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NVIDIA RTX Neural Texture Compression change ce modèle : une partie des données de texture devient une petite représentation neuronale qui peut être décodée par le GPU lui-même.\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\">Réponse directe\u003C\u002Fstrong>\u003Cdiv class=\"text-gray-700 dark:text-gray-200\">\u003Cstrong>RTX Neural Texture Compression n'est pas de la mise à l'échelle de texture.\u003C\u002Fstrong> Il compresse plusieurs textures de matériau en poids de réseau neuronal plus des données latentes compactes, puis reconstruit les valeurs de texture demandées avec un petit réseau neuronal. Selon le mode d'intégration, le jeu peut échanger du stockage et de l'utilisation de VRAM contre un travail d'inférence GPU supplémentaire.\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\">Statut actuel\u003C\u002Fstrong>\u003Cdiv class=\"text-gray-700 dark:text-gray-200\">RTX Neural Texture Compression est encore un \u003Cstrong>SDK bêta\u003C\u002Fstrong>. La version bêta actuelle v0.10.0 a ajouté le support d'inférence DirectX 12 Linear Algebra, connectant NTC directement à la nouvelle infrastructure de shaders neuronaux discutée ailleurs sur Figure Rocks.\u003C\u002Fdiv>\u003C\u002Faside>\n\u003Cnav class=\"editorjs-toc\" data-editorjs-toc=\"true\" aria-label=\"Contenu\">\u003Cstrong class=\"editorjs-toc__title\">Contenu\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\">Pourquoi la compression de texture normale utilise encore beaucoup de mémoire\u003C\u002Fa>\u003C\u002Fli>\u003Cli class=\"editorjs-toc__item\">\u003Ca href=\"#section-9\" class=\"editorjs-toc__link\">Ce que RTX Neural Texture Compression stocke réellement\u003C\u002Fa>\u003C\u002Fli>\u003Cli class=\"editorjs-toc__item\">\u003Ca href=\"#section-13\" class=\"editorjs-toc__link\">Pourquoi compresser les canaux ensemble peut aider\u003C\u002Fa>\u003C\u002Fli>\u003Cli class=\"editorjs-toc__item\">\u003Ca href=\"#section-17\" class=\"editorjs-toc__link\">Les trois modes d'exécution NTC sont la clé pour comprendre la technologie\u003C\u002Fa>\u003C\u002Fli>\u003Cli class=\"editorjs-toc__item\">\u003Ca href=\"#section-20\" class=\"editorjs-toc__link\">Inférence au chargement : la compression neuronale comme format de stockage\u003C\u002Fa>\u003C\u002Fli>\u003Cli class=\"editorjs-toc__item\">\u003Ca href=\"#section-25\" class=\"editorjs-toc__link\">Inférence à l'échantillonnage : garder la texture neuronale en VRAM\u003C\u002Fa>\u003C\u002Fli>\u003Cli class=\"editorjs-toc__item\">\u003Ca href=\"#section-32\" class=\"editorjs-toc__link\">Inférence sur retour : ne décoder que ce que le joueur voit réellement\u003C\u002Fa>\u003C\u002Fli>\u003Cli class=\"editorjs-toc__item\">\u003Ca href=\"#section-37\" class=\"editorjs-toc__link\">L'échange stockage-calcul des textures\u003C\u002Fa>\u003C\u002Fli>\u003Cli class=\"editorjs-toc__item\">\u003Ca href=\"#section-40\" class=\"editorjs-toc__link\">Pourquoi le matériel matriciel spécialisé est important\u003C\u002Fa>\u003C\u002Fli>\u003Cli class=\"editorjs-toc__item\">\u003Ca href=\"#section-45\" class=\"editorjs-toc__link\">Pourquoi la compression neuronale de textures n'est pas de la mise à l'échelle de textures\u003C\u002Fa>\u003C\u002Fli>\u003Cli class=\"editorjs-toc__item\">\u003Ca href=\"#section-49\" class=\"editorjs-toc__link\">Le réglage de qualité est le nombre de bits par pixel\u003C\u002Fa>\u003C\u002Fli>\u003Cli class=\"editorjs-toc__item\">\u003Ca href=\"#section-54\" class=\"editorjs-toc__link\">Pourquoi les canaux de matériau corrélés sont importants\u003C\u002Fa>\u003C\u002Fli>\u003Cli class=\"editorjs-toc__item\">\u003Ca href=\"#section-58\" class=\"editorjs-toc__link\">Le test de valeur des textures neuronales\u003C\u002Fa>\u003C\u002Fli>\u003Cli class=\"editorjs-toc__item\">\u003Ca href=\"#section-60\" class=\"editorjs-toc__link\">Pourquoi « 8× plus petit » nécessite du contexte\u003C\u002Fa>\u003C\u002Fli>\u003Cli class=\"editorjs-toc__item\">\u003Ca href=\"#section-64\" class=\"editorjs-toc__link\">La taille du décodeur est un autre compromis performance-qualité\u003C\u002Fa>\u003C\u002Fli>\u003Cli class=\"editorjs-toc__item\">\u003Ca href=\"#section-68\" class=\"editorjs-toc__link\">Pourquoi cela compte pour les futures installations de jeux\u003C\u002Fa>\u003C\u002Fli>\u003Cli class=\"editorjs-toc__item\">\u003Ca href=\"#section-72\" class=\"editorjs-toc__link\">Cela change aussi ce que signifie « mémoire de texture »\u003C\u002Fa>\u003C\u002Fli>\u003Cli class=\"editorjs-toc__item\">\u003Ca href=\"#section-76\" class=\"editorjs-toc__link\">La prise en charge multi-fournisseurs est plus nuancée que ne le suggère le nom RTX\u003C\u002Fa>\u003C\u002Fli>\u003Cli class=\"editorjs-toc__item\">\u003Ca href=\"#section-80\" class=\"editorjs-toc__link\">Qu'est-ce qui changerait cette réponse ?\u003C\u002Fa>\u003C\u002Fli>\u003Cli class=\"editorjs-toc__item\">\u003Ca href=\"#section-83\" class=\"editorjs-toc__link\">Limitations\u003C\u002Fa>\u003C\u002Fli>\u003Cli class=\"editorjs-toc__item\">\u003Ca href=\"#section-86\" class=\"editorjs-toc__link\">Conclusion\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\">Glossaire\u003C\u002Fa>\u003C\u002Fli>\u003Cli class=\"editorjs-toc__item\">\u003Ca href=\"#section-95\" class=\"editorjs-toc__link\">Sources principales\u003C\u002Fa>\u003C\u002Fli>\u003C\u002Fol>\u003C\u002Fnav>\n\u003Ch2 id=\"section-5\">Pourquoi la compression de texture normale utilise encore beaucoup de mémoire\u003C\u002Fh2>\n\u003Cp>Un matériau moderne basé sur la physique se compose rarement d'une seule image. Une seule surface peut utiliser l'albédo, la normale, la rugosité, la métallicité, l'occlusion ambiante, l'opacité et d'autres canaux.\u003C\u002Fp>\n\u003Cp>Les formats de compression de blocs GPU traditionnels tels que BC1 à BC7 réduisent le coût, mais le GPU finit toujours par stocker des blocs de texture conventionnels pour le matériau.\u003C\u002Fp>\n\u003Cp>À mesure que la résolution des textures et la complexité des matériaux augmentent, ces canaux consomment de l'espace disque, de la bande passante de streaming et de la mémoire GPU.\u003C\u002Fp>\n\u003Ch2 id=\"section-9\">Ce que RTX Neural Texture Compression stocke réellement\u003C\u002Fh2>\n\u003Cp>Le SDK RTXNTC de NVIDIA compresse ensemble les canaux appartenant à un même matériau. Le SDK actuel prend en charge jusqu'à 16 canaux de texture dans un ensemble de textures NTC.\u003C\u002Fp>\n\u003Cp>Au lieu de conserver uniquement des texels compressés conventionnels, le processus de compression produit deux éléments principaux : les poids d'un petit décodeur neuronal et des données de caractéristiques latentes compactes.\u003C\u002Fp>\n\u003Csection class=\"editorjs-process my-6\">\u003Ch3 class=\"mb-3 text-lg font-semibold\">Le pipeline de texture neuronale\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. Textures de matériau originales\u003C\u002Fdiv>\u003Cdiv class=\"mt-1 text-sm text-gray-600 dark:text-gray-300\">L'albédo, la normale, la rugosité, la métallicité et d'autres canaux de matériau sont fournis ensemble.\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. Compression hors ligne\u003C\u002Fdiv>\u003Cdiv class=\"mt-1 text-sm text-gray-600 dark:text-gray-300\">Le SDK apprend une représentation compacte du matériau.\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. Poids neuronaux\u003C\u002Fdiv>\u003Cdiv class=\"mt-1 text-sm text-gray-600 dark:text-gray-300\">Un petit réseau décodeur stocke une partie de ce qui est nécessaire pour reconstruire le matériau.\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. Données latentes\u003C\u002Fdiv>\u003Cdiv class=\"mt-1 text-sm text-gray-600 dark:text-gray-300\">Des tenseurs de caractéristiques compacts stockent des informations spécifiques au matériau.\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. Inférence GPU\u003C\u002Fdiv>\u003Cdiv class=\"mt-1 text-sm text-gray-600 dark:text-gray-300\">À l'exécution, le décodeur combine les données latentes et les poids neuronaux pour reconstruire les valeurs de texture.\u003C\u002Fdiv>\u003C\u002Fdiv>\u003C\u002Fdiv>\u003C\u002Fsection>\n\u003Ch2 id=\"section-13\">Pourquoi compresser les canaux ensemble peut aider\u003C\u002Fh2>\n\u003Cp>Les canaux de matériau sont souvent liés. Une rayure visible dans la couleur de base peut également apparaître dans la carte de normale ou de rugosité. Un motif de tissu peut influencer plusieurs canaux au même emplacement spatial.\u003C\u002Fp>\n\u003Cp>NVIDIA a conçu NTC pour exploiter ces corrélations au lieu de compresser chaque texture indépendamment.\u003C\u002Fp>\n\u003Cp>C'est l'une des raisons pour lesquelles la technologie est décrite comme une compression orientée matériau plutôt que simplement un autre format d'image.\u003C\u002Fp>\n\u003Ch2 id=\"section-17\">Les trois modes d'exécution NTC sont la clé pour comprendre la technologie\u003C\u002Fh2>\n\u003Cp>La partie la plus importante de RTXNTC n'est pas seulement la façon dont le matériau est compressé. C'est le moment où le jeu choisit de le décompresser.\u003C\u002Fp>\n\u003Csection class=\"editorjs-comparison my-6\">\u003Ch3 class=\"mb-3 text-lg font-semibold\">Inférence au chargement vs Inférence à l'échantillonnage vs Inférence sur retour\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\">Quand le décodage neuronal a lieu\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\">Comportement de la mémoire de textures à l'exécution\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\">Principal compromis\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\">Inférence au chargement\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\">Inférence à l'échantillonnage\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\">Inférence sur retour\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\">Inférence au chargement : la compression neuronale comme format de stockage\u003C\u002Fh2>\n\u003Cp>L'inférence au chargement est le mode le plus simple à comprendre.\u003C\u002Fp>\n\u003Cp>Le jeu stocke le matériau sous forme NTC compacte. Lorsque l'actif est chargé, le GPU reconstruit les données de texture et peut les transcoder dans des formats de texture BCn ordinaires.\u003C\u002Fp>\n\u003Cp>Après cette étape, le rendu peut utiliser l'échantillonnage de texture normal. L'économie importante se situe principalement avant la décompression : taille du jeu empaqueté, taille du téléchargement ou bande passante de streaming des actifs.\u003C\u002Fp>\n\u003Cp>Mais une fois le matériau entièrement décompressé en textures conventionnelles, son empreinte VRAM à l'exécution se rapproche à nouveau de la représentation conventionnelle.\u003C\u002Fp>\n\u003Ch2 id=\"section-25\">Inférence à l'échantillonnage : garder la texture neuronale en VRAM\u003C\u002Fh2>\n\u003Cp>L'inférence à l'échantillonnage est le mode le plus radical.\u003C\u002Fp>\n\u003Cp>Au lieu de décompresser le matériau en textures conventionnelles avant le rendu, le shader lit des données latentes compactes et exécute le décodeur neuronal lorsqu'il a besoin de valeurs de texture.\u003C\u002Fp>\n\u003Cp>L'exemple du SDK de NVIDIA compare une représentation de matériau BCn de 12 Mo à une représentation NTC de 2,5 Mo lors de l'utilisation de l'inférence à l'échantillonnage.\u003C\u002Fp>\n\u003Cp>L'économie est réelle car les données de texture conventionnelles n'ont pas besoin de rester entièrement résidentes. Mais le coût se déplace ailleurs : le shader de pixels ou de collision effectue désormais l'inférence neuronale.\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 compression ne fait pas disparaître le travail\u003C\u002Fstrong>\u003Cdiv class=\"text-gray-700 dark:text-gray-200\">L'inférence à l'échantillonnage échange de la \u003Cstrong>mémoire et de la bande passante\u003C\u002Fstrong> contre du \u003Cstrong>calcul GPU\u003C\u002Fstrong>. La bonne question n'est pas « De combien la texture est-elle plus petite ? » mais « L'économie de mémoire vaut-elle le coût d'inférence ajouté sur cette charge de travail ? »\u003C\u002Fdiv>\u003C\u002Faside>\n\u003Caside class=\"editorjs-referral my-6\">\u003Ca href=\"https:\u002F\u002Ffigure.rocks\u002Ffr\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'utilisation de la VRAM n'est pas une exigence de VRAM : pourquoi un indicateur de mémoire plein ne raconte pas toute l'histoire\u003C\u002Fstrong>\u003Cp class=\"mt-2 text-sm text-gray-600 dark:text-gray-300\">Pourquoi la capacité VRAM, les budgets, la résidence et la pression mémoire réelle sont des choses différentes.\u003C\u002Fp>\u003Cspan class=\"mt-3 inline-flex text-sm font-medium text-primary-600 dark:text-primary-400\">Lire le guide VRAM →\u003C\u002Fspan>\u003C\u002Fdiv>\u003C\u002Fa>\u003C\u002Faside>\n\u003Ch2 id=\"section-32\">Inférence sur retour : ne décoder que ce que le joueur voit réellement\u003C\u002Fh2>\n\u003Cp>L'inférence sur retour se situe entre les deux extrêmes.\u003C\u002Fp>\n\u003Cp>Le moteur de rendu suit les tuiles de texture réellement demandées. Au lieu de décompresser immédiatement tout le matériau, le système peut décoder les tuiles demandées par lots et conserver un cache de travail.\u003C\u002Fp>\n\u003Cp>Conceptuellement, cela combine la compression neuronale avec le streaming de textures : le système ne paie le coût de décodage que pour les régions qui deviennent pertinentes.\u003C\u002Fp>\n\u003Cp>L'implémentation actuelle de l'exemple est plus spécialisée que les autres modes et ses contraintes de prise en charge diffèrent, elle doit donc être traitée comme une stratégie d'intégration plutôt que comme un remplacement universel du streaming de textures ordinaire.\u003C\u002Fp>\n\u003Ch2 id=\"section-37\">L'échange stockage-calcul des textures\u003C\u002Fh2>\n\u003Cp>La façon la plus simple de comprendre la compression neuronale de textures est de la voir comme un échange entre ressources.\u003C\u002Fp>\n\u003Csection class=\"editorjs-comparison my-6\">\u003Ch3 class=\"mb-3 text-lg font-semibold\">Ce qui change lorsque le stockage des textures devient neuronal\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 compressée traditionnelle\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\">Représentation neuronale de 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\">Disque\u002Fstockage\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\">Coût d'échantillonnage\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\">Contrôle de qualité\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\">Pourquoi le matériel matriciel spécialisé est important\u003C\u002Fh2>\n\u003Cp>Exécuter un réseau de neurones pour l'échantillonnage de textures serait trop coûteux si chaque multiplication devait être traitée comme un simple calcul scalaire de shader.\u003C\u002Fp>\n\u003Cp>RTXNTC bénéficie donc de Cooperative Vector et désormais des chemins DirectX 12 Linear Algebra qui permettent aux shaders d'utiliser le matériel d'accélération matricielle du GPU.\u003C\u002Fp>\n\u003Cp>La bêta v0.10.0 a explicitement ajouté l'inférence via l'API DirectX 12 Linear Algebra introduite avec Shader Model 6.10.\u003C\u002Fp>\n\u003Caside class=\"editorjs-referral my-6\">\u003Ca href=\"https:\u002F\u002Ffigure.rocks\u002Ffr\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 devient une plateforme de ML : ce que Linear Algebra et les shaders neuronaux signifient pour les futurs jeux\u003C\u002Fstrong>\u003Cp class=\"mt-2 text-sm text-gray-600 dark:text-gray-300\">Comment DirectX déplace les opérations matricielles et neuronales directement dans HLSL et le pipeline graphique.\u003C\u002Fp>\u003Cspan class=\"mt-3 inline-flex text-sm font-medium text-primary-600 dark:text-primary-400\">Lire le guide des shaders neuronaux DirectX →\u003C\u002Fspan>\u003C\u002Fdiv>\u003C\u002Fa>\u003C\u002Faside>\n\u003Ch2 id=\"section-45\">Pourquoi la compression neuronale de textures n'est pas de la mise à l'échelle de textures\u003C\u002Fh2>\n\u003Cp>Les deux techniques peuvent toutes deux utiliser l'apprentissage automatique, mais elles résolvent des problèmes différents.\u003C\u002Fp>\n\u003Csection class=\"editorjs-comparison my-6\">\u003Ch3 class=\"mb-3 text-lg font-semibold\">Compression neuronale de textures vs Super Résolution\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\">Compression neuronale de textures\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 Résolution\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\">Entrée\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\">Objectif\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\">Quand elle s'exécute\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\">Sortie\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 compression neuronale de textures peut donc exister sous DLSS, FSR, XeSS ou le rendu natif. Elle modifie la façon dont les données de matériau sont stockées et reconstruites, pas la résolution d'affichage finale.\u003C\u002Fp>\n\u003Ch2 id=\"section-49\">Le réglage de qualité est le nombre de bits par pixel\u003C\u002Fh2>\n\u003Cp>NTC est une compression avec perte. La quantité d'informations compressées est contrôlée en grande partie par le réglage des bits par pixel.\u003C\u002Fp>\n\u003Cp>Un débit binaire plus élevé donne plus d'informations au modèle et améliore généralement la qualité de reconstruction. Un débit binaire plus faible améliore la compression mais augmente le risque d'erreurs visibles.\u003C\u002Fp>\n\u003Cp>Comme plusieurs canaux partagent la même représentation, ajouter davantage de canaux de matériau sans augmenter le débit binaire peut réduire la qualité disponible pour chaque canal.\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\">Neuronal ne signifie pas sans perte\u003C\u002Fstrong>\u003Cdiv class=\"text-gray-700 dark:text-gray-200\">La documentation du SDK note explicitement que l'erreur de compression est normale. L'objectif pratique est \u003Cstrong>une erreur visuelle acceptable à un coût de stockage et d'exécution utile\u003C\u002Fstrong>.\u003C\u002Fdiv>\u003C\u002Faside>\n\u003Ch2 id=\"section-54\">Pourquoi les canaux de matériau corrélés sont importants\u003C\u002Fh2>\n\u003Cp>Une représentation neuronale devient plus précieuse lorsque plusieurs canaux de matière décrivent une structure apparentée.\u003C\u002Fp>\n\u003Cp>Si l'albédo, la normale et la rugosité contiennent tous les mêmes rayures, coutures ou tissage, le décodeur peut exploiter des informations spatiales partagées.\u003C\u002Fp>\n\u003Cp>Si les canaux sont du bruit non corrélé, il y a moins de structure commune à exploiter et le problème de compression devient plus difficile.\u003C\u002Fp>\n\u003Ch2 id=\"section-58\">Le test de valeur des textures neuronales\u003C\u002Fh2>\n\u003Csection class=\"editorjs-process my-6\">\u003Ch3 class=\"mb-3 text-lg font-semibold\">Quand la compression de textures neuronale est-elle réellement 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. Mesurer le coût des textures conventionnelles\u003C\u002Fdiv>\u003Cdiv class=\"mt-1 text-sm text-gray-600 dark:text-gray-300\">Quel espace disque, quelle bande passante de streaming et quelle VRAM les textures de matière existantes consomment-elles ?\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. Choisir le mode d'exécution\u003C\u002Fdiv>\u003Cdiv class=\"mt-1 text-sm text-gray-600 dark:text-gray-300\">Voulez-vous uniquement des économies de stockage, des économies persistantes de VRAM ou une reconstruction de tuiles en 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. Fixer un objectif de qualité acceptable\u003C\u002Fdiv>\u003Cdiv class=\"mt-1 text-sm text-gray-600 dark:text-gray-300\">Comparez les canaux reconstruits à la matière d'origine, pas seulement à l'image 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. Mesurer le coût d'inférence\u003C\u002Fdiv>\u003Cdiv class=\"mt-1 text-sm text-gray-600 dark:text-gray-300\">Enregistrez le temps supplémentaire de shader ou de décompression sur le GPU cible réel.\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. Mesurer les économies de mémoire\u003C\u002Fdiv>\u003Cdiv class=\"mt-1 text-sm text-gray-600 dark:text-gray-300\">Vérifiez l'ensemble de travail réel à l'exécution plutôt que seulement la taille du fichier compressé.\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. Tester les matières difficiles\u003C\u002Fdiv>\u003Cdiv class=\"mt-1 text-sm text-gray-600 dark:text-gray-300\">Les normales fines, les masques nets, l'opacité, le texte et les canaux non corrélés peuvent révéler des échecs de compression.\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. Décider selon le bénéfice net\u003C\u002Fdiv>\u003Cdiv class=\"mt-1 text-sm text-gray-600 dark:text-gray-300\">N'utilisez NTC que là où la mémoire\u002Fbande passante économisée vaut la complexité supplémentaire d'inférence et d'intégration.\u003C\u002Fdiv>\u003C\u002Fdiv>\u003C\u002Fdiv>\u003C\u002Fsection>\n\u003Ch2 id=\"section-60\">Pourquoi « 8× plus petit » nécessite du contexte\u003C\u002Fh2>\n\u003Cp>Le RTX Kit de NVIDIA décrit la RTX Neural Texture Compression comme offrant jusqu'à 8× d'amélioration de la mémoire disque à une fidélité visuelle similaire à la compression par blocs traditionnelle.\u003C\u002Fp>\n\u003Cp>L'expression « jusqu'à » compte. Le taux de compression dépend de la matière, du nombre de canaux, du débit binaire cible, de la configuration du décodeur et du seuil de qualité.\u003C\u002Fp>\n\u003Cp>Le même taux ne décrit pas non plus automatiquement les économies de VRAM. L'inférence au chargement peut partir d'un fichier compact et s'étendre ensuite en textures GPU conventionnelles. L'inférence à l'échantillonnage préserve la représentation compacte en mémoire GPU mais dépense plus de calcul pendant l'ombrage.\u003C\u002Fp>\n\u003Ch2 id=\"section-64\">La taille du décodeur est un autre compromis performance-qualité\u003C\u002Fh2>\n\u003Cp>L'environnement d'exécution NTC utilise un petit perceptron multicouche pour décoder les valeurs de texture.\u003C\u002Fp>\n\u003Cp>La bibliothèque actuelle de NVIDIA utilise une architecture de décodeur configurable. Des réseaux plus grands peuvent améliorer la qualité de compression mais coûtent plus cher à exécuter ; des réseaux plus petits peuvent s'exécuter plus rapidement avec une certaine perte de qualité de reconstruction.\u003C\u002Fp>\n\u003Cp>Cela donne aux développeurs de moteurs une dimension de réglage supplémentaire au-delà de la résolution de texture et du débit binaire.\u003C\u002Fp>\n\u003Ch2 id=\"section-68\">Pourquoi cela compte pour les futures installations de jeux\u003C\u002Fh2>\n\u003Cp>Les jeux modernes livrent de plus en plus d'ensembles de matières haute résolution qui affectent la taille de téléchargement ainsi que la mémoire d'exécution.\u003C\u002Fp>\n\u003Cp>La compression de textures neuronale crée une nouvelle option : livrer une représentation apprise compacte et décider plus tard de l'étendre au chargement, de la reconstruire directement pendant l'ombrage ou de ne décoder que les tuiles demandées.\u003C\u002Fp>\n\u003Cp>Cela signifie qu'une seule représentation d'actif compressé peut participer à plusieurs stratégies de mémoire d'exécution différentes.\u003C\u002Fp>\n\u003Ch2 id=\"section-72\">Cela change aussi ce que signifie « mémoire de texture »\u003C\u002Fh2>\n\u003Cp>Avec le rendu traditionnel, un budget de mémoire de textures concerne principalement les formats de textures, les niveaux de mip, la résolution et la résidence.\u003C\u002Fp>\n\u003Cp>Avec les textures neuronales, les développeurs peuvent également budgétiser les données latentes, les poids du décodeur, les tampons d'inférence, les caches transcodés et la puissance de calcul nécessaire pour reconstruire les valeurs demandées.\u003C\u002Fp>\n\u003Cp>Ainsi, l'actif n'a plus une seule identité mémoire fixe et simple.\u003C\u002Fp>\n\u003Ch2 id=\"section-76\">La prise en charge multi-fournisseurs est plus nuancée que ne le suggère le nom RTX\u003C\u002Fh2>\n\u003Cp>RTXNTC est un SDK NVIDIA, et la compression elle-même nécessite actuellement un GPU NVIDIA selon les exigences du SDK.\u003C\u002Fp>\n\u003Cp>La décompression à l'exécution est plus large. NVIDIA documente des chemins fonctionnels sur le matériel Shader Model 6 et note une validation sur les GPU NVIDIA, AMD et Intel, tandis que les chemins avancés Cooperative Vector \u002F Linear Algebra dépendent de la prise en charge des API et des pilotes.\u003C\u002Fp>\n\u003Cp>La performance et la parité des fonctionnalités ne doivent donc pas être supposées entre les fournisseurs simplement parce que le décodeur de base peut s'exécuter.\u003C\u002Fp>\n\u003Ch2 id=\"section-80\">Qu'est-ce qui changerait cette réponse ?\u003C\u002Fh2>\n\u003Cp>NTC est encore en version bêta. Les modes d'exécution, les architectures de décodeur, la prise en charge des pilotes et les chemins d'intégration peuvent changer avant une version de production stable.\u003C\u002Fp>\n\u003Cp>Le changement le plus important à long terme serait l'adoption généralisée de primitives de shaders neuronaux standardisées dans DirectX et Vulkan. Cela rendrait le décodage de textures neuronales moins dépendant de chemins d'exécution personnalisés propres à un fournisseur.\u003C\u002Fp>\n\u003Ch2 id=\"section-83\">Limitations\u003C\u002Fh2>\n\u003Cp>Cet article décrit l'architecture et le comportement actuel du SDK RTXNTC public. Les affirmations de compression citées par NVIDIA sont fournies par le fournisseur et ne doivent pas être considérées comme des résultats garantis pour chaque matériau.\u003C\u002Fp>\n\u003Cp>Le SDK actuel est un logiciel bêta, et certains chemins de prévisualisation ont des limitations documentées de pilote et de plateforme.\u003C\u002Fp>\n\u003Ch2 id=\"section-86\">Conclusion\u003C\u002Fh2>\n\u003Cp>RTX Neural Texture Compression est intéressant car il change une hypothèse très ancienne : le détail des textures ne doit pas toujours exister en mémoire sous forme de texels conventionnels.\u003C\u002Fp>\n\u003Cp>Un matériau peut plutôt être stocké en partie sous forme de représentation apprise compacte et reconstruit lorsque nécessaire.\u003C\u002Fp>\n\u003Cp>Cela ne donne pas une qualité gratuite ni de la mémoire gratuite. Cela crée un nouvel échange : moins de stockage, de bande passante et potentiellement de VRAM en échange d'un travail d'inférence neuronale.\u003C\u002Fp>\n\u003Cp>La véritable innovation n'est pas « l'IA rend les textures plus nettes ». C'est qu'une partie des données de matériaux d'un jeu peut devenir du calcul.\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\">La compression de textures neuronale RTX expliquée simplement\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\">La compression de textures neuronale RTX est-elle un upscaler ?\u003C\u002Fh4>\u003Cdiv class=\"mt-2 text-gray-600 dark:text-gray-300\">Non. Elle compresse et reconstruit les données de texture des matériaux. Les technologies de super-résolution reconstruisent l'image finale rendue à une résolution d'affichage plus élevée.\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 peut-il réduire l'utilisation de la VRAM ?\u003C\u002Fh4>\u003Cdiv class=\"mt-2 text-gray-600 dark:text-gray-300\">Oui, en particulier avec l'inférence au chargement, car la représentation neuronale compacte peut rester en mémoire GPU au lieu de textures conventionnelles entièrement décompressées. L'inférence au chargement préserve principalement les économies de stockage avant l'expansion.\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\">Que stocke le réseau de neurones ?\u003C\u002Fh4>\u003Cdiv class=\"mt-2 text-gray-600 dark:text-gray-300\">Le matériau compressé contient des données de caractéristiques latentes compactes ainsi que les poids d'un petit réseau décodeur qui reconstruit les valeurs de 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 décode-t-il toute la texture avant le rendu ?\u003C\u002Fh4>\u003Cdiv class=\"mt-2 text-gray-600 dark:text-gray-300\">Pas nécessairement. L'inférence au chargement le fait, l'inférence à l'échantillonnage reconstruit les valeurs pendant l'échantillonnage du shader, et l'inférence sur retour peut décoder les tuiles de texture demandées.\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 compression de textures neuronale est-elle sans perte ?\u003C\u002Fh4>\u003Cdiv class=\"mt-2 text-gray-600 dark:text-gray-300\">Non. RTXNTC est une compression avec perte et la qualité dépend du débit, du nombre de canaux, de la configuration du décodeur et du matériau lui-même.\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 est-il prêt pour la production ?\u003C\u002Fh4>\u003Cdiv class=\"mt-2 text-gray-600 dark:text-gray-300\">Le SDK public actuel est encore étiqueté bêta, donc les API, le support et les caractéristiques de performance peuvent continuer à changer.\u003C\u002Fdiv>\u003C\u002Fdiv>\u003C\u002Fsection>\n\u003Ch2 id=\"section-93\">Glossaire\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\">Termes clés des textures neuronales\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\">Compression de textures neuronale\u003C\u002Fdt>\u003Cdd class=\"mt-1 text-gray-600 dark:text-gray-300\">Une technique qui stocke les informations de texture sous forme de données latentes compactes plus les poids du décodeur neuronal au lieu de seulement des blocs de texels conventionnels.\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\">Données latentes\u003C\u002Fdt>\u003Cdd class=\"mt-1 text-gray-600 dark:text-gray-300\">Caractéristiques apprises compactes que le décodeur neuronal utilise pour reconstruire les valeurs de 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\">Décodeur\u003C\u002Fdt>\u003Cdd class=\"mt-1 text-gray-600 dark:text-gray-300\">Un petit réseau de neurones qui convertit les caractéristiques latentes en canaux de texture reconstruits.\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\">Inférence au chargement\u003C\u002Fdt>\u003Cdd class=\"mt-1 text-gray-600 dark:text-gray-300\">Mode d'exécution qui décode la texture neuronale lors du chargement d'un actif, généralement vers des formats de texture conventionnels.\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\">Inférence à l'échantillonnage\u003C\u002Fdt>\u003Cdd class=\"mt-1 text-gray-600 dark:text-gray-300\">Mode d'exécution qui effectue le décodage neuronal directement pendant l'échantillonnage de texture afin que la représentation compressée puisse rester résidente.\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\">Inférence sur retour\u003C\u002Fdt>\u003Cdd class=\"mt-1 text-gray-600 dark:text-gray-300\">Stratégie d'exécution qui utilise le retour de texture pour décoder et mettre en cache uniquement les tuiles demandées.\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\">Échange stockage-calcul de texture\u003C\u002Fdt>\u003Cdd class=\"mt-1 text-gray-600 dark:text-gray-300\">Un modèle Figure Rocks décrivant l'échange de stockage de texture, de bande passante et de VRAM contre un travail d'inférence neuronale supplémentaire sur le 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\">Test de valeur de texture neuronale\u003C\u002Fdt>\u003Cdd class=\"mt-1 text-gray-600 dark:text-gray-300\">Un flux de travail Figure Rocks pour décider si la compression de textures neuronale crée un bénéfice net pour un matériau particulier et un GPU cible.\u003C\u002Fdd>\u003C\u002Fdiv>\u003C\u002Fdl>\u003C\u002Fsection>\n\u003Ch2 id=\"section-95\">Sources principales\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\">Présentation officielle de la compression de textures neuronale RTX et du positionnement publié par NVIDIA en matière de réduction du stockage.\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\">Documentation officielle du SDK décrivant la compression des canaux de matériaux, la représentation décodeur\u002Flatente, les modes d'exécution, des exemples de mémoire, les exigences système et la prise en charge de 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 — Versions\u003C\u002Fstrong>\u003Cp class=\"text-sm text-gray-600 dark:text-gray-400\">Historique officiel des versions, y compris la version bêta v0.10.0 et la prise en charge de l'inférence 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 — Paramètres de compression et qualité d'image\u003C\u002Fstrong>\u003Cp class=\"text-sm text-gray-600 dark:text-gray-400\">Documentation officielle couvrant le débit, les interactions entre canaux, la mesure de la qualité et le comportement de la compression avec perte.\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\">Documentation officielle de la bibliothèque d'exécution décrivant la configuration du décodeur et le compromis qualité\u002Fperformance des différentes tailles de réseau de neurones.\u003C\u002Fp>\u003C\u002Fa>",{"time":538,"blocks":539,"version":1246},1790379063331,[540,546,554,561,568,574,579,584,589,594,599,604,627,632,637,642,647,652,657,687,692,697,702,707,712,717,722,727,732,737,743,752,757,762,767,772,777,782,787,816,821,826,831,836,844,849,854,882,887,892,897,902,907,913,918,923,928,933,938,965,970,975,980,985,990,995,1000,1005,1010,1015,1020,1025,1030,1035,1040,1045,1050,1055,1060,1065,1070,1075,1080,1085,1090,1095,1100,1105,1110,1115,1120,1125,1155,1160,1195,1200,1210,1219,1228,1237],{"id":541,"data":542,"type":544,"tunes":545},"intro",{"text":543},"La compression de texture signifie normalement stocker une version plus petite des données de texture et la développer dans un format GPU conventionnel avant ou pendant l'utilisation. NVIDIA RTX Neural Texture Compression change ce modèle : une partie des données de texture devient une petite représentation neuronale qui peut être décodée par le GPU lui-même.","paragraph",{},{"id":547,"data":548,"type":552,"tunes":553},"direct",{"body":549,"title":550,"variant":551},"\u003Cstrong>RTX Neural Texture Compression n'est pas de la mise à l'échelle de texture.\u003C\u002Fstrong> Il compresse plusieurs textures de matériau en poids de réseau neuronal plus des données latentes compactes, puis reconstruit les valeurs de texture demandées avec un petit réseau neuronal. Selon le mode d'intégration, le jeu peut échanger du stockage et de l'utilisation de VRAM contre un travail d'inférence GPU supplémentaire.","Réponse directe","info","callout",{},{"id":555,"data":556,"type":552,"tunes":560},"status",{"body":557,"title":558,"variant":559},"RTX Neural Texture Compression est encore un \u003Cstrong>SDK bêta\u003C\u002Fstrong>. La version bêta actuelle v0.10.0 a ajouté le support d'inférence DirectX 12 Linear Algebra, connectant NTC directement à la nouvelle infrastructure de shaders neuronaux discutée ailleurs sur Figure Rocks.","Statut actuel","note",{},{"id":562,"data":563,"type":566,"tunes":567},"toc",{"title":564,"maxLevel":565,"minLevel":47},"Contenu",3,"tableOfContents",{},{"id":569,"data":570,"type":572,"tunes":573},"h-normal",{"text":571,"level":47},"Pourquoi la compression de texture normale utilise encore beaucoup de mémoire","header",{},{"id":575,"data":576,"type":544,"tunes":578},"p-normal-1",{"text":577},"Un matériau moderne basé sur la physique se compose rarement d'une seule image. Une seule surface peut utiliser l'albédo, la normale, la rugosité, la métallicité, l'occlusion ambiante, l'opacité et d'autres canaux.",{},{"id":580,"data":581,"type":544,"tunes":583},"p-normal-2",{"text":582},"Les formats de compression de blocs GPU traditionnels tels que BC1 à BC7 réduisent le coût, mais le GPU finit toujours par stocker des blocs de texture conventionnels pour le matériau.",{},{"id":585,"data":586,"type":544,"tunes":588},"p-normal-3",{"text":587},"À mesure que la résolution des textures et la complexité des matériaux augmentent, ces canaux consomment de l'espace disque, de la bande passante de streaming et de la mémoire GPU.",{},{"id":590,"data":591,"type":572,"tunes":593},"h-store",{"text":592,"level":47},"Ce que RTX Neural Texture Compression stocke réellement",{},{"id":595,"data":596,"type":544,"tunes":598},"p-store-1",{"text":597},"Le SDK RTXNTC de NVIDIA compresse ensemble les canaux appartenant à un même matériau. Le SDK actuel prend en charge jusqu'à 16 canaux de texture dans un ensemble de textures NTC.",{},{"id":600,"data":601,"type":544,"tunes":603},"p-store-2",{"text":602},"Au lieu de conserver uniquement des texels compressés conventionnels, le processus de compression produit deux éléments principaux : les poids d'un petit décodeur neuronal et des données de caractéristiques latentes compactes.",{},{"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. Textures de matériau originales","L'albédo, la normale, la rugosité, la métallicité et d'autres canaux de matériau sont fournis ensemble.",{"label":612,"description":613},"2. Compression hors ligne","Le SDK apprend une représentation compacte du matériau.",{"label":615,"description":616},"3. Poids neuronaux","Un petit réseau décodeur stocke une partie de ce qui est nécessaire pour reconstruire le matériau.",{"label":618,"description":619},"4. Données latentes","Des tenseurs de caractéristiques compacts stockent des informations spécifiques au matériau.",{"label":621,"description":622},"5. Inférence GPU","À l'exécution, le décodeur combine les données latentes et les poids neuronaux pour reconstruire les valeurs de texture.","Le pipeline de texture neuronale","auto","processFlow",{},{"id":628,"data":629,"type":572,"tunes":631},"h-correlation",{"text":630,"level":47},"Pourquoi compresser les canaux ensemble peut aider",{},{"id":633,"data":634,"type":544,"tunes":636},"p-cor-1",{"text":635},"Les canaux de matériau sont souvent liés. Une rayure visible dans la couleur de base peut également apparaître dans la carte de normale ou de rugosité. Un motif de tissu peut influencer plusieurs canaux au même emplacement spatial.",{},{"id":638,"data":639,"type":544,"tunes":641},"p-cor-2",{"text":640},"NVIDIA a conçu NTC pour exploiter ces corrélations au lieu de compresser chaque texture indépendamment.",{},{"id":643,"data":644,"type":544,"tunes":646},"p-cor-3",{"text":645},"C'est l'une des raisons pour lesquelles la technologie est décrite comme une compression orientée matériau plutôt que simplement un autre format d'image.",{},{"id":648,"data":649,"type":572,"tunes":651},"h-modes",{"text":650,"level":47},"Les trois modes d'exécution NTC sont la clé pour comprendre la technologie",{},{"id":653,"data":654,"type":544,"tunes":656},"p-modes-1",{"text":655},"La partie la plus importante de RTXNTC n'est pas seulement la façon dont le matériau est compressé. C'est le moment où le jeu choisit de le décompresser.",{},{"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","Inférence au chargement",[13,13,13],{"id":666,"label":667,"values":668},"sample","Inférence à l'échantillonnage",[13,13,13],{"id":670,"label":671,"values":672},"feedback","Inférence sur retour",[13,13,13],"Inférence au chargement vs Inférence à l'échantillonnage vs Inférence sur retour","table",[676,679,682],{"id":677,"label":678},"when","Quand le décodage neuronal a lieu",{"id":680,"label":681},"memory","Comportement de la mémoire de textures à l'exécution",{"id":683,"label":684},"tradeoff","Principal compromis","comparison",{},{"id":688,"data":689,"type":572,"tunes":691},"h-load",{"text":690,"level":47},"Inférence au chargement : la compression neuronale comme format de stockage",{},{"id":693,"data":694,"type":544,"tunes":696},"p-load-1",{"text":695},"L'inférence au chargement est le mode le plus simple à comprendre.",{},{"id":698,"data":699,"type":544,"tunes":701},"p-load-2",{"text":700},"Le jeu stocke le matériau sous forme NTC compacte. Lorsque l'actif est chargé, le GPU reconstruit les données de texture et peut les transcoder dans des formats de texture BCn ordinaires.",{},{"id":703,"data":704,"type":544,"tunes":706},"p-load-3",{"text":705},"Après cette étape, le rendu peut utiliser l'échantillonnage de texture normal. L'économie importante se situe principalement avant la décompression : taille du jeu empaqueté, taille du téléchargement ou bande passante de streaming des actifs.",{},{"id":708,"data":709,"type":544,"tunes":711},"p-load-4",{"text":710},"Mais une fois le matériau entièrement décompressé en textures conventionnelles, son empreinte VRAM à l'exécution se rapproche à nouveau de la représentation conventionnelle.",{},{"id":713,"data":714,"type":572,"tunes":716},"h-sample",{"text":715,"level":47},"Inférence à l'échantillonnage : garder la texture neuronale en VRAM",{},{"id":718,"data":719,"type":544,"tunes":721},"p-sample-1",{"text":720},"L'inférence à l'échantillonnage est le mode le plus radical.",{},{"id":723,"data":724,"type":544,"tunes":726},"p-sample-2",{"text":725},"Au lieu de décompresser le matériau en textures conventionnelles avant le rendu, le shader lit des données latentes compactes et exécute le décodeur neuronal lorsqu'il a besoin de valeurs de texture.",{},{"id":728,"data":729,"type":544,"tunes":731},"p-sample-3",{"text":730},"L'exemple du SDK de NVIDIA compare une représentation de matériau BCn de 12 Mo à une représentation NTC de 2,5 Mo lors de l'utilisation de l'inférence à l'échantillonnage.",{},{"id":733,"data":734,"type":544,"tunes":736},"p-sample-4",{"text":735},"L'économie est réelle car les données de texture conventionnelles n'ont pas besoin de rester entièrement résidentes. Mais le coût se déplace ailleurs : le shader de pixels ou de collision effectue désormais l'inférence neuronale.",{},{"id":683,"data":738,"type":552,"tunes":742},{"body":739,"title":740,"variant":741},"L'inférence à l'échantillonnage échange de la \u003Cstrong>mémoire et de la bande passante\u003C\u002Fstrong> contre du \u003Cstrong>calcul GPU\u003C\u002Fstrong>. La bonne question n'est pas « De combien la texture est-elle plus petite ? » mais « L'économie de mémoire vaut-elle le coût d'inférence ajouté sur cette charge de travail ? »","La compression ne fait pas disparaître le travail","warning",{},{"id":744,"data":745,"type":750,"tunes":751},"ref-vram",{"url":746,"title":747,"excerpt":748,"ctaLabel":749},"https:\u002F\u002Ffigure.rocks\u002Ffr\u002Fblog\u002Fvram-usage-is-not-vram-requirement-why-a-full-memory-meter-does-not-tell-the-whole-story","L'utilisation de la VRAM n'est pas une exigence de VRAM : pourquoi un indicateur de mémoire plein ne raconte pas toute l'histoire","Pourquoi la capacité VRAM, les budgets, la résidence et la pression mémoire réelle sont des choses différentes.","Lire le guide VRAM","referralArticle",{},{"id":753,"data":754,"type":572,"tunes":756},"h-feedback",{"text":755,"level":47},"Inférence sur retour : ne décoder que ce que le joueur voit réellement",{},{"id":758,"data":759,"type":544,"tunes":761},"p-feed-1",{"text":760},"L'inférence sur retour se situe entre les deux extrêmes.",{},{"id":763,"data":764,"type":544,"tunes":766},"p-feed-2",{"text":765},"Le moteur de rendu suit les tuiles de texture réellement demandées. Au lieu de décompresser immédiatement tout le matériau, le système peut décoder les tuiles demandées par lots et conserver un cache de travail.",{},{"id":768,"data":769,"type":544,"tunes":771},"p-feed-3",{"text":770},"Conceptuellement, cela combine la compression neuronale avec le streaming de textures : le système ne paie le coût de décodage que pour les régions qui deviennent pertinentes.",{},{"id":773,"data":774,"type":544,"tunes":776},"p-feed-4",{"text":775},"L'implémentation actuelle de l'exemple est plus spécialisée que les autres modes et ses contraintes de prise en charge diffèrent, elle doit donc être traitée comme une stratégie d'intégration plutôt que comme un remplacement universel du streaming de textures ordinaire.",{},{"id":778,"data":779,"type":572,"tunes":781},"h-exchange",{"text":780,"level":47},"L'échange stockage-calcul des textures",{},{"id":783,"data":784,"type":544,"tunes":786},"p-exchange-1",{"text":785},"La façon la plus simple de comprendre la compression neuronale de textures est de la voir comme un échange entre ressources.",{},{"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","Disque\u002Fstockage",[13,13],{"id":796,"label":797,"values":798},"vram","VRAM",[13,13],{"id":800,"label":801,"values":802},"sampling","Coût d'échantillonnage",[13,13],{"id":804,"label":805,"values":806},"quality","Contrôle de qualité",[13,13],"Ce qui change lorsque le stockage des textures devient neuronal",[809,812],{"id":810,"label":811},"traditional","Texture compressée traditionnelle",{"id":813,"label":814},"neural","Représentation neuronale de texture",{},{"id":817,"data":818,"type":572,"tunes":820},"h-matrix",{"text":819,"level":47},"Pourquoi le matériel matriciel spécialisé est important",{},{"id":822,"data":823,"type":544,"tunes":825},"p-matrix-1",{"text":824},"Exécuter un réseau de neurones pour l'échantillonnage de textures serait trop coûteux si chaque multiplication devait être traitée comme un simple calcul scalaire de shader.",{},{"id":827,"data":828,"type":544,"tunes":830},"p-matrix-2",{"text":829},"RTXNTC bénéficie donc de Cooperative Vector et désormais des chemins DirectX 12 Linear Algebra qui permettent aux shaders d'utiliser le matériel d'accélération matricielle du GPU.",{},{"id":832,"data":833,"type":544,"tunes":835},"p-matrix-3",{"text":834},"La bêta v0.10.0 a explicitement ajouté l'inférence via l'API DirectX 12 Linear Algebra introduite avec 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\u002Ffr\u002Fblog\u002Fdirectx-is-becoming-an-ml-platform-what-linear-algebra-and-neural-shaders-mean-for-future-games","DirectX devient une plateforme de ML : ce que Linear Algebra et les shaders neuronaux signifient pour les futurs jeux","Comment DirectX déplace les opérations matricielles et neuronales directement dans HLSL et le pipeline graphique.","Lire le guide des shaders neuronaux DirectX",{},{"id":845,"data":846,"type":572,"tunes":848},"h-not-upscale",{"text":847,"level":47},"Pourquoi la compression neuronale de textures n'est pas de la mise à l'échelle de textures",{},{"id":850,"data":851,"type":544,"tunes":853},"p-notup-1",{"text":852},"Les deux techniques peuvent toutes deux utiliser l'apprentissage automatique, mais elles résolvent des problèmes différents.",{},{"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","Entrée",[13,13],{"id":863,"label":864,"values":865},"goal","Objectif",[13,13],{"id":677,"label":867,"values":868},"Quand elle s'exécute",[13,13],{"id":870,"label":871,"values":872},"output","Sortie",[13,13],"Compression neuronale de textures vs Super Résolution",[875,878],{"id":876,"label":877},"ntc","Compression neuronale de textures",{"id":879,"label":880},"sr","Super Résolution",{},{"id":883,"data":884,"type":544,"tunes":886},"p-notup-2",{"text":885},"La compression neuronale de textures peut donc exister sous DLSS, FSR, XeSS ou le rendu natif. Elle modifie la façon dont les données de matériau sont stockées et reconstruites, pas la résolution d'affichage finale.",{},{"id":888,"data":889,"type":572,"tunes":891},"h-bpp",{"text":890,"level":47},"Le réglage de qualité est le nombre de bits par pixel",{},{"id":893,"data":894,"type":544,"tunes":896},"p-bpp-1",{"text":895},"NTC est une compression avec perte. La quantité d'informations compressées est contrôlée en grande partie par le réglage des bits par pixel.",{},{"id":898,"data":899,"type":544,"tunes":901},"p-bpp-2",{"text":900},"Un débit binaire plus élevé donne plus d'informations au modèle et améliore généralement la qualité de reconstruction. Un débit binaire plus faible améliore la compression mais augmente le risque d'erreurs visibles.",{},{"id":903,"data":904,"type":544,"tunes":906},"p-bpp-3",{"text":905},"Comme plusieurs canaux partagent la même représentation, ajouter davantage de canaux de matériau sans augmenter le débit binaire peut réduire la qualité disponible pour chaque canal.",{},{"id":908,"data":909,"type":552,"tunes":912},"lossy-note",{"body":910,"title":911,"variant":559},"La documentation du SDK note explicitement que l'erreur de compression est normale. L'objectif pratique est \u003Cstrong>une erreur visuelle acceptable à un coût de stockage et d'exécution utile\u003C\u002Fstrong>.","Neuronal ne signifie pas sans perte",{},{"id":914,"data":915,"type":572,"tunes":917},"h-channels",{"text":916,"level":47},"Pourquoi les canaux de matériau corrélés sont importants",{},{"id":919,"data":920,"type":544,"tunes":922},"p-channels-1",{"text":921},"Une représentation neuronale devient plus précieuse lorsque plusieurs canaux de matière décrivent une structure apparentée.",{},{"id":924,"data":925,"type":544,"tunes":927},"p-channels-2",{"text":926},"Si l'albédo, la normale et la rugosité contiennent tous les mêmes rayures, coutures ou tissage, le décodeur peut exploiter des informations spatiales partagées.",{},{"id":929,"data":930,"type":544,"tunes":932},"p-channels-3",{"text":931},"Si les canaux sont du bruit non corrélé, il y a moins de structure commune à exploiter et le problème de compression devient plus difficile.",{},{"id":934,"data":935,"type":572,"tunes":937},"h-test",{"text":936,"level":47},"Le test de valeur des textures neuronales",{},{"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. Mesurer le coût des textures conventionnelles","Quel espace disque, quelle bande passante de streaming et quelle VRAM les textures de matière existantes consomment-elles ?",{"label":946,"description":947},"2. Choisir le mode d'exécution","Voulez-vous uniquement des économies de stockage, des économies persistantes de VRAM ou une reconstruction de tuiles en streaming ?",{"label":949,"description":950},"3. Fixer un objectif de qualité acceptable","Comparez les canaux reconstruits à la matière d'origine, pas seulement à l'image finale.",{"label":952,"description":953},"4. Mesurer le coût d'inférence","Enregistrez le temps supplémentaire de shader ou de décompression sur le GPU cible réel.",{"label":955,"description":956},"5. Mesurer les économies de mémoire","Vérifiez l'ensemble de travail réel à l'exécution plutôt que seulement la taille du fichier compressé.",{"label":958,"description":959},"6. Tester les matières difficiles","Les normales fines, les masques nets, l'opacité, le texte et les canaux non corrélés peuvent révéler des échecs de compression.",{"label":961,"description":962},"7. Décider selon le bénéfice net","N'utilisez NTC que là où la mémoire\u002Fbande passante économisée vaut la complexité supplémentaire d'inférence et d'intégration.","Quand la compression de textures neuronale est-elle réellement utile ?",{},{"id":966,"data":967,"type":572,"tunes":969},"h-8x",{"text":968,"level":47},"Pourquoi « 8× plus petit » nécessite du contexte",{},{"id":971,"data":972,"type":544,"tunes":974},"p-8x-1",{"text":973},"Le RTX Kit de NVIDIA décrit la RTX Neural Texture Compression comme offrant jusqu'à 8× d'amélioration de la mémoire disque à une fidélité visuelle similaire à la compression par blocs traditionnelle.",{},{"id":976,"data":977,"type":544,"tunes":979},"p-8x-2",{"text":978},"L'expression « jusqu'à » compte. Le taux de compression dépend de la matière, du nombre de canaux, du débit binaire cible, de la configuration du décodeur et du seuil de qualité.",{},{"id":981,"data":982,"type":544,"tunes":984},"p-8x-3",{"text":983},"Le même taux ne décrit pas non plus automatiquement les économies de VRAM. L'inférence au chargement peut partir d'un fichier compact et s'étendre ensuite en textures GPU conventionnelles. L'inférence à l'échantillonnage préserve la représentation compacte en mémoire GPU mais dépense plus de calcul pendant l'ombrage.",{},{"id":986,"data":987,"type":572,"tunes":989},"h-decoder",{"text":988,"level":47},"La taille du décodeur est un autre compromis performance-qualité",{},{"id":991,"data":992,"type":544,"tunes":994},"p-dec-1",{"text":993},"L'environnement d'exécution NTC utilise un petit perceptron multicouche pour décoder les valeurs de texture.",{},{"id":996,"data":997,"type":544,"tunes":999},"p-dec-2",{"text":998},"La bibliothèque actuelle de NVIDIA utilise une architecture de décodeur configurable. Des réseaux plus grands peuvent améliorer la qualité de compression mais coûtent plus cher à exécuter ; des réseaux plus petits peuvent s'exécuter plus rapidement avec une certaine perte de qualité de reconstruction.",{},{"id":1001,"data":1002,"type":544,"tunes":1004},"p-dec-3",{"text":1003},"Cela donne aux développeurs de moteurs une dimension de réglage supplémentaire au-delà de la résolution de texture et du débit binaire.",{},{"id":1006,"data":1007,"type":572,"tunes":1009},"h-install",{"text":1008,"level":47},"Pourquoi cela compte pour les futures installations de jeux",{},{"id":1011,"data":1012,"type":544,"tunes":1014},"p-install-1",{"text":1013},"Les jeux modernes livrent de plus en plus d'ensembles de matières haute résolution qui affectent la taille de téléchargement ainsi que la mémoire d'exécution.",{},{"id":1016,"data":1017,"type":544,"tunes":1019},"p-install-2",{"text":1018},"La compression de textures neuronale crée une nouvelle option : livrer une représentation apprise compacte et décider plus tard de l'étendre au chargement, de la reconstruire directement pendant l'ombrage ou de ne décoder que les tuiles demandées.",{},{"id":1021,"data":1022,"type":544,"tunes":1024},"p-install-3",{"text":1023},"Cela signifie qu'une seule représentation d'actif compressé peut participer à plusieurs stratégies de mémoire d'exécution différentes.",{},{"id":1026,"data":1027,"type":572,"tunes":1029},"h-meaning",{"text":1028,"level":47},"Cela change aussi ce que signifie « mémoire de texture »",{},{"id":1031,"data":1032,"type":544,"tunes":1034},"p-meaning-1",{"text":1033},"Avec le rendu traditionnel, un budget de mémoire de textures concerne principalement les formats de textures, les niveaux de mip, la résolution et la résidence.",{},{"id":1036,"data":1037,"type":544,"tunes":1039},"p-meaning-2",{"text":1038},"Avec les textures neuronales, les développeurs peuvent également budgétiser les données latentes, les poids du décodeur, les tampons d'inférence, les caches transcodés et la puissance de calcul nécessaire pour reconstruire les valeurs demandées.",{},{"id":1041,"data":1042,"type":544,"tunes":1044},"p-meaning-3",{"text":1043},"Ainsi, l'actif n'a plus une seule identité mémoire fixe et simple.",{},{"id":1046,"data":1047,"type":572,"tunes":1049},"h-cross",{"text":1048,"level":47},"La prise en charge multi-fournisseurs est plus nuancée que ne le suggère le nom RTX",{},{"id":1051,"data":1052,"type":544,"tunes":1054},"p-cross-1",{"text":1053},"RTXNTC est un SDK NVIDIA, et la compression elle-même nécessite actuellement un GPU NVIDIA selon les exigences du SDK.",{},{"id":1056,"data":1057,"type":544,"tunes":1059},"p-cross-2",{"text":1058},"La décompression à l'exécution est plus large. NVIDIA documente des chemins fonctionnels sur le matériel Shader Model 6 et note une validation sur les GPU NVIDIA, AMD et Intel, tandis que les chemins avancés Cooperative Vector \u002F Linear Algebra dépendent de la prise en charge des API et des pilotes.",{},{"id":1061,"data":1062,"type":544,"tunes":1064},"p-cross-3",{"text":1063},"La performance et la parité des fonctionnalités ne doivent donc pas être supposées entre les fournisseurs simplement parce que le décodeur de base peut s'exécuter.",{},{"id":1066,"data":1067,"type":572,"tunes":1069},"h-change",{"text":1068,"level":47},"Qu'est-ce qui changerait cette réponse ?",{},{"id":1071,"data":1072,"type":544,"tunes":1074},"p-change-1",{"text":1073},"NTC est encore en version bêta. Les modes d'exécution, les architectures de décodeur, la prise en charge des pilotes et les chemins d'intégration peuvent changer avant une version de production stable.",{},{"id":1076,"data":1077,"type":544,"tunes":1079},"p-change-2",{"text":1078},"Le changement le plus important à long terme serait l'adoption généralisée de primitives de shaders neuronaux standardisées dans DirectX et Vulkan. Cela rendrait le décodage de textures neuronales moins dépendant de chemins d'exécution personnalisés propres à un fournisseur.",{},{"id":1081,"data":1082,"type":572,"tunes":1084},"h-limit",{"text":1083,"level":47},"Limitations",{},{"id":1086,"data":1087,"type":544,"tunes":1089},"p-limit-1",{"text":1088},"Cet article décrit l'architecture et le comportement actuel du SDK RTXNTC public. Les affirmations de compression citées par NVIDIA sont fournies par le fournisseur et ne doivent pas être considérées comme des résultats garantis pour chaque matériau.",{},{"id":1091,"data":1092,"type":544,"tunes":1094},"p-limit-2",{"text":1093},"Le SDK actuel est un logiciel bêta, et certains chemins de prévisualisation ont des limitations documentées de pilote et de plateforme.",{},{"id":1096,"data":1097,"type":572,"tunes":1099},"h-conclusion",{"text":1098,"level":47},"Conclusion",{},{"id":1101,"data":1102,"type":544,"tunes":1104},"p-conc-1",{"text":1103},"RTX Neural Texture Compression est intéressant car il change une hypothèse très ancienne : le détail des textures ne doit pas toujours exister en mémoire sous forme de texels conventionnels.",{},{"id":1106,"data":1107,"type":544,"tunes":1109},"p-conc-2",{"text":1108},"Un matériau peut plutôt être stocké en partie sous forme de représentation apprise compacte et reconstruit lorsque nécessaire.",{},{"id":1111,"data":1112,"type":544,"tunes":1114},"p-conc-3",{"text":1113},"Cela ne donne pas une qualité gratuite ni de la mémoire gratuite. Cela crée un nouvel échange : moins de stockage, de bande passante et potentiellement de VRAM en échange d'un travail d'inférence neuronale.",{},{"id":1116,"data":1117,"type":544,"tunes":1119},"p-conc-4",{"text":1118},"La véritable innovation n'est pas « l'IA rend les textures plus nettes ». C'est qu'une partie des données de matériaux d'un jeu peut devenir du calcul.",{},{"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","Non. Elle compresse et reconstruit les données de texture des matériaux. Les technologies de super-résolution reconstruisent l'image finale rendue à une résolution d'affichage plus élevée.","La compression de textures neuronale RTX est-elle un upscaler ?",{"id":1134,"answer":1135,"question":1136},"faq2","Oui, en particulier avec l'inférence au chargement, car la représentation neuronale compacte peut rester en mémoire GPU au lieu de textures conventionnelles entièrement décompressées. L'inférence au chargement préserve principalement les économies de stockage avant l'expansion.","NTC peut-il réduire l'utilisation de la VRAM ?",{"id":1138,"answer":1139,"question":1140},"faq3","Le matériau compressé contient des données de caractéristiques latentes compactes ainsi que les poids d'un petit réseau décodeur qui reconstruit les valeurs de texture.","Que stocke le réseau de neurones ?",{"id":1142,"answer":1143,"question":1144},"faq4","Pas nécessairement. L'inférence au chargement le fait, l'inférence à l'échantillonnage reconstruit les valeurs pendant l'échantillonnage du shader, et l'inférence sur retour peut décoder les tuiles de texture demandées.","NTC décode-t-il toute la texture avant le rendu ?",{"id":1146,"answer":1147,"question":1148},"faq5","Non. RTXNTC est une compression avec perte et la qualité dépend du débit, du nombre de canaux, de la configuration du décodeur et du matériau lui-même.","La compression de textures neuronale est-elle sans perte ?",{"id":1150,"answer":1151,"question":1152},"faq6","Le SDK public actuel est encore étiqueté bêta, donc les API, le support et les caractéristiques de performance peuvent continuer à changer.","RTXNTC est-il prêt pour la production ?","La compression de textures neuronale RTX expliquée simplement",{},{"id":1156,"data":1157,"type":572,"tunes":1159},"h-glossary",{"text":1158,"level":47},"Glossaire",{},{"id":1161,"data":1162,"type":1161,"tunes":1194},"glossary",{"title":1163,"entries":1164},"Termes clés des textures neuronales",[1165,1169,1173,1177,1180,1183,1186,1190],{"term":1166,"anchor":1167,"definition":1168},"Compression de textures neuronale","neural-texture-compression","Une technique qui stocke les informations de texture sous forme de données latentes compactes plus les poids du décodeur neuronal au lieu de seulement des blocs de texels conventionnels.",{"term":1170,"anchor":1171,"definition":1172},"Données latentes","latent-data","Caractéristiques apprises compactes que le décodeur neuronal utilise pour reconstruire les valeurs de texture.",{"term":1174,"anchor":1175,"definition":1176},"Décodeur","decoder","Un petit réseau de neurones qui convertit les caractéristiques latentes en canaux de texture reconstruits.",{"term":663,"anchor":1178,"definition":1179},"inference-on-load","Mode d'exécution qui décode la texture neuronale lors du chargement d'un actif, généralement vers des formats de texture conventionnels.",{"term":667,"anchor":1181,"definition":1182},"inference-on-sample","Mode d'exécution qui effectue le décodage neuronal directement pendant l'échantillonnage de texture afin que la représentation compressée puisse rester résidente.",{"term":671,"anchor":1184,"definition":1185},"inference-on-feedback","Stratégie d'exécution qui utilise le retour de texture pour décoder et mettre en cache uniquement les tuiles demandées.",{"term":1187,"anchor":1188,"definition":1189},"Échange stockage-calcul de texture","texture-storage-compute-exchange","Un modèle Figure Rocks décrivant l'échange de stockage de texture, de bande passante et de VRAM contre un travail d'inférence neuronale supplémentaire sur le GPU.",{"term":1191,"anchor":1192,"definition":1193},"Test de valeur de texture neuronale","neural-texture-value-test","Un flux de travail Figure Rocks pour décider si la compression de textures neuronale crée un bénéfice net pour un matériau particulier et un GPU cible.",{},{"id":1196,"data":1197,"type":572,"tunes":1199},"h-sources",{"text":1198,"level":47},"Sources principales",{},{"id":1201,"data":1202,"type":1208,"tunes":1209},"src-rtxkit",{"link":1203,"meta":1204},"https:\u002F\u002Fdeveloper.nvidia.com\u002Frtx-kit",{"image":1205,"title":1206,"description":1207},{"url":13},"NVIDIA Developer — RTX Kit","Présentation officielle de la compression de textures neuronale RTX et du positionnement publié par NVIDIA en matière de réduction du stockage.","linkTool",{},{"id":1211,"data":1212,"type":1208,"tunes":1218},"src-rtxntc-readme",{"link":1213,"meta":1214},"https:\u002F\u002Fgithub.com\u002FNVIDIA-RTX\u002FRTXNTC\u002Fblob\u002Fmain\u002FREADME.md",{"image":1215,"title":1216,"description":1217},{"url":13},"NVIDIA RTXNTC — SDK README","Documentation officielle du SDK décrivant la compression des canaux de matériaux, la représentation décodeur\u002Flatente, les modes d'exécution, des exemples de mémoire, les exigences système et la prise en charge de Cooperative Vector.",{},{"id":1220,"data":1221,"type":1208,"tunes":1227},"src-rtxntc-release",{"link":1222,"meta":1223},"https:\u002F\u002Fgithub.com\u002FNVIDIA-RTX\u002FRTXNTC\u002Freleases",{"image":1224,"title":1225,"description":1226},{"url":13},"NVIDIA RTXNTC — Versions","Historique officiel des versions, y compris la version bêta v0.10.0 et la prise en charge de l'inférence DirectX 12 Linear Algebra.",{},{"id":1229,"data":1230,"type":1208,"tunes":1236},"src-rtxntc-quality",{"link":1231,"meta":1232},"https:\u002F\u002Fgithub.com\u002FNVIDIA-RTX\u002FRTXNTC\u002Fblob\u002Fmain\u002Fdocs\u002FSettingsAndQuality.md",{"image":1233,"title":1234,"description":1235},{"url":13},"NVIDIA RTXNTC — Paramètres de compression et qualité d'image","Documentation officielle couvrant le débit, les interactions entre canaux, la mesure de la qualité et le comportement de la compression avec perte.",{},{"id":1238,"data":1239,"type":1208,"tunes":1245},"src-libntc",{"link":1240,"meta":1241},"https:\u002F\u002Fgithub.com\u002FNVIDIA-RTX\u002FRTXNTC-Library",{"image":1242,"title":1243,"description":1244},{"url":13},"NVIDIA — LibNTC","Documentation officielle de la bibliothèque d'exécution décrivant la configuration du décodeur et le compromis qualité\u002Fperformance des différentes tailles de réseau de neurones.",{},"2.31","NVIDIA RTX Neural Texture Compression change la façon dont les matériaux de jeu peuvent être stockés. 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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":1294,"blocks":1295,"version":1845},1790378899383,[1296,1300,1305,1310,1314,1318,1322,1326,1330,1334,1338,1342,1362,1366,1370,1374,1378,1382,1386,1407,1411,1415,1419,1423,1427,1431,1435,1439,1443,1447,1452,1459,1463,1467,1471,1475,1479,1483,1487,1508,1512,1516,1520,1524,1531,1535,1539,1561,1565,1569,1573,1577,1581,1586,1590,1594,1598,1602,1606,1632,1636,1640,1644,1648,1652,1656,1660,1664,1668,1672,1676,1680,1684,1688,1692,1696,1700,1704,1708,1712,1716,1720,1724,1727,1731,1735,1738,1742,1746,1750,1754,1757,1780,1784,1809,1813,1819,1825,1832,1839],{"id":541,"data":1297,"type":544,"tunes":1299},{"text":1298},"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":1301,"type":552,"tunes":1304},{"body":1302,"title":1303,"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":1306,"type":552,"tunes":1309},{"body":1307,"title":1308,"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":1311,"type":566,"tunes":1313},{"title":1312,"maxLevel":565,"minLevel":47},"Contents",{},{"id":569,"data":1315,"type":572,"tunes":1317},{"text":1316,"level":47},"Why normal texture compression still uses a lot of memory",{},{"id":575,"data":1319,"type":544,"tunes":1321},{"text":1320},"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":1323,"type":544,"tunes":1325},{"text":1324},"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":1327,"type":544,"tunes":1329},{"text":1328},"As texture resolution and material complexity increase, those channels consume disk space, streaming bandwidth and GPU memory.",{},{"id":590,"data":1331,"type":572,"tunes":1333},{"text":1332,"level":47},"What RTX Neural Texture Compression actually stores",{},{"id":595,"data":1335,"type":544,"tunes":1337},{"text":1336},"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":1339,"type":544,"tunes":1341},{"text":1340},"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":1343,"type":625,"tunes":1361},{"steps":1344,"title":1360,"orientation":624},[1345,1348,1351,1354,1357],{"label":1346,"description":1347},"1. Original material textures","Albedo, normal, roughness, metalness and other material channels are provided together.",{"label":1349,"description":1350},"2. Offline compression","The SDK learns a compact representation of the material.",{"label":1352,"description":1353},"3. Neural weights","A small decoder network stores part of what is needed to reconstruct the material.",{"label":1355,"description":1356},"4. Latent data","Compact feature tensors store material-specific information.",{"label":1358,"description":1359},"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":1363,"type":572,"tunes":1365},{"text":1364,"level":47},"Why compressing channels together can help",{},{"id":633,"data":1367,"type":544,"tunes":1369},{"text":1368},"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":1371,"type":544,"tunes":1373},{"text":1372},"NVIDIA designed NTC to exploit those correlations instead of compressing every texture independently.",{},{"id":643,"data":1375,"type":544,"tunes":1377},{"text":1376},"That is one reason the technology is described as material-oriented compression rather than merely another image format.",{},{"id":648,"data":1379,"type":572,"tunes":1381},{"text":1380,"level":47},"The three NTC runtime modes are the key to understanding the technology",{},{"id":653,"data":1383,"type":544,"tunes":1385},{"text":1384},"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":1387,"type":685,"tunes":1406},{"rows":1388,"title":1398,"layout":674,"columns":1399},[1389,1392,1395],{"id":662,"label":1390,"values":1391},"Inference on Load",[13,13,13],{"id":666,"label":1393,"values":1394},"Inference on Sample",[13,13,13],{"id":670,"label":1396,"values":1397},"Inference on Feedback",[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":1560},{"rows":1541,"title":1554,"layout":674,"columns":1555},[1542,1545,1548,1551],{"id":859,"label":1543,"values":1544},"Input",[13,13],{"id":863,"label":1546,"values":1547},"Goal",[13,13],{"id":677,"label":1549,"values":1550},"When it runs",[13,13],{"id":870,"label":1552,"values":1553},"Output",[13,13],"Neural Texture Compression vs Super Resolution",[1556,1558],{"id":876,"label":1557},"Neural Texture Compression",{"id":879,"label":1559},"Super Resolution",{},{"id":883,"data":1562,"type":544,"tunes":1564},{"text":1563},"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":1566,"type":572,"tunes":1568},{"text":1567,"level":47},"The quality knob is bits per pixel",{},{"id":893,"data":1570,"type":544,"tunes":1572},{"text":1571},"NTC is lossy compression. The amount of compressed information is controlled largely through the bits-per-pixel setting.",{},{"id":898,"data":1574,"type":544,"tunes":1576},{"text":1575},"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":1578,"type":544,"tunes":1580},{"text":1579},"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":1582,"type":552,"tunes":1585},{"body":1583,"title":1584,"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":1587,"type":572,"tunes":1589},{"text":1588,"level":47},"Why correlated material channels are important",{},{"id":919,"data":1591,"type":544,"tunes":1593},{"text":1592},"A neural representation becomes more valuable when several material channels describe related structure.",{},{"id":924,"data":1595,"type":544,"tunes":1597},{"text":1596},"If albedo, normal and roughness all contain the same scratches, seams or fabric weave, the decoder can exploit shared spatial information.",{},{"id":929,"data":1599,"type":544,"tunes":1601},{"text":1600},"If the channels are unrelated noise, there is less common structure to exploit and the compression problem becomes harder.",{},{"id":934,"data":1603,"type":572,"tunes":1605},{"text":1604,"level":47},"The Neural Texture Value Test",{},{"id":939,"data":1607,"type":625,"tunes":1631},{"steps":1608,"title":1630,"orientation":624},[1609,1612,1615,1618,1621,1624,1627],{"label":1610,"description":1611},"1. Measure conventional texture cost","How much disk space, streaming bandwidth and VRAM do the existing material textures consume?",{"label":1613,"description":1614},"2. Choose the runtime mode","Do you want storage savings only, persistent VRAM savings or streamed tile reconstruction?",{"label":1616,"description":1617},"3. Set an acceptable quality target","Compare reconstructed channels against the original material, not only the final beauty image.",{"label":1619,"description":1620},"4. Measure inference cost","Record added shader or decompression time on the actual target GPU.",{"label":1622,"description":1623},"5. Measure memory savings","Check the real runtime working set rather than only the compressed file size.",{"label":1625,"description":1626},"6. Test difficult materials","Fine normals, sharp masks, opacity, text and unrelated channels can expose compression failures.",{"label":1628,"description":1629},"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":1633,"type":572,"tunes":1635},{"text":1634,"level":47},"Why “8× smaller” needs context",{},{"id":971,"data":1637,"type":544,"tunes":1639},{"text":1638},"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":1641,"type":544,"tunes":1643},{"text":1642},"The phrase “up to” matters. Compression ratio depends on the material, number of channels, target bitrate, decoder configuration and quality threshold.",{},{"id":981,"data":1645,"type":544,"tunes":1647},{"text":1646},"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":1649,"type":572,"tunes":1651},{"text":1650,"level":47},"Decoder size is another performance-quality trade-off",{},{"id":991,"data":1653,"type":544,"tunes":1655},{"text":1654},"The NTC runtime uses a small multilayer perceptron to decode texture values.",{},{"id":996,"data":1657,"type":544,"tunes":1659},{"text":1658},"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":1661,"type":544,"tunes":1663},{"text":1662},"That gives engine developers another tuning dimension beyond texture resolution and bitrate.",{},{"id":1006,"data":1665,"type":572,"tunes":1667},{"text":1666,"level":47},"Why this matters for future game installations",{},{"id":1011,"data":1669,"type":544,"tunes":1671},{"text":1670},"Modern games increasingly ship high-resolution material sets that affect download size as well as runtime memory.",{},{"id":1016,"data":1673,"type":544,"tunes":1675},{"text":1674},"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":1677,"type":544,"tunes":1679},{"text":1678},"That means one compressed asset representation can participate in several different runtime memory strategies.",{},{"id":1026,"data":1681,"type":572,"tunes":1683},{"text":1682,"level":47},"It also changes what 'texture memory' means",{},{"id":1031,"data":1685,"type":544,"tunes":1687},{"text":1686},"With traditional rendering, a texture-memory budget is mostly about texture formats, mip levels, resolution and residency.",{},{"id":1036,"data":1689,"type":544,"tunes":1691},{"text":1690},"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":1693,"type":544,"tunes":1695},{"text":1694},"So the asset no longer has one simple fixed memory identity.",{},{"id":1046,"data":1697,"type":572,"tunes":1699},{"text":1698,"level":47},"Cross-vendor support is more nuanced than the RTX name suggests",{},{"id":1051,"data":1701,"type":544,"tunes":1703},{"text":1702},"RTXNTC is an NVIDIA SDK, and compression itself currently requires an NVIDIA GPU according to the SDK requirements.",{},{"id":1056,"data":1705,"type":544,"tunes":1707},{"text":1706},"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":1709,"type":544,"tunes":1711},{"text":1710},"Performance and feature parity therefore should not be assumed across vendors merely because the basic decoder can run.",{},{"id":1066,"data":1713,"type":572,"tunes":1715},{"text":1714,"level":47},"What would change this answer?",{},{"id":1071,"data":1717,"type":544,"tunes":1719},{"text":1718},"NTC is still beta. Runtime modes, decoder architectures, driver support and integration paths can change before a stable production release.",{},{"id":1076,"data":1721,"type":544,"tunes":1723},{"text":1722},"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":1725,"type":572,"tunes":1726},{"text":1083,"level":47},{},{"id":1086,"data":1728,"type":544,"tunes":1730},{"text":1729},"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":1732,"type":544,"tunes":1734},{"text":1733},"The current SDK is beta software, and some preview paths have documented driver and platform limitations.",{},{"id":1096,"data":1736,"type":572,"tunes":1737},{"text":1098,"level":47},{},{"id":1101,"data":1739,"type":544,"tunes":1741},{"text":1740},"RTX Neural Texture Compression is interesting because it changes a very old assumption: texture detail does not always have to exist in memory as conventional texels.",{},{"id":1106,"data":1743,"type":544,"tunes":1745},{"text":1744},"A material can instead be stored partly as a compact learned representation and reconstructed when needed.",{},{"id":1111,"data":1747,"type":544,"tunes":1749},{"text":1748},"That does not give free quality or free memory. It creates a new exchange: less storage, bandwidth and potentially VRAM in return for neural inference work.",{},{"id":1116,"data":1751,"type":544,"tunes":1753},{"text":1752},"The real innovation is not “AI makes textures sharper.” It is that part of a game's material data can become computation.",{},{"id":1121,"data":1755,"type":572,"tunes":1756},{"text":1123,"level":47},{},{"id":1126,"data":1758,"type":1126,"tunes":1779},{"items":1759,"title":1778},[1760,1763,1766,1769,1772,1775],{"id":1130,"answer":1761,"question":1762},"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":1764,"question":1765},"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":1767,"question":1768},"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":1770,"question":1771},"Not necessarily. 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