanto suggerisca il nome RTX\u003C\u002Fh2>\n\u003Cp>RTXNTC è un SDK NVIDIA, e la compressione stessa attualmente richiede una GPU NVIDIA secondo i requisiti dell'SDK.\u003C\u002Fp>\n\u003Cp>La decompressione a runtime è più ampia. NVIDIA documenta percorsi funzionali su hardware Shader Model 6 e segnala la validazione su GPU NVIDIA, AMD e Intel, mentre i percorsi avanzati Cooperative Vector \u002F Linear Algebra dipendono dal supporto API e driver.\u003C\u002Fp>\n\u003Cp>Prestazioni e parità di funzionalità non dovrebbero quindi essere date per scontate tra i vari vendor solo perché il decoder di base può essere eseguito.\u003C\u002Fp>\n\u003Ch2 id=\"section-80\">Cosa cambierebbe questa risposta?\u003C\u002Fh2>\n\u003Cp>NTC è ancora in beta. Modalità di runtime, architetture dei decoder, supporto dei driver e percorsi di integrazione possono cambiare prima di una release stabile per la produzione.\u003C\u002Fp>\n\u003Cp>Il cambiamento più grande a lungo termine sarebbe l'adozione diffusa di primitive standardizzate per shader neurali in DirectX e Vulkan. Ciò renderebbe la decodifica delle texture neurali meno dipendente da percorsi di esecuzione personalizzati specifici del vendor.\u003C\u002Fp>\n\u003Ch2 id=\"section-83\">Limitazioni\u003C\u002Fh2>\n\u003Cp>Questo articolo descrive l'architettura e il comportamento attuale dell'SDK RTXNTC pubblico. Le dichiarazioni di compressione citate da NVIDIA sono fornite dal vendor e non dovrebbero essere considerate risultati garantiti per ogni materiale.\u003C\u002Fp>\n\u003Cp>L'SDK attuale è software in beta, e alcuni percorsi di anteprima hanno limitazioni documentate di driver e piattaforma.\u003C\u002Fp>\n\u003Ch2 id=\"section-86\">Conclusione\u003C\u002Fh2>\n\u003Cp>RTX Neural Texture Compression è interessante perché cambia un presupposto molto vecchio: il dettaglio delle texture non deve sempre esistere in memoria come texel convenzionali.\u003C\u002Fp>\n\u003Cp>Un materiale può invece essere memorizzato in parte come una rappresentazione appresa compatta e ricostruito quando necessario.\u003C\u002Fp>\n\u003Cp>Ciò non offre qualità gratuita o memoria gratuita. Crea un nuovo scambio: meno archiviazione, larghezza di banda e potenzialmente VRAM in cambio di lavoro di inferenza neurale.\u003C\u002Fp>\n\u003Cp>La vera innovazione non è \"l'IA rende le texture più nitide\". È che parte dei dati dei materiali di un gioco può diventare calcolo.\u003C\u002Fp>\n\u003Ch2 id=\"section-91\">FAQ\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 spiegato in parole semplici\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\">RTX Neural Texture Compression è un upscaler?\u003C\u002Fh4>\u003Cdiv class=\"mt-2 text-gray-600 dark:text-gray-300\">No. Comprime e ricostruisce i dati delle texture dei materiali. Le tecnologie di super-risoluzione ricostruiscono l'immagine renderizzata finale a una risoluzione di visualizzazione superiore.\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\">NTC può ridurre l'utilizzo della VRAM?\u003C\u002Fh4>\u003Cdiv class=\"mt-2 text-gray-600 dark:text-gray-300\">Sì, in particolare con Inference on Sample perché la rappresentazione neurale compatta può rimanere nella memoria della GPU invece di essere completamente espansa in texture convenzionali. Inference on Load preserva principalmente il risparmio di archiviazione prima dell'espansione.\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\">Cosa memorizza la rete neurale?\u003C\u002Fh4>\u003Cdiv class=\"mt-2 text-gray-600 dark:text-gray-300\">Il materiale compresso contiene dati compatti di feature latenti più i pesi per una piccola rete decoder che ricostruisce i valori delle texture.\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\">NTC decodifica l'intera texture prima del rendering?\u003C\u002Fh4>\u003Cdiv class=\"mt-2 text-gray-600 dark:text-gray-300\">Non necessariamente. Inference on Load lo fa, Inference on Sample ricostruisce i valori durante il campionamento nello shader, e Inference on Feedback può decodificare i tile di texture richiesti.\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\">La compressione neurale delle texture è senza perdita?\u003C\u002Fh4>\u003Cdiv class=\"mt-2 text-gray-600 dark:text-gray-300\">No. RTXNTC è una compressione con perdita e la qualità dipende dal bitrate, dal numero di canali, dalla configurazione del decoder e dal materiale stesso.\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\">RTXNTC è pronto per la produzione?\u003C\u002Fh4>\u003Cdiv class=\"mt-2 text-gray-600 dark:text-gray-300\">L'attuale SDK pubblico è ancora etichettato come beta, quindi API, supporto e caratteristiche prestazionali potrebbero continuare a cambiare.\u003C\u002Fdiv>\u003C\u002Fdiv>\u003C\u002Fsection>\n\u003Ch2 id=\"section-93\">Glossario\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\">Termini chiave delle texture neurali\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\">Una tecnica che memorizza le informazioni delle texture come dati latenti compatti più i pesi del decoder neurale invece che solo come blocchi di texel convenzionali.\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\">Dati latenti\u003C\u002Fdt>\u003Cdd class=\"mt-1 text-gray-600 dark:text-gray-300\">Feature compatte apprese che il decoder neurale utilizza per ricostruire i valori delle texture.\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\">Decoder\u003C\u002Fdt>\u003Cdd class=\"mt-1 text-gray-600 dark:text-gray-300\">Una piccola rete neurale che converte le feature latenti in canali di texture ricostruiti.\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\">Modalità di runtime che decodifica la texture neurale quando un asset viene caricato, solitamente in formati di texture convenzionali.\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\">Modalità di runtime che esegue la decodifica neurale direttamente durante il campionamento della texture, così la rappresentazione compressa può rimanere residente.\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\">Strategia di runtime che utilizza il feedback delle texture per decodificare e memorizzare nella cache solo i tile richiesti.\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\">Un modello di Figure Rocks che descrive lo scambio di archiviazione delle texture, larghezza di banda e VRAM per ulteriore lavoro di inferenza neurale sulla GPU.\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\">Un flusso di lavoro di Figure Rocks per decidere se la compressione neurale delle texture crea un beneficio netto per un particolare materiale e una GPU target.\u003C\u002Fdd>\u003C\u002Fdiv>\u003C\u002Fdl>\u003C\u002Fsection>\n\u003Ch2 id=\"section-95\">Fonti primarie\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\">Panoramica ufficiale di RTX Neural Texture Compression e del posizionamento pubblicato da NVIDIA sulla riduzione dell'archiviazione.\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\">Documentazione ufficiale dell'SDK che descrive la compressione dei canali dei materiali, la rappresentazione decoder\u002Flatente, le modalità di runtime, esempi di memoria, requisiti di sistema e supporto 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 — Releases\u003C\u002Fstrong>\u003Cp class=\"text-sm text-gray-600 dark:text-gray-400\">Cronologia ufficiale delle release, inclusa la beta v0.10.0 e il supporto all'inferenza DirectX 12 Linear Algebra.\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 — Compression Settings and Image Quality\u003C\u002Fstrong>\u003Cp class=\"text-sm text-gray-600 dark:text-gray-400\">Documentazione ufficiale che copre bitrate, interazioni tra canali, misurazione della qualità e comportamento della compressione con perdita.\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\">Documentazione ufficiale della libreria di runtime che descrive la configurazione del decoder e il compromesso qualità\u002Fprestazioni delle diverse dimensioni della rete neurale.\u003C\u002Fp>\u003C\u002Fa>",{"time":538,"blocks":539,"version":1249},1790379095970,[540,546,554,561,568,574,579,584,589,594,599,604,627,632,637,642,647,652,657,687,692,697,702,707,712,717,722,727,732,737,743,752,757,762,767,772,777,782,787,816,821,826,831,836,844,849,854,882,887,892,897,902,907,913,918,923,928,933,938,965,970,975,980,985,990,995,1000,1005,1010,1015,1020,1025,1030,1035,1040,1045,1050,1055,1060,1065,1070,1075,1080,1085,1090,1095,1100,1105,1110,1115,1120,1125,1155,1160,1198,1203,1213,1222,1231,1240],{"id":541,"data":542,"type":544,"tunes":545},"intro",{"text":543},"La compressione delle texture normalmente significa memorizzare una versione più piccola dei dati della texture ed espanderla in un formato GPU convenzionale prima o durante l'uso. NVIDIA RTX Neural Texture Compression cambia questo modello: parte dei dati della texture diventa una piccola rappresentazione neurale che può essere decodificata dalla GPU stessa.","paragraph",{},{"id":547,"data":548,"type":552,"tunes":553},"direct",{"body":549,"title":550,"variant":551},"\u003Cstrong>RTX Neural Texture Compression non è upscaling delle texture.\u003C\u002Fstrong> Comprime più texture di materiale in pesi di rete neurale più dati latenti compatti, poi ricostruisce i valori delle texture richiesti con una piccola rete neurale. A seconda della modalità di integrazione, il gioco può scambiare spazio di archiviazione e utilizzo della VRAM con lavoro di inferenza GPU aggiuntivo.","Risposta diretta","info","callout",{},{"id":555,"data":556,"type":552,"tunes":560},"status",{"body":557,"title":558,"variant":559},"RTX Neural Texture Compression è ancora un \u003Cstrong>SDK beta\u003C\u002Fstrong>. L'attuale beta v0.10.0 ha aggiunto il supporto all'inferenza DirectX 12 Linear Algebra, collegando NTC direttamente alla nuova infrastruttura di shader neurali discussa altrove su Figure Rocks.","Stato attuale","note",{},{"id":562,"data":563,"type":566,"tunes":567},"toc",{"title":564,"maxLevel":565,"minLevel":47},"Contenuti",3,"tableOfContents",{},{"id":569,"data":570,"type":572,"tunes":573},"h-normal",{"text":571,"level":47},"Perché la normale compressione delle texture usa ancora molta memoria","header",{},{"id":575,"data":576,"type":544,"tunes":578},"p-normal-1",{"text":577},"Un moderno materiale basato sulla fisica raramente consiste in una sola immagine. Una singola superficie può utilizzare albedo, normal, roughness, metalness, occlusione ambientale, opacità e altri canali.",{},{"id":580,"data":581,"type":544,"tunes":583},"p-normal-2",{"text":582},"I formati tradizionali di compressione a blocchi GPU come BC1 fino a BC7 riducono il costo, ma la GPU finisce comunque per memorizzare blocchi di texture convenzionali per il materiale.",{},{"id":585,"data":586,"type":544,"tunes":588},"p-normal-3",{"text":587},"Man mano che la risoluzione delle texture e la complessità dei materiali aumentano, quei canali consumano spazio su disco, larghezza di banda in streaming e memoria GPU.",{},{"id":590,"data":591,"type":572,"tunes":593},"h-store",{"text":592,"level":47},"Cosa memorizza effettivamente RTX Neural Texture Compression",{},{"id":595,"data":596,"type":544,"tunes":598},"p-store-1",{"text":597},"L'SDK RTXNTC di NVIDIA comprime insieme i canali appartenenti a un materiale. L'SDK attuale supporta fino a 16 canali di texture in un set di texture NTC.",{},{"id":600,"data":601,"type":544,"tunes":603},"p-store-2",{"text":602},"Invece di mantenere solo texel compressi convenzionali, il processo di compressione produce due cose principali: pesi per un piccolo decoder neurale e dati di feature latenti compatti.",{},{"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. Texture originali del materiale","Albedo, normal, roughness, metalness e altri canali del materiale sono forniti insieme.",{"label":612,"description":613},"2. Compressione offline","L'SDK apprende una rappresentazione compatta del materiale.",{"label":615,"description":616},"3. Pesi neurali","Una piccola rete decoder memorizza parte di ciò che serve per ricostruire il materiale.",{"label":618,"description":619},"4. Dati latenti","Tensori di feature compatti memorizzano informazioni specifiche del materiale.",{"label":621,"description":622},"5. Inferenza GPU","A runtime, il decoder combina i dati latenti e i pesi neurali per ricostruire i valori delle texture.","La pipeline delle texture neurali","auto","processFlow",{},{"id":628,"data":629,"type":572,"tunes":631},"h-correlation",{"text":630,"level":47},"Perché comprimere i canali insieme può aiutare",{},{"id":633,"data":634,"type":544,"tunes":636},"p-cor-1",{"text":635},"I canali del materiale sono spesso correlati. Un graffio visibile nel colore di base può apparire anche nella mappa normal o roughness. Un motivo di tessuto può influenzare diversi canali nella stessa posizione spaziale.",{},{"id":638,"data":639,"type":544,"tunes":641},"p-cor-2",{"text":640},"NVIDIA ha progettato NTC per sfruttare queste correlazioni invece di comprimere ogni texture in modo indipendente.",{},{"id":643,"data":644,"type":544,"tunes":646},"p-cor-3",{"text":645},"Questo è uno dei motivi per cui la tecnologia è descritta come compressione orientata al materiale piuttosto che semplicemente un altro formato immagine.",{},{"id":648,"data":649,"type":572,"tunes":651},"h-modes",{"text":650,"level":47},"Le tre modalità runtime NTC sono la chiave per comprendere la tecnologia",{},{"id":653,"data":654,"type":544,"tunes":656},"p-modes-1",{"text":655},"La parte più importante di RTXNTC non è solo come viene compresso il materiale. È quando il gioco sceglie di decomprimerlo.",{},{"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","Inferenza al caricamento",[13,13,13],{"id":666,"label":667,"values":668},"sample","Inferenza al campionamento",[13,13,13],{"id":670,"label":671,"values":672},"feedback","Inferenza al feedback",[13,13,13],"Inferenza al caricamento vs Inferenza al campionamento vs Inferenza al feedback","table",[676,679,682],{"id":677,"label":678},"when","Quando avviene la decodifica neurale",{"id":680,"label":681},"memory","Comportamento della memoria texture a runtime",{"id":683,"label":684},"tradeoff","Compromesso principale","comparison",{},{"id":688,"data":689,"type":572,"tunes":691},"h-load",{"text":690,"level":47},"Inferenza al caricamento: compressione neurale come formato di archiviazione",{},{"id":693,"data":694,"type":544,"tunes":696},"p-load-1",{"text":695},"L'Inferenza al caricamento è la modalità più semplice da comprendere.",{},{"id":698,"data":699,"type":544,"tunes":701},"p-load-2",{"text":700},"Il gioco memorizza il materiale in forma NTC compatta. Quando l'asset viene caricato, la GPU ricostruisce i dati della texture e può transcodificarli nei formati di texture BCn ordinari.",{},{"id":703,"data":704,"type":544,"tunes":706},"p-load-3",{"text":705},"Dopo questo passaggio, il rendering può utilizzare il campionamento texture normale. Il risparmio importante avviene principalmente prima della decompressione: dimensione del gioco pacchettizzato, dimensione del download o larghezza di banda dello streaming degli asset.",{},{"id":708,"data":709,"type":544,"tunes":711},"p-load-4",{"text":710},"Ma una volta che il materiale è completamente espanso in texture convenzionali, il suo ingombro in VRAM a runtime si avvicina di nuovo alla rappresentazione convenzionale.",{},{"id":713,"data":714,"type":572,"tunes":716},"h-sample",{"text":715,"level":47},"Inferenza al campionamento: mantenere la texture neurale in VRAM",{},{"id":718,"data":719,"type":544,"tunes":721},"p-sample-1",{"text":720},"L'Inferenza al campionamento è la modalità più radicale.",{},{"id":723,"data":724,"type":544,"tunes":726},"p-sample-2",{"text":725},"Invece di espandere il materiale in texture convenzionali prima del rendering, lo shader legge dati latenti compatti ed esegue il decodificatore neurale quando ha bisogno dei valori della texture.",{},{"id":728,"data":729,"type":544,"tunes":731},"p-sample-3",{"text":730},"L'esempio dell'SDK di NVIDIA confronta una rappresentazione del materiale BCn da 12 MB con una rappresentazione NTC da 2,5 MB quando si utilizza l'Inferenza al campionamento.",{},{"id":733,"data":734,"type":544,"tunes":736},"p-sample-4",{"text":735},"Il risparmio è reale perché i dati della texture convenzionale non devono rimanere completamente residenti. Ma il costo si sposta altrove: il pixel shader o l'hit shader ora esegue l'inferenza neurale.",{},{"id":683,"data":738,"type":552,"tunes":742},{"body":739,"title":740,"variant":741},"L'Inferenza al campionamento scambia \u003Cstrong>memoria e larghezza di banda\u003C\u002Fstrong> con \u003Cstrong>calcolo GPU\u003C\u002Fstrong>. La domanda giusta non è \"Quanto è più piccola la texture?\" ma \"Il risparmio di memoria vale il costo di inferenza aggiuntivo su questo carico di lavoro?\"","La compressione non fa sparire il lavoro","warning",{},{"id":744,"data":745,"type":750,"tunes":751},"ref-vram",{"url":746,"title":747,"excerpt":748,"ctaLabel":749},"https:\u002F\u002Ffigure.rocks\u002Fit\u002Fblog\u002Fvram-usage-is-not-vram-requirement-why-a-full-memory-meter-does-not-tell-the-whole-story","L'uso della VRAM non è il fabbisogno di VRAM: perché un misuratore di memoria pieno non racconta tutta la storia","Perché capacità VRAM, budget, residenza e pressione di memoria effettiva sono cose diverse.","Leggi la guida sulla VRAM","referralArticle",{},{"id":753,"data":754,"type":572,"tunes":756},"h-feedback",{"text":755,"level":47},"Inferenza al feedback: decodificare solo ciò che il giocatore vede effettivamente",{},{"id":758,"data":759,"type":544,"tunes":761},"p-feed-1",{"text":760},"L'Inferenza al feedback si colloca tra i due estremi.",{},{"id":763,"data":764,"type":544,"tunes":766},"p-feed-2",{"text":765},"Il renderer tiene traccia di quali tile di texture sono effettivamente richiesti. Invece di espandere immediatamente l'intero materiale, il sistema può decodificare i tile richiesti in batch e mantenere una cache di lavoro.",{},{"id":768,"data":769,"type":544,"tunes":771},"p-feed-3",{"text":770},"Concettualmente, questo combina la compressione neurale con lo streaming delle texture: il sistema paga il costo di decodifica solo per le regioni che diventano rilevanti.",{},{"id":773,"data":774,"type":544,"tunes":776},"p-feed-4",{"text":775},"L'attuale implementazione di esempio è più specializzata rispetto alle altre modalità e i suoi vincoli di supporto differiscono, quindi dovrebbe essere trattata come una strategia di integrazione piuttosto che come un sostituto universale dello streaming di texture ordinario.",{},{"id":778,"data":779,"type":572,"tunes":781},"h-exchange",{"text":780,"level":47},"Lo scambio tra archiviazione delle texture e calcolo",{},{"id":783,"data":784,"type":544,"tunes":786},"p-exchange-1",{"text":785},"Il modo più semplice per comprendere la compressione neurale delle texture è considerarla uno scambio tra risorse.",{},{"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","Disco\u002Farchiviazione",[13,13],{"id":796,"label":797,"values":798},"vram","VRAM",[13,13],{"id":800,"label":801,"values":802},"sampling","Costo di campionamento",[13,13],{"id":804,"label":805,"values":806},"quality","Controllo qualità",[13,13],"Cosa cambia quando l'archiviazione delle texture diventa neurale",[809,812],{"id":810,"label":811},"traditional","Texture compressa tradizionale",{"id":813,"label":814},"neural","Rappresentazione neurale della texture",{},{"id":817,"data":818,"type":572,"tunes":820},"h-matrix",{"text":819,"level":47},"Perché l'hardware specializzato per matrici è importante",{},{"id":822,"data":823,"type":544,"tunes":825},"p-matrix-1",{"text":824},"Eseguire una rete neurale per il campionamento delle texture sarebbe troppo costoso se ogni moltiplicazione dovesse essere gestita come un normale lavoro scalare dello shader.",{},{"id":827,"data":828,"type":544,"tunes":830},"p-matrix-2",{"text":829},"RTXNTC trae quindi vantaggio da Cooperative Vector e ora dai percorsi DirectX 12 Linear Algebra che consentono agli shader di utilizzare l'hardware di accelerazione matriciale della GPU.",{},{"id":832,"data":833,"type":544,"tunes":835},"p-matrix-3",{"text":834},"La beta v0.10.0 ha aggiunto esplicitamente l'inferenza tramite l'API DirectX 12 Linear Algebra introdotta con 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\u002Fit\u002Fblog\u002Fdirectx-is-becoming-an-ml-platform-what-linear-algebra-and-neural-shaders-mean-for-future-games","DirectX sta diventando una piattaforma ML: cosa significano Linear Algebra e Neural Shaders per i giochi futuri","Come DirectX sta spostando le operazioni matriciali e neurali direttamente in HLSL e nella pipeline grafica.","Leggi la guida ai neural shader di DirectX",{},{"id":845,"data":846,"type":572,"tunes":848},"h-not-upscale",{"text":847,"level":47},"Perché la compressione neurale delle texture non è upscaling delle texture",{},{"id":850,"data":851,"type":544,"tunes":853},"p-notup-1",{"text":852},"Entrambe le tecniche possono utilizzare il machine learning, ma risolvono problemi diversi.",{},{"id":855,"data":856,"type":685,"tunes":881},"ntc-vs-sr",{"rows":857,"title":873,"layout":674,"columns":874},[858,862,866,869],{"id":859,"label":860,"values":861},"input","Input",[13,13],{"id":863,"label":864,"values":865},"goal","Obiettivo",[13,13],{"id":677,"label":867,"values":868},"Quando viene eseguita",[13,13],{"id":870,"label":871,"values":872},"output","Output",[13,13],"Compressione neurale delle texture vs Super Resolution",[875,878],{"id":876,"label":877},"ntc","Compressione neurale delle texture",{"id":879,"label":880},"sr","Super Resolution",{},{"id":883,"data":884,"type":544,"tunes":886},"p-notup-2",{"text":885},"La compressione neurale delle texture può quindi esistere al di sotto di DLSS, FSR, XeSS o rendering nativo. Cambia il modo in cui i dati dei materiali vengono archiviati e ricostruiti, non la risoluzione finale di visualizzazione.",{},{"id":888,"data":889,"type":572,"tunes":891},"h-bpp",{"text":890,"level":47},"Il parametro di qualità è i bit per pixel",{},{"id":893,"data":894,"type":544,"tunes":896},"p-bpp-1",{"text":895},"NTC è una compressione con perdita. La quantità di informazioni compresse è controllata in gran parte tramite l'impostazione dei bit per pixel.",{},{"id":898,"data":899,"type":544,"tunes":901},"p-bpp-2",{"text":900},"Un bitrate più elevato fornisce al modello più informazioni e generalmente migliora la qualità della ricostruzione. Un bitrate più basso migliora la compressione ma aumenta il rischio di errori visibili.",{},{"id":903,"data":904,"type":544,"tunes":906},"p-bpp-3",{"text":905},"Poiché più canali condividono la stessa rappresentazione, aggiungere più canali del materiale senza aumentare il bitrate può ridurre la qualità disponibile per ciascun canale.",{},{"id":908,"data":909,"type":552,"tunes":912},"lossy-note",{"body":910,"title":911,"variant":559},"La documentazione dell'SDK osserva esplicitamente che l'errore di compressione è normale. L'obiettivo pratico è \u003Cstrong>un errore visivo accettabile a un costo utile di archiviazione e runtime\u003C\u002Fstrong>.","Neurale non significa senza perdita",{},{"id":914,"data":915,"type":572,"tunes":917},"h-channels",{"text":916,"level":47},"Perché i canali del materiale correlati sono importanti",{},{"id":919,"data":920,"type":544,"tunes":922},"p-channels-1",{"text":921},"Una rappresentazione neurale diventa più preziosa quando diversi canali di materiale descrivono una struttura correlata.",{},{"id":924,"data":925,"type":544,"tunes":927},"p-channels-2",{"text":926},"Se albedo, normal e roughness contengono tutti gli stessi graffi, cuciture o trama del tessuto, il decoder può sfruttare le informazioni spaziali condivise.",{},{"id":929,"data":930,"type":544,"tunes":932},"p-channels-3",{"text":931},"Se i canali sono rumore non correlato, c'è meno struttura comune da sfruttare e il problema di compressione diventa più difficile.",{},{"id":934,"data":935,"type":572,"tunes":937},"h-test",{"text":936,"level":47},"Il test del valore della texture neurale",{},{"id":939,"data":940,"type":625,"tunes":964},"value-test",{"steps":941,"title":963,"orientation":624},[942,945,948,951,954,957,960],{"label":943,"description":944},"1. Misurare il costo convenzionale della texture","Quanto spazio su disco, larghezza di banda in streaming e VRAM consumano le texture dei materiali esistenti?",{"label":946,"description":947},"2. Scegliere la modalità di runtime","Vuoi solo risparmiare spazio di archiviazione, risparmiare VRAM in modo persistente o ricostruire tile in streaming?",{"label":949,"description":950},"3. Impostare un obiettivo di qualità accettabile","Confronta i canali ricostruiti con il materiale originale, non solo con l'immagine finale.",{"label":952,"description":953},"4. Misurare il costo di inferenza","Registra il tempo aggiuntivo dello shader o di decompressione sulla GPU di destinazione effettiva.",{"label":955,"description":956},"5. Misurare il risparmio di memoria","Controlla il working set reale a runtime, non solo la dimensione del file compresso.",{"label":958,"description":959},"6. Testare materiali difficili","Normali fini, maschere nitide, opacità, testo e canali non correlati possono esporre fallimenti di compressione.",{"label":961,"description":962},"7. Decidere in base al beneficio netto","Usa NTC solo dove la memoria\u002Flarghezza di banda risparmiata vale l'inferenza extra e la complessità di integrazione.","Quando la compressione neurale delle texture è realmente utile?",{},{"id":966,"data":967,"type":572,"tunes":969},"h-8x",{"text":968,"level":47},"Perché “8× più piccolo” necessita di contesto",{},{"id":971,"data":972,"type":544,"tunes":974},"p-8x-1",{"text":973},"RTX Kit di NVIDIA descrive RTX Neural Texture Compression come in grado di offrire fino a 8× di miglioramento della memoria su disco a una fedeltà visiva simile alla compressione a blocchi tradizionale.",{},{"id":976,"data":977,"type":544,"tunes":979},"p-8x-2",{"text":978},"La frase “fino a” è importante. Il rapporto di compressione dipende dal materiale, dal numero di canali, dal bitrate target, dalla configurazione del decoder e dalla soglia di qualità.",{},{"id":981,"data":982,"type":544,"tunes":984},"p-8x-3",{"text":983},"Lo stesso rapporto non descrive automaticamente il risparmio di VRAM. L'inferenza al caricamento può partire da un file compatto e comunque espandersi in texture GPU convenzionali. L'inferenza al campionamento preserva la rappresentazione compatta nella memoria GPU ma spende più calcolo durante lo shading.",{},{"id":986,"data":987,"type":572,"tunes":989},"h-decoder",{"text":988,"level":47},"La dimensione del decoder è un altro compromesso prestazioni-qualità",{},{"id":991,"data":992,"type":544,"tunes":994},"p-dec-1",{"text":993},"Il runtime NTC utilizza un piccolo percettrone multistrato per decodificare i valori delle texture.",{},{"id":996,"data":997,"type":544,"tunes":999},"p-dec-2",{"text":998},"L'attuale libreria di NVIDIA utilizza un'architettura decoder configurabile. Reti più grandi possono migliorare la qualità di compressione ma costano di più da eseguire; reti più piccole possono funzionare più velocemente con qualche perdita di qualità di ricostruzione.",{},{"id":1001,"data":1002,"type":544,"tunes":1004},"p-dec-3",{"text":1003},"Ciò offre agli sviluppatori di engine un'altra dimensione di tuning oltre alla risoluzione delle texture e al bitrate.",{},{"id":1006,"data":1007,"type":572,"tunes":1009},"h-install",{"text":1008,"level":47},"Perché questo è importante per le future installazioni di giochi",{},{"id":1011,"data":1012,"type":544,"tunes":1014},"p-install-1",{"text":1013},"I giochi moderni distribuiscono sempre più set di materiali ad alta risoluzione che influenzano sia la dimensione del download che la memoria a runtime.",{},{"id":1016,"data":1017,"type":544,"tunes":1019},"p-install-2",{"text":1018},"La compressione neurale delle texture crea una nuova opzione: distribuire una rappresentazione compatta appresa e decidere in seguito se espanderla al caricamento, ricostruirla direttamente durante lo shading o decodificare solo le tile richieste.",{},{"id":1021,"data":1022,"type":544,"tunes":1024},"p-install-3",{"text":1023},"Ciò significa che una singola rappresentazione compressa di asset può partecipare a diverse strategie di memoria a runtime.",{},{"id":1026,"data":1027,"type":572,"tunes":1029},"h-meaning",{"text":1028,"level":47},"Cambia anche cosa significa 'memoria delle texture'",{},{"id":1031,"data":1032,"type":544,"tunes":1034},"p-meaning-1",{"text":1033},"Con il rendering tradizionale, un budget di memoria delle texture riguarda principalmente formati delle texture, livelli mip, risoluzione e residenza.",{},{"id":1036,"data":1037,"type":544,"tunes":1039},"p-meaning-2",{"text":1038},"Con le texture neurali, gli sviluppatori possono anche preventivare dati latenti, pesi del decoder, buffer di inferenza, cache transcodificate e la potenza di calcolo necessaria per ricostruire i valori richiesti.",{},{"id":1041,"data":1042,"type":544,"tunes":1044},"p-meaning-3",{"text":1043},"Quindi l'asset non ha più una sola semplice identità di memoria fissa.",{},{"id":1046,"data":1047,"type":572,"tunes":1049},"h-cross",{"text":1048,"level":47},"Il supporto cross-vendor è più sfumato di quanto suggerisca il nome RTX",{},{"id":1051,"data":1052,"type":544,"tunes":1054},"p-cross-1",{"text":1053},"RTXNTC è un SDK NVIDIA, e la compressione stessa attualmente richiede una GPU NVIDIA secondo i requisiti dell'SDK.",{},{"id":1056,"data":1057,"type":544,"tunes":1059},"p-cross-2",{"text":1058},"La decompressione a runtime è più ampia. NVIDIA documenta percorsi funzionali su hardware Shader Model 6 e segnala la validazione su GPU NVIDIA, AMD e Intel, mentre i percorsi avanzati Cooperative Vector \u002F Linear Algebra dipendono dal supporto API e driver.",{},{"id":1061,"data":1062,"type":544,"tunes":1064},"p-cross-3",{"text":1063},"Prestazioni e parità di funzionalità non dovrebbero quindi essere date per scontate tra i vari vendor solo perché il decoder di base può essere eseguito.",{},{"id":1066,"data":1067,"type":572,"tunes":1069},"h-change",{"text":1068,"level":47},"Cosa cambierebbe questa risposta?",{},{"id":1071,"data":1072,"type":544,"tunes":1074},"p-change-1",{"text":1073},"NTC è ancora in beta. Modalità di runtime, architetture dei decoder, supporto dei driver e percorsi di integrazione possono cambiare prima di una release stabile per la produzione.",{},{"id":1076,"data":1077,"type":544,"tunes":1079},"p-change-2",{"text":1078},"Il cambiamento più grande a lungo termine sarebbe l'adozione diffusa di primitive standardizzate per shader neurali in DirectX e Vulkan. Ciò renderebbe la decodifica delle texture neurali meno dipendente da percorsi di esecuzione personalizzati specifici del vendor.",{},{"id":1081,"data":1082,"type":572,"tunes":1084},"h-limit",{"text":1083,"level":47},"Limitazioni",{},{"id":1086,"data":1087,"type":544,"tunes":1089},"p-limit-1",{"text":1088},"Questo articolo descrive l'architettura e il comportamento attuale dell'SDK RTXNTC pubblico. Le dichiarazioni di compressione citate da NVIDIA sono fornite dal vendor e non dovrebbero essere considerate risultati garantiti per ogni materiale.",{},{"id":1091,"data":1092,"type":544,"tunes":1094},"p-limit-2",{"text":1093},"L'SDK attuale è software in beta, e alcuni percorsi di anteprima hanno limitazioni documentate di driver e piattaforma.",{},{"id":1096,"data":1097,"type":572,"tunes":1099},"h-conclusion",{"text":1098,"level":47},"Conclusione",{},{"id":1101,"data":1102,"type":544,"tunes":1104},"p-conc-1",{"text":1103},"RTX Neural Texture Compression è interessante perché cambia un presupposto molto vecchio: il dettaglio delle texture non deve sempre esistere in memoria come texel convenzionali.",{},{"id":1106,"data":1107,"type":544,"tunes":1109},"p-conc-2",{"text":1108},"Un materiale può invece essere memorizzato in parte come una rappresentazione appresa compatta e ricostruito quando necessario.",{},{"id":1111,"data":1112,"type":544,"tunes":1114},"p-conc-3",{"text":1113},"Ciò non offre qualità gratuita o memoria gratuita. Crea un nuovo scambio: meno archiviazione, larghezza di banda e potenzialmente VRAM in cambio di lavoro di inferenza neurale.",{},{"id":1116,"data":1117,"type":544,"tunes":1119},"p-conc-4",{"text":1118},"La vera innovazione non è \"l'IA rende le texture più nitide\". È che parte dei dati dei materiali di un gioco può diventare calcolo.",{},{"id":1121,"data":1122,"type":572,"tunes":1124},"h-faq",{"text":1123,"level":47},"FAQ",{},{"id":1126,"data":1127,"type":1126,"tunes":1154},"faq",{"items":1128,"title":1153},[1129,1133,1137,1141,1145,1149],{"id":1130,"answer":1131,"question":1132},"faq1","No. Comprime e ricostruisce i dati delle texture dei materiali. Le tecnologie di super-risoluzione ricostruiscono l'immagine renderizzata finale a una risoluzione di visualizzazione superiore.","RTX Neural Texture Compression è un upscaler?",{"id":1134,"answer":1135,"question":1136},"faq2","Sì, in particolare con Inference on Sample perché la rappresentazione neurale compatta può rimanere nella memoria della GPU invece di essere completamente espansa in texture convenzionali. Inference on Load preserva principalmente il risparmio di archiviazione prima dell'espansione.","NTC può ridurre l'utilizzo della VRAM?",{"id":1138,"answer":1139,"question":1140},"faq3","Il materiale compresso contiene dati compatti di feature latenti più i pesi per una piccola rete decoder che ricostruisce i valori delle texture.","Cosa memorizza la rete neurale?",{"id":1142,"answer":1143,"question":1144},"faq4","Non necessariamente. Inference on Load lo fa, Inference on Sample ricostruisce i valori durante il campionamento nello shader, e Inference on Feedback può decodificare i tile di texture richiesti.","NTC decodifica l'intera texture prima del rendering?",{"id":1146,"answer":1147,"question":1148},"faq5","No. RTXNTC è una compressione con perdita e la qualità dipende dal bitrate, dal numero di canali, dalla configurazione del decoder e dal materiale stesso.","La compressione neurale delle texture è senza perdita?",{"id":1150,"answer":1151,"question":1152},"faq6","L'attuale SDK pubblico è ancora etichettato come beta, quindi API, supporto e caratteristiche prestazionali potrebbero continuare a cambiare.","RTXNTC è pronto per la produzione?","RTX Neural Texture Compression spiegato in parole semplici",{},{"id":1156,"data":1157,"type":572,"tunes":1159},"h-glossary",{"text":1158,"level":47},"Glossario",{},{"id":1161,"data":1162,"type":1161,"tunes":1197},"glossary",{"title":1163,"entries":1164},"Termini chiave delle texture neurali",[1165,1169,1173,1177,1181,1185,1189,1193],{"term":1166,"anchor":1167,"definition":1168},"Neural Texture Compression","neural-texture-compression","Una tecnica che memorizza le informazioni delle texture come dati latenti compatti più i pesi del decoder neurale invece che solo come blocchi di texel convenzionali.",{"term":1170,"anchor":1171,"definition":1172},"Dati latenti","latent-data","Feature compatte apprese che il decoder neurale utilizza per ricostruire i valori delle texture.",{"term":1174,"anchor":1175,"definition":1176},"Decoder","decoder","Una piccola rete neurale che converte le feature latenti in canali di texture ricostruiti.",{"term":1178,"anchor":1179,"definition":1180},"Inference on Load","inference-on-load","Modalità di runtime che decodifica la texture neurale quando un asset viene caricato, solitamente in formati di texture convenzionali.",{"term":1182,"anchor":1183,"definition":1184},"Inference on Sample","inference-on-sample","Modalità di runtime che esegue la decodifica neurale direttamente durante il campionamento della texture, così la rappresentazione compressa può rimanere residente.",{"term":1186,"anchor":1187,"definition":1188},"Inference on Feedback","inference-on-feedback","Strategia di runtime che utilizza il feedback delle texture per decodificare e memorizzare nella cache solo i tile richiesti.",{"term":1190,"anchor":1191,"definition":1192},"Texture Storage–Compute Exchange","texture-storage-compute-exchange","Un modello di Figure Rocks che descrive lo scambio di archiviazione delle texture, larghezza di banda e VRAM per ulteriore lavoro di inferenza neurale sulla GPU.",{"term":1194,"anchor":1195,"definition":1196},"Neural Texture Value Test","neural-texture-value-test","Un flusso di lavoro di Figure Rocks per decidere se la compressione neurale delle texture crea un beneficio netto per un particolare materiale e una GPU target.",{},{"id":1199,"data":1200,"type":572,"tunes":1202},"h-sources",{"text":1201,"level":47},"Fonti primarie",{},{"id":1204,"data":1205,"type":1211,"tunes":1212},"src-rtxkit",{"link":1206,"meta":1207},"https:\u002F\u002Fdeveloper.nvidia.com\u002Frtx-kit",{"image":1208,"title":1209,"description":1210},{"url":13},"NVIDIA Developer — RTX Kit","Panoramica ufficiale di RTX Neural Texture Compression e del posizionamento pubblicato da NVIDIA sulla riduzione dell'archiviazione.","linkTool",{},{"id":1214,"data":1215,"type":1211,"tunes":1221},"src-rtxntc-readme",{"link":1216,"meta":1217},"https:\u002F\u002Fgithub.com\u002FNVIDIA-RTX\u002FRTXNTC\u002Fblob\u002Fmain\u002FREADME.md",{"image":1218,"title":1219,"description":1220},{"url":13},"NVIDIA RTXNTC — SDK README","Documentazione ufficiale dell'SDK che descrive la compressione dei canali dei materiali, la rappresentazione decoder\u002Flatente, le modalità di runtime, esempi di memoria, requisiti di sistema e supporto Cooperative Vector.",{},{"id":1223,"data":1224,"type":1211,"tunes":1230},"src-rtxntc-release",{"link":1225,"meta":1226},"https:\u002F\u002Fgithub.com\u002FNVIDIA-RTX\u002FRTXNTC\u002Freleases",{"image":1227,"title":1228,"description":1229},{"url":13},"NVIDIA RTXNTC — Releases","Cronologia ufficiale delle release, inclusa la beta v0.10.0 e il supporto all'inferenza DirectX 12 Linear Algebra.",{},{"id":1232,"data":1233,"type":1211,"tunes":1239},"src-rtxntc-quality",{"link":1234,"meta":1235},"https:\u002F\u002Fgithub.com\u002FNVIDIA-RTX\u002FRTXNTC\u002Fblob\u002Fmain\u002Fdocs\u002FSettingsAndQuality.md",{"image":1236,"title":1237,"description":1238},{"url":13},"NVIDIA RTXNTC — Compression Settings and Image Quality","Documentazione ufficiale che copre bitrate, interazioni tra canali, misurazione della qualità e comportamento della compressione con perdita.",{},{"id":1241,"data":1242,"type":1211,"tunes":1248},"src-libntc",{"link":1243,"meta":1244},"https:\u002F\u002Fgithub.com\u002FNVIDIA-RTX\u002FRTXNTC-Library",{"image":1245,"title":1246,"description":1247},{"url":13},"NVIDIA — LibNTC","Documentazione ufficiale della libreria di runtime che descrive la configurazione del decoder e il compromesso qualità\u002Fprestazioni delle diverse dimensioni della rete neurale.",{},"2.31","NVIDIA RTX Neural Texture Compression cambia il modo in cui i materiali di gioco possono essere archiviati. Invece di mantenere ogni canale di texture solo come texel convenzionali, un materiale può essere compresso in dati latenti compatti e un piccolo decoder neurale, per poi essere ricostruito dalla GPU quando necessario.","\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":1259,"de":1260,"sr":1261,"es":1262,"fr":1263,"it":1264,"ru":1265,"zh":1266},"\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",[1268,1272,1276,1280,1284],{"id":1269,"name":1270,"slug":1271},152,"VRAM e streaming","vram-and-streaming",{"id":1273,"name":1274,"slug":1275},330,"Correzioni di streaming e IO","streaming-and-io-fixes",{"id":1277,"name":1278,"slug":1279},173,"CPU, memoria, archiviazione","cpus-memory-storage",{"id":1281,"name":1282,"slug":1283},210,"Cosa significa qualità","what-quality-means",{"id":1285,"name":1286,"slug":1287},214,"Marketing vs Realtà","marketing-vs-reality",{"id":283,"login":1289,"email":1290,"displayName":1291},"aleksandar","aleksandar@stajic.de","Aleksandar Stajic",[1293,1840],{"lang":8,"title":1294,"content":1295,"contentJson":1296,"excerpt":1839},"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":1297,"blocks":1298,"version":1838},1790378899383,[1299,1303,1308,1313,1317,1321,1325,1329,1333,1337,1341,1345,1365,1369,1373,1377,1381,1385,1389,1407,1411,1415,1419,1423,1427,1431,1435,1439,1443,1447,1452,1459,1463,1467,1471,1475,1479,1483,1487,1508,1512,1516,1520,1524,1531,1535,1539,1557,1561,1565,1569,1573,1577,1582,1586,1590,1594,1598,1602,1628,1632,1636,1640,1644,1648,1652,1656,1660,1664,1668,1672,1676,1680,1684,1688,1692,1696,1700,1704,1708,1712,1716,1720,1724,1728,1732,1736,1740,1744,1748,1752,1755,1778,1782,1804,1808,1814,1820,1826,1832],{"id":541,"data":1300,"type":544,"tunes":1302},{"text":1301},"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":1304,"type":552,"tunes":1307},{"body":1305,"title":1306,"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":1309,"type":552,"tunes":1312},{"body":1310,"title":1311,"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":1314,"type":566,"tunes":1316},{"title":1315,"maxLevel":565,"minLevel":47},"Contents",{},{"id":569,"data":1318,"type":572,"tunes":1320},{"text":1319,"level":47},"Why normal texture compression still uses a lot of memory",{},{"id":575,"data":1322,"type":544,"tunes":1324},{"text":1323},"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":1326,"type":544,"tunes":1328},{"text":1327},"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":1330,"type":544,"tunes":1332},{"text":1331},"As texture resolution and material complexity increase, those channels consume disk space, streaming bandwidth and GPU memory.",{},{"id":590,"data":1334,"type":572,"tunes":1336},{"text":1335,"level":47},"What RTX Neural Texture Compression actually stores",{},{"id":595,"data":1338,"type":544,"tunes":1340},{"text":1339},"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":1342,"type":544,"tunes":1344},{"text":1343},"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":1346,"type":625,"tunes":1364},{"steps":1347,"title":1363,"orientation":624},[1348,1351,1354,1357,1360],{"label":1349,"description":1350},"1. Original material textures","Albedo, normal, roughness, metalness and other material channels are provided together.",{"label":1352,"description":1353},"2. Offline compression","The SDK learns a compact representation of the material.",{"label":1355,"description":1356},"3. Neural weights","A small decoder network stores part of what is needed to reconstruct the material.",{"label":1358,"description":1359},"4. Latent data","Compact feature tensors store material-specific information.",{"label":1361,"description":1362},"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":1366,"type":572,"tunes":1368},{"text":1367,"level":47},"Why compressing channels together can help",{},{"id":633,"data":1370,"type":544,"tunes":1372},{"text":1371},"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":1374,"type":544,"tunes":1376},{"text":1375},"NVIDIA designed NTC to exploit those correlations instead of compressing every texture independently.",{},{"id":643,"data":1378,"type":544,"tunes":1380},{"text":1379},"That is one reason the technology is described as material-oriented compression rather than merely another image format.",{},{"id":648,"data":1382,"type":572,"tunes":1384},{"text":1383,"level":47},"The three NTC runtime modes are the key to understanding the technology",{},{"id":653,"data":1386,"type":544,"tunes":1388},{"text":1387},"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":1390,"type":685,"tunes":1406},{"rows":1391,"title":1398,"layout":674,"columns":1399},[1392,1394,1396],{"id":662,"label":1178,"values":1393},[13,13,13],{"id":666,"label":1182,"values":1395},[13,13,13],{"id":670,"label":1186,"values":1397},[13,13,13],"Inference on Load vs Inference on Sample vs Inference on Feedback",[1400,1402,1404],{"id":677,"label":1401},"When neural decoding happens",{"id":680,"label":1403},"Runtime texture-memory behavior",{"id":683,"label":1405},"Main trade-off",{},{"id":688,"data":1408,"type":572,"tunes":1410},{"text":1409,"level":47},"Inference on Load: neural compression as a storage format",{},{"id":693,"data":1412,"type":544,"tunes":1414},{"text":1413},"Inference on Load is the easiest mode to understand.",{},{"id":698,"data":1416,"type":544,"tunes":1418},{"text":1417},"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":1420,"type":544,"tunes":1422},{"text":1421},"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":1424,"type":544,"tunes":1426},{"text":1425},"But once the material is fully expanded into conventional textures, its runtime VRAM footprint approaches the conventional representation again.",{},{"id":713,"data":1428,"type":572,"tunes":1430},{"text":1429,"level":47},"Inference on Sample: keep the texture neural in VRAM",{},{"id":718,"data":1432,"type":544,"tunes":1434},{"text":1433},"Inference on Sample is the more radical mode.",{},{"id":723,"data":1436,"type":544,"tunes":1438},{"text":1437},"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":1440,"type":544,"tunes":1442},{"text":1441},"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":1444,"type":544,"tunes":1446},{"text":1445},"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":1448,"type":552,"tunes":1451},{"body":1449,"title":1450,"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":1453,"type":750,"tunes":1458},{"url":1454,"title":1455,"excerpt":1456,"ctaLabel":1457},"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":1460,"type":572,"tunes":1462},{"text":1461,"level":47},"Inference on Feedback: decode only what the player actually sees",{},{"id":758,"data":1464,"type":544,"tunes":1466},{"text":1465},"Inference on Feedback sits between the two extremes.",{},{"id":763,"data":1468,"type":544,"tunes":1470},{"text":1469},"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":1472,"type":544,"tunes":1474},{"text":1473},"Conceptually, this combines neural compression with texture streaming: the system pays the decoding cost only for regions that become relevant.",{},{"id":773,"data":1476,"type":544,"tunes":1478},{"text":1477},"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":1480,"type":572,"tunes":1482},{"text":1481,"level":47},"The Texture Storage–Compute Exchange",{},{"id":783,"data":1484,"type":544,"tunes":1486},{"text":1485},"The easiest way to understand neural texture compression is as an exchange between resources.",{},{"id":788,"data":1488,"type":685,"tunes":1507},{"rows":1489,"title":1501,"layout":674,"columns":1502},[1490,1493,1495,1498],{"id":792,"label":1491,"values":1492},"Disk\u002Fstorage",[13,13],{"id":796,"label":797,"values":1494},[13,13],{"id":800,"label":1496,"values":1497},"Sampling cost",[13,13],{"id":804,"label":1499,"values":1500},"Quality control",[13,13],"What moves when texture storage becomes neural",[1503,1505],{"id":810,"label":1504},"Traditional compressed texture",{"id":813,"label":1506},"Neural texture representation",{},{"id":817,"data":1509,"type":572,"tunes":1511},{"text":1510,"level":47},"Why special matrix hardware matters",{},{"id":822,"data":1513,"type":544,"tunes":1515},{"text":1514},"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":1517,"type":544,"tunes":1519},{"text":1518},"RTXNTC therefore benefits from Cooperative Vector and now DirectX 12 Linear Algebra paths that let shaders use GPU matrix-acceleration hardware.",{},{"id":832,"data":1521,"type":544,"tunes":1523},{"text":1522},"The v0.10.0 beta explicitly added inference through the DirectX 12 Linear Algebra API introduced with Shader Model 6.10.",{},{"id":837,"data":1525,"type":750,"tunes":1530},{"url":1526,"title":1527,"excerpt":1528,"ctaLabel":1529},"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":1532,"type":572,"tunes":1534},{"text":1533,"level":47},"Why neural texture compression is not texture upscaling",{},{"id":850,"data":1536,"type":544,"tunes":1538},{"text":1537},"The two techniques can both use machine learning, but they solve different problems.",{},{"id":855,"data":1540,"type":685,"tunes":1556},{"rows":1541,"title":1552,"layout":674,"columns":1553},[1542,1544,1547,1550],{"id":859,"label":860,"values":1543},[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":871,"values":1551},[13,13],"Neural Texture Compression vs Super Resolution",[1554,1555],{"id":876,"label":1166},{"id":879,"label":880},{},{"id":883,"data":1558,"type":544,"tunes":1560},{"text":1559},"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":888,"data":1562,"type":572,"tunes":1564},{"text":1563,"level":47},"The quality knob is bits per pixel",{},{"id":893,"data":1566,"type":544,"tunes":1568},{"text":1567},"NTC is lossy compression. The amount of compressed information is controlled largely through the bits-per-pixel setting.",{},{"id":898,"data":1570,"type":544,"tunes":1572},{"text":1571},"Higher bitrate gives the model more information and generally improves reconstruction quality. Lower bitrate improves compression but increases the risk of visible error.",{},{"id":903,"data":1574,"type":544,"tunes":1576},{"text":1575},"Because multiple channels share the same representation, adding more material channels without increasing the bitrate can reduce the quality available to each channel.",{},{"id":908,"data":1578,"type":552,"tunes":1581},{"body":1579,"title":1580,"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":914,"data":1583,"type":572,"tunes":1585},{"text":1584,"level":47},"Why correlated material channels are important",{},{"id":919,"data":1587,"type":544,"tunes":1589},{"text":1588},"A neural representation becomes more valuable when several material channels describe related structure.",{},{"id":924,"data":1591,"type":544,"tunes":1593},{"text":1592},"If albedo, normal and roughness all contain the same scratches, seams or fabric weave, the decoder can exploit shared spatial information.",{},{"id":929,"data":1595,"type":544,"tunes":1597},{"text":1596},"If the channels are unrelated noise, there is less common structure to exploit and the compression problem becomes harder.",{},{"id":934,"data":1599,"type":572,"tunes":1601},{"text":1600,"level":47},"The Neural Texture Value Test",{},{"id":939,"data":1603,"type":625,"tunes":1627},{"steps":1604,"title":1626,"orientation":624},[1605,1608,1611,1614,1617,1620,1623],{"label":1606,"description":1607},"1. Measure conventional texture cost","How much disk space, streaming bandwidth and VRAM do the existing material textures consume?",{"label":1609,"description":1610},"2. Choose the runtime mode","Do you want storage savings only, persistent VRAM savings or streamed tile reconstruction?",{"label":1612,"description":1613},"3. Set an acceptable quality target","Compare reconstructed channels against the original material, not only the final beauty image.",{"label":1615,"description":1616},"4. Measure inference cost","Record added shader or decompression time on the actual target GPU.",{"label":1618,"description":1619},"5. Measure memory savings","Check the real runtime working set rather than only the compressed file size.",{"label":1621,"description":1622},"6. Test difficult materials","Fine normals, sharp masks, opacity, text and unrelated channels can expose compression failures.",{"label":1624,"description":1625},"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":966,"data":1629,"type":572,"tunes":1631},{"text":1630,"level":47},"Why “8× smaller” needs context",{},{"id":971,"data":1633,"type":544,"tunes":1635},{"text":1634},"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":976,"data":1637,"type":544,"tunes":1639},{"text":1638},"The phrase “up to” matters. Compression ratio depends on the material, number of channels, target bitrate, decoder configuration and quality threshold.",{},{"id":981,"data":1641,"type":544,"tunes":1643},{"text":1642},"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":986,"data":1645,"type":572,"tunes":1647},{"text":1646,"level":47},"Decoder size is another performance-quality trade-off",{},{"id":991,"data":1649,"type":544,"tunes":1651},{"text":1650},"The NTC runtime uses a small multilayer perceptron to decode texture values.",{},{"id":996,"data":1653,"type":544,"tunes":1655},{"text":1654},"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":1001,"data":1657,"type":544,"tunes":1659},{"text":1658},"That gives engine developers another tuning dimension beyond texture resolution and bitrate.",{},{"id":1006,"data":1661,"type":572,"tunes":1663},{"text":1662,"level":47},"Why this matters for future game installations",{},{"id":1011,"data":1665,"type":544,"tunes":1667},{"text":1666},"Modern games increasingly ship high-resolution material sets that affect download size as well as runtime memory.",{},{"id":1016,"data":1669,"type":544,"tunes":1671},{"text":1670},"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":1021,"data":1673,"type":544,"tunes":1675},{"text":1674},"That means one compressed asset representation can participate in several different runtime memory strategies.",{},{"id":1026,"data":1677,"type":572,"tunes":1679},{"text":1678,"level":47},"It also changes what 'texture memory' means",{},{"id":1031,"data":1681,"type":544,"tunes":1683},{"text":1682},"With traditional rendering, a texture-memory budget is mostly about texture formats, mip levels, resolution and residency.",{},{"id":1036,"data":1685,"type":544,"tunes":1687},{"text":1686},"With neural textures, developers may also budget latent data, decoder weights, inference buffers, transcoded caches and the compute needed to reconstruct requested values.",{},{"id":1041,"data":1689,"type":544,"tunes":1691},{"text":1690},"So the asset no longer has one simple fixed memory identity.",{},{"id":1046,"data":1693,"type":572,"tunes":1695},{"text":1694,"level":47},"Cross-vendor support is more nuanced than the RTX name suggests",{},{"id":1051,"data":1697,"type":544,"tunes":1699},{"text":1698},"RTXNTC is an NVIDIA SDK, and compression itself currently requires an NVIDIA GPU according to the SDK requirements.",{},{"id":1056,"data":1701,"type":544,"tunes":1703},{"text":1702},"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":1061,"data":1705,"type":544,"tunes":1707},{"text":1706},"Performance and feature parity therefore should not be assumed across vendors merely because the basic decoder can run.",{},{"id":1066,"data":1709,"type":572,"tunes":1711},{"text":1710,"level":47},"What would change this answer?",{},{"id":1071,"data":1713,"type":544,"tunes":1715},{"text":1714},"NTC is still beta. Runtime modes, decoder architectures, driver support and integration paths can change before a stable production release.",{},{"id":1076,"data":1717,"type":544,"tunes":1719},{"text":1718},"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":1081,"data":1721,"type":572,"tunes":1723},{"text":1722,"level":47},"Limitations",{},{"id":1086,"data":1725,"type":544,"tunes":1727},{"text":1726},"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":1091,"data":1729,"type":544,"tunes":1731},{"text":1730},"The current SDK is beta software, and some preview paths have documented driver and platform limitations.",{},{"id":1096,"data":1733,"type":572,"tunes":1735},{"text":1734,"level":47},"Conclusion",{},{"id":1101,"data":1737,"type":544,"tunes":1739},{"text":1738},"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":1106,"data":1741,"type":544,"tunes":1743},{"text":1742},"A material can instead be stored partly as a compact learned representation and reconstructed when needed.",{},{"id":1111,"data":1745,"type":544,"tunes":1747},{"text":1746},"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":1116,"data":1749,"type":544,"tunes":1751},{"text":1750},"The real innovation is not “AI makes textures sharper.” It is that part of a game's material data can become computation.",{},{"id":1121,"data":1753,"type":572,"tunes":1754},{"text":1123,"level":47},{},{"id":1126,"data":1756,"type":1126,"tunes":1777},{"items":1757,"title":1776},[1758,1761,1764,1767,1770,1773],{"id":1130,"answer":1759,"question":1760},"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":1134,"answer":1762,"question":1763},"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":1138,"answer":1765,"question":1766},"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":1142,"answer":1768,"question":1769},"Not necessarily. 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