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NVIDIA RTX Neural Texture Compression cambia ese modelo: parte de los datos de textura se convierte en una pequeña representación neuronal que puede ser decodificada por la propia GPU.\u003C\u002Fp>\n\u003Caside class=\"editorjs-callout editorjs-callout--info my-6 rounded-xl border p-5 border-blue-300 bg-blue-50 dark:border-blue-900 dark:bg-blue-950\u002F20\" role=\"note\">\u003Cstrong class=\"block mb-2 text-gray-900 dark:text-gray-100\">Respuesta directa\u003C\u002Fstrong>\u003Cdiv class=\"text-gray-700 dark:text-gray-200\">\u003Cstrong>RTX Neural Texture Compression no es escalado de texturas.\u003C\u002Fstrong> Comprime múltiples texturas de materiales en pesos de redes neuronales más datos latentes compactos, y luego reconstruye los valores de textura solicitados con una pequeña red neuronal. Según el modo de integración, el juego puede intercambiar almacenamiento y uso de VRAM por trabajo adicional de inferencia en la GPU.\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\">Estado actual\u003C\u002Fstrong>\u003Cdiv class=\"text-gray-700 dark:text-gray-200\">RTX Neural Texture Compression sigue siendo un \u003Cstrong>SDK beta\u003C\u002Fstrong>. La beta actual v0.10.0 añadió soporte de inferencia DirectX 12 Linear Algebra, conectando NTC directamente con la nueva infraestructura de shaders neuronales discutida en otra parte de Figure Rocks.\u003C\u002Fdiv>\u003C\u002Faside>\n\u003Cnav class=\"editorjs-toc\" data-editorjs-toc=\"true\" aria-label=\"Contenido\">\u003Cstrong class=\"editorjs-toc__title\">Contenido\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\">Por qué la compresión de texturas normal sigue usando mucha memoria\u003C\u002Fa>\u003C\u002Fli>\u003Cli class=\"editorjs-toc__item\">\u003Ca href=\"#section-9\" class=\"editorjs-toc__link\">Qué almacena realmente RTX Neural Texture Compression\u003C\u002Fa>\u003C\u002Fli>\u003Cli class=\"editorjs-toc__item\">\u003Ca href=\"#section-13\" class=\"editorjs-toc__link\">Por qué comprimir canales juntos puede ayudar\u003C\u002Fa>\u003C\u002Fli>\u003Cli class=\"editorjs-toc__item\">\u003Ca href=\"#section-17\" class=\"editorjs-toc__link\">Los tres modos de ejecución de NTC son la clave para entender la tecnología\u003C\u002Fa>\u003C\u002Fli>\u003Cli class=\"editorjs-toc__item\">\u003Ca href=\"#section-20\" class=\"editorjs-toc__link\">Inferencia al cargar: compresión neuronal como formato de almacenamiento\u003C\u002Fa>\u003C\u002Fli>\u003Cli class=\"editorjs-toc__item\">\u003Ca href=\"#section-25\" class=\"editorjs-toc__link\">Inferencia al muestrear: mantener la textura neuronal en VRAM\u003C\u002Fa>\u003C\u002Fli>\u003Cli class=\"editorjs-toc__item\">\u003Ca href=\"#section-32\" class=\"editorjs-toc__link\">Inferencia por retroalimentación: decodificar solo lo que el jugador ve realmente\u003C\u002Fa>\u003C\u002Fli>\u003Cli class=\"editorjs-toc__item\">\u003Ca href=\"#section-37\" class=\"editorjs-toc__link\">El intercambio entre almacenamiento de texturas y cómputo\u003C\u002Fa>\u003C\u002Fli>\u003Cli class=\"editorjs-toc__item\">\u003Ca href=\"#section-40\" class=\"editorjs-toc__link\">Por qué importa el hardware especializado en matrices\u003C\u002Fa>\u003C\u002Fli>\u003Cli class=\"editorjs-toc__item\">\u003Ca href=\"#section-45\" class=\"editorjs-toc__link\">Por qué la compresión de texturas neuronales no es escalado de texturas\u003C\u002Fa>\u003C\u002Fli>\u003Cli class=\"editorjs-toc__item\">\u003Ca href=\"#section-49\" class=\"editorjs-toc__link\">El control de calidad son los bits por píxel\u003C\u002Fa>\u003C\u002Fli>\u003Cli class=\"editorjs-toc__item\">\u003Ca href=\"#section-54\" class=\"editorjs-toc__link\">Por qué son importantes los canales de material correlacionados\u003C\u002Fa>\u003C\u002Fli>\u003Cli class=\"editorjs-toc__item\">\u003Ca href=\"#section-58\" class=\"editorjs-toc__link\">La prueba de valor de la textura neuronal\u003C\u002Fa>\u003C\u002Fli>\u003Cli class=\"editorjs-toc__item\">\u003Ca href=\"#section-60\" class=\"editorjs-toc__link\">Por qué “8× más pequeño” necesita contexto\u003C\u002Fa>\u003C\u002Fli>\u003Cli class=\"editorjs-toc__item\">\u003Ca href=\"#section-64\" class=\"editorjs-toc__link\">El tamaño del decodificador es otro compromiso entre rendimiento y calidad\u003C\u002Fa>\u003C\u002Fli>\u003Cli class=\"editorjs-toc__item\">\u003Ca href=\"#section-68\" class=\"editorjs-toc__link\">Por qué esto importa para futuras instalaciones de juegos\u003C\u002Fa>\u003C\u002Fli>\u003Cli class=\"editorjs-toc__item\">\u003Ca href=\"#section-72\" class=\"editorjs-toc__link\">También cambia lo que significa 'memoria de textura'\u003C\u002Fa>\u003C\u002Fli>\u003Cli class=\"editorjs-toc__item\">\u003Ca href=\"#section-76\" class=\"editorjs-toc__link\">El soporte entre proveedores es más matizado de lo que sugiere el nombre RTX\u003C\u002Fa>\u003C\u002Fli>\u003Cli class=\"editorjs-toc__item\">\u003Ca href=\"#section-80\" class=\"editorjs-toc__link\">¿Qué cambiaría esta respuesta?\u003C\u002Fa>\u003C\u002Fli>\u003Cli class=\"editorjs-toc__item\">\u003Ca href=\"#section-83\" class=\"editorjs-toc__link\">Limitaciones\u003C\u002Fa>\u003C\u002Fli>\u003Cli class=\"editorjs-toc__item\">\u003Ca href=\"#section-86\" class=\"editorjs-toc__link\">Conclusión\u003C\u002Fa>\u003C\u002Fli>\u003Cli class=\"editorjs-toc__item\">\u003Ca href=\"#section-91\" class=\"editorjs-toc__link\">Preguntas frecuentes\u003C\u002Fa>\u003C\u002Fli>\u003Cli class=\"editorjs-toc__item\">\u003Ca href=\"#section-93\" class=\"editorjs-toc__link\">Glosario\u003C\u002Fa>\u003C\u002Fli>\u003Cli class=\"editorjs-toc__item\">\u003Ca href=\"#section-95\" class=\"editorjs-toc__link\">Fuentes primarias\u003C\u002Fa>\u003C\u002Fli>\u003C\u002Fol>\u003C\u002Fnav>\n\u003Ch2 id=\"section-5\">Por qué la compresión de texturas normal sigue usando mucha memoria\u003C\u002Fh2>\n\u003Cp>Un material moderno basado en física rara vez consiste en una sola imagen. Una única superficie puede usar albedo, normal, rugosidad, metalicidad, oclusión ambiental, opacidad y otros canales.\u003C\u002Fp>\n\u003Cp>Los formatos tradicionales de compresión de bloques para GPU, como BC1 a BC7, reducen el coste, pero la GPU sigue almacenando bloques de textura convencionales para el material.\u003C\u002Fp>\n\u003Cp>A medida que aumentan la resolución de textura y la complejidad del material, esos canales consumen espacio en disco, ancho de banda de streaming y memoria de GPU.\u003C\u002Fp>\n\u003Ch2 id=\"section-9\">Qué almacena realmente RTX Neural Texture Compression\u003C\u002Fh2>\n\u003Cp>El SDK RTXNTC de NVIDIA comprime juntos los canales que pertenecen a un material. El SDK actual admite hasta 16 canales de textura en un conjunto de texturas NTC.\u003C\u002Fp>\n\u003Cp>En lugar de conservar solo texeles comprimidos convencionales, el proceso de compresión produce dos cosas principales: pesos para un pequeño decodificador neuronal y datos de características latentes compactos.\u003C\u002Fp>\n\u003Csection class=\"editorjs-process my-6\">\u003Ch3 class=\"mb-3 text-lg font-semibold\">El pipeline de texturas neuronales\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. Texturas de material originales\u003C\u002Fdiv>\u003Cdiv class=\"mt-1 text-sm text-gray-600 dark:text-gray-300\">Albedo, normal, rugosidad, metalicidad y otros canales de material se proporcionan juntos.\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. Compresión offline\u003C\u002Fdiv>\u003Cdiv class=\"mt-1 text-sm text-gray-600 dark:text-gray-300\">El SDK aprende una representación compacta del material.\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. Pesos neuronales\u003C\u002Fdiv>\u003Cdiv class=\"mt-1 text-sm text-gray-600 dark:text-gray-300\">Una pequeña red decodificadora almacena parte de lo necesario para reconstruir el material.\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. Datos latentes\u003C\u002Fdiv>\u003Cdiv class=\"mt-1 text-sm text-gray-600 dark:text-gray-300\">Tensores de características compactos almacenan información específica del material.\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. Inferencia en GPU\u003C\u002Fdiv>\u003Cdiv class=\"mt-1 text-sm text-gray-600 dark:text-gray-300\">En tiempo de ejecución, el decodificador combina los datos latentes y los pesos neuronales para reconstruir los valores de textura.\u003C\u002Fdiv>\u003C\u002Fdiv>\u003C\u002Fdiv>\u003C\u002Fsection>\n\u003Ch2 id=\"section-13\">Por qué comprimir canales juntos puede ayudar\u003C\u002Fh2>\n\u003Cp>Los canales de material suelen estar relacionados. Un arañazo visible en el color base también puede aparecer en el mapa normal o de rugosidad. Un patrón de tela puede influir en varios canales en la misma ubicación espacial.\u003C\u002Fp>\n\u003Cp>NVIDIA diseñó NTC para aprovechar esas correlaciones en lugar de comprimir cada textura de forma independiente.\u003C\u002Fp>\n\u003Cp>Esa es una de las razones por las que la tecnología se describe como compresión orientada a materiales en lugar de ser simplemente otro formato de imagen.\u003C\u002Fp>\n\u003Ch2 id=\"section-17\">Los tres modos de ejecución de NTC son la clave para entender la tecnología\u003C\u002Fh2>\n\u003Cp>La parte más importante de RTXNTC no es solo cómo se comprime el material. Es cuándo elige el juego descomprimirlo.\u003C\u002Fp>\n\u003Csection class=\"editorjs-comparison my-6\">\u003Ch3 class=\"mb-3 text-lg font-semibold\">Inferencia al cargar vs Inferencia al muestrear vs Inferencia por retroalimentación\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\">Cuándo ocurre la decodificación neuronal\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\">Comportamiento de la memoria de texturas en tiempo de ejecución\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 compensación\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\">Inferencia al cargar\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\">Inferencia al muestrear\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\">Inferencia por retroalimentación\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\">Inferencia al cargar: compresión neuronal como formato de almacenamiento\u003C\u002Fh2>\n\u003Cp>La Inferencia al cargar es el modo más fácil de entender.\u003C\u002Fp>\n\u003Cp>El juego almacena el material en forma NTC compacta. Cuando se carga el recurso, la GPU reconstruye los datos de textura y puede transcodificarlos a formatos de textura BCn ordinarios.\u003C\u002Fp>\n\u003Cp>Después de ese paso, el renderizado puede usar muestreo de textura normal. El ahorro importante se produce principalmente antes de la descompresión: tamaño del juego empaquetado, tamaño de descarga o ancho de banda de transmisión de recursos.\u003C\u002Fp>\n\u003Cp>Pero una vez que el material se expande completamente a texturas convencionales, su huella de VRAM en tiempo de ejecución se acerca de nuevo a la representación convencional.\u003C\u002Fp>\n\u003Ch2 id=\"section-25\">Inferencia al muestrear: mantener la textura neuronal en VRAM\u003C\u002Fh2>\n\u003Cp>La Inferencia al muestrear es el modo más radical.\u003C\u002Fp>\n\u003Cp>En lugar de expandir el material a texturas convencionales antes del renderizado, el shader lee datos latentes compactos y ejecuta el decodificador neuronal cuando necesita valores de textura.\u003C\u002Fp>\n\u003Cp>El propio ejemplo del SDK de NVIDIA compara una representación de material BCn de 12 MB con una representación NTC de 2.5 MB cuando se usa Inferencia al muestrear.\u003C\u002Fp>\n\u003Cp>El ahorro es real porque los datos de textura convencionales no necesitan permanecer completamente residentes. Pero el costo se traslada a otro lugar: el shader de píxeles o de impactos ahora realiza inferencia neuronal.\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 compresión no hace desaparecer el trabajo\u003C\u002Fstrong>\u003Cdiv class=\"text-gray-700 dark:text-gray-200\">La Inferencia al muestrear intercambia \u003Cstrong>memoria y ancho de banda\u003C\u002Fstrong> por \u003Cstrong>computación de GPU\u003C\u002Fstrong>. La pregunta correcta no es “¿Cuánto más pequeña es la textura?” sino “¿Vale la pena el ahorro de memoria frente al costo de inferencia añadido en esta carga de trabajo?”\u003C\u002Fdiv>\u003C\u002Faside>\n\u003Caside class=\"editorjs-referral my-6\">\u003Ca href=\"https:\u002F\u002Ffigure.rocks\u002Fes\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\">El uso de VRAM no es el requisito de VRAM: por qué un medidor de memoria lleno no cuenta toda la historia\u003C\u002Fstrong>\u003Cp class=\"mt-2 text-sm text-gray-600 dark:text-gray-300\">Por qué la capacidad de VRAM, los presupuestos, la residencia y la presión de memoria real son cosas diferentes.\u003C\u002Fp>\u003Cspan class=\"mt-3 inline-flex text-sm font-medium text-primary-600 dark:text-primary-400\">Leer la guía de VRAM →\u003C\u002Fspan>\u003C\u002Fdiv>\u003C\u002Fa>\u003C\u002Faside>\n\u003Ch2 id=\"section-32\">Inferencia por retroalimentación: decodificar solo lo que el jugador ve realmente\u003C\u002Fh2>\n\u003Cp>La Inferencia por retroalimentación se sitúa entre los dos extremos.\u003C\u002Fp>\n\u003Cp>El renderizador rastrea qué mosaicos de textura se solicitan realmente. En lugar de expandir el material completo de inmediato, el sistema puede decodificar los mosaicos solicitados por lotes y mantener una caché de trabajo.\u003C\u002Fp>\n\u003Cp>Conceptualmente, esto combina compresión neuronal con transmisión de texturas: el sistema paga el costo de decodificación solo por las regiones que se vuelven relevantes.\u003C\u002Fp>\n\u003Cp>La implementación de ejemplo actual es más especializada que los otros modos y sus restricciones de soporte difieren, por lo que debe tratarse como una estrategia de integración en lugar de un reemplazo universal para la transmisión de texturas ordinaria.\u003C\u002Fp>\n\u003Ch2 id=\"section-37\">El intercambio entre almacenamiento de texturas y cómputo\u003C\u002Fh2>\n\u003Cp>La forma más sencilla de entender la compresión de texturas neuronales es como un intercambio entre recursos.\u003C\u002Fp>\n\u003Csection class=\"editorjs-comparison my-6\">\u003Ch3 class=\"mb-3 text-lg font-semibold\">Qué cambia cuando el almacenamiento de texturas se vuelve 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\">Textura comprimida tradicional\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\">Representación de textura neuronal\u003C\u002Fth>\u003C\u002Ftr>\u003C\u002Fthead>\u003Ctbody>\u003Ctr>\u003Cth class=\"border border-gray-300 px-4 py-3 text-left font-semibold dark:border-gray-700\">Disco\u002Falmacenamiento\u003C\u002Fth>\u003Ctd class=\"border border-gray-300 px-4 py-3 dark:border-gray-700\">\u003C\u002Ftd>\u003Ctd class=\"border border-gray-300 px-4 py-3 dark:border-gray-700\">\u003C\u002Ftd>\u003C\u002Ftr>\u003Ctr>\u003Cth class=\"border border-gray-300 px-4 py-3 text-left font-semibold dark:border-gray-700\">VRAM\u003C\u002Fth>\u003Ctd class=\"border border-gray-300 px-4 py-3 dark:border-gray-700\">\u003C\u002Ftd>\u003Ctd class=\"border border-gray-300 px-4 py-3 dark:border-gray-700\">\u003C\u002Ftd>\u003C\u002Ftr>\u003Ctr>\u003Cth class=\"border border-gray-300 px-4 py-3 text-left font-semibold dark:border-gray-700\">Costo de muestreo\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\">Control de calidad\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\">Por qué importa el hardware especializado en matrices\u003C\u002Fh2>\n\u003Cp>Ejecutar una red neuronal para el muestreo de texturas sería demasiado costoso si cada multiplicación tuviera que manejarse como trabajo escalar ordinario de sombreadores.\u003C\u002Fp>\n\u003Cp>Por lo tanto, RTXNTC se beneficia de Cooperative Vector y ahora de las rutas de Álgebra Lineal de DirectX 12 que permiten a los sombreadores usar hardware de aceleración de matrices de la GPU.\u003C\u002Fp>\n\u003Cp>La beta v0.10.0 añadió explícitamente la inferencia a través de la API de Álgebra Lineal de DirectX 12 introducida con Shader Model 6.10.\u003C\u002Fp>\n\u003Caside class=\"editorjs-referral my-6\">\u003Ca href=\"https:\u002F\u002Ffigure.rocks\u002Fes\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 se está convirtiendo en una plataforma de ML: qué significan el álgebra lineal y los sombreadores neuronales para los futuros juegos\u003C\u002Fstrong>\u003Cp class=\"mt-2 text-sm text-gray-600 dark:text-gray-300\">Cómo DirectX está trasladando las operaciones de matrices y neuronales directamente a HLSL y a la canalización de gráficos.\u003C\u002Fp>\u003Cspan class=\"mt-3 inline-flex text-sm font-medium text-primary-600 dark:text-primary-400\">Leer la guía de sombreadores neuronales de DirectX →\u003C\u002Fspan>\u003C\u002Fdiv>\u003C\u002Fa>\u003C\u002Faside>\n\u003Ch2 id=\"section-45\">Por qué la compresión de texturas neuronales no es escalado de texturas\u003C\u002Fh2>\n\u003Cp>Ambas técnicas pueden usar aprendizaje automático, pero resuelven problemas diferentes.\u003C\u002Fp>\n\u003Csection class=\"editorjs-comparison my-6\">\u003Ch3 class=\"mb-3 text-lg font-semibold\">Compresión de texturas neuronales vs superresolución\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\">Compresión de texturas neuronales\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\">Superresolución\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\">Entrada\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\">Objetivo\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\">Cuándo se ejecuta\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\">Salida\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>Por lo tanto, la compresión de texturas neuronales puede existir por debajo de DLSS, FSR, XeSS o renderizado nativo. Cambia cómo se almacenan y reconstruyen los datos de materiales, no la resolución final de visualización.\u003C\u002Fp>\n\u003Ch2 id=\"section-49\">El control de calidad son los bits por píxel\u003C\u002Fh2>\n\u003Cp>NTC es compresión con pérdida. La cantidad de información comprimida se controla en gran medida mediante la configuración de bits por píxel.\u003C\u002Fp>\n\u003Cp>Una tasa de bits más alta proporciona más información al modelo y generalmente mejora la calidad de reconstrucción. Una tasa de bits más baja mejora la compresión pero aumenta el riesgo de error visible.\u003C\u002Fp>\n\u003Cp>Debido a que múltiples canales comparten la misma representación, añadir más canales de material sin aumentar la tasa de bits puede reducir la calidad disponible para cada 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 no significa sin pérdida\u003C\u002Fstrong>\u003Cdiv class=\"text-gray-700 dark:text-gray-200\">La documentación del SDK señala explícitamente que el error de compresión es normal. El objetivo práctico es \u003Cstrong>un error visual aceptable a un costo de almacenamiento y tiempo de ejecución útil\u003C\u002Fstrong>.\u003C\u002Fdiv>\u003C\u002Faside>\n\u003Ch2 id=\"section-54\">Por qué son importantes los canales de material correlacionados\u003C\u002Fh2>\n\u003Cp>Una representación neuronal se vuelve más valiosa cuando varios canales de material describen una estructura relacionada.\u003C\u002Fp>\n\u003Cp>Si el albedo, la normal y la rugosidad contienen todos los mismos arañazos, costuras o tejido, el decodificador puede explotar la información espacial compartida.\u003C\u002Fp>\n\u003Cp>Si los canales son ruido no relacionado, hay menos estructura común que explotar y el problema de compresión se vuelve más difícil.\u003C\u002Fp>\n\u003Ch2 id=\"section-58\">La prueba de valor de la textura neuronal\u003C\u002Fh2>\n\u003Csection class=\"editorjs-process my-6\">\u003Ch3 class=\"mb-3 text-lg font-semibold\">¿Cuándo es realmente útil la compresión de texturas neuronales?\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. Medir el coste de la textura convencional\u003C\u002Fdiv>\u003Cdiv class=\"mt-1 text-sm text-gray-600 dark:text-gray-300\">¿Cuánto espacio en disco, ancho de banda de streaming y VRAM consumen las texturas de material existentes?\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. Elegir el modo de ejecución\u003C\u002Fdiv>\u003Cdiv class=\"mt-1 text-sm text-gray-600 dark:text-gray-300\">¿Solo quieres ahorro de almacenamiento, ahorro persistente de VRAM o reconstrucción de tiles 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. Establecer un objetivo de calidad aceptable\u003C\u002Fdiv>\u003Cdiv class=\"mt-1 text-sm text-gray-600 dark:text-gray-300\">Compara los canales reconstruidos con el material original, no solo con la imagen final embellecida.\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. Medir el coste de inferencia\u003C\u002Fdiv>\u003Cdiv class=\"mt-1 text-sm text-gray-600 dark:text-gray-300\">Registra el tiempo añadido de shader o descompresión en la GPU objetivo real.\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. Medir el ahorro de memoria\u003C\u002Fdiv>\u003Cdiv class=\"mt-1 text-sm text-gray-600 dark:text-gray-300\">Comprueba el conjunto de trabajo real en tiempo de ejecución, no solo el tamaño del archivo comprimido.\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. Probar materiales difíciles\u003C\u002Fdiv>\u003Cdiv class=\"mt-1 text-sm text-gray-600 dark:text-gray-300\">Las normales finas, las máscaras nítidas, la opacidad, el texto y los canales no relacionados pueden exponer fallos de compresión.\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. Decidir por beneficio neto\u003C\u002Fdiv>\u003Cdiv class=\"mt-1 text-sm text-gray-600 dark:text-gray-300\">Usa NTC solo donde la memoria\u002Fancho de banda ahorrados merezcan la complejidad extra de inferencia e integración.\u003C\u002Fdiv>\u003C\u002Fdiv>\u003C\u002Fdiv>\u003C\u002Fsection>\n\u003Ch2 id=\"section-60\">Por qué “8× más pequeño” necesita contexto\u003C\u002Fh2>\n\u003Cp>El RTX Kit de NVIDIA describe la RTX Neural Texture Compression como una tecnología que ofrece hasta 8× de mejora en memoria de disco con una fidelidad visual similar a la compresión de bloques tradicional.\u003C\u002Fp>\n\u003Cp>La frase “hasta” importa. La relación de compresión depende del material, el número de canales, el bitrate objetivo, la configuración del decodificador y el umbral de calidad.\u003C\u002Fp>\n\u003Cp>La misma relación tampoco describe automáticamente el ahorro de VRAM. La inferencia en carga puede partir de un archivo compacto y aun así expandirse a texturas GPU convencionales. La inferencia en muestreo conserva la representación compacta en la memoria GPU, pero gasta más cómputo durante el sombreado.\u003C\u002Fp>\n\u003Ch2 id=\"section-64\">El tamaño del decodificador es otro compromiso entre rendimiento y calidad\u003C\u002Fh2>\n\u003Cp>El runtime de NTC usa un pequeño perceptrón multicapa para decodificar los valores de textura.\u003C\u002Fp>\n\u003Cp>La biblioteca actual de NVIDIA usa una arquitectura de decodificador configurable. Las redes más grandes pueden mejorar la calidad de compresión, pero cuestan más de ejecutar; las redes más pequeñas pueden ejecutarse más rápido con cierta pérdida en la calidad de reconstrucción.\u003C\u002Fp>\n\u003Cp>Eso da a los desarrolladores de motores otra dimensión de ajuste más allá de la resolución de textura y el bitrate.\u003C\u002Fp>\n\u003Ch2 id=\"section-68\">Por qué esto importa para futuras instalaciones de juegos\u003C\u002Fh2>\n\u003Cp>Los juegos modernos incluyen cada vez más conjuntos de materiales de alta resolución que afectan tanto al tamaño de descarga como a la memoria en tiempo de ejecución.\u003C\u002Fp>\n\u003Cp>La compresión de texturas neuronales crea una nueva opción: distribuir una representación aprendida compacta y decidir después si expandirla al cargar, reconstruirla directamente durante el sombreado o decodificar solo los tiles solicitados.\u003C\u002Fp>\n\u003Cp>Eso significa que una representación de recurso comprimido puede participar en varias estrategias de memoria en tiempo de ejecución diferentes.\u003C\u002Fp>\n\u003Ch2 id=\"section-72\">También cambia lo que significa 'memoria de textura'\u003C\u002Fh2>\n\u003Cp>Con la renderización tradicional, un presupuesto de memoria de texturas se basa principalmente en formatos de textura, niveles de mip, resolución y residencia.\u003C\u002Fp>\n\u003Cp>Con las texturas neuronales, los desarrolladores también pueden presupuestar datos latentes, pesos del decodificador, búferes de inferencia, cachés transcodificadas y el cómputo necesario para reconstruir los valores solicitados.\u003C\u002Fp>\n\u003Cp>Así que el recurso ya no tiene una única identidad de memoria fija y simple.\u003C\u002Fp>\n\u003Ch2 id=\"section-76\">El soporte entre proveedores es más matizado de lo que sugiere el nombre RTX\u003C\u002Fh2>\n\u003Cp>RTXNTC es un SDK de NVIDIA, y la compresión en sí actualmente requiere una GPU NVIDIA según los requisitos del SDK.\u003C\u002Fp>\n\u003Cp>La descompresión en tiempo de ejecución es más amplia. NVIDIA documenta rutas funcionales en hardware Shader Model 6 y señala validación en GPU NVIDIA, AMD e Intel, mientras que las rutas avanzadas de Cooperative Vector \u002F Linear Algebra dependen del soporte de API y controladores.\u003C\u002Fp>\n\u003Cp>Por lo tanto, no se debe asumir paridad de rendimiento y características entre proveedores simplemente porque el decodificador básico pueda ejecutarse.\u003C\u002Fp>\n\u003Ch2 id=\"section-80\">¿Qué cambiaría esta respuesta?\u003C\u002Fh2>\n\u003Cp>NTC todavía está en beta. Los modos de tiempo de ejecución, las arquitecturas de decodificador, el soporte de controladores y las rutas de integración pueden cambiar antes de un lanzamiento de producción estable.\u003C\u002Fp>\n\u003Cp>El mayor cambio a largo plazo sería la adopción generalizada de primitivas de sombreado neuronal estandarizadas en DirectX y Vulkan. Eso haría que la decodificación de texturas neuronales dependiera menos de rutas de ejecución personalizadas específicas de cada proveedor.\u003C\u002Fp>\n\u003Ch2 id=\"section-83\">Limitaciones\u003C\u002Fh2>\n\u003Cp>Este artículo describe la arquitectura y el comportamiento público actual del SDK RTXNTC. Las afirmaciones de compresión citadas por NVIDIA son proporcionadas por el proveedor y no deben tratarse como resultados garantizados para todos los materiales.\u003C\u002Fp>\n\u003Cp>El SDK actual es software beta, y algunas rutas de vista previa tienen limitaciones documentadas de controladores y plataforma.\u003C\u002Fp>\n\u003Ch2 id=\"section-86\">Conclusión\u003C\u002Fh2>\n\u003Cp>RTX Neural Texture Compression es interesante porque cambia una suposición muy antigua: el detalle de textura no siempre tiene que existir en memoria como texeles convencionales.\u003C\u002Fp>\n\u003Cp>En su lugar, un material puede almacenarse parcialmente como una representación aprendida compacta y reconstruirse cuando sea necesario.\u003C\u002Fp>\n\u003Cp>Eso no proporciona calidad gratuita ni memoria gratuita. Crea un nuevo intercambio: menos almacenamiento, ancho de banda y potencialmente VRAM a cambio de trabajo de inferencia neuronal.\u003C\u002Fp>\n\u003Cp>La verdadera innovación no es \"la IA hace que las texturas sean más nítidas\". Es que parte de los datos de materiales de un juego pueden convertirse en cómputo.\u003C\u002Fp>\n\u003Ch2 id=\"section-91\">Preguntas frecuentes\u003C\u002Fh2>\n\u003Csection class=\"editorjs-faq my-6 rounded-xl border border-gray-200 p-5 dark:border-gray-700\">\u003Ch3 class=\"mb-3 text-lg font-semibold\">RTX Neural Texture Compression en lenguaje sencillo\u003C\u002Fh3>\u003Cdiv id=\"faq1\" class=\"border-t border-gray-200 py-4 first:border-t-0 dark:border-gray-700\">\u003Ch4 class=\"font-semibold text-gray-900 dark:text-gray-100\">¿RTX Neural Texture Compression es un escalador?\u003C\u002Fh4>\u003Cdiv class=\"mt-2 text-gray-600 dark:text-gray-300\">No. Comprime y reconstruye datos de texturas de materiales. Las tecnologías de superresolución reconstruyen la imagen renderizada final a una resolución de pantalla mayor.\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\">¿Puede NTC reducir el uso de VRAM?\u003C\u002Fh4>\u003Cdiv class=\"mt-2 text-gray-600 dark:text-gray-300\">Sí, particularmente con Inference on Sample porque la representación neuronal compacta puede permanecer en la memoria de la GPU en lugar de texturas convencionales completamente expandidas. Inference on Load principalmente conserva el ahorro de almacenamiento antes de la expansión.\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\">¿Qué almacena la red neuronal?\u003C\u002Fh4>\u003Cdiv class=\"mt-2 text-gray-600 dark:text-gray-300\">El material comprimido contiene datos de características latentes compactos más pesos para una pequeña red decodificadora que reconstruye los valores de textura.\u003C\u002Fdiv>\u003C\u002Fdiv>\u003Cdiv id=\"faq4\" class=\"border-t border-gray-200 py-4 first:border-t-0 dark:border-gray-700\">\u003Ch4 class=\"font-semibold text-gray-900 dark:text-gray-100\">¿NTC decodifica toda la textura antes de renderizar?\u003C\u002Fh4>\u003Cdiv class=\"mt-2 text-gray-600 dark:text-gray-300\">No necesariamente. Inference on Load lo hace, Inference on Sample reconstruye valores durante el muestreo del shader, e Inference on Feedback puede decodificar los mosaicos de textura solicitados.\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 compresión neuronal de texturas es sin pérdida?\u003C\u002Fh4>\u003Cdiv class=\"mt-2 text-gray-600 dark:text-gray-300\">No. RTXNTC es compresión con pérdida y la calidad depende de la tasa de bits, el número de canales, la configuración del decodificador y el material en sí.\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 está listo para producción?\u003C\u002Fh4>\u003Cdiv class=\"mt-2 text-gray-600 dark:text-gray-300\">El SDK público actual todavía está etiquetado como beta, por lo que las API, el soporte y las características de rendimiento pueden seguir cambiando.\u003C\u002Fdiv>\u003C\u002Fdiv>\u003C\u002Fsection>\n\u003Ch2 id=\"section-93\">Glosario\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\">Términos clave de texturas 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\">Neural Texture Compression\u003C\u002Fdt>\u003Cdd class=\"mt-1 text-gray-600 dark:text-gray-300\">Una técnica que almacena información de textura como datos latentes compactos más pesos de decodificador neuronal en lugar de solo bloques de texeles convencionales.\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\">Datos latentes\u003C\u002Fdt>\u003Cdd class=\"mt-1 text-gray-600 dark:text-gray-300\">Características aprendidas compactas que el decodificador neuronal utiliza para reconstruir los valores de textura.\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\">Decodificador\u003C\u002Fdt>\u003Cdd class=\"mt-1 text-gray-600 dark:text-gray-300\">Una pequeña red neuronal que convierte las características latentes en canales de textura reconstruidos.\u003C\u002Fdd>\u003C\u002Fdiv>\u003Cdiv id=\"inference-on-load\" class=\"border-t border-gray-200 dark:border-gray-700 py-3 first:border-t-0\">\u003Cdt class=\"font-semibold text-gray-900 dark:text-gray-100\">Inference on Load\u003C\u002Fdt>\u003Cdd class=\"mt-1 text-gray-600 dark:text-gray-300\">Modo de tiempo de ejecución que decodifica la textura neuronal cuando se carga un recurso, generalmente a formatos de textura convencionales.\u003C\u002Fdd>\u003C\u002Fdiv>\u003Cdiv id=\"inference-on-sample\" class=\"border-t border-gray-200 dark:border-gray-700 py-3 first:border-t-0\">\u003Cdt class=\"font-semibold text-gray-900 dark:text-gray-100\">Inference on Sample\u003C\u002Fdt>\u003Cdd class=\"mt-1 text-gray-600 dark:text-gray-300\">Modo de tiempo de ejecución que realiza la decodificación neuronal directamente durante el muestreo de textura para que la representación comprimida pueda permanecer residente.\u003C\u002Fdd>\u003C\u002Fdiv>\u003Cdiv id=\"inference-on-feedback\" class=\"border-t border-gray-200 dark:border-gray-700 py-3 first:border-t-0\">\u003Cdt class=\"font-semibold text-gray-900 dark:text-gray-100\">Inference on Feedback\u003C\u002Fdt>\u003Cdd class=\"mt-1 text-gray-600 dark:text-gray-300\">Estrategia de tiempo de ejecución que utiliza retroalimentación de textura para decodificar y almacenar en caché solo los mosaicos solicitados.\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\">Intercambio de almacenamiento de texturas por cómputo\u003C\u002Fdt>\u003Cdd class=\"mt-1 text-gray-600 dark:text-gray-300\">Un modelo de Figure Rocks que describe el intercambio de almacenamiento de texturas, ancho de banda y VRAM por trabajo adicional de inferencia neuronal en la 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\">Prueba de valor de textura neuronal\u003C\u002Fdt>\u003Cdd class=\"mt-1 text-gray-600 dark:text-gray-300\">Un flujo de trabajo de Figure Rocks para decidir si la compresión neuronal de texturas genera un beneficio neto para un material particular y una GPU objetivo.\u003C\u002Fdd>\u003C\u002Fdiv>\u003C\u002Fdl>\u003C\u002Fsection>\n\u003Ch2 id=\"section-95\">Fuentes primarias\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\">Descripción general oficial de RTX Neural Texture Compression y el posicionamiento publicado por NVIDIA sobre reducción de almacenamiento.\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\">Documentación oficial del SDK que describe la compresión de canales de material, la representación decodificador\u002Flatente, los modos de tiempo de ejecución, ejemplos de memoria, requisitos del sistema y soporte 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 — Versiones\u003C\u002Fstrong>\u003Cp class=\"text-sm text-gray-600 dark:text-gray-400\">Historial oficial de versiones, incluida la beta v0.10.0 y el soporte de inferencia de 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 — Configuración de compresión y calidad de imagen\u003C\u002Fstrong>\u003Cp class=\"text-sm text-gray-600 dark:text-gray-400\">Documentación oficial que cubre la tasa de bits, las interacciones entre canales, la medición de calidad y el comportamiento de la compresión con pérdida.\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\">Documentación oficial de la biblioteca de tiempo de ejecución que describe la configuración del decodificador y el equilibrio entre calidad y rendimiento de los diferentes tamaños de red neuronal.\u003C\u002Fp>\u003C\u002Fa>",{"time":538,"blocks":539,"version":1249},1790379031375,[540,546,554,561,568,574,579,584,589,594,599,604,627,632,637,642,647,652,657,687,692,697,702,707,712,717,722,727,732,737,743,752,757,762,767,772,777,782,787,816,821,826,831,836,844,849,854,882,887,892,897,902,907,913,918,923,928,933,938,965,970,975,980,985,990,995,1000,1005,1010,1015,1020,1025,1030,1035,1040,1045,1050,1055,1060,1065,1070,1075,1080,1085,1090,1095,1100,1105,1110,1115,1120,1125,1155,1160,1198,1203,1213,1222,1231,1240],{"id":541,"data":542,"type":544,"tunes":545},"intro",{"text":543},"La compresión de texturas normalmente significa almacenar una versión más pequeña de los datos de textura y expandirla a un formato de GPU convencional antes o durante su uso. NVIDIA RTX Neural Texture Compression cambia ese modelo: parte de los datos de textura se convierte en una pequeña representación neuronal que puede ser decodificada por la propia GPU.","paragraph",{},{"id":547,"data":548,"type":552,"tunes":553},"direct",{"body":549,"title":550,"variant":551},"\u003Cstrong>RTX Neural Texture Compression no es escalado de texturas.\u003C\u002Fstrong> Comprime múltiples texturas de materiales en pesos de redes neuronales más datos latentes compactos, y luego reconstruye los valores de textura solicitados con una pequeña red neuronal. Según el modo de integración, el juego puede intercambiar almacenamiento y uso de VRAM por trabajo adicional de inferencia en la GPU.","Respuesta directa","info","callout",{},{"id":555,"data":556,"type":552,"tunes":560},"status",{"body":557,"title":558,"variant":559},"RTX Neural Texture Compression sigue siendo un \u003Cstrong>SDK beta\u003C\u002Fstrong>. La beta actual v0.10.0 añadió soporte de inferencia DirectX 12 Linear Algebra, conectando NTC directamente con la nueva infraestructura de shaders neuronales discutida en otra parte de Figure Rocks.","Estado actual","note",{},{"id":562,"data":563,"type":566,"tunes":567},"toc",{"title":564,"maxLevel":565,"minLevel":47},"Contenido",3,"tableOfContents",{},{"id":569,"data":570,"type":572,"tunes":573},"h-normal",{"text":571,"level":47},"Por qué la compresión de texturas normal sigue usando mucha memoria","header",{},{"id":575,"data":576,"type":544,"tunes":578},"p-normal-1",{"text":577},"Un material moderno basado en física rara vez consiste en una sola imagen. Una única superficie puede usar albedo, normal, rugosidad, metalicidad, oclusión ambiental, opacidad y otros canales.",{},{"id":580,"data":581,"type":544,"tunes":583},"p-normal-2",{"text":582},"Los formatos tradicionales de compresión de bloques para GPU, como BC1 a BC7, reducen el coste, pero la GPU sigue almacenando bloques de textura convencionales para el material.",{},{"id":585,"data":586,"type":544,"tunes":588},"p-normal-3",{"text":587},"A medida que aumentan la resolución de textura y la complejidad del material, esos canales consumen espacio en disco, ancho de banda de streaming y memoria de GPU.",{},{"id":590,"data":591,"type":572,"tunes":593},"h-store",{"text":592,"level":47},"Qué almacena realmente RTX Neural Texture Compression",{},{"id":595,"data":596,"type":544,"tunes":598},"p-store-1",{"text":597},"El SDK RTXNTC de NVIDIA comprime juntos los canales que pertenecen a un material. El SDK actual admite hasta 16 canales de textura en un conjunto de texturas NTC.",{},{"id":600,"data":601,"type":544,"tunes":603},"p-store-2",{"text":602},"En lugar de conservar solo texeles comprimidos convencionales, el proceso de compresión produce dos cosas principales: pesos para un pequeño decodificador neuronal y datos de características latentes compactos.",{},{"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. Texturas de material originales","Albedo, normal, rugosidad, metalicidad y otros canales de material se proporcionan juntos.",{"label":612,"description":613},"2. Compresión offline","El SDK aprende una representación compacta del material.",{"label":615,"description":616},"3. Pesos neuronales","Una pequeña red decodificadora almacena parte de lo necesario para reconstruir el material.",{"label":618,"description":619},"4. Datos latentes","Tensores de características compactos almacenan información específica del material.",{"label":621,"description":622},"5. Inferencia en GPU","En tiempo de ejecución, el decodificador combina los datos latentes y los pesos neuronales para reconstruir los valores de textura.","El pipeline de texturas neuronales","auto","processFlow",{},{"id":628,"data":629,"type":572,"tunes":631},"h-correlation",{"text":630,"level":47},"Por qué comprimir canales juntos puede ayudar",{},{"id":633,"data":634,"type":544,"tunes":636},"p-cor-1",{"text":635},"Los canales de material suelen estar relacionados. Un arañazo visible en el color base también puede aparecer en el mapa normal o de rugosidad. Un patrón de tela puede influir en varios canales en la misma ubicación espacial.",{},{"id":638,"data":639,"type":544,"tunes":641},"p-cor-2",{"text":640},"NVIDIA diseñó NTC para aprovechar esas correlaciones en lugar de comprimir cada textura de forma independiente.",{},{"id":643,"data":644,"type":544,"tunes":646},"p-cor-3",{"text":645},"Esa es una de las razones por las que la tecnología se describe como compresión orientada a materiales en lugar de ser simplemente otro formato de imagen.",{},{"id":648,"data":649,"type":572,"tunes":651},"h-modes",{"text":650,"level":47},"Los tres modos de ejecución de NTC son la clave para entender la tecnología",{},{"id":653,"data":654,"type":544,"tunes":656},"p-modes-1",{"text":655},"La parte más importante de RTXNTC no es solo cómo se comprime el material. Es cuándo elige el juego descomprimirlo.",{},{"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","Inferencia al cargar",[13,13,13],{"id":666,"label":667,"values":668},"sample","Inferencia al muestrear",[13,13,13],{"id":670,"label":671,"values":672},"feedback","Inferencia por retroalimentación",[13,13,13],"Inferencia al cargar vs Inferencia al muestrear vs Inferencia por retroalimentación","table",[676,679,682],{"id":677,"label":678},"when","Cuándo ocurre la decodificación neuronal",{"id":680,"label":681},"memory","Comportamiento de la memoria de texturas en tiempo de ejecución",{"id":683,"label":684},"tradeoff","Principal compensación","comparison",{},{"id":688,"data":689,"type":572,"tunes":691},"h-load",{"text":690,"level":47},"Inferencia al cargar: compresión neuronal como formato de almacenamiento",{},{"id":693,"data":694,"type":544,"tunes":696},"p-load-1",{"text":695},"La Inferencia al cargar es el modo más fácil de entender.",{},{"id":698,"data":699,"type":544,"tunes":701},"p-load-2",{"text":700},"El juego almacena el material en forma NTC compacta. Cuando se carga el recurso, la GPU reconstruye los datos de textura y puede transcodificarlos a formatos de textura BCn ordinarios.",{},{"id":703,"data":704,"type":544,"tunes":706},"p-load-3",{"text":705},"Después de ese paso, el renderizado puede usar muestreo de textura normal. El ahorro importante se produce principalmente antes de la descompresión: tamaño del juego empaquetado, tamaño de descarga o ancho de banda de transmisión de recursos.",{},{"id":708,"data":709,"type":544,"tunes":711},"p-load-4",{"text":710},"Pero una vez que el material se expande completamente a texturas convencionales, su huella de VRAM en tiempo de ejecución se acerca de nuevo a la representación convencional.",{},{"id":713,"data":714,"type":572,"tunes":716},"h-sample",{"text":715,"level":47},"Inferencia al muestrear: mantener la textura neuronal en VRAM",{},{"id":718,"data":719,"type":544,"tunes":721},"p-sample-1",{"text":720},"La Inferencia al muestrear es el modo más radical.",{},{"id":723,"data":724,"type":544,"tunes":726},"p-sample-2",{"text":725},"En lugar de expandir el material a texturas convencionales antes del renderizado, el shader lee datos latentes compactos y ejecuta el decodificador neuronal cuando necesita valores de textura.",{},{"id":728,"data":729,"type":544,"tunes":731},"p-sample-3",{"text":730},"El propio ejemplo del SDK de NVIDIA compara una representación de material BCn de 12 MB con una representación NTC de 2.5 MB cuando se usa Inferencia al muestrear.",{},{"id":733,"data":734,"type":544,"tunes":736},"p-sample-4",{"text":735},"El ahorro es real porque los datos de textura convencionales no necesitan permanecer completamente residentes. Pero el costo se traslada a otro lugar: el shader de píxeles o de impactos ahora realiza inferencia neuronal.",{},{"id":683,"data":738,"type":552,"tunes":742},{"body":739,"title":740,"variant":741},"La Inferencia al muestrear intercambia \u003Cstrong>memoria y ancho de banda\u003C\u002Fstrong> por \u003Cstrong>computación de GPU\u003C\u002Fstrong>. La pregunta correcta no es “¿Cuánto más pequeña es la textura?” sino “¿Vale la pena el ahorro de memoria frente al costo de inferencia añadido en esta carga de trabajo?”","La compresión no hace desaparecer el trabajo","warning",{},{"id":744,"data":745,"type":750,"tunes":751},"ref-vram",{"url":746,"title":747,"excerpt":748,"ctaLabel":749},"https:\u002F\u002Ffigure.rocks\u002Fes\u002Fblog\u002Fvram-usage-is-not-vram-requirement-why-a-full-memory-meter-does-not-tell-the-whole-story","El uso de VRAM no es el requisito de VRAM: por qué un medidor de memoria lleno no cuenta toda la historia","Por qué la capacidad de VRAM, los presupuestos, la residencia y la presión de memoria real son cosas diferentes.","Leer la guía de VRAM","referralArticle",{},{"id":753,"data":754,"type":572,"tunes":756},"h-feedback",{"text":755,"level":47},"Inferencia por retroalimentación: decodificar solo lo que el jugador ve realmente",{},{"id":758,"data":759,"type":544,"tunes":761},"p-feed-1",{"text":760},"La Inferencia por retroalimentación se sitúa entre los dos extremos.",{},{"id":763,"data":764,"type":544,"tunes":766},"p-feed-2",{"text":765},"El renderizador rastrea qué mosaicos de textura se solicitan realmente. En lugar de expandir el material completo de inmediato, el sistema puede decodificar los mosaicos solicitados por lotes y mantener una caché de trabajo.",{},{"id":768,"data":769,"type":544,"tunes":771},"p-feed-3",{"text":770},"Conceptualmente, esto combina compresión neuronal con transmisión de texturas: el sistema paga el costo de decodificación solo por las regiones que se vuelven relevantes.",{},{"id":773,"data":774,"type":544,"tunes":776},"p-feed-4",{"text":775},"La implementación de ejemplo actual es más especializada que los otros modos y sus restricciones de soporte difieren, por lo que debe tratarse como una estrategia de integración en lugar de un reemplazo universal para la transmisión de texturas ordinaria.",{},{"id":778,"data":779,"type":572,"tunes":781},"h-exchange",{"text":780,"level":47},"El intercambio entre almacenamiento de texturas y cómputo",{},{"id":783,"data":784,"type":544,"tunes":786},"p-exchange-1",{"text":785},"La forma más sencilla de entender la compresión de texturas neuronales es como un intercambio entre recursos.",{},{"id":788,"data":789,"type":685,"tunes":815},"exchange-table",{"rows":790,"title":807,"layout":674,"columns":808},[791,795,799,803],{"id":792,"label":793,"values":794},"storage","Disco\u002Falmacenamiento",[13,13],{"id":796,"label":797,"values":798},"vram","VRAM",[13,13],{"id":800,"label":801,"values":802},"sampling","Costo de muestreo",[13,13],{"id":804,"label":805,"values":806},"quality","Control de calidad",[13,13],"Qué cambia cuando el almacenamiento de texturas se vuelve neuronal",[809,812],{"id":810,"label":811},"traditional","Textura comprimida tradicional",{"id":813,"label":814},"neural","Representación de textura neuronal",{},{"id":817,"data":818,"type":572,"tunes":820},"h-matrix",{"text":819,"level":47},"Por qué importa el hardware especializado en matrices",{},{"id":822,"data":823,"type":544,"tunes":825},"p-matrix-1",{"text":824},"Ejecutar una red neuronal para el muestreo de texturas sería demasiado costoso si cada multiplicación tuviera que manejarse como trabajo escalar ordinario de sombreadores.",{},{"id":827,"data":828,"type":544,"tunes":830},"p-matrix-2",{"text":829},"Por lo tanto, RTXNTC se beneficia de Cooperative Vector y ahora de las rutas de Álgebra Lineal de DirectX 12 que permiten a los sombreadores usar hardware de aceleración de matrices de la GPU.",{},{"id":832,"data":833,"type":544,"tunes":835},"p-matrix-3",{"text":834},"La beta v0.10.0 añadió explícitamente la inferencia a través de la API de Álgebra Lineal de DirectX 12 introducida con Shader Model 6.10.",{},{"id":837,"data":838,"type":750,"tunes":843},"ref-directx",{"url":839,"title":840,"excerpt":841,"ctaLabel":842},"https:\u002F\u002Ffigure.rocks\u002Fes\u002Fblog\u002Fdirectx-is-becoming-an-ml-platform-what-linear-algebra-and-neural-shaders-mean-for-future-games","DirectX se está convirtiendo en una plataforma de ML: qué significan el álgebra lineal y los sombreadores neuronales para los futuros juegos","Cómo DirectX está trasladando las operaciones de matrices y neuronales directamente a HLSL y a la canalización de gráficos.","Leer la guía de sombreadores neuronales de DirectX",{},{"id":845,"data":846,"type":572,"tunes":848},"h-not-upscale",{"text":847,"level":47},"Por qué la compresión de texturas neuronales no es escalado de texturas",{},{"id":850,"data":851,"type":544,"tunes":853},"p-notup-1",{"text":852},"Ambas técnicas pueden usar aprendizaje automático, pero resuelven problemas diferentes.",{},{"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","Entrada",[13,13],{"id":863,"label":864,"values":865},"goal","Objetivo",[13,13],{"id":677,"label":867,"values":868},"Cuándo se ejecuta",[13,13],{"id":870,"label":871,"values":872},"output","Salida",[13,13],"Compresión de texturas neuronales vs superresolución",[875,878],{"id":876,"label":877},"ntc","Compresión de texturas neuronales",{"id":879,"label":880},"sr","Superresolución",{},{"id":883,"data":884,"type":544,"tunes":886},"p-notup-2",{"text":885},"Por lo tanto, la compresión de texturas neuronales puede existir por debajo de DLSS, FSR, XeSS o renderizado nativo. Cambia cómo se almacenan y reconstruyen los datos de materiales, no la resolución final de visualización.",{},{"id":888,"data":889,"type":572,"tunes":891},"h-bpp",{"text":890,"level":47},"El control de calidad son los bits por píxel",{},{"id":893,"data":894,"type":544,"tunes":896},"p-bpp-1",{"text":895},"NTC es compresión con pérdida. La cantidad de información comprimida se controla en gran medida mediante la configuración de bits por píxel.",{},{"id":898,"data":899,"type":544,"tunes":901},"p-bpp-2",{"text":900},"Una tasa de bits más alta proporciona más información al modelo y generalmente mejora la calidad de reconstrucción. Una tasa de bits más baja mejora la compresión pero aumenta el riesgo de error visible.",{},{"id":903,"data":904,"type":544,"tunes":906},"p-bpp-3",{"text":905},"Debido a que múltiples canales comparten la misma representación, añadir más canales de material sin aumentar la tasa de bits puede reducir la calidad disponible para cada canal.",{},{"id":908,"data":909,"type":552,"tunes":912},"lossy-note",{"body":910,"title":911,"variant":559},"La documentación del SDK señala explícitamente que el error de compresión es normal. El objetivo práctico es \u003Cstrong>un error visual aceptable a un costo de almacenamiento y tiempo de ejecución útil\u003C\u002Fstrong>.","Neuronal no significa sin pérdida",{},{"id":914,"data":915,"type":572,"tunes":917},"h-channels",{"text":916,"level":47},"Por qué son importantes los canales de material correlacionados",{},{"id":919,"data":920,"type":544,"tunes":922},"p-channels-1",{"text":921},"Una representación neuronal se vuelve más valiosa cuando varios canales de material describen una estructura relacionada.",{},{"id":924,"data":925,"type":544,"tunes":927},"p-channels-2",{"text":926},"Si el albedo, la normal y la rugosidad contienen todos los mismos arañazos, costuras o tejido, el decodificador puede explotar la información espacial compartida.",{},{"id":929,"data":930,"type":544,"tunes":932},"p-channels-3",{"text":931},"Si los canales son ruido no relacionado, hay menos estructura común que explotar y el problema de compresión se vuelve más difícil.",{},{"id":934,"data":935,"type":572,"tunes":937},"h-test",{"text":936,"level":47},"La prueba de valor de la textura neuronal",{},{"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. Medir el coste de la textura convencional","¿Cuánto espacio en disco, ancho de banda de streaming y VRAM consumen las texturas de material existentes?",{"label":946,"description":947},"2. Elegir el modo de ejecución","¿Solo quieres ahorro de almacenamiento, ahorro persistente de VRAM o reconstrucción de tiles en streaming?",{"label":949,"description":950},"3. Establecer un objetivo de calidad aceptable","Compara los canales reconstruidos con el material original, no solo con la imagen final embellecida.",{"label":952,"description":953},"4. Medir el coste de inferencia","Registra el tiempo añadido de shader o descompresión en la GPU objetivo real.",{"label":955,"description":956},"5. Medir el ahorro de memoria","Comprueba el conjunto de trabajo real en tiempo de ejecución, no solo el tamaño del archivo comprimido.",{"label":958,"description":959},"6. Probar materiales difíciles","Las normales finas, las máscaras nítidas, la opacidad, el texto y los canales no relacionados pueden exponer fallos de compresión.",{"label":961,"description":962},"7. Decidir por beneficio neto","Usa NTC solo donde la memoria\u002Fancho de banda ahorrados merezcan la complejidad extra de inferencia e integración.","¿Cuándo es realmente útil la compresión de texturas neuronales?",{},{"id":966,"data":967,"type":572,"tunes":969},"h-8x",{"text":968,"level":47},"Por qué “8× más pequeño” necesita contexto",{},{"id":971,"data":972,"type":544,"tunes":974},"p-8x-1",{"text":973},"El RTX Kit de NVIDIA describe la RTX Neural Texture Compression como una tecnología que ofrece hasta 8× de mejora en memoria de disco con una fidelidad visual similar a la compresión de bloques tradicional.",{},{"id":976,"data":977,"type":544,"tunes":979},"p-8x-2",{"text":978},"La frase “hasta” importa. La relación de compresión depende del material, el número de canales, el bitrate objetivo, la configuración del decodificador y el umbral de calidad.",{},{"id":981,"data":982,"type":544,"tunes":984},"p-8x-3",{"text":983},"La misma relación tampoco describe automáticamente el ahorro de VRAM. La inferencia en carga puede partir de un archivo compacto y aun así expandirse a texturas GPU convencionales. La inferencia en muestreo conserva la representación compacta en la memoria GPU, pero gasta más cómputo durante el sombreado.",{},{"id":986,"data":987,"type":572,"tunes":989},"h-decoder",{"text":988,"level":47},"El tamaño del decodificador es otro compromiso entre rendimiento y calidad",{},{"id":991,"data":992,"type":544,"tunes":994},"p-dec-1",{"text":993},"El runtime de NTC usa un pequeño perceptrón multicapa para decodificar los valores de textura.",{},{"id":996,"data":997,"type":544,"tunes":999},"p-dec-2",{"text":998},"La biblioteca actual de NVIDIA usa una arquitectura de decodificador configurable. Las redes más grandes pueden mejorar la calidad de compresión, pero cuestan más de ejecutar; las redes más pequeñas pueden ejecutarse más rápido con cierta pérdida en la calidad de reconstrucción.",{},{"id":1001,"data":1002,"type":544,"tunes":1004},"p-dec-3",{"text":1003},"Eso da a los desarrolladores de motores otra dimensión de ajuste más allá de la resolución de textura y el bitrate.",{},{"id":1006,"data":1007,"type":572,"tunes":1009},"h-install",{"text":1008,"level":47},"Por qué esto importa para futuras instalaciones de juegos",{},{"id":1011,"data":1012,"type":544,"tunes":1014},"p-install-1",{"text":1013},"Los juegos modernos incluyen cada vez más conjuntos de materiales de alta resolución que afectan tanto al tamaño de descarga como a la memoria en tiempo de ejecución.",{},{"id":1016,"data":1017,"type":544,"tunes":1019},"p-install-2",{"text":1018},"La compresión de texturas neuronales crea una nueva opción: distribuir una representación aprendida compacta y decidir después si expandirla al cargar, reconstruirla directamente durante el sombreado o decodificar solo los tiles solicitados.",{},{"id":1021,"data":1022,"type":544,"tunes":1024},"p-install-3",{"text":1023},"Eso significa que una representación de recurso comprimido puede participar en varias estrategias de memoria en tiempo de ejecución diferentes.",{},{"id":1026,"data":1027,"type":572,"tunes":1029},"h-meaning",{"text":1028,"level":47},"También cambia lo que significa 'memoria de textura'",{},{"id":1031,"data":1032,"type":544,"tunes":1034},"p-meaning-1",{"text":1033},"Con la renderización tradicional, un presupuesto de memoria de texturas se basa principalmente en formatos de textura, niveles de mip, resolución y residencia.",{},{"id":1036,"data":1037,"type":544,"tunes":1039},"p-meaning-2",{"text":1038},"Con las texturas neuronales, los desarrolladores también pueden presupuestar datos latentes, pesos del decodificador, búferes de inferencia, cachés transcodificadas y el cómputo necesario para reconstruir los valores solicitados.",{},{"id":1041,"data":1042,"type":544,"tunes":1044},"p-meaning-3",{"text":1043},"Así que el recurso ya no tiene una única identidad de memoria fija y simple.",{},{"id":1046,"data":1047,"type":572,"tunes":1049},"h-cross",{"text":1048,"level":47},"El soporte entre proveedores es más matizado de lo que sugiere el nombre RTX",{},{"id":1051,"data":1052,"type":544,"tunes":1054},"p-cross-1",{"text":1053},"RTXNTC es un SDK de NVIDIA, y la compresión en sí actualmente requiere una GPU NVIDIA según los requisitos del SDK.",{},{"id":1056,"data":1057,"type":544,"tunes":1059},"p-cross-2",{"text":1058},"La descompresión en tiempo de ejecución es más amplia. NVIDIA documenta rutas funcionales en hardware Shader Model 6 y señala validación en GPU NVIDIA, AMD e Intel, mientras que las rutas avanzadas de Cooperative Vector \u002F Linear Algebra dependen del soporte de API y controladores.",{},{"id":1061,"data":1062,"type":544,"tunes":1064},"p-cross-3",{"text":1063},"Por lo tanto, no se debe asumir paridad de rendimiento y características entre proveedores simplemente porque el decodificador básico pueda ejecutarse.",{},{"id":1066,"data":1067,"type":572,"tunes":1069},"h-change",{"text":1068,"level":47},"¿Qué cambiaría esta respuesta?",{},{"id":1071,"data":1072,"type":544,"tunes":1074},"p-change-1",{"text":1073},"NTC todavía está en beta. Los modos de tiempo de ejecución, las arquitecturas de decodificador, el soporte de controladores y las rutas de integración pueden cambiar antes de un lanzamiento de producción estable.",{},{"id":1076,"data":1077,"type":544,"tunes":1079},"p-change-2",{"text":1078},"El mayor cambio a largo plazo sería la adopción generalizada de primitivas de sombreado neuronal estandarizadas en DirectX y Vulkan. Eso haría que la decodificación de texturas neuronales dependiera menos de rutas de ejecución personalizadas específicas de cada proveedor.",{},{"id":1081,"data":1082,"type":572,"tunes":1084},"h-limit",{"text":1083,"level":47},"Limitaciones",{},{"id":1086,"data":1087,"type":544,"tunes":1089},"p-limit-1",{"text":1088},"Este artículo describe la arquitectura y el comportamiento público actual del SDK RTXNTC. Las afirmaciones de compresión citadas por NVIDIA son proporcionadas por el proveedor y no deben tratarse como resultados garantizados para todos los materiales.",{},{"id":1091,"data":1092,"type":544,"tunes":1094},"p-limit-2",{"text":1093},"El SDK actual es software beta, y algunas rutas de vista previa tienen limitaciones documentadas de controladores y plataforma.",{},{"id":1096,"data":1097,"type":572,"tunes":1099},"h-conclusion",{"text":1098,"level":47},"Conclusión",{},{"id":1101,"data":1102,"type":544,"tunes":1104},"p-conc-1",{"text":1103},"RTX Neural Texture Compression es interesante porque cambia una suposición muy antigua: el detalle de textura no siempre tiene que existir en memoria como texeles convencionales.",{},{"id":1106,"data":1107,"type":544,"tunes":1109},"p-conc-2",{"text":1108},"En su lugar, un material puede almacenarse parcialmente como una representación aprendida compacta y reconstruirse cuando sea necesario.",{},{"id":1111,"data":1112,"type":544,"tunes":1114},"p-conc-3",{"text":1113},"Eso no proporciona calidad gratuita ni memoria gratuita. Crea un nuevo intercambio: menos almacenamiento, ancho de banda y potencialmente VRAM a cambio de trabajo de inferencia neuronal.",{},{"id":1116,"data":1117,"type":544,"tunes":1119},"p-conc-4",{"text":1118},"La verdadera innovación no es \"la IA hace que las texturas sean más nítidas\". Es que parte de los datos de materiales de un juego pueden convertirse en cómputo.",{},{"id":1121,"data":1122,"type":572,"tunes":1124},"h-faq",{"text":1123,"level":47},"Preguntas frecuentes",{},{"id":1126,"data":1127,"type":1126,"tunes":1154},"faq",{"items":1128,"title":1153},[1129,1133,1137,1141,1145,1149],{"id":1130,"answer":1131,"question":1132},"faq1","No. Comprime y reconstruye datos de texturas de materiales. Las tecnologías de superresolución reconstruyen la imagen renderizada final a una resolución de pantalla mayor.","¿RTX Neural Texture Compression es un escalador?",{"id":1134,"answer":1135,"question":1136},"faq2","Sí, particularmente con Inference on Sample porque la representación neuronal compacta puede permanecer en la memoria de la GPU en lugar de texturas convencionales completamente expandidas. Inference on Load principalmente conserva el ahorro de almacenamiento antes de la expansión.","¿Puede NTC reducir el uso de VRAM?",{"id":1138,"answer":1139,"question":1140},"faq3","El material comprimido contiene datos de características latentes compactos más pesos para una pequeña red decodificadora que reconstruye los valores de textura.","¿Qué almacena la red neuronal?",{"id":1142,"answer":1143,"question":1144},"faq4","No necesariamente. Inference on Load lo hace, Inference on Sample reconstruye valores durante el muestreo del shader, e Inference on Feedback puede decodificar los mosaicos de textura solicitados.","¿NTC decodifica toda la textura antes de renderizar?",{"id":1146,"answer":1147,"question":1148},"faq5","No. RTXNTC es compresión con pérdida y la calidad depende de la tasa de bits, el número de canales, la configuración del decodificador y el material en sí.","¿La compresión neuronal de texturas es sin pérdida?",{"id":1150,"answer":1151,"question":1152},"faq6","El SDK público actual todavía está etiquetado como beta, por lo que las API, el soporte y las características de rendimiento pueden seguir cambiando.","¿RTXNTC está listo para producción?","RTX Neural Texture Compression en lenguaje sencillo",{},{"id":1156,"data":1157,"type":572,"tunes":1159},"h-glossary",{"text":1158,"level":47},"Glosario",{},{"id":1161,"data":1162,"type":1161,"tunes":1197},"glossary",{"title":1163,"entries":1164},"Términos clave de texturas neuronales",[1165,1169,1173,1177,1181,1185,1189,1193],{"term":1166,"anchor":1167,"definition":1168},"Neural Texture Compression","neural-texture-compression","Una técnica que almacena información de textura como datos latentes compactos más pesos de decodificador neuronal en lugar de solo bloques de texeles convencionales.",{"term":1170,"anchor":1171,"definition":1172},"Datos latentes","latent-data","Características aprendidas compactas que el decodificador neuronal utiliza para reconstruir los valores de textura.",{"term":1174,"anchor":1175,"definition":1176},"Decodificador","decoder","Una pequeña red neuronal que convierte las características latentes en canales de textura reconstruidos.",{"term":1178,"anchor":1179,"definition":1180},"Inference on Load","inference-on-load","Modo de tiempo de ejecución que decodifica la textura neuronal cuando se carga un recurso, generalmente a formatos de textura convencionales.",{"term":1182,"anchor":1183,"definition":1184},"Inference on Sample","inference-on-sample","Modo de tiempo de ejecución que realiza la decodificación neuronal directamente durante el muestreo de textura para que la representación comprimida pueda permanecer residente.",{"term":1186,"anchor":1187,"definition":1188},"Inference on Feedback","inference-on-feedback","Estrategia de tiempo de ejecución que utiliza retroalimentación de textura para decodificar y almacenar en caché solo los mosaicos solicitados.",{"term":1190,"anchor":1191,"definition":1192},"Intercambio de almacenamiento de texturas por cómputo","texture-storage-compute-exchange","Un modelo de Figure Rocks que describe el intercambio de almacenamiento de texturas, ancho de banda y VRAM por trabajo adicional de inferencia neuronal en la GPU.",{"term":1194,"anchor":1195,"definition":1196},"Prueba de valor de textura neuronal","neural-texture-value-test","Un flujo de trabajo de Figure Rocks para decidir si la compresión neuronal de texturas genera un beneficio neto para un material particular y una GPU objetivo.",{},{"id":1199,"data":1200,"type":572,"tunes":1202},"h-sources",{"text":1201,"level":47},"Fuentes primarias",{},{"id":1204,"data":1205,"type":1211,"tunes":1212},"src-rtxkit",{"link":1206,"meta":1207},"https:\u002F\u002Fdeveloper.nvidia.com\u002Frtx-kit",{"image":1208,"title":1209,"description":1210},{"url":13},"NVIDIA Developer — RTX Kit","Descripción general oficial de RTX Neural Texture Compression y el posicionamiento publicado por NVIDIA sobre reducción de almacenamiento.","linkTool",{},{"id":1214,"data":1215,"type":1211,"tunes":1221},"src-rtxntc-readme",{"link":1216,"meta":1217},"https:\u002F\u002Fgithub.com\u002FNVIDIA-RTX\u002FRTXNTC\u002Fblob\u002Fmain\u002FREADME.md",{"image":1218,"title":1219,"description":1220},{"url":13},"NVIDIA RTXNTC — SDK README","Documentación oficial del SDK que describe la compresión de canales de material, la representación decodificador\u002Flatente, los modos de tiempo de ejecución, ejemplos de memoria, requisitos del sistema y soporte de Cooperative Vector.",{},{"id":1223,"data":1224,"type":1211,"tunes":1230},"src-rtxntc-release",{"link":1225,"meta":1226},"https:\u002F\u002Fgithub.com\u002FNVIDIA-RTX\u002FRTXNTC\u002Freleases",{"image":1227,"title":1228,"description":1229},{"url":13},"NVIDIA RTXNTC — Versiones","Historial oficial de versiones, incluida la beta v0.10.0 y el soporte de inferencia de DirectX 12 Linear Algebra.",{},{"id":1232,"data":1233,"type":1211,"tunes":1239},"src-rtxntc-quality",{"link":1234,"meta":1235},"https:\u002F\u002Fgithub.com\u002FNVIDIA-RTX\u002FRTXNTC\u002Fblob\u002Fmain\u002Fdocs\u002FSettingsAndQuality.md",{"image":1236,"title":1237,"description":1238},{"url":13},"NVIDIA RTXNTC — Configuración de compresión y calidad de imagen","Documentación oficial que cubre la tasa de bits, las interacciones entre canales, la medición de calidad y el comportamiento de la compresión con pérdida.",{},{"id":1241,"data":1242,"type":1211,"tunes":1248},"src-libntc",{"link":1243,"meta":1244},"https:\u002F\u002Fgithub.com\u002FNVIDIA-RTX\u002FRTXNTC-Library",{"image":1245,"title":1246,"description":1247},{"url":13},"NVIDIA — LibNTC","Documentación oficial de la biblioteca de tiempo de ejecución que describe la configuración del decodificador y el equilibrio entre calidad y rendimiento de los diferentes tamaños de red neuronal.",{},"2.31","NVIDIA RTX Neural Texture Compression cambia cómo se pueden almacenar los materiales de los juegos. 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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":1297,"blocks":1298,"version":1847},1790378899383,[1299,1303,1308,1313,1317,1321,1325,1329,1333,1337,1341,1345,1365,1369,1373,1377,1381,1385,1389,1407,1411,1415,1419,1423,1427,1431,1435,1439,1443,1447,1452,1459,1463,1467,1471,1475,1479,1483,1487,1508,1512,1516,1520,1524,1531,1535,1539,1560,1564,1568,1572,1576,1580,1585,1589,1593,1597,1601,1605,1631,1635,1639,1643,1647,1651,1655,1659,1663,1667,1671,1675,1679,1683,1687,1691,1695,1699,1703,1707,1711,1715,1719,1723,1727,1731,1735,1739,1743,1747,1751,1755,1759,1782,1786,1811,1815,1821,1827,1834,1841],{"id":541,"data":1300,"type":544,"tunes":1302},{"text":1301},"Texture compression normally means storing a smaller version of texture data and expanding it into a conventional GPU format before or during use. NVIDIA RTX Neural Texture Compression changes that model: part of the texture data becomes a small neural representation that can be decoded by the GPU itself.",{},{"id":547,"data":1304,"type":552,"tunes":1307},{"body":1305,"title":1306,"variant":551},"\u003Cstrong>RTX Neural Texture Compression is not texture upscaling.\u003C\u002Fstrong> It compresses multiple material textures into neural-network weights plus compact latent data, then reconstructs the requested texture values with a small neural network. Depending on the integration mode, the game can trade storage and VRAM usage for additional GPU inference work.","Direct answer",{},{"id":555,"data":1309,"type":552,"tunes":1312},{"body":1310,"title":1311,"variant":559},"RTX Neural Texture Compression is still a \u003Cstrong>beta SDK\u003C\u002Fstrong>. The current v0.10.0 beta added DirectX 12 Linear Algebra inference support, connecting NTC directly to the new neural-shader infrastructure discussed elsewhere on Figure Rocks.","Current status",{},{"id":562,"data":1314,"type":566,"tunes":1316},{"title":1315,"maxLevel":565,"minLevel":47},"Contents",{},{"id":569,"data":1318,"type":572,"tunes":1320},{"text":1319,"level":47},"Why normal texture compression still uses a lot of memory",{},{"id":575,"data":1322,"type":544,"tunes":1324},{"text":1323},"A modern physically based material rarely consists of one image. A single surface may use albedo, normal, roughness, metalness, ambient occlusion, opacity and other channels.",{},{"id":580,"data":1326,"type":544,"tunes":1328},{"text":1327},"Traditional GPU block-compression formats such as BC1 through BC7 reduce the cost, but the GPU still ends up storing conventional texture blocks for the material.",{},{"id":585,"data":1330,"type":544,"tunes":1332},{"text":1331},"As texture resolution and material complexity increase, those channels consume disk space, streaming bandwidth and GPU memory.",{},{"id":590,"data":1334,"type":572,"tunes":1336},{"text":1335,"level":47},"What RTX Neural Texture Compression actually stores",{},{"id":595,"data":1338,"type":544,"tunes":1340},{"text":1339},"NVIDIA's RTXNTC SDK compresses the channels belonging to one material together. The current SDK supports up to 16 texture channels in one NTC texture set.",{},{"id":600,"data":1342,"type":544,"tunes":1344},{"text":1343},"Instead of keeping only conventional compressed texels, the compression process produces two main things: weights for a small neural decoder and compact latent feature data.",{},{"id":605,"data":1346,"type":625,"tunes":1364},{"steps":1347,"title":1363,"orientation":624},[1348,1351,1354,1357,1360],{"label":1349,"description":1350},"1. Original material textures","Albedo, normal, roughness, metalness and other material channels are provided together.",{"label":1352,"description":1353},"2. Offline compression","The SDK learns a compact representation of the material.",{"label":1355,"description":1356},"3. Neural weights","A small decoder network stores part of what is needed to reconstruct the material.",{"label":1358,"description":1359},"4. Latent data","Compact feature tensors store material-specific information.",{"label":1361,"description":1362},"5. GPU inference","At runtime, the decoder combines the latent data and neural weights to reconstruct texture values.","The neural texture pipeline",{},{"id":628,"data":1366,"type":572,"tunes":1368},{"text":1367,"level":47},"Why compressing channels together can help",{},{"id":633,"data":1370,"type":544,"tunes":1372},{"text":1371},"Material channels are often related. A scratch visible in the base color may also appear in the normal or roughness map. A fabric pattern can influence several channels at the same spatial location.",{},{"id":638,"data":1374,"type":544,"tunes":1376},{"text":1375},"NVIDIA designed NTC to exploit those correlations instead of compressing every texture independently.",{},{"id":643,"data":1378,"type":544,"tunes":1380},{"text":1379},"That is one reason the technology is described as material-oriented compression rather than merely another image format.",{},{"id":648,"data":1382,"type":572,"tunes":1384},{"text":1383,"level":47},"The three NTC runtime modes are the key to understanding the technology",{},{"id":653,"data":1386,"type":544,"tunes":1388},{"text":1387},"The most important part of RTXNTC is not only how the material is compressed. It is when the game chooses to decompress it.",{},{"id":658,"data":1390,"type":685,"tunes":1406},{"rows":1391,"title":1398,"layout":674,"columns":1399},[1392,1394,1396],{"id":662,"label":1178,"values":1393},[13,13,13],{"id":666,"label":1182,"values":1395},[13,13,13],{"id":670,"label":1186,"values":1397},[13,13,13],"Inference on Load vs Inference on Sample vs Inference on Feedback",[1400,1402,1404],{"id":677,"label":1401},"When neural decoding happens",{"id":680,"label":1403},"Runtime texture-memory behavior",{"id":683,"label":1405},"Main trade-off",{},{"id":688,"data":1408,"type":572,"tunes":1410},{"text":1409,"level":47},"Inference on Load: neural compression as a storage format",{},{"id":693,"data":1412,"type":544,"tunes":1414},{"text":1413},"Inference on Load is the easiest mode to understand.",{},{"id":698,"data":1416,"type":544,"tunes":1418},{"text":1417},"The game stores the material in compact NTC form. When the asset is loaded, the GPU reconstructs the texture data and can transcode it into ordinary BCn texture formats.",{},{"id":703,"data":1420,"type":544,"tunes":1422},{"text":1421},"After that step, rendering can use normal texture sampling. The important saving is primarily before decompression: packaged game size, download size or asset-streaming bandwidth.",{},{"id":708,"data":1424,"type":544,"tunes":1426},{"text":1425},"But once the material is fully expanded into conventional textures, its runtime VRAM footprint approaches the conventional representation again.",{},{"id":713,"data":1428,"type":572,"tunes":1430},{"text":1429,"level":47},"Inference on Sample: keep the texture neural in VRAM",{},{"id":718,"data":1432,"type":544,"tunes":1434},{"text":1433},"Inference on Sample is the more radical mode.",{},{"id":723,"data":1436,"type":544,"tunes":1438},{"text":1437},"Instead of expanding the material into conventional textures before rendering, the shader reads compact latent data and runs the neural decoder when it needs texture values.",{},{"id":728,"data":1440,"type":544,"tunes":1442},{"text":1441},"NVIDIA's own SDK example compares a 12 MB BCn material representation with a 2.5 MB NTC representation when using Inference on Sample.",{},{"id":733,"data":1444,"type":544,"tunes":1446},{"text":1445},"The saving is real because the conventional texture data does not need to remain fully resident. But the cost moves somewhere else: the pixel or hit shader now performs neural inference.",{},{"id":683,"data":1448,"type":552,"tunes":1451},{"body":1449,"title":1450,"variant":741},"Inference on Sample trades \u003Cstrong>memory and bandwidth\u003C\u002Fstrong> for \u003Cstrong>GPU computation\u003C\u002Fstrong>. The right question is not “How much smaller is the texture?” but “Is the memory saving worth the added inference cost on this workload?”","Compression does not make the work disappear",{},{"id":744,"data":1453,"type":750,"tunes":1458},{"url":1454,"title":1455,"excerpt":1456,"ctaLabel":1457},"https:\u002F\u002Ffigure.rocks\u002Fblog\u002Fvram-usage-is-not-vram-requirement-why-a-full-memory-meter-does-not-tell-the-whole-story","VRAM Usage Is Not VRAM Requirement: Why a Full Memory Meter Does Not Tell the Whole Story","Why VRAM capacity, budgets, residency and actual memory pressure are different things.","Read the VRAM guide",{},{"id":753,"data":1460,"type":572,"tunes":1462},{"text":1461,"level":47},"Inference on Feedback: decode only what the player actually sees",{},{"id":758,"data":1464,"type":544,"tunes":1466},{"text":1465},"Inference on Feedback sits between the two extremes.",{},{"id":763,"data":1468,"type":544,"tunes":1470},{"text":1469},"The renderer tracks which texture tiles are actually requested. Instead of expanding the full material immediately, the system can decode requested tiles in batches and keep a working cache.",{},{"id":768,"data":1472,"type":544,"tunes":1474},{"text":1473},"Conceptually, this combines neural compression with texture streaming: the system pays the decoding cost only for regions that become relevant.",{},{"id":773,"data":1476,"type":544,"tunes":1478},{"text":1477},"The current sample implementation is more specialized than the other modes and its support constraints differ, so it should be treated as an integration strategy rather than a universal replacement for ordinary texture streaming.",{},{"id":778,"data":1480,"type":572,"tunes":1482},{"text":1481,"level":47},"The Texture Storage–Compute Exchange",{},{"id":783,"data":1484,"type":544,"tunes":1486},{"text":1485},"The easiest way to understand neural texture compression is as an exchange between resources.",{},{"id":788,"data":1488,"type":685,"tunes":1507},{"rows":1489,"title":1501,"layout":674,"columns":1502},[1490,1493,1495,1498],{"id":792,"label":1491,"values":1492},"Disk\u002Fstorage",[13,13],{"id":796,"label":797,"values":1494},[13,13],{"id":800,"label":1496,"values":1497},"Sampling cost",[13,13],{"id":804,"label":1499,"values":1500},"Quality control",[13,13],"What moves when texture storage becomes neural",[1503,1505],{"id":810,"label":1504},"Traditional compressed texture",{"id":813,"label":1506},"Neural texture representation",{},{"id":817,"data":1509,"type":572,"tunes":1511},{"text":1510,"level":47},"Why special matrix hardware matters",{},{"id":822,"data":1513,"type":544,"tunes":1515},{"text":1514},"Running a neural network for texture sampling would be too expensive if every multiply had to be handled like ordinary scalar shader work.",{},{"id":827,"data":1517,"type":544,"tunes":1519},{"text":1518},"RTXNTC therefore benefits from Cooperative Vector and now DirectX 12 Linear Algebra paths that let shaders use GPU matrix-acceleration hardware.",{},{"id":832,"data":1521,"type":544,"tunes":1523},{"text":1522},"The v0.10.0 beta explicitly added inference through the DirectX 12 Linear Algebra API introduced with Shader Model 6.10.",{},{"id":837,"data":1525,"type":750,"tunes":1530},{"url":1526,"title":1527,"excerpt":1528,"ctaLabel":1529},"https:\u002F\u002Ffigure.rocks\u002Fblog\u002Fdirectx-is-becoming-an-ml-platform-what-linear-algebra-and-neural-shaders-mean-for-future-games","DirectX Is Becoming an ML Platform: What Linear Algebra and Neural Shaders Mean for Future Games","How DirectX is moving matrix and neural operations directly into HLSL and the graphics pipeline.","Read the DirectX neural-shader guide",{},{"id":845,"data":1532,"type":572,"tunes":1534},{"text":1533,"level":47},"Why neural texture compression is not texture upscaling",{},{"id":850,"data":1536,"type":544,"tunes":1538},{"text":1537},"The two techniques can both use machine learning, but they solve different problems.",{},{"id":855,"data":1540,"type":685,"tunes":1559},{"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",[1556,1557],{"id":876,"label":1166},{"id":879,"label":1558},"Super Resolution",{},{"id":883,"data":1561,"type":544,"tunes":1563},{"text":1562},"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":1565,"type":572,"tunes":1567},{"text":1566,"level":47},"The quality knob is bits per pixel",{},{"id":893,"data":1569,"type":544,"tunes":1571},{"text":1570},"NTC is lossy compression. The amount of compressed information is controlled largely through the bits-per-pixel setting.",{},{"id":898,"data":1573,"type":544,"tunes":1575},{"text":1574},"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":1577,"type":544,"tunes":1579},{"text":1578},"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":1581,"type":552,"tunes":1584},{"body":1582,"title":1583,"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":1586,"type":572,"tunes":1588},{"text":1587,"level":47},"Why correlated material channels are important",{},{"id":919,"data":1590,"type":544,"tunes":1592},{"text":1591},"A neural representation becomes more valuable when several material channels describe related structure.",{},{"id":924,"data":1594,"type":544,"tunes":1596},{"text":1595},"If albedo, normal and roughness all contain the same scratches, seams or fabric weave, the decoder can exploit shared spatial information.",{},{"id":929,"data":1598,"type":544,"tunes":1600},{"text":1599},"If the channels are unrelated noise, there is less common structure to exploit and the compression problem becomes harder.",{},{"id":934,"data":1602,"type":572,"tunes":1604},{"text":1603,"level":47},"The Neural Texture Value Test",{},{"id":939,"data":1606,"type":625,"tunes":1630},{"steps":1607,"title":1629,"orientation":624},[1608,1611,1614,1617,1620,1623,1626],{"label":1609,"description":1610},"1. Measure conventional texture cost","How much disk space, streaming bandwidth and VRAM do the existing material textures consume?",{"label":1612,"description":1613},"2. Choose the runtime mode","Do you want storage savings only, persistent VRAM savings or streamed tile reconstruction?",{"label":1615,"description":1616},"3. Set an acceptable quality target","Compare reconstructed channels against the original material, not only the final beauty image.",{"label":1618,"description":1619},"4. Measure inference cost","Record added shader or decompression time on the actual target GPU.",{"label":1621,"description":1622},"5. Measure memory savings","Check the real runtime working set rather than only the compressed file size.",{"label":1624,"description":1625},"6. Test difficult materials","Fine normals, sharp masks, opacity, text and unrelated channels can expose compression failures.",{"label":1627,"description":1628},"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":1632,"type":572,"tunes":1634},{"text":1633,"level":47},"Why “8× smaller” needs context",{},{"id":971,"data":1636,"type":544,"tunes":1638},{"text":1637},"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":1640,"type":544,"tunes":1642},{"text":1641},"The phrase “up to” matters. Compression ratio depends on the material, number of channels, target bitrate, decoder configuration and quality threshold.",{},{"id":981,"data":1644,"type":544,"tunes":1646},{"text":1645},"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":1648,"type":572,"tunes":1650},{"text":1649,"level":47},"Decoder size is another performance-quality trade-off",{},{"id":991,"data":1652,"type":544,"tunes":1654},{"text":1653},"The NTC runtime uses a small multilayer perceptron to decode texture values.",{},{"id":996,"data":1656,"type":544,"tunes":1658},{"text":1657},"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":1660,"type":544,"tunes":1662},{"text":1661},"That gives engine developers another tuning dimension beyond texture resolution and bitrate.",{},{"id":1006,"data":1664,"type":572,"tunes":1666},{"text":1665,"level":47},"Why this matters for future game installations",{},{"id":1011,"data":1668,"type":544,"tunes":1670},{"text":1669},"Modern games increasingly ship high-resolution material sets that affect download size as well as runtime memory.",{},{"id":1016,"data":1672,"type":544,"tunes":1674},{"text":1673},"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":1676,"type":544,"tunes":1678},{"text":1677},"That means one compressed asset representation can participate in several different runtime memory strategies.",{},{"id":1026,"data":1680,"type":572,"tunes":1682},{"text":1681,"level":47},"It also changes what 'texture memory' means",{},{"id":1031,"data":1684,"type":544,"tunes":1686},{"text":1685},"With traditional rendering, a texture-memory budget is mostly about texture formats, mip levels, resolution and residency.",{},{"id":1036,"data":1688,"type":544,"tunes":1690},{"text":1689},"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":1692,"type":544,"tunes":1694},{"text":1693},"So the asset no longer has one simple fixed memory identity.",{},{"id":1046,"data":1696,"type":572,"tunes":1698},{"text":1697,"level":47},"Cross-vendor support is more nuanced than the RTX name suggests",{},{"id":1051,"data":1700,"type":544,"tunes":1702},{"text":1701},"RTXNTC is an NVIDIA SDK, and compression itself currently requires an NVIDIA GPU according to the SDK requirements.",{},{"id":1056,"data":1704,"type":544,"tunes":1706},{"text":1705},"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":1708,"type":544,"tunes":1710},{"text":1709},"Performance and feature parity therefore should not be assumed across vendors merely because the basic decoder can run.",{},{"id":1066,"data":1712,"type":572,"tunes":1714},{"text":1713,"level":47},"What would change this answer?",{},{"id":1071,"data":1716,"type":544,"tunes":1718},{"text":1717},"NTC is still beta. Runtime modes, decoder architectures, driver support and integration paths can change before a stable production release.",{},{"id":1076,"data":1720,"type":544,"tunes":1722},{"text":1721},"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":1724,"type":572,"tunes":1726},{"text":1725,"level":47},"Limitations",{},{"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":1738},{"text":1737,"level":47},"Conclusion",{},{"id":1101,"data":1740,"type":544,"tunes":1742},{"text":1741},"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":1744,"type":544,"tunes":1746},{"text":1745},"A material can instead be stored partly as a compact learned representation and reconstructed when needed.",{},{"id":1111,"data":1748,"type":544,"tunes":1750},{"text":1749},"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":1752,"type":544,"tunes":1754},{"text":1753},"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":1756,"type":572,"tunes":1758},{"text":1757,"level":47},"FAQ",{},{"id":1126,"data":1760,"type":1126,"tunes":1781},{"items":1761,"title":1780},[1762,1765,1768,1771,1774,1777],{"id":1130,"answer":1763,"question":1764},"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":1766,"question":1767},"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":1769,"question":1770},"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":1772,"question":1773},"Not necessarily. Inference on Load does, Inference on Sample reconstructs values during shader sampling, and Inference on Feedback can decode requested texture tiles.","Does NTC decode the whole texture before rendering?",{"id":1146,"answer":1775,"question":1776},"No. RTXNTC is lossy compression and quality depends on bitrate, channel count, decoder configuration and the material itself.","Is neural texture compression lossless?",{"id":1150,"answer":1778,"question":1779},"The current public SDK is still labeled beta, so APIs, support and performance characteristics may continue to change.","Is RTXNTC production-ready?","RTX Neural Texture Compression in plain English",{},{"id":1156,"data":1783,"type":572,"tunes":1785},{"text":1784,"level":47},"Glossary",{},{"id":1161,"data":1787,"type":1161,"tunes":1810},{"title":1788,"entries":1789},"Key neural texture terms",[1790,1792,1795,1798,1800,1802,1804,1807],{"term":1166,"anchor":1167,"definition":1791},"A technique that stores texture information as compact latent data plus neural decoder weights instead of only conventional texel blocks.",{"term":1793,"anchor":1171,"definition":1794},"Latent data","Compact learned features that the neural decoder uses to reconstruct texture values.",{"term":1796,"anchor":1175,"definition":1797},"Decoder","A small neural network that converts latent features into reconstructed texture channels.",{"term":1178,"anchor":1179,"definition":1799},"Runtime mode that decodes the neural texture when an asset is loaded, usually into conventional texture formats.",{"term":1182,"anchor":1183,"definition":1801},"Runtime mode that performs neural decoding directly during texture sampling so the compressed representation can remain resident.",{"term":1186,"anchor":1187,"definition":1803},"Runtime strategy that uses texture feedback to decode and cache only requested tiles.",{"term":1805,"anchor":1191,"definition":1806},"Texture Storage–Compute Exchange","A Figure Rocks model describing the trade of texture storage, bandwidth and VRAM for additional GPU neural-inference work.",{"term":1808,"anchor":1195,"definition":1809},"Neural Texture Value Test","A Figure Rocks workflow for deciding whether neural texture compression creates a net benefit for a particular material and target GPU.",{},{"id":1199,"data":1812,"type":572,"tunes":1814},{"text":1813,"level":47},"Primary sources",{},{"id":1204,"data":1816,"type":1211,"tunes":1820},{"link":1206,"meta":1817},{"image":1818,"title":1209,"description":1819},{"url":13},"Official overview of RTX Neural Texture Compression and NVIDIA's published storage-reduction positioning.",{},{"id":1214,"data":1822,"type":1211,"tunes":1826},{"link":1216,"meta":1823},{"image":1824,"title":1219,"description":1825},{"url":13},"Official SDK documentation describing material-channel compression, decoder\u002Flatent representation, runtime modes, memory examples, system requirements and Cooperative Vector support.",{},{"id":1223,"data":1828,"type":1211,"tunes":1833},{"link":1225,"meta":1829},{"image":1830,"title":1831,"description":1832},{"url":13},"NVIDIA RTXNTC — Releases","Official release history, including v0.10.0 beta and DirectX 12 Linear Algebra inference support.",{},{"id":1232,"data":1835,"type":1211,"tunes":1840},{"link":1234,"meta":1836},{"image":1837,"title":1838,"description":1839},{"url":13},"NVIDIA RTXNTC — Compression Settings and Image Quality","Official documentation covering bitrate, channel interactions, quality measurement and lossy compression behavior.",{},{"id":1241,"data":1842,"type":1211,"tunes":1846},{"link":1243,"meta":1843},{"image":1844,"title":1246,"description":1845},{"url":13},"Official runtime library documentation describing decoder configuration and the quality\u002Fperformance trade-off of different neural-network sizes.",{},"2.31.6","NVIDIA RTX Neural Texture Compression changes how game materials can be stored. 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