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Microsoft está agregando primitivas de aprendizaje automático directamente a HLSL y una segunda ruta para ejecutar grafos de ML más grandes dentro del ecosistema de DirectX. Eso cambia dónde puede vivir la representación neuronal dentro de los futuros juegos de PC.\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>DirectX se está convirtiendo en una plataforma de gráficos con capacidad de ML.\u003C\u002Fstrong> Las cargas de trabajo neuronales pequeñas pueden ejecutarse directamente dentro de los sombreadores a través de DX Linear Algebra, mientras que los modelos más grandes están siendo objetivo del DirectX Compute Graph Compiler. El objetivo es permitir que los motores de juegos usen hardware de IA de GPU sin construir una ruta separada específica de cada proveedor para cada técnica.\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\">A partir de septiembre de 2026, \u003Cstrong>DX Linear Algebra sigue siendo una tecnología en vista previa\u003C\u002Fstrong> en la ruta de vista previa de Shader Model 6.10 \u002F Agility SDK. Microsoft anunció el DirectX Compute Graph Compiler para vista previa privada en lugar de como una característica de venta al por menor ampliamente disponible. Este artículo explica la arquitectura y la dirección, no una afirmación de que todos los juegos actuales puedan usarlo hoy.\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 representación neuronal necesita nuevas primitivas de DirectX\u003C\u002Fa>\u003C\u002Fli>\u003Cli class=\"editorjs-toc__item\">\u003Ca href=\"#section-9\" class=\"editorjs-toc__link\">El modelo de ML de DirectX de dos niveles\u003C\u002Fa>\u003C\u002Fli>\u003Cli class=\"editorjs-toc__item\">\u003Ca href=\"#section-12\" class=\"editorjs-toc__link\">Qué le da realmente DX Linear Algebra a un sombreador\u003C\u002Fa>\u003C\u002Fli>\u003Cli class=\"editorjs-toc__item\">\u003Ca href=\"#section-17\" class=\"editorjs-toc__link\">Por qué Cooperative Vector fue solo el comienzo\u003C\u002Fa>\u003C\u002Fli>\u003Cli class=\"editorjs-toc__item\">\u003Ca href=\"#section-21\" class=\"editorjs-toc__link\">¿Qué puede ejecutarse realmente dentro de un shader?\u003C\u002Fa>\u003C\u002Fli>\u003Cli class=\"editorjs-toc__item\">\u003Ca href=\"#section-24\" class=\"editorjs-toc__link\">Por qué esto importa para la memoria de texturas\u003C\u002Fa>\u003C\u002Fli>\u003Cli class=\"editorjs-toc__item\">\u003Ca href=\"#section-29\" class=\"editorjs-toc__link\">Por qué los modelos completos necesitan un camino diferente\u003C\u002Fa>\u003C\u002Fli>\u003Cli class=\"editorjs-toc__item\">\u003Ca href=\"#section-33\" class=\"editorjs-toc__link\">La frontera entre shader o grafo\u003C\u002Fa>\u003C\u002Fli>\u003Cli class=\"editorjs-toc__item\">\u003Ca href=\"#section-35\" class=\"editorjs-toc__link\">Por qué el soporte entre proveedores importa más que otra función de IA\u003C\u002Fa>\u003C\u002Fli>\u003Cli class=\"editorjs-toc__item\">\u003Ca href=\"#section-40\" class=\"editorjs-toc__link\">¿Qué hardware admite la vista previa actual?\u003C\u002Fa>\u003C\u002Fli>\u003Cli class=\"editorjs-toc__item\">\u003Ca href=\"#section-44\" class=\"editorjs-toc__link\">El renderizado neuronal se está convirtiendo en infraestructura\u003C\u002Fa>\u003C\u002Fli>\u003Cli class=\"editorjs-toc__item\">\u003Ca href=\"#section-49\" class=\"editorjs-toc__link\">La pila de renderizado neuronal se está volviendo por capas\u003C\u002Fa>\u003C\u002Fli>\u003Cli class=\"editorjs-toc__item\">\u003Ca href=\"#section-51\" class=\"editorjs-toc__link\">Por qué importa la creación de perfiles unificada\u003C\u002Fa>\u003C\u002Fli>\u003Cli class=\"editorjs-toc__item\">\u003Ca href=\"#section-55\" class=\"editorjs-toc__link\">Esto no significa que todos los shaders se convertirán en redes neuronales\u003C\u002Fa>\u003C\u002Fli>\u003Cli class=\"editorjs-toc__item\">\u003Ca href=\"#section-59\" class=\"editorjs-toc__link\">La prueba de valor del shader neuronal\u003C\u002Fa>\u003C\u002Fli>\u003Cli class=\"editorjs-toc__item\">\u003Ca href=\"#section-61\" class=\"editorjs-toc__link\">Qué significa esto para los jugadores\u003C\u002Fa>\u003C\u002Fli>\u003Cli class=\"editorjs-toc__item\">\u003Ca href=\"#section-65\" class=\"editorjs-toc__link\">¿Qué cambiaría esta respuesta?\u003C\u002Fa>\u003C\u002Fli>\u003Cli class=\"editorjs-toc__item\">\u003Ca href=\"#section-69\" class=\"editorjs-toc__link\">Limitaciones\u003C\u002Fa>\u003C\u002Fli>\u003Cli class=\"editorjs-toc__item\">\u003Ca href=\"#section-72\" class=\"editorjs-toc__link\">Conclusión\u003C\u002Fa>\u003C\u002Fli>\u003Cli class=\"editorjs-toc__item\">\u003Ca href=\"#section-76\" class=\"editorjs-toc__link\">Preguntas frecuentes\u003C\u002Fa>\u003C\u002Fli>\u003Cli class=\"editorjs-toc__item\">\u003Ca href=\"#section-78\" class=\"editorjs-toc__link\">Glosario\u003C\u002Fa>\u003C\u002Fli>\u003Cli class=\"editorjs-toc__item\">\u003Ca href=\"#section-80\" class=\"editorjs-toc__link\">Fuentes primarias\u003C\u002Fa>\u003C\u002Fli>\u003C\u002Fol>\u003C\u002Fnav>\n\u003Ch2 id=\"section-5\">Por qué la representación neuronal necesita nuevas primitivas de DirectX\u003C\u002Fh2>\n\u003Cp>Las GPU modernas ya contienen hardware especializado para operaciones matriciales utilizadas por el aprendizaje automático. El problema para un desarrollador de juegos no es solo si ese hardware existe, sino cómo acceder a él de manera eficiente desde una canalización de gráficos en tiempo real.\u003C\u002Fp>\n\u003Cp>Microsoft exploró esto por primera vez con el soporte de Cooperative Vector. En 2026, ese trabajo evolucionó hacia un diseño más amplio de DirectX Linear Algebra que admite operaciones de vector-matriz y matriz-matriz.\u003C\u002Fp>\n\u003Cp>Eso importa porque diferentes trabajos de representación neuronal tienen diferentes formas. Un modelo pequeño que evalúa el comportamiento del material por píxel no es la misma carga de trabajo que un grafo grande de superresolución o eliminación de ruido.\u003C\u002Fp>\n\u003Ch2 id=\"section-9\">El modelo de ML de DirectX de dos niveles\u003C\u002Fh2>\n\u003Csection class=\"editorjs-process my-6\">\u003Ch3 class=\"mb-3 text-lg font-semibold\">Dos formas en que el ML puede entrar en la canalización de gráficos de DirectX\u003C\u002Fh3>\u003Cdiv class=\"flex flex-col sm:flex-row gap-3\">\u003Cdiv class=\"editorjs-process__step min-w-0 flex-1 rounded-xl border border-gray-200 dark:border-gray-700 p-4\">\u003Cdiv class=\"text-xs font-semibold text-gray-500 dark:text-gray-400\">1\u003C\u002Fdiv>\u003Cdiv class=\"mt-1 font-semibold text-gray-900 dark:text-gray-100\">1. ML a nivel de sombreador\u003C\u002Fdiv>\u003Cdiv class=\"mt-1 text-sm text-gray-600 dark:text-gray-300\">Las cargas de trabajo neuronales pequeñas o de álgebra lineal se ejecutan directamente desde HLSL junto con el código de sombreador tradicional.\u003C\u002Fdiv>\u003C\u002Fdiv>\u003Cdiv class=\"editorjs-process__arrow shrink-0 self-center text-xl text-gray-400 rotate-90 sm:rotate-0\" aria-hidden=\"true\">→\u003C\u002Fdiv>\u003Cdiv class=\"editorjs-process__step min-w-0 flex-1 rounded-xl border border-gray-200 dark:border-gray-700 p-4\">\u003Cdiv class=\"text-xs font-semibold text-gray-500 dark:text-gray-400\">2\u003C\u002Fdiv>\u003Cdiv class=\"mt-1 font-semibold text-gray-900 dark:text-gray-100\">2. DX Linear Algebra\u003C\u002Fdiv>\u003Cdiv class=\"mt-1 text-sm text-gray-600 dark:text-gray-300\">El sombreador puede solicitar operaciones vectoriales y matriciales aceleradas por hardware en lugar de implementar manualmente cada primitiva de ML.\u003C\u002Fdiv>\u003C\u002Fdiv>\u003Cdiv class=\"editorjs-process__arrow shrink-0 self-center text-xl text-gray-400 rotate-90 sm:rotate-0\" aria-hidden=\"true\">→\u003C\u002Fdiv>\u003Cdiv class=\"editorjs-process__step min-w-0 flex-1 rounded-xl border border-gray-200 dark:border-gray-700 p-4\">\u003Cdiv class=\"text-xs font-semibold text-gray-500 dark:text-gray-400\">3\u003C\u002Fdiv>\u003Cdiv class=\"mt-1 font-semibold text-gray-900 dark:text-gray-100\">3. ML a nivel de modelo\u003C\u002Fdiv>\u003Cdiv class=\"mt-1 text-sm text-gray-600 dark:text-gray-300\">Las redes neuronales más grandes se representan como grafos de computación completos en lugar de fragmentos de sombreador escritos a mano.\u003C\u002Fdiv>\u003C\u002Fdiv>\u003Cdiv class=\"editorjs-process__arrow shrink-0 self-center text-xl text-gray-400 rotate-90 sm:rotate-0\" aria-hidden=\"true\">→\u003C\u002Fdiv>\u003Cdiv class=\"editorjs-process__step min-w-0 flex-1 rounded-xl border border-gray-200 dark:border-gray-700 p-4\">\u003Cdiv class=\"text-xs font-semibold text-gray-500 dark:text-gray-400\">4\u003C\u002Fdiv>\u003Cdiv class=\"mt-1 font-semibold text-gray-900 dark:text-gray-100\">4. DirectX Compute Graph Compiler\u003C\u002Fdiv>\u003Cdiv class=\"mt-1 text-sm text-gray-600 dark:text-gray-300\">La ruta del compilador planificada por Microsoft analiza y reduce esos grafos en cargas de trabajo de GPU optimizadas integradas con colas y listas de comandos de D3D12.\u003C\u002Fdiv>\u003C\u002Fdiv>\u003C\u002Fdiv>\u003C\u002Fsection>\n\u003Caside class=\"editorjs-callout editorjs-callout--success my-6 rounded-xl border p-5 border-emerald-300 bg-emerald-50 dark:border-emerald-900 dark:bg-emerald-950\u002F20\" role=\"note\">\u003Cstrong class=\"block mb-2 text-gray-900 dark:text-gray-100\">La diferencia simple\u003C\u002Fstrong>\u003Cdiv class=\"text-gray-700 dark:text-gray-200\">\u003Cstrong>DX Linear Algebra:\u003C\u002Fstrong> coloca matemáticas de ML pequeñas dentro del sombreador.\u003Cbr>\u003Cstrong>Compute Graph Compiler:\u003C\u002Fstrong> trae un modelo de ML más grande al motor como un grafo.\u003C\u002Fdiv>\u003C\u002Faside>\n\u003Ch2 id=\"section-12\">Qué le da realmente DX Linear Algebra a un sombreador\u003C\u002Fh2>\n\u003Cp>El HLSL tradicional está construido en torno a operaciones de gráficos y cómputo. Las cargas de trabajo neuronales dependen en gran medida del álgebra lineal: vectores, matrices, multiplicación, acumulación y diseños de datos optimizados para esas operaciones.\u003C\u002Fp>\n\u003Cp>La vista previa de Shader Model 6.10 agrega API de matrices de primera clase para que los desarrolladores puedan expresar esas cargas de trabajo de manera más directa y permitir que el controlador las asigne a hardware especializado.\u003C\u002Fp>\n\u003Cp>La vista previa de Microsoft de abril de 2026 describe explícitamente esto como una ruta unificada para la representación neuronal, el ML y las cargas de trabajo de procesamiento de imágenes en lugar de una característica solo para gráficos.\u003C\u002Fp>\n\u003Csection class=\"editorjs-comparison my-6\">\u003Ch3 class=\"mb-3 text-lg font-semibold\">Matemáticas de sombreador tradicionales vs matemáticas de sombreador orientadas a ML\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\">Enfoque de sombreador 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\">Enfoque orientado a ML\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\">Trabajo típico\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\">Ruta de hardware\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\">Expresión del desarrollador\u003C\u002Fth>\u003Ctd class=\"border border-gray-300 px-4 py-3 dark:border-gray-700\">\u003C\u002Ftd>\u003Ctd class=\"border border-gray-300 px-4 py-3 dark:border-gray-700\">\u003C\u002Ftd>\u003C\u002Ftr>\u003C\u002Ftbody>\u003C\u002Ftable>\u003C\u002Fdiv>\u003C\u002Fsection>\n\u003Ch2 id=\"section-17\">Por qué Cooperative Vector fue solo el comienzo\u003C\u002Fh2>\n\u003Cp>Cooperative Vector permitía a los hilos de sombreador solicitar trabajo de vector-matriz que los controladores podían asignar a hardware especializado. Eso era útil para cargas de trabajo altamente paralelas por píxel.\u003C\u002Fp>\n\u003Cp>Microsoft concluyó más tarde que muchas cargas de trabajo importantes de ML necesitan más que operaciones de vector-matriz. La superresolución, la eliminación de ruido, la reconstrucción temporal, los modelos de imagen más grandes y la inferencia general pueden requerir operaciones de matriz-matriz y trabajo compartido entre muchos hilos.\u003C\u002Fp>\n\u003Cp>Por lo tanto, DX Linear Algebra amplía el modelo en lugar de tratar Cooperative Vector como la abstracción final.\u003C\u002Fp>\n\u003Ch2 id=\"section-21\">¿Qué puede ejecutarse realmente dentro de un shader?\u003C\u002Fh2>\n\u003Cp>Las cargas de trabajo de ML a nivel de shader más interesantes son lo suficientemente pequeñas como para ejecutarse cerca de los datos gráficos sobre los que operan.\u003C\u002Fp>\n\u003Cdiv class=\"overflow-x-auto\">\u003Ctable class=\"w-full border-collapse\">\u003Cthead>\u003Ctr>\u003Cth class=\"border border-gray-300 px-4 py-2 text-left font-semibold\">Carga de trabajo\u003C\u002Fth>\u003Cth class=\"border border-gray-300 px-4 py-2 text-left font-semibold\">Por qué el ML a nivel de shader encaja\u003C\u002Fth>\u003C\u002Ftr>\u003C\u002Fthead>\u003Ctbody>\u003Ctr>\u003Ctd class=\"border border-gray-300 px-4 py-2\">Compresión de texturas neuronales\u003C\u002Ftd>\u003Ctd class=\"border border-gray-300 px-4 py-2\">Una red pequeña puede reconstruir información de textura cerca del punto donde el shader la necesita\u003C\u002Ftd>\u003C\u002Ftr>\u003Ctr>\u003Ctd class=\"border border-gray-300 px-4 py-2\">Evaluación de materiales neuronales\u003C\u002Ftd>\u003Ctd class=\"border border-gray-300 px-4 py-2\">Una función aprendida puede reemplazar o aumentar cálculos de materiales costosos escritos a mano\u003C\u002Ftd>\u003C\u002Ftr>\u003Ctr>\u003Ctd class=\"border border-gray-300 px-4 py-2\">Caché de radiancia neuronal\u003C\u002Ftd>\u003Ctd class=\"border border-gray-300 px-4 py-2\">La inferencia por píxel o local puede estimar información de iluminación a partir del comportamiento aprendido de la escena\u003C\u002Ftd>\u003C\u002Ftr>\u003Ctr>\u003Ctd class=\"border border-gray-300 px-4 py-2\">Pequeños kernels de eliminación de ruido\u002Freconstrucción\u003C\u002Ftd>\u003Ctd class=\"border border-gray-300 px-4 py-2\">Las operaciones de ML pueden situarse directamente junto a la etapa de renderizado que mejoran\u003C\u002Ftd>\u003C\u002Ftr>\u003Ctr>\u003Ctd class=\"border border-gray-300 px-4 py-2\">Inferencia de procesamiento de imágenes\u003C\u002Ftd>\u003Ctd class=\"border border-gray-300 px-4 py-2\">Las operaciones matriciales pueden integrarse en el procesamiento de GPU sin un runtime externo separado\u003C\u002Ftd>\u003C\u002Ftr>\u003C\u002Ftbody>\u003C\u002Ftable>\u003C\u002Fdiv>\n\u003Ch2 id=\"section-24\">Por qué esto importa para la memoria de texturas\u003C\u002Fh2>\n\u003Cp>Uno de los ejemplos recurrentes de Microsoft es la compresión de texturas neuronales.\u003C\u002Fp>\n\u003Cp>En lugar de almacenar cada canal de textura con fidelidad convencional, un juego puede almacenar una representación más compacta y usar una pequeña red neuronal para reconstruir el detalle durante el renderizado.\u003C\u002Fp>\n\u003Cp>Eso intercambia algo de trabajo de inferencia en la GPU por menor almacenamiento o presión de memoria. El beneficio exacto depende de la técnica y el hardware, pero el cambio arquitectónico es importante: parte del detalle visual puede convertirse en cómputo en lugar de datos almacenados.\u003C\u002Fp>\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\">Una guía práctica sobre la capacidad de VRAM, los presupuestos de residencia, los conjuntos de trabajo y por qué la presión de memoria es más complicada que un solo número de uso.\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-29\">Por qué los modelos completos necesitan un camino diferente\u003C\u002Fh2>\n\u003Cp>Escribir a mano unas pocas operaciones matriciales en HLSL es práctico para funciones neuronales pequeñas. Se vuelve mucho menos práctico cuando la carga de trabajo es un modelo moderno completo con muchas capas, dependencias y tensores intermedios.\u003C\u002Fp>\n\u003Cp>El DirectX Compute Graph Compiler de Microsoft está diseñado para esta clase más amplia de cargas de trabajo.\u003C\u002Fp>\n\u003Cp>En lugar de reescribir el modelo como código de shader personalizado, el compilador puede aceptar un grafo de cómputo, analizar todo el grafo, planificar la memoria, fusionar operaciones y reducir el resultado a trabajo de GPU que se integra con DirectX 12.\u003C\u002Fp>\n\u003Ch2 id=\"section-33\">La frontera entre shader o grafo\u003C\u002Fh2>\n\u003Csection class=\"editorjs-comparison my-6\">\u003Ch3 class=\"mb-3 text-lg font-semibold\">Cuándo la carga de trabajo de ML pertenece a HLSL frente a un compilador de modelos\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\">Ruta a nivel de shader\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\">Ruta a nivel de modelo\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\">Tamaño del modelo\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\">Estilo de ejecución\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\">Autoría\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\">Alcance de la optimización\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\">Uso típico\u003C\u002Fth>\u003Ctd class=\"border border-gray-300 px-4 py-3 dark:border-gray-700\">\u003C\u002Ftd>\u003Ctd class=\"border border-gray-300 px-4 py-3 dark:border-gray-700\">\u003C\u002Ftd>\u003C\u002Ftr>\u003C\u002Ftbody>\u003C\u002Ftable>\u003C\u002Fdiv>\u003C\u002Fsection>\n\u003Ch2 id=\"section-35\">Por qué el soporte entre proveedores importa más que otra función de IA\u003C\u002Fh2>\n\u003Cp>AMD, Intel, NVIDIA y Qualcomm exponen diferentes arquitecturas de GPU y diferentes formas de aceleración matricial dedicada.\u003C\u002Fp>\n\u003Cp>Una abstracción de DirectX ofrece a Microsoft y a los proveedores de controladores un lugar para traducir HLSL común o ML a nivel de grafo en la ruta de hardware correcta.\u003C\u002Fp>\n\u003Cp>Eso no hace que todas las GPU sean igual de rápidas, pero puede reducir la necesidad de que un motor de juego implemente una API de renderizado neuronal completamente diferente para cada proveedor.\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\">La portabilidad es la verdadera historia de la plataforma\u003C\u002Fstrong>\u003Cdiv class=\"text-gray-700 dark:text-gray-200\">Lo interesante no es que DirectX pueda “ejecutar IA”. Las GPU ya podían. El cambio de plataforma es que \u003Cstrong>el ML se vuelve expresable a través de una API de gráficos y una cadena de herramientas comunes\u003C\u002Fstrong>.\u003C\u002Fdiv>\u003C\u002Faside>\n\u003Ch2 id=\"section-40\">¿Qué hardware admite la vista previa actual?\u003C\u002Fh2>\n\u003Cp>La respuesta depende de la operación específica de álgebra lineal y del controlador de vista previa.\u003C\u002Fp>\n\u003Cp>La tabla de compatibilidad de la vista previa del Agility SDK 1.721 de Microsoft incluye LinAlg VectorAccumulate para hardware AMD Radeon RX Serie 9000, hardware Intel Xe2 o más reciente mediante un controlador próximo, y hardware NVIDIA RTX mediante la ruta de vista previa compatible.\u003C\u002Fp>\n\u003Cp>Esto es compatibilidad de la era de vista previa, no una garantía universal de venta al público. La compatibilidad de hardware y controladores puede cambiar antes de la finalización.\u003C\u002Fp>\n\u003Ch2 id=\"section-44\">El renderizado neuronal se está convirtiendo en infraestructura\u003C\u002Fh2>\n\u003Cp>DLSS, FSR y otras tecnologías de gráficos neuronales suelen discutirse como características de marca visibles en el menú de configuración de un juego.\u003C\u002Fp>\n\u003Cp>DirectX Linear Algebra apunta a un cambio más profundo. La operación neuronal puede convertirse en un detalle de implementación interno dentro del renderizador en lugar de una única característica opcional de posprocesamiento.\u003C\u002Fp>\n\u003Cp>Un desarrollador podría usar ML para texturas, materiales, iluminación, reconstrucción u otras funciones locales sin exponer cada una como un interruptor de IA orientado al consumidor.\u003C\u002Fp>\n\u003Caside class=\"editorjs-referral my-6\">\u003Ca href=\"https:\u002F\u002Ffigure.rocks\u002Fes\u002Fblog\u002Fdlss-5-is-not-just-upscaling-what-3d-guided-neural-rendering-actually-changes\" class=\"flex flex-col sm:flex-row gap-4 rounded-xl border border-gray-200 dark:border-gray-700 p-4 transition hover:border-primary-500\">\u003Cdiv class=\"min-w-0 flex-1\">\u003Cstrong class=\"block text-lg text-gray-900 dark:text-gray-100\">DLSS 5 no es solo escalado: qué cambia realmente el renderizado neuronal guiado por 3D\u003C\u002Fstrong>\u003Cp class=\"mt-2 text-sm text-gray-600 dark:text-gray-300\">Cómo el renderizado neuronal está yendo más allá del escalado y los fotogramas generados hacia la reconstrucción de materiales e iluminación dentro de 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 renderizado neuronal de DLSS 5 →\u003C\u002Fspan>\u003C\u002Fdiv>\u003C\u002Fa>\u003C\u002Faside>\n\u003Ch2 id=\"section-49\">La pila de renderizado neuronal se está volviendo por capas\u003C\u002Fh2>\n\u003Csection class=\"editorjs-process my-6\">\u003Ch3 class=\"mb-3 text-lg font-semibold\">Una posible futura canalización de juego con DirectX\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\">Trabajo tradicional del motor\u003C\u002Fdiv>\u003Cdiv class=\"mt-1 text-sm text-gray-600 dark:text-gray-300\">La simulación, la geometría, la visibilidad y el renderizado base siguen siendo responsabilidades estándar del motor.\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\">Shaders neuronales en línea\u003C\u002Fdiv>\u003Cdiv class=\"mt-1 text-sm text-gray-600 dark:text-gray-300\">Pequeñas funciones aprendidas reconstruyen texturas, materiales o iluminación dentro de HLSL.\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\">Grafos de ML más grandes\u003C\u002Fdiv>\u003Cdiv class=\"mt-1 text-sm text-gray-600 dark:text-gray-300\">Los modelos completos de reconstrucción o inferencia se ejecutan a través de una ruta de DirectX a nivel de grafo.\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\">Asignación de hardware del proveedor\u003C\u002Fdiv>\u003Cdiv class=\"mt-1 text-sm text-gray-600 dark:text-gray-300\">Los controladores asignan operaciones comunes de DirectX a las unidades especializadas de IA y matrices de la GPU.\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\">Visibilidad en PIX\u003C\u002Fdiv>\u003Cdiv class=\"mt-1 text-sm text-gray-600 dark:text-gray-300\">El trabajo de gráficos y ML se puede perfilar en conjunto en lugar de vivir en tiempos de ejecución opacos separados.\u003C\u002Fdiv>\u003C\u002Fdiv>\u003C\u002Fdiv>\u003C\u002Fsection>\n\u003Ch2 id=\"section-51\">Por qué importa la creación de perfiles unificada\u003C\u002Fh2>\n\u003Cp>Una carga de trabajo neuronal que mejora la calidad de imagen aún puede dañar un juego si consume inesperadamente tiempo de fotograma, ancho de banda de memoria o VRAM.\u003C\u002Fp>\n\u003Cp>Microsoft incluye explícitamente la visibilidad unificada de PIX como parte de la dirección del Compute Graph Compiler. Eso importa porque los desarrolladores necesitan ver el trabajo de gráficos y ML en la misma captura de fotograma.\u003C\u002Fp>\n\u003Cp>Si el renderizado neuronal se convierte en infraestructura, debe ser medible como cualquier otra etapa de renderizado.\u003C\u002Fp>\n\u003Ch2 id=\"section-55\">Esto no significa que todos los shaders se convertirán en redes neuronales\u003C\u002Fh2>\n\u003Cp>Las matemáticas tradicionales de los shaders siguen siendo eficientes, deterministas y fáciles de razonar para muchas cargas de trabajo.\u003C\u002Fp>\n\u003Cp>Una función neuronal tiene sentido cuando puede aproximar o reconstruir algo costoso de manera más eficiente, comprimir datos o producir una calidad que de otro modo requeriría demasiado cómputo o memoria convencionales.\u003C\u002Fp>\n\u003Cp>La arquitectura correcta seguirá siendo híbrida.\u003C\u002Fp>\n\u003Ch2 id=\"section-59\">La prueba de valor del shader neuronal\u003C\u002Fh2>\n\u003Csection class=\"editorjs-process my-6\">\u003Ch3 class=\"mb-3 text-lg font-semibold\">¿Cuándo tiene realmente sentido incorporar ML en la canalización de renderizado?\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. Identificar la operación tradicional costosa\u003C\u002Fdiv>\u003Cdiv class=\"mt-1 text-sm text-gray-600 dark:text-gray-300\">¿Qué costo de cómputo, ancho de banda, memoria o almacenamiento intentas reemplazar?\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. Definir el sustituto neuronal\u003C\u002Fdiv>\u003Cdiv class=\"mt-1 text-sm text-gray-600 dark:text-gray-300\">¿Qué puede reconstruir, predecir o comprimir un modelo pequeño?\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. Medir el costo de inferencia\u003C\u002Fdiv>\u003Cdiv class=\"mt-1 text-sm text-gray-600 dark:text-gray-300\">El propio modelo de ML consume tiempo de GPU, memoria y ancho de banda.\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 la estabilidad de la calidad\u003C\u002Fdiv>\u003Cdiv class=\"mt-1 text-sm text-gray-600 dark:text-gray-300\">Busca artefactos temporales, errores de reconstrucción y casos de fallo.\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 neto\u003C\u002Fdiv>\u003Cdiv class=\"mt-1 text-sm text-gray-600 dark:text-gray-300\">La técnica solo es útil si el costo tradicional evitado vale la pena frente al costo adicional de ML.\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 en distintos proveedores\u003C\u002Fdiv>\u003Cdiv class=\"mt-1 text-sm text-gray-600 dark:text-gray-300\">Una abstracción de DirectX ayuda a la portabilidad, pero el rendimiento real del hardware sigue siendo diferente.\u003C\u002Fdiv>\u003C\u002Fdiv>\u003C\u002Fdiv>\u003C\u002Fsection>\n\u003Ch2 id=\"section-61\">Qué significa esto para los jugadores\u003C\u002Fh2>\n\u003Cp>Es posible que los jugadores nunca vean un interruptor de “DX Linear Algebra” en un menú de gráficos.\u003C\u002Fp>\n\u003Cp>El impacto probable es indirecto: texturas más pequeñas, mejor reconstrucción de iluminación, gráficos neuronales más eficientes o nuevas técnicas visuales que se vuelven prácticas porque el motor puede acceder a la aceleración de matrices a través de una ruta estándar.\u003C\u002Fp>\n\u003Cp>La característica es más importante como infraestructura que como marca.\u003C\u002Fp>\n\u003Ch2 id=\"section-65\">¿Qué cambiaría esta respuesta?\u003C\u002Fh2>\n\u003Cp>La mayor incertidumbre es la forma final de venta al público de estas API. DX Linear Algebra sigue en vista previa, y el Compute Graph Compiler aún no ha alcanzado una disponibilidad amplia para el público.\u003C\u002Fp>\n\u003Cp>El soporte final de hardware, los detalles de la API, el comportamiento del compilador y la adopción por parte de los motores pueden cambiar antes de que estos sistemas se conviertan en infraestructura normal para juegos comerciales.\u003C\u002Fp>\n\u003Cp>Si los motores principales adoptan las abstracciones directamente, los shaders neuronales podrían volverse mucho más comunes sin que los equipos individuales de juegos implementen cada técnica desde cero.\u003C\u002Fp>\n\u003Ch2 id=\"section-69\">Limitaciones\u003C\u002Fh2>\n\u003Cp>Este artículo explica la arquitectura DirectX documentada por Microsoft y las API en vista previa. No afirma que DX Linear Algebra mejore actualmente el rendimiento en todos los juegos ni que el Compute Graph Compiler sea un producto final para el público.\u003C\u002Fp>\n\u003Cp>Los ejemplos de Microsoft describen capacidades y el uso previsto. Los beneficios reales dependen del modelo, la integración con el motor, la arquitectura de la GPU, los controladores y la carga de trabajo.\u003C\u002Fp>\n\u003Ch2 id=\"section-72\">Conclusión\u003C\u002Fh2>\n\u003Cp>El cambio importante de DirectX no es otra casilla de verificación llamada IA.\u003C\u002Fp>\n\u003Cp>Es que las matemáticas de aprendizaje automático se están trasladando al propio modelo de programación de gráficos. Las funciones neuronales pequeñas pueden residir dentro de los sombreadores, los modelos más grandes pueden avanzar hacia la compilación a nivel de grafo, y la misma cadena de herramientas de DirectX puede exponer esas cargas de trabajo en múltiples proveedores de GPU.\u003C\u002Fp>\n\u003Cp>Así es como la representación neuronal deja de ser una característica de marca y comienza a formar parte de la infraestructura de representación.\u003C\u002Fp>\n\u003Ch2 id=\"section-76\">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\">Álgebra lineal de DirectX y sombreadores neuronales\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\">¿Qué es el Álgebra lineal de DirectX?\u003C\u002Fh4>\u003Cdiv class=\"mt-2 text-gray-600 dark:text-gray-300\">Es un conjunto de características de DirectX\u002FHLSL para operaciones vectoriales y matriciales aceleradas por hardware utilizadas por cargas de trabajo de aprendizaje automático, representación neuronal y procesamiento de imágenes.\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\">¿Qué es un sombreador neuronal?\u003C\u002Fh4>\u003Cdiv class=\"mt-2 text-gray-600 dark:text-gray-300\">Una descripción útil en lenguaje sencillo es un sombreador que incluye una función neuronal aprendida o un paso de inferencia de aprendizaje automático como parte de su trabajo gráfico.\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\">¿Es el Álgebra lineal de DX ya una característica normal de DirectX para el consumidor?\u003C\u002Fh4>\u003Cdiv class=\"mt-2 text-gray-600 dark:text-gray-300\">A partir de septiembre de 2026, permanece en la ruta de vista previa del Shader Model 6.10 \u002F Agility SDK.\u003C\u002Fdiv>\u003C\u002Fdiv>\u003Cdiv id=\"faq4\" class=\"border-t border-gray-200 py-4 first:border-t-0 dark:border-gray-700\">\u003Ch4 class=\"font-semibold text-gray-900 dark:text-gray-100\">¿Qué es el Compilador de grafos de cómputo de DirectX?\u003C\u002Fh4>\u003Cdiv class=\"mt-2 text-gray-600 dark:text-gray-300\">Es la API de compilador de aprendizaje automático a nivel de modelo anunciada por Microsoft, destinada a tomar grafos de cómputo más grandes y convertirlos en cargas de trabajo de GPU optimizadas integradas con D3D12.\u003C\u002Fdiv>\u003C\u002Fdiv>\u003Cdiv id=\"faq5\" class=\"border-t border-gray-200 py-4 first:border-t-0 dark:border-gray-700\">\u003Ch4 class=\"font-semibold text-gray-900 dark:text-gray-100\">¿Por qué no ejecutar todos los modelos de aprendizaje automático directamente en HLSL?\u003C\u002Fh4>\u003Cdiv class=\"mt-2 text-gray-600 dark:text-gray-300\">Las funciones pequeñas se adaptan bien a la autoría a nivel de sombreador, mientras que los modelos grandes se benefician de la optimización de todo el grafo, la planificación de memoria y la fusión de operadores.\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\">¿Reemplazará esto a DLSS o FSR?\u003C\u002Fh4>\u003Cdiv class=\"mt-2 text-gray-600 dark:text-gray-300\">No directamente. DirectX proporciona infraestructura de nivel inferior que los proveedores y desarrolladores pueden utilizar para gráficos neuronales. Las tecnologías de marca aún pueden implementar sus propios modelos y estrategias de integración.\u003C\u002Fdiv>\u003C\u002Fdiv>\u003C\u002Fsection>\n\u003Ch2 id=\"section-78\">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 aprendizaje automático de DirectX\u003C\u002Fh3>\u003Cdl>\u003Cdiv id=\"dx-linear-algebra\" class=\"border-t border-gray-200 dark:border-gray-700 py-3 first:border-t-0\">\u003Cdt class=\"font-semibold text-gray-900 dark:text-gray-100\">Álgebra lineal de DX\u003C\u002Fdt>\u003Cdd class=\"mt-1 text-gray-600 dark:text-gray-300\">API de DirectX\u002FHLSL para operaciones vectoriales y matriciales aceleradas destinadas a cargas de trabajo de representación neuronal, aprendizaje automático y procesamiento de imágenes.\u003C\u002Fdd>\u003C\u002Fdiv>\u003Cdiv id=\"cooperative-vector\" class=\"border-t border-gray-200 dark:border-gray-700 py-3 first:border-t-0\">\u003Cdt class=\"font-semibold text-gray-900 dark:text-gray-100\">Vector cooperativo\u003C\u002Fdt>\u003Cdd class=\"mt-1 text-gray-600 dark:text-gray-300\">Un enfoque anterior de DirectX para operaciones vector-matriz aceleradas dentro de sombreadores que ayudó a establecer la ruta de representación neuronal a nivel de sombreador.\u003C\u002Fdd>\u003C\u002Fdiv>\u003Cdiv id=\"shader-model-6-10\" class=\"border-t border-gray-200 dark:border-gray-700 py-3 first:border-t-0\">\u003Cdt class=\"font-semibold text-gray-900 dark:text-gray-100\">Shader Model 6.10\u003C\u002Fdt>\u003Cdd class=\"mt-1 text-gray-600 dark:text-gray-300\">La generación de modelo de sombreador en vista previa que contiene las API de matriz de Álgebra lineal de DirectX actuales.\u003C\u002Fdd>\u003C\u002Fdiv>\u003Cdiv id=\"compute-graph-compiler\" class=\"border-t border-gray-200 dark:border-gray-700 py-3 first:border-t-0\">\u003Cdt class=\"font-semibold text-gray-900 dark:text-gray-100\">Compilador de grafos de cómputo de DirectX\u003C\u002Fdt>\u003Cdd class=\"mt-1 text-gray-600 dark:text-gray-300\">La API de compilador anunciada por Microsoft para optimizar y ejecutar grafos de cómputo de aprendizaje automático más grandes como cargas de trabajo nativas de GPU de DirectX.\u003C\u002Fdd>\u003C\u002Fdiv>\u003Cdiv id=\"neural-shader\" class=\"border-t border-gray-200 dark:border-gray-700 py-3 first:border-t-0\">\u003Cdt class=\"font-semibold text-gray-900 dark:text-gray-100\">Sombreador neuronal\u003C\u002Fdt>\u003Cdd class=\"mt-1 text-gray-600 dark:text-gray-300\">Un término práctico para un sombreador que realiza inferencia aprendida o computación neuronal como parte del procesamiento gráfico.\u003C\u002Fdd>\u003C\u002Fdiv>\u003Cdiv id=\"shader-or-graph-boundary\" class=\"border-t border-gray-200 dark:border-gray-700 py-3 first:border-t-0\">\u003Cdt class=\"font-semibold text-gray-900 dark:text-gray-100\">Frontera sombreador-o-grafo\u003C\u002Fdt>\u003Cdd class=\"mt-1 text-gray-600 dark:text-gray-300\">Un modelo de Figure Rocks para decidir si una carga de trabajo de aprendizaje automático pertenece como matemáticas de sombreador en línea pequeñas o como un grafo de cómputo a nivel de modelo más grande.\u003C\u002Fdd>\u003C\u002Fdiv>\u003Cdiv id=\"neural-shader-value-test\" class=\"border-t border-gray-200 dark:border-gray-700 py-3 first:border-t-0\">\u003Cdt class=\"font-semibold text-gray-900 dark:text-gray-100\">Prueba de valor del sombreador neuronal\u003C\u002Fdt>\u003Cdd class=\"mt-1 text-gray-600 dark:text-gray-300\">Un flujo de trabajo de Figure Rocks para decidir si el costo tradicional de cómputo o memoria evitado por una técnica neuronal vale sus costos de inferencia y compensaciones de calidad.\u003C\u002Fdd>\u003C\u002Fdiv>\u003C\u002Fdl>\u003C\u002Fsection>\n\u003Ch2 id=\"section-80\">Fuentes primarias\u003C\u002Fh2>\n\u003Ca href=\"https:\u002F\u002Fdevblogs.microsoft.com\u002Fdirectx\u002Fevolving-directx-for-the-ml-era-on-windows\u002F\" target=\"_blank\" rel=\"noopener noreferrer\" class=\"editorjs-link-tool block border border-gray-200 dark:border-gray-700 rounded-lg p-4 transition text-gray-900 dark:text-gray-100 hover:border-primary-500 hover:bg-primary-50 dark:hover:bg-gray-900 hover:text-gray-900 dark:hover:text-gray-100\">\u003Cstrong class=\"block font-semibold\">Microsoft DirectX — Evolucionando DirectX para la era del aprendizaje automático en Windows\u003C\u002Fstrong>\u003Cp class=\"text-sm text-gray-600 dark:text-gray-400\">Descripción general oficial de la arquitectura de GDC 2026 que cubre aprendizaje automático a nivel de sombreador, Álgebra lineal de DX y el Compilador de grafos de cómputo de DirectX.\u003C\u002Fp>\u003C\u002Fa>\n\u003Ca href=\"https:\u002F\u002Fdevblogs.microsoft.com\u002Fdirectx\u002Fd3d12-linalg-preview\u002F\" target=\"_blank\" rel=\"noopener noreferrer\" class=\"editorjs-link-tool block border border-gray-200 dark:border-gray-700 rounded-lg p-4 transition text-gray-900 dark:text-gray-100 hover:border-primary-500 hover:bg-primary-50 dark:hover:bg-gray-900 hover:text-gray-900 dark:hover:text-gray-100\">\u003Cstrong class=\"block font-semibold\">Microsoft DirectX — Vista previa de D3D12 LinAlg Matrix\u003C\u002Fstrong>\u003Cp class=\"text-sm text-gray-600 dark:text-gray-400\">Vista previa oficial de abril de 2026 que explica las API unificadas de Álgebra lineal, operaciones matriciales y la motivación de representación neuronal.\u003C\u002Fp>\u003C\u002Fa>\n\u003Ca href=\"https:\u002F\u002Fdevblogs.microsoft.com\u002Fdirectx\u002Fannouncing-agilitysdk-721-preview-and-more-shader-model-6-10-features\u002F\" target=\"_blank\" rel=\"noopener noreferrer\" class=\"editorjs-link-tool block border border-gray-200 dark:border-gray-700 rounded-lg p-4 transition text-gray-900 dark:text-gray-100 hover:border-primary-500 hover:bg-primary-50 dark:hover:bg-gray-900 hover:text-gray-900 dark:hover:text-gray-100\">\u003Cstrong class=\"block font-semibold\">Microsoft DirectX — Vista previa de Agility SDK 1.721\u003C\u002Fstrong>\u003Cp class=\"text-sm text-gray-600 dark:text-gray-400\">Lanzamiento oficial de mayo de 2026 que documenta las actualizaciones de Álgebra lineal de Shader Model 6.10 y el soporte de hardware en vista previa.\u003C\u002Fp>\u003C\u002Fa>\n\u003Ca href=\"https:\u002F\u002Fdeveloper.microsoft.com\u002Fen-us\u002Fgames\u002Farticles\u002F2026\u002F03\u002Fgdc-2026-evolving-directx-for-ml-era-on-windows\u002F\" target=\"_blank\" rel=\"noopener noreferrer\" class=\"editorjs-link-tool block border border-gray-200 dark:border-gray-700 rounded-lg p-4 transition text-gray-900 dark:text-gray-100 hover:border-primary-500 hover:bg-primary-50 dark:hover:bg-gray-900 hover:text-gray-900 dark:hover:text-gray-100\">\u003Cstrong class=\"block font-semibold\">Microsoft Game Dev — GDC 2026: Evolucionando DirectX para la era del aprendizaje automático\u003C\u002Fstrong>\u003Cp class=\"text-sm text-gray-600 dark:text-gray-400\">Resumen oficial de Microsoft Game Dev que explica el aprendizaje automático a nivel de sombreador y a nivel de modelo y el papel del Compilador de grafos de cómputo.\u003C\u002Fp>\u003C\u002Fa>\n\u003Ca href=\"https:\u002F\u002Fdevblogs.microsoft.com\u002Fdirectx\u002Fcooperative-vector\u002F\" target=\"_blank\" rel=\"noopener noreferrer\" class=\"editorjs-link-tool block border border-gray-200 dark:border-gray-700 rounded-lg p-4 transition text-gray-900 dark:text-gray-100 hover:border-primary-500 hover:bg-primary-50 dark:hover:bg-gray-900 hover:text-gray-900 dark:hover:text-gray-100\">\u003Cstrong class=\"block font-semibold\">Microsoft DirectX — Vector cooperativo de D3D12\u003C\u002Fstrong>\u003Cp class=\"text-sm text-gray-600 dark:text-gray-400\">Antecedentes oficiales sobre operaciones vectoriales\u002Fmatriciales aceleradas por hardware y representación neuronal directamente desde hilos de sombreador.\u003C\u002Fp>\u003C\u002Fa>",{"time":538,"blocks":539,"version":1178},1790378124192,[540,546,554,561,568,574,579,584,589,594,614,621,626,631,636,641,668,673,678,683,688,693,698,722,727,732,737,742,751,756,761,766,771,776,809,814,819,824,829,835,840,845,850,855,860,865,870,875,883,888,909,914,919,924,929,934,939,944,949,954,978,983,988,993,998,1003,1008,1013,1018,1023,1028,1033,1038,1043,1048,1053,1058,1088,1093,1127,1132,1142,1151,1160,1169],{"id":541,"data":542,"type":544,"tunes":545},"intro",{"text":543},"DirectX ya no es solo una API de gráficos que envía sombreadores tradicionales a la GPU. Microsoft está agregando primitivas de aprendizaje automático directamente a HLSL y una segunda ruta para ejecutar grafos de ML más grandes dentro del ecosistema de DirectX. Eso cambia dónde puede vivir la representación neuronal dentro de los futuros juegos de PC.","paragraph",{},{"id":547,"data":548,"type":552,"tunes":553},"direct",{"body":549,"title":550,"variant":551},"\u003Cstrong>DirectX se está convirtiendo en una plataforma de gráficos con capacidad de ML.\u003C\u002Fstrong> Las cargas de trabajo neuronales pequeñas pueden ejecutarse directamente dentro de los sombreadores a través de DX Linear Algebra, mientras que los modelos más grandes están siendo objetivo del DirectX Compute Graph Compiler. El objetivo es permitir que los motores de juegos usen hardware de IA de GPU sin construir una ruta separada específica de cada proveedor para cada técnica.","Respuesta directa","info","callout",{},{"id":555,"data":556,"type":552,"tunes":560},"status-note",{"body":557,"title":558,"variant":559},"A partir de septiembre de 2026, \u003Cstrong>DX Linear Algebra sigue siendo una tecnología en vista previa\u003C\u002Fstrong> en la ruta de vista previa de Shader Model 6.10 \u002F Agility SDK. Microsoft anunció el DirectX Compute Graph Compiler para vista previa privada en lugar de como una característica de venta al por menor ampliamente disponible. Este artículo explica la arquitectura y la dirección, no una afirmación de que todos los juegos actuales puedan usarlo hoy.","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-why",{"text":571,"level":47},"Por qué la representación neuronal necesita nuevas primitivas de DirectX","header",{},{"id":575,"data":576,"type":544,"tunes":578},"p-why-1",{"text":577},"Las GPU modernas ya contienen hardware especializado para operaciones matriciales utilizadas por el aprendizaje automático. El problema para un desarrollador de juegos no es solo si ese hardware existe, sino cómo acceder a él de manera eficiente desde una canalización de gráficos en tiempo real.",{},{"id":580,"data":581,"type":544,"tunes":583},"p-why-2",{"text":582},"Microsoft exploró esto por primera vez con el soporte de Cooperative Vector. En 2026, ese trabajo evolucionó hacia un diseño más amplio de DirectX Linear Algebra que admite operaciones de vector-matriz y matriz-matriz.",{},{"id":585,"data":586,"type":544,"tunes":588},"p-why-3",{"text":587},"Eso importa porque diferentes trabajos de representación neuronal tienen diferentes formas. Un modelo pequeño que evalúa el comportamiento del material por píxel no es la misma carga de trabajo que un grafo grande de superresolución o eliminación de ruido.",{},{"id":590,"data":591,"type":572,"tunes":593},"h-two-level",{"text":592,"level":47},"El modelo de ML de DirectX de dos niveles",{},{"id":595,"data":596,"type":612,"tunes":613},"two-level-flow",{"steps":597,"title":610,"orientation":611},[598,601,604,607],{"label":599,"description":600},"1. ML a nivel de sombreador","Las cargas de trabajo neuronales pequeñas o de álgebra lineal se ejecutan directamente desde HLSL junto con el código de sombreador tradicional.",{"label":602,"description":603},"2. DX Linear Algebra","El sombreador puede solicitar operaciones vectoriales y matriciales aceleradas por hardware en lugar de implementar manualmente cada primitiva de ML.",{"label":605,"description":606},"3. ML a nivel de modelo","Las redes neuronales más grandes se representan como grafos de computación completos en lugar de fragmentos de sombreador escritos a mano.",{"label":608,"description":609},"4. DirectX Compute Graph Compiler","La ruta del compilador planificada por Microsoft analiza y reduce esos grafos en cargas de trabajo de GPU optimizadas integradas con colas y listas de comandos de D3D12.","Dos formas en que el ML puede entrar en la canalización de gráficos de DirectX","auto","processFlow",{},{"id":615,"data":616,"type":552,"tunes":620},"simple-diff",{"body":617,"title":618,"variant":619},"\u003Cstrong>DX Linear Algebra:\u003C\u002Fstrong> coloca matemáticas de ML pequeñas dentro del sombreador.\u003Cbr>\u003Cstrong>Compute Graph Compiler:\u003C\u002Fstrong> trae un modelo de ML más grande al motor como un grafo.","La diferencia simple","success",{},{"id":622,"data":623,"type":572,"tunes":625},"h-linalg",{"text":624,"level":47},"Qué le da realmente DX Linear Algebra a un sombreador",{},{"id":627,"data":628,"type":544,"tunes":630},"p-linalg-1",{"text":629},"El HLSL tradicional está construido en torno a operaciones de gráficos y cómputo. Las cargas de trabajo neuronales dependen en gran medida del álgebra lineal: vectores, matrices, multiplicación, acumulación y diseños de datos optimizados para esas operaciones.",{},{"id":632,"data":633,"type":544,"tunes":635},"p-linalg-2",{"text":634},"La vista previa de Shader Model 6.10 agrega API de matrices de primera clase para que los desarrolladores puedan expresar esas cargas de trabajo de manera más directa y permitir que el controlador las asigne a hardware especializado.",{},{"id":637,"data":638,"type":544,"tunes":640},"p-linalg-3",{"text":639},"La vista previa de Microsoft de abril de 2026 describe explícitamente esto como una ruta unificada para la representación neuronal, el ML y las cargas de trabajo de procesamiento de imágenes en lugar de una característica solo para gráficos.",{},{"id":642,"data":643,"type":666,"tunes":667},"math-table",{"rows":644,"title":657,"layout":658,"columns":659},[645,649,653],{"id":646,"label":647,"values":648},"work","Trabajo típico",[13,13],{"id":650,"label":651,"values":652},"hardware","Ruta de hardware",[13,13],{"id":654,"label":655,"values":656},"code","Expresión del desarrollador",[13,13],"Matemáticas de sombreador tradicionales vs matemáticas de sombreador orientadas a ML","table",[660,663],{"id":661,"label":662},"traditional","Enfoque de sombreador tradicional",{"id":664,"label":665},"ml","Enfoque orientado a ML","comparison",{},{"id":669,"data":670,"type":572,"tunes":672},"h-coop",{"text":671,"level":47},"Por qué Cooperative Vector fue solo el comienzo",{},{"id":674,"data":675,"type":544,"tunes":677},"p-coop-1",{"text":676},"Cooperative Vector permitía a los hilos de sombreador solicitar trabajo de vector-matriz que los controladores podían asignar a hardware especializado. Eso era útil para cargas de trabajo altamente paralelas por píxel.",{},{"id":679,"data":680,"type":544,"tunes":682},"p-coop-2",{"text":681},"Microsoft concluyó más tarde que muchas cargas de trabajo importantes de ML necesitan más que operaciones de vector-matriz. La superresolución, la eliminación de ruido, la reconstrucción temporal, los modelos de imagen más grandes y la inferencia general pueden requerir operaciones de matriz-matriz y trabajo compartido entre muchos hilos.",{},{"id":684,"data":685,"type":544,"tunes":687},"p-coop-3",{"text":686},"Por lo tanto, DX Linear Algebra amplía el modelo en lugar de tratar Cooperative Vector como la abstracción final.",{},{"id":689,"data":690,"type":572,"tunes":692},"h-usecases",{"text":691,"level":47},"¿Qué puede ejecutarse realmente dentro de un shader?",{},{"id":694,"data":695,"type":544,"tunes":697},"p-use-1",{"text":696},"Las cargas de trabajo de ML a nivel de shader más interesantes son lo suficientemente pequeñas como para ejecutarse cerca de los datos gráficos sobre los que operan.",{},{"id":699,"data":700,"type":658,"tunes":721},"usecases-table",{"content":701,"stretched":720,"withHeadings":15},[702,705,708,711,714,717],[703,704],"Carga de trabajo","Por qué el ML a nivel de shader encaja",[706,707],"Compresión de texturas neuronales","Una red pequeña puede reconstruir información de textura cerca del punto donde el shader la necesita",[709,710],"Evaluación de materiales neuronales","Una función aprendida puede reemplazar o aumentar cálculos de materiales costosos escritos a mano",[712,713],"Caché de radiancia neuronal","La inferencia por píxel o local puede estimar información de iluminación a partir del comportamiento aprendido de la escena",[715,716],"Pequeños kernels de eliminación de ruido\u002Freconstrucción","Las operaciones de ML pueden situarse directamente junto a la etapa de renderizado que mejoran",[718,719],"Inferencia de procesamiento de imágenes","Las operaciones matriciales pueden integrarse en el procesamiento de GPU sin un runtime externo separado",false,{},{"id":723,"data":724,"type":572,"tunes":726},"h-texture",{"text":725,"level":47},"Por qué esto importa para la memoria de texturas",{},{"id":728,"data":729,"type":544,"tunes":731},"p-tex-1",{"text":730},"Uno de los ejemplos recurrentes de Microsoft es la compresión de texturas neuronales.",{},{"id":733,"data":734,"type":544,"tunes":736},"p-tex-2",{"text":735},"En lugar de almacenar cada canal de textura con fidelidad convencional, un juego puede almacenar una representación más compacta y usar una pequeña red neuronal para reconstruir el detalle durante el renderizado.",{},{"id":738,"data":739,"type":544,"tunes":741},"p-tex-3",{"text":740},"Eso intercambia algo de trabajo de inferencia en la GPU por menor almacenamiento o presión de memoria. El beneficio exacto depende de la técnica y el hardware, pero el cambio arquitectónico es importante: parte del detalle visual puede convertirse en cómputo en lugar de datos almacenados.",{},{"id":743,"data":744,"type":749,"tunes":750},"ref-vram",{"url":745,"title":746,"excerpt":747,"ctaLabel":748},"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","Una guía práctica sobre la capacidad de VRAM, los presupuestos de residencia, los conjuntos de trabajo y por qué la presión de memoria es más complicada que un solo número de uso.","Leer la guía de VRAM","referralArticle",{},{"id":752,"data":753,"type":572,"tunes":755},"h-models",{"text":754,"level":47},"Por qué los modelos completos necesitan un camino diferente",{},{"id":757,"data":758,"type":544,"tunes":760},"p-models-1",{"text":759},"Escribir a mano unas pocas operaciones matriciales en HLSL es práctico para funciones neuronales pequeñas. Se vuelve mucho menos práctico cuando la carga de trabajo es un modelo moderno completo con muchas capas, dependencias y tensores intermedios.",{},{"id":762,"data":763,"type":544,"tunes":765},"p-models-2",{"text":764},"El DirectX Compute Graph Compiler de Microsoft está diseñado para esta clase más amplia de cargas de trabajo.",{},{"id":767,"data":768,"type":544,"tunes":770},"p-models-3",{"text":769},"En lugar de reescribir el modelo como código de shader personalizado, el compilador puede aceptar un grafo de cómputo, analizar todo el grafo, planificar la memoria, fusionar operaciones y reducir el resultado a trabajo de GPU que se integra con DirectX 12.",{},{"id":772,"data":773,"type":572,"tunes":775},"h-boundary",{"text":774,"level":47},"La frontera entre shader o grafo",{},{"id":777,"data":778,"type":666,"tunes":808},"boundary-table",{"rows":779,"title":800,"layout":658,"columns":801},[780,784,788,792,796],{"id":781,"label":782,"values":783},"size","Tamaño del modelo",[13,13],{"id":785,"label":786,"values":787},"location","Estilo de ejecución",[13,13],{"id":789,"label":790,"values":791},"authoring","Autoría",[13,13],{"id":793,"label":794,"values":795},"optimization","Alcance de la optimización",[13,13],{"id":797,"label":798,"values":799},"use","Uso típico",[13,13],"Cuándo la carga de trabajo de ML pertenece a HLSL frente a un compilador de modelos",[802,805],{"id":803,"label":804},"shader","Ruta a nivel de shader",{"id":806,"label":807},"graph","Ruta a nivel de modelo",{},{"id":810,"data":811,"type":572,"tunes":813},"h-crossvendor",{"text":812,"level":47},"Por qué el soporte entre proveedores importa más que otra función de IA",{},{"id":815,"data":816,"type":544,"tunes":818},"p-cross-1",{"text":817},"AMD, Intel, NVIDIA y Qualcomm exponen diferentes arquitecturas de GPU y diferentes formas de aceleración matricial dedicada.",{},{"id":820,"data":821,"type":544,"tunes":823},"p-cross-2",{"text":822},"Una abstracción de DirectX ofrece a Microsoft y a los proveedores de controladores un lugar para traducir HLSL común o ML a nivel de grafo en la ruta de hardware correcta.",{},{"id":825,"data":826,"type":544,"tunes":828},"p-cross-3",{"text":827},"Eso no hace que todas las GPU sean igual de rápidas, pero puede reducir la necesidad de que un motor de juego implemente una API de renderizado neuronal completamente diferente para cada proveedor.",{},{"id":830,"data":831,"type":552,"tunes":834},"portability-note",{"body":832,"title":833,"variant":559},"Lo interesante no es que DirectX pueda “ejecutar IA”. Las GPU ya podían. El cambio de plataforma es que \u003Cstrong>el ML se vuelve expresable a través de una API de gráficos y una cadena de herramientas comunes\u003C\u002Fstrong>.","La portabilidad es la verdadera historia de la plataforma",{},{"id":836,"data":837,"type":572,"tunes":839},"h-support",{"text":838,"level":47},"¿Qué hardware admite la vista previa actual?",{},{"id":841,"data":842,"type":544,"tunes":844},"p-support-1",{"text":843},"La respuesta depende de la operación específica de álgebra lineal y del controlador de vista previa.",{},{"id":846,"data":847,"type":544,"tunes":849},"p-support-2",{"text":848},"La tabla de compatibilidad de la vista previa del Agility SDK 1.721 de Microsoft incluye LinAlg VectorAccumulate para hardware AMD Radeon RX Serie 9000, hardware Intel Xe2 o más reciente mediante un controlador próximo, y hardware NVIDIA RTX mediante la ruta de vista previa compatible.",{},{"id":851,"data":852,"type":544,"tunes":854},"p-support-3",{"text":853},"Esto es compatibilidad de la era de vista previa, no una garantía universal de venta al público. La compatibilidad de hardware y controladores puede cambiar antes de la finalización.",{},{"id":856,"data":857,"type":572,"tunes":859},"h-infra",{"text":858,"level":47},"El renderizado neuronal se está convirtiendo en infraestructura",{},{"id":861,"data":862,"type":544,"tunes":864},"p-infra-1",{"text":863},"DLSS, FSR y otras tecnologías de gráficos neuronales suelen discutirse como características de marca visibles en el menú de configuración de un juego.",{},{"id":866,"data":867,"type":544,"tunes":869},"p-infra-2",{"text":868},"DirectX Linear Algebra apunta a un cambio más profundo. La operación neuronal puede convertirse en un detalle de implementación interno dentro del renderizador en lugar de una única característica opcional de posprocesamiento.",{},{"id":871,"data":872,"type":544,"tunes":874},"p-infra-3",{"text":873},"Un desarrollador podría usar ML para texturas, materiales, iluminación, reconstrucción u otras funciones locales sin exponer cada una como un interruptor de IA orientado al consumidor.",{},{"id":876,"data":877,"type":749,"tunes":882},"ref-dlss5",{"url":878,"title":879,"excerpt":880,"ctaLabel":881},"https:\u002F\u002Ffigure.rocks\u002Fes\u002Fblog\u002Fdlss-5-is-not-just-upscaling-what-3d-guided-neural-rendering-actually-changes","DLSS 5 no es solo escalado: qué cambia realmente el renderizado neuronal guiado por 3D","Cómo el renderizado neuronal está yendo más allá del escalado y los fotogramas generados hacia la reconstrucción de materiales e iluminación dentro de la canalización de gráficos.","Leer la guía de renderizado neuronal de DLSS 5",{},{"id":884,"data":885,"type":572,"tunes":887},"h-stack",{"text":886,"level":47},"La pila de renderizado neuronal se está volviendo por capas",{},{"id":889,"data":890,"type":612,"tunes":908},"stack-flow",{"steps":891,"title":907,"orientation":611},[892,895,898,901,904],{"label":893,"description":894},"Trabajo tradicional del motor","La simulación, la geometría, la visibilidad y el renderizado base siguen siendo responsabilidades estándar del motor.",{"label":896,"description":897},"Shaders neuronales en línea","Pequeñas funciones aprendidas reconstruyen texturas, materiales o iluminación dentro de HLSL.",{"label":899,"description":900},"Grafos de ML más grandes","Los modelos completos de reconstrucción o inferencia se ejecutan a través de una ruta de DirectX a nivel de grafo.",{"label":902,"description":903},"Asignación de hardware del proveedor","Los controladores asignan operaciones comunes de DirectX a las unidades especializadas de IA y matrices de la GPU.",{"label":905,"description":906},"Visibilidad en PIX","El trabajo de gráficos y ML se puede perfilar en conjunto en lugar de vivir en tiempos de ejecución opacos separados.","Una posible futura canalización de juego con DirectX",{},{"id":910,"data":911,"type":572,"tunes":913},"h-pix",{"text":912,"level":47},"Por qué importa la creación de perfiles unificada",{},{"id":915,"data":916,"type":544,"tunes":918},"p-pix-1",{"text":917},"Una carga de trabajo neuronal que mejora la calidad de imagen aún puede dañar un juego si consume inesperadamente tiempo de fotograma, ancho de banda de memoria o VRAM.",{},{"id":920,"data":921,"type":544,"tunes":923},"p-pix-2",{"text":922},"Microsoft incluye explícitamente la visibilidad unificada de PIX como parte de la dirección del Compute Graph Compiler. Eso importa porque los desarrolladores necesitan ver el trabajo de gráficos y ML en la misma captura de fotograma.",{},{"id":925,"data":926,"type":544,"tunes":928},"p-pix-3",{"text":927},"Si el renderizado neuronal se convierte en infraestructura, debe ser medible como cualquier otra etapa de renderizado.",{},{"id":930,"data":931,"type":572,"tunes":933},"h-not-everything",{"text":932,"level":47},"Esto no significa que todos los shaders se convertirán en redes neuronales",{},{"id":935,"data":936,"type":544,"tunes":938},"p-not-1",{"text":937},"Las matemáticas tradicionales de los shaders siguen siendo eficientes, deterministas y fáciles de razonar para muchas cargas de trabajo.",{},{"id":940,"data":941,"type":544,"tunes":943},"p-not-2",{"text":942},"Una función neuronal tiene sentido cuando puede aproximar o reconstruir algo costoso de manera más eficiente, comprimir datos o producir una calidad que de otro modo requeriría demasiado cómputo o memoria convencionales.",{},{"id":945,"data":946,"type":544,"tunes":948},"p-not-3",{"text":947},"La arquitectura correcta seguirá siendo híbrida.",{},{"id":950,"data":951,"type":572,"tunes":953},"h-test",{"text":952,"level":47},"La prueba de valor del shader neuronal",{},{"id":955,"data":956,"type":612,"tunes":977},"value-test",{"steps":957,"title":976,"orientation":611},[958,961,964,967,970,973],{"label":959,"description":960},"1. Identificar la operación tradicional costosa","¿Qué costo de cómputo, ancho de banda, memoria o almacenamiento intentas reemplazar?",{"label":962,"description":963},"2. Definir el sustituto neuronal","¿Qué puede reconstruir, predecir o comprimir un modelo pequeño?",{"label":965,"description":966},"3. Medir el costo de inferencia","El propio modelo de ML consume tiempo de GPU, memoria y ancho de banda.",{"label":968,"description":969},"4. Medir la estabilidad de la calidad","Busca artefactos temporales, errores de reconstrucción y casos de fallo.",{"label":971,"description":972},"5. Medir el ahorro neto","La técnica solo es útil si el costo tradicional evitado vale la pena frente al costo adicional de ML.",{"label":974,"description":975},"6. Probar en distintos proveedores","Una abstracción de DirectX ayuda a la portabilidad, pero el rendimiento real del hardware sigue siendo diferente.","¿Cuándo tiene realmente sentido incorporar ML en la canalización de renderizado?",{},{"id":979,"data":980,"type":572,"tunes":982},"h-gamers",{"text":981,"level":47},"Qué significa esto para los jugadores",{},{"id":984,"data":985,"type":544,"tunes":987},"p-gamers-1",{"text":986},"Es posible que los jugadores nunca vean un interruptor de “DX Linear Algebra” en un menú de gráficos.",{},{"id":989,"data":990,"type":544,"tunes":992},"p-gamers-2",{"text":991},"El impacto probable es indirecto: texturas más pequeñas, mejor reconstrucción de iluminación, gráficos neuronales más eficientes o nuevas técnicas visuales que se vuelven prácticas porque el motor puede acceder a la aceleración de matrices a través de una ruta estándar.",{},{"id":994,"data":995,"type":544,"tunes":997},"p-gamers-3",{"text":996},"La característica es más importante como infraestructura que como marca.",{},{"id":999,"data":1000,"type":572,"tunes":1002},"h-change",{"text":1001,"level":47},"¿Qué cambiaría esta respuesta?",{},{"id":1004,"data":1005,"type":544,"tunes":1007},"p-change-1",{"text":1006},"La mayor incertidumbre es la forma final de venta al público de estas API. DX Linear Algebra sigue en vista previa, y el Compute Graph Compiler aún no ha alcanzado una disponibilidad amplia para el público.",{},{"id":1009,"data":1010,"type":544,"tunes":1012},"p-change-2",{"text":1011},"El soporte final de hardware, los detalles de la API, el comportamiento del compilador y la adopción por parte de los motores pueden cambiar antes de que estos sistemas se conviertan en infraestructura normal para juegos comerciales.",{},{"id":1014,"data":1015,"type":544,"tunes":1017},"p-change-3",{"text":1016},"Si los motores principales adoptan las abstracciones directamente, los shaders neuronales podrían volverse mucho más comunes sin que los equipos individuales de juegos implementen cada técnica desde cero.",{},{"id":1019,"data":1020,"type":572,"tunes":1022},"h-limit",{"text":1021,"level":47},"Limitaciones",{},{"id":1024,"data":1025,"type":544,"tunes":1027},"p-limit-1",{"text":1026},"Este artículo explica la arquitectura DirectX documentada por Microsoft y las API en vista previa. No afirma que DX Linear Algebra mejore actualmente el rendimiento en todos los juegos ni que el Compute Graph Compiler sea un producto final para el público.",{},{"id":1029,"data":1030,"type":544,"tunes":1032},"p-limit-2",{"text":1031},"Los ejemplos de Microsoft describen capacidades y el uso previsto. Los beneficios reales dependen del modelo, la integración con el motor, la arquitectura de la GPU, los controladores y la carga de trabajo.",{},{"id":1034,"data":1035,"type":572,"tunes":1037},"h-conclusion",{"text":1036,"level":47},"Conclusión",{},{"id":1039,"data":1040,"type":544,"tunes":1042},"p-conc-1",{"text":1041},"El cambio importante de DirectX no es otra casilla de verificación llamada IA.",{},{"id":1044,"data":1045,"type":544,"tunes":1047},"p-conc-2",{"text":1046},"Es que las matemáticas de aprendizaje automático se están trasladando al propio modelo de programación de gráficos. Las funciones neuronales pequeñas pueden residir dentro de los sombreadores, los modelos más grandes pueden avanzar hacia la compilación a nivel de grafo, y la misma cadena de herramientas de DirectX puede exponer esas cargas de trabajo en múltiples proveedores de GPU.",{},{"id":1049,"data":1050,"type":544,"tunes":1052},"p-conc-3",{"text":1051},"Así es como la representación neuronal deja de ser una característica de marca y comienza a formar parte de la infraestructura de representación.",{},{"id":1054,"data":1055,"type":572,"tunes":1057},"h-faq",{"text":1056,"level":47},"Preguntas frecuentes",{},{"id":1059,"data":1060,"type":1059,"tunes":1087},"faq",{"items":1061,"title":1086},[1062,1066,1070,1074,1078,1082],{"id":1063,"answer":1064,"question":1065},"faq1","Es un conjunto de características de DirectX\u002FHLSL para operaciones vectoriales y matriciales aceleradas por hardware utilizadas por cargas de trabajo de aprendizaje automático, representación neuronal y procesamiento de imágenes.","¿Qué es el Álgebra lineal de DirectX?",{"id":1067,"answer":1068,"question":1069},"faq2","Una descripción útil en lenguaje sencillo es un sombreador que incluye una función neuronal aprendida o un paso de inferencia de aprendizaje automático como parte de su trabajo gráfico.","¿Qué es un sombreador neuronal?",{"id":1071,"answer":1072,"question":1073},"faq3","A partir de septiembre de 2026, permanece en la ruta de vista previa del Shader Model 6.10 \u002F Agility SDK.","¿Es el Álgebra lineal de DX ya una característica normal de DirectX para el consumidor?",{"id":1075,"answer":1076,"question":1077},"faq4","Es la API de compilador de aprendizaje automático a nivel de modelo anunciada por Microsoft, destinada a tomar grafos de cómputo más grandes y convertirlos en cargas de trabajo de GPU optimizadas integradas con D3D12.","¿Qué es el Compilador de grafos de cómputo de DirectX?",{"id":1079,"answer":1080,"question":1081},"faq5","Las funciones pequeñas se adaptan bien a la autoría a nivel de sombreador, mientras que los modelos grandes se benefician de la optimización de todo el grafo, la planificación de memoria y la fusión de operadores.","¿Por qué no ejecutar todos los modelos de aprendizaje automático directamente en HLSL?",{"id":1083,"answer":1084,"question":1085},"faq6","No directamente. DirectX proporciona infraestructura de nivel inferior que los proveedores y desarrolladores pueden utilizar para gráficos neuronales. Las tecnologías de marca aún pueden implementar sus propios modelos y estrategias de integración.","¿Reemplazará esto a DLSS o FSR?","Álgebra lineal de DirectX y sombreadores neuronales",{},{"id":1089,"data":1090,"type":572,"tunes":1092},"h-glossary",{"text":1091,"level":47},"Glosario",{},{"id":1094,"data":1095,"type":1094,"tunes":1126},"glossary",{"title":1096,"entries":1097},"Términos clave de aprendizaje automático de DirectX",[1098,1102,1106,1110,1114,1118,1122],{"term":1099,"anchor":1100,"definition":1101},"Álgebra lineal de DX","dx-linear-algebra","API de DirectX\u002FHLSL para operaciones vectoriales y matriciales aceleradas destinadas a cargas de trabajo de representación neuronal, aprendizaje automático y procesamiento de imágenes.",{"term":1103,"anchor":1104,"definition":1105},"Vector cooperativo","cooperative-vector","Un enfoque anterior de DirectX para operaciones vector-matriz aceleradas dentro de sombreadores que ayudó a establecer la ruta de representación neuronal a nivel de sombreador.",{"term":1107,"anchor":1108,"definition":1109},"Shader Model 6.10","shader-model-6-10","La generación de modelo de sombreador en vista previa que contiene las API de matriz de Álgebra lineal de DirectX actuales.",{"term":1111,"anchor":1112,"definition":1113},"Compilador de grafos de cómputo de DirectX","compute-graph-compiler","La API de compilador anunciada por Microsoft para optimizar y ejecutar grafos de cómputo de aprendizaje automático más grandes como cargas de trabajo nativas de GPU de DirectX.",{"term":1115,"anchor":1116,"definition":1117},"Sombreador neuronal","neural-shader","Un término práctico para un sombreador que realiza inferencia aprendida o computación neuronal como parte del procesamiento gráfico.",{"term":1119,"anchor":1120,"definition":1121},"Frontera sombreador-o-grafo","shader-or-graph-boundary","Un modelo de Figure Rocks para decidir si una carga de trabajo de aprendizaje automático pertenece como matemáticas de sombreador en línea pequeñas o como un grafo de cómputo a nivel de modelo más grande.",{"term":1123,"anchor":1124,"definition":1125},"Prueba de valor del sombreador neuronal","neural-shader-value-test","Un flujo de trabajo de Figure Rocks para decidir si el costo tradicional de cómputo o memoria evitado por una técnica neuronal vale sus costos de inferencia y compensaciones de calidad.",{},{"id":1128,"data":1129,"type":572,"tunes":1131},"h-sources",{"text":1130,"level":47},"Fuentes primarias",{},{"id":1133,"data":1134,"type":1140,"tunes":1141},"src-ms-ml-era",{"link":1135,"meta":1136},"https:\u002F\u002Fdevblogs.microsoft.com\u002Fdirectx\u002Fevolving-directx-for-the-ml-era-on-windows\u002F",{"image":1137,"title":1138,"description":1139},{"url":13},"Microsoft DirectX — Evolucionando DirectX para la era del aprendizaje automático en Windows","Descripción general oficial de la arquitectura de GDC 2026 que cubre aprendizaje automático a nivel de sombreador, Álgebra lineal de DX y el Compilador de grafos de cómputo de DirectX.","linkTool",{},{"id":1143,"data":1144,"type":1140,"tunes":1150},"src-ms-linalg",{"link":1145,"meta":1146},"https:\u002F\u002Fdevblogs.microsoft.com\u002Fdirectx\u002Fd3d12-linalg-preview\u002F",{"image":1147,"title":1148,"description":1149},{"url":13},"Microsoft DirectX — Vista previa de D3D12 LinAlg Matrix","Vista previa oficial de abril de 2026 que explica las API unificadas de Álgebra lineal, operaciones matriciales y la motivación de representación neuronal.",{},{"id":1152,"data":1153,"type":1140,"tunes":1159},"src-ms-agility721",{"link":1154,"meta":1155},"https:\u002F\u002Fdevblogs.microsoft.com\u002Fdirectx\u002Fannouncing-agilitysdk-721-preview-and-more-shader-model-6-10-features\u002F",{"image":1156,"title":1157,"description":1158},{"url":13},"Microsoft DirectX — Vista previa de Agility SDK 1.721","Lanzamiento oficial de mayo de 2026 que documenta las actualizaciones de Álgebra lineal de Shader Model 6.10 y el soporte de hardware en vista previa.",{},{"id":1161,"data":1162,"type":1140,"tunes":1168},"src-ms-gdc",{"link":1163,"meta":1164},"https:\u002F\u002Fdeveloper.microsoft.com\u002Fen-us\u002Fgames\u002Farticles\u002F2026\u002F03\u002Fgdc-2026-evolving-directx-for-ml-era-on-windows\u002F",{"image":1165,"title":1166,"description":1167},{"url":13},"Microsoft Game Dev — GDC 2026: Evolucionando DirectX para la era del aprendizaje automático","Resumen oficial de Microsoft Game Dev que explica el aprendizaje automático a nivel de sombreador y a nivel de modelo y el papel del Compilador de grafos de cómputo.",{},{"id":1170,"data":1171,"type":1140,"tunes":1177},"src-ms-coop",{"link":1172,"meta":1173},"https:\u002F\u002Fdevblogs.microsoft.com\u002Fdirectx\u002Fcooperative-vector\u002F",{"image":1174,"title":1175,"description":1176},{"url":13},"Microsoft DirectX — Vector cooperativo de D3D12","Antecedentes oficiales sobre operaciones vectoriales\u002Fmatriciales aceleradas por hardware y representación neuronal directamente desde hilos de sombreador.",{},"2.31","DirectX está yendo más allá de los sombreadores gráficos tradicionales. Microsoft está añadiendo álgebra lineal acelerada por hardware directamente a HLSL y una ruta separada para modelos de ML más grandes, sentando las bases para texturas neuronales, materiales aprendidos, iluminación neuronal y otras técnicas de renderizado impulsadas por IA.","\u002Fuploads\u002F2026\u002F09\u002Fdirectx-is-becoming-an-ml-platform-what-linear-algebra-and-neural-shaders-mean-for-future-games-1790378019857-utigyk.webp","directx-is-becoming-an-ml-platform-what-linear-algebra-and-neural-shaders-mean-for-future-games-1790378019857-utigyk","PUBLISHED","2026-09-25T15:12:00.000Z","2026-09-25T23:12:37.728Z","2026-09-26T07:39:23.873Z",{"en":1187,"de":1188,"sr":1189,"es":1190,"fr":1191,"it":1192,"ru":1193,"zh":1194},"\u002Fblog\u002Fdirectx-is-becoming-an-ml-platform-what-linear-algebra-and-neural-shaders-mean-for-future-games","\u002Fde\u002Fblog\u002Fdirectx-is-becoming-an-ml-platform-what-linear-algebra-and-neural-shaders-mean-for-future-games","\u002Fsr\u002Fblog\u002Fdirectx-is-becoming-an-ml-platform-what-linear-algebra-and-neural-shaders-mean-for-future-games","\u002Fes\u002Fblog\u002Fdirectx-is-becoming-an-ml-platform-what-linear-algebra-and-neural-shaders-mean-for-future-games","\u002Ffr\u002Fblog\u002Fdirectx-is-becoming-an-ml-platform-what-linear-algebra-and-neural-shaders-mean-for-future-games","\u002Fit\u002Fblog\u002Fdirectx-is-becoming-an-ml-platform-what-linear-algebra-and-neural-shaders-mean-for-future-games","\u002Fru\u002Fblog\u002Fdirectx-is-becoming-an-ml-platform-what-linear-algebra-and-neural-shaders-mean-for-future-games","\u002Fzh\u002Fblog\u002Fdirectx-is-becoming-an-ml-platform-what-linear-algebra-and-neural-shaders-mean-for-future-games",[1196,1200,1204,1208],{"id":1197,"name":1198,"slug":1199},251,"Desenfoque y persistencia","blur-and-persistence",{"id":1201,"name":1202,"slug":1203},252,"Overdrive y Smearing","overdrive-and-smearing",{"id":1205,"name":1206,"slug":1207},253,"Frecuencia de actualización y claridad","refresh-and-clarity",{"id":1209,"name":1210,"slug":1211},220,"Diseño al servicio del uso","design-that-serves-use",{"id":283,"login":1213,"email":1214,"displayName":1215},"aleksandar","aleksandar@stajic.de","Aleksandar Stajic",[1217,1730],{"lang":8,"title":1218,"content":1219,"contentJson":1220,"excerpt":1729},"DirectX Is Becoming an ML Platform: What Linear Algebra and Neural Shaders Mean for Future Games","{\"time\":1790408311441,\"blocks\":[{\"id\":\"intro\",\"type\":\"paragraph\",\"data\":{\"text\":\"DirectX is no longer only a graphics API that sends traditional shaders to the GPU. Microsoft is adding machine-learning primitives directly to HLSL and a second path for running larger ML graphs inside the DirectX ecosystem. That changes where neural rendering can live inside future PC games.\"},\"tunes\":{}},{\"id\":\"direct\",\"type\":\"callout\",\"data\":{\"variant\":\"info\",\"title\":\"Direct answer\",\"body\":\"\u003Cstrong>DirectX is becoming an ML-capable graphics platform.\u003C\u002Fstrong> Small neural workloads can run directly inside shaders through DX Linear Algebra, while larger models are being targeted by the DirectX Compute Graph Compiler. The goal is to let game engines use GPU AI hardware without building a separate vendor-specific path for every technique.\"},\"tunes\":{}},{\"id\":\"status-note\",\"type\":\"callout\",\"data\":{\"variant\":\"note\",\"title\":\"Current status\",\"body\":\"As of September 2026, \u003Cstrong>DX Linear Algebra is still a preview technology\u003C\u002Fstrong> in the Shader Model 6.10 \u002F Agility SDK preview path. Microsoft announced the DirectX Compute Graph Compiler for private preview rather than as a broadly shipping retail feature. This article explains the architecture and direction, not a claim that every current game can use it today.\"},\"tunes\":{}},{\"id\":\"toc\",\"type\":\"tableOfContents\",\"data\":{\"title\":\"Contents\",\"minLevel\":2,\"maxLevel\":3},\"tunes\":{}},{\"id\":\"h-why\",\"type\":\"header\",\"data\":{\"text\":\"Why neural rendering needs new DirectX primitives\",\"level\":2},\"tunes\":{}},{\"id\":\"p-why-1\",\"type\":\"paragraph\",\"data\":{\"text\":\"Modern GPUs already contain specialized hardware for matrix operations used by machine learning. The problem for a game developer is not only whether that hardware exists, but how to access it efficiently from a real-time graphics pipeline.\"},\"tunes\":{}},{\"id\":\"p-why-2\",\"type\":\"paragraph\",\"data\":{\"text\":\"Microsoft first explored this with Cooperative Vector support. In 2026, that work evolved into a broader DirectX Linear Algebra design that supports both vector-matrix and matrix-matrix operations.\"},\"tunes\":{}},{\"id\":\"p-why-3\",\"type\":\"paragraph\",\"data\":{\"text\":\"That matters because different neural-rendering jobs have different shapes. A tiny model evaluating material behavior per pixel is not the same workload as a large super-resolution or denoising graph.\"},\"tunes\":{}},{\"id\":\"h-two-level\",\"type\":\"header\",\"data\":{\"text\":\"The Two-Level DirectX ML Model\",\"level\":2},\"tunes\":{}},{\"id\":\"two-level-flow\",\"type\":\"processFlow\",\"data\":{\"title\":\"Two ways ML can enter the DirectX graphics pipeline\",\"orientation\":\"auto\",\"steps\":[{\"label\":\"1. Shader-level ML\",\"description\":\"Small neural or linear-algebra workloads execute directly from HLSL alongside traditional shader code.\"},{\"label\":\"2. DX Linear Algebra\",\"description\":\"The shader can request hardware-accelerated vector and matrix operations instead of manually implementing every ML primitive.\"},{\"label\":\"3. Model-level ML\",\"description\":\"Larger neural networks are represented as complete computation graphs rather than hand-written shader fragments.\"},{\"label\":\"4. DirectX Compute Graph Compiler\",\"description\":\"Microsoft's planned compiler path analyzes and lowers those graphs into optimized GPU workloads integrated with D3D12 queues and command lists.\"}]},\"tunes\":{}},{\"id\":\"simple-diff\",\"type\":\"callout\",\"data\":{\"variant\":\"success\",\"title\":\"The simple difference\",\"body\":\"\u003Cstrong>DX Linear Algebra:\u003C\u002Fstrong> put small ML math inside the shader.\u003Cbr>\u003Cstrong>Compute Graph Compiler:\u003C\u002Fstrong> bring a larger ML model into the engine as a graph.\"},\"tunes\":{}},{\"id\":\"h-linalg\",\"type\":\"header\",\"data\":{\"text\":\"What DX Linear Algebra actually gives a shader\",\"level\":2},\"tunes\":{}},{\"id\":\"p-linalg-1\",\"type\":\"paragraph\",\"data\":{\"text\":\"Traditional HLSL is built around graphics and compute operations. Neural workloads rely heavily on linear algebra: vectors, matrices, multiplication, accumulation and data layouts optimized for those operations.\"},\"tunes\":{}},{\"id\":\"p-linalg-2\",\"type\":\"paragraph\",\"data\":{\"text\":\"The Shader Model 6.10 preview adds first-class matrix APIs so developers can express those workloads more directly and let the driver map them to specialized hardware.\"},\"tunes\":{}},{\"id\":\"p-linalg-3\",\"type\":\"paragraph\",\"data\":{\"text\":\"Microsoft's April 2026 preview explicitly describes this as a unified path for neural rendering, ML and image-processing workloads rather than a graphics-only feature.\"},\"tunes\":{}},{\"id\":\"math-table\",\"type\":\"comparison\",\"data\":{\"title\":\"Traditional shader math vs ML-oriented shader math\",\"layout\":\"table\",\"columns\":[{\"id\":\"traditional\",\"label\":\"Traditional shader focus\"},{\"id\":\"ml\",\"label\":\"ML-oriented focus\"}],\"rows\":[{\"id\":\"work\",\"label\":\"Typical work\",\"values\":[\"\",\"\"]},{\"id\":\"hardware\",\"label\":\"Hardware path\",\"values\":[\"\",\"\"]},{\"id\":\"code\",\"label\":\"Developer expression\",\"values\":[\"\",\"\"]}]},\"tunes\":{}},{\"id\":\"h-coop\",\"type\":\"header\",\"data\":{\"text\":\"Why Cooperative Vector was only the beginning\",\"level\":2},\"tunes\":{}},{\"id\":\"p-coop-1\",\"type\":\"paragraph\",\"data\":{\"text\":\"Cooperative Vector allowed shader threads to request vector-matrix work that drivers could map to specialized hardware. That was useful for highly parallel per-pixel workloads.\"},\"tunes\":{}},{\"id\":\"p-coop-2\",\"type\":\"paragraph\",\"data\":{\"text\":\"Microsoft later concluded that many important ML workloads need more than vector-matrix operations. Super resolution, denoising, temporal reconstruction, larger image models and general inference can require matrix-matrix operations and shared work across many threads.\"},\"tunes\":{}},{\"id\":\"p-coop-3\",\"type\":\"paragraph\",\"data\":{\"text\":\"DX Linear Algebra therefore broadens the model instead of treating Cooperative Vector as the final abstraction.\"},\"tunes\":{}},{\"id\":\"h-usecases\",\"type\":\"header\",\"data\":{\"text\":\"What can actually run inside a shader?\",\"level\":2},\"tunes\":{}},{\"id\":\"p-use-1\",\"type\":\"paragraph\",\"data\":{\"text\":\"The most interesting shader-level ML workloads are small enough to execute close to the graphics data they operate on.\"},\"tunes\":{}},{\"id\":\"usecases-table\",\"type\":\"table\",\"data\":{\"withHeadings\":true,\"stretched\":false,\"content\":[[\"Workload\",\"Why shader-level ML fits\"],[\"Neural texture compression\",\"A small network can reconstruct texture information near the point where the shader needs it\"],[\"Neural material evaluation\",\"A learned function can replace or augment expensive hand-authored material math\"],[\"Neural radiance caching\",\"Per-pixel or local inference can estimate lighting information from learned scene behavior\"],[\"Small denoising\u002Freconstruction kernels\",\"ML operations can sit directly beside the rendering stage they improve\"],[\"Image-processing inference\",\"Matrix operations can be embedded into GPU processing without a separate external runtime\"]]},\"tunes\":{}},{\"id\":\"h-texture\",\"type\":\"header\",\"data\":{\"text\":\"Why this matters for texture memory\",\"level\":2},\"tunes\":{}},{\"id\":\"p-tex-1\",\"type\":\"paragraph\",\"data\":{\"text\":\"One of Microsoft's recurring examples is neural texture compression.\"},\"tunes\":{}},{\"id\":\"p-tex-2\",\"type\":\"paragraph\",\"data\":{\"text\":\"Instead of storing every texture channel at conventional fidelity, a game can store a more compact representation and use a small neural network to reconstruct detail during rendering.\"},\"tunes\":{}},{\"id\":\"p-tex-3\",\"type\":\"paragraph\",\"data\":{\"text\":\"That trades some GPU inference work for lower storage or memory pressure. The exact benefit depends on the technique and hardware, but the architectural change is important: some visual detail can become computation instead of stored data.\"},\"tunes\":{}},{\"id\":\"ref-vram\",\"type\":\"referralArticle\",\"data\":{\"url\":\"https:\u002F\u002Ffigure.rocks\u002Fblog\u002Fvram-usage-is-not-vram-requirement-why-a-full-memory-meter-does-not-tell-the-whole-story\",\"title\":\"VRAM Usage Is Not VRAM Requirement: Why a Full Memory Meter Does Not Tell the Whole Story\",\"excerpt\":\"A practical guide to VRAM capacity, residency budgets, working sets and why memory pressure is more complicated than one usage number.\",\"ctaLabel\":\"Read the VRAM guide\"},\"tunes\":{}},{\"id\":\"h-models\",\"type\":\"header\",\"data\":{\"text\":\"Why full models need a different path\",\"level\":2},\"tunes\":{}},{\"id\":\"p-models-1\",\"type\":\"paragraph\",\"data\":{\"text\":\"Hand-writing a few matrix operations in HLSL is practical for small neural functions. It becomes much less practical when the workload is a complete modern model with many layers, dependencies and intermediate tensors.\"},\"tunes\":{}},{\"id\":\"p-models-2\",\"type\":\"paragraph\",\"data\":{\"text\":\"Microsoft's DirectX Compute Graph Compiler is designed for this larger class of workload.\"},\"tunes\":{}},{\"id\":\"p-models-3\",\"type\":\"paragraph\",\"data\":{\"text\":\"Instead of rewriting the model as custom shader code, the compiler can accept a computation graph, analyze the whole graph, plan memory, fuse operations and lower the result into GPU work that integrates with DirectX 12.\"},\"tunes\":{}},{\"id\":\"h-boundary\",\"type\":\"header\",\"data\":{\"text\":\"The Shader-or-Graph Boundary\",\"level\":2},\"tunes\":{}},{\"id\":\"boundary-table\",\"type\":\"comparison\",\"data\":{\"title\":\"When the ML workload belongs in HLSL vs a model compiler\",\"layout\":\"table\",\"columns\":[{\"id\":\"shader\",\"label\":\"Shader-level path\"},{\"id\":\"graph\",\"label\":\"Model-level path\"}],\"rows\":[{\"id\":\"size\",\"label\":\"Model size\",\"values\":[\"\",\"\"]},{\"id\":\"location\",\"label\":\"Execution style\",\"values\":[\"\",\"\"]},{\"id\":\"authoring\",\"label\":\"Authoring\",\"values\":[\"\",\"\"]},{\"id\":\"optimization\",\"label\":\"Optimization scope\",\"values\":[\"\",\"\"]},{\"id\":\"use\",\"label\":\"Typical use\",\"values\":[\"\",\"\"]}]},\"tunes\":{}},{\"id\":\"h-crossvendor\",\"type\":\"header\",\"data\":{\"text\":\"Why cross-vendor support matters more than another AI feature\",\"level\":2},\"tunes\":{}},{\"id\":\"p-cross-1\",\"type\":\"paragraph\",\"data\":{\"text\":\"AMD, Intel, NVIDIA and Qualcomm expose different GPU architectures and different forms of dedicated matrix acceleration.\"},\"tunes\":{}},{\"id\":\"p-cross-2\",\"type\":\"paragraph\",\"data\":{\"text\":\"A DirectX abstraction gives Microsoft and the driver vendors a place to translate common HLSL or graph-level ML into the correct hardware path.\"},\"tunes\":{}},{\"id\":\"p-cross-3\",\"type\":\"paragraph\",\"data\":{\"text\":\"That does not make every GPU equally fast, but it can reduce the need for a game engine to implement a completely different neural-rendering API for every vendor.\"},\"tunes\":{}},{\"id\":\"portability-note\",\"type\":\"callout\",\"data\":{\"variant\":\"note\",\"title\":\"Portability is the real platform story\",\"body\":\"The interesting part is not that DirectX can “run AI.” GPUs already could. The platform change is that \u003Cstrong>ML becomes expressible through a common graphics API and toolchain\u003C\u002Fstrong>.\"},\"tunes\":{}},{\"id\":\"h-support\",\"type\":\"header\",\"data\":{\"text\":\"What hardware supports the current preview?\",\"level\":2},\"tunes\":{}},{\"id\":\"p-support-1\",\"type\":\"paragraph\",\"data\":{\"text\":\"The answer depends on the specific Linear Algebra operation and preview driver.\"},\"tunes\":{}},{\"id\":\"p-support-2\",\"type\":\"paragraph\",\"data\":{\"text\":\"Microsoft's Agility SDK 1.721 preview support table lists LinAlg VectorAccumulate for AMD Radeon RX 9000 Series hardware, Intel Xe2-or-newer hardware through an upcoming driver, and NVIDIA RTX hardware through the supported preview path.\"},\"tunes\":{}},{\"id\":\"p-support-3\",\"type\":\"paragraph\",\"data\":{\"text\":\"This is preview-era support, not a universal retail guarantee. Hardware and driver support can change before finalization.\"},\"tunes\":{}},{\"id\":\"h-infra\",\"type\":\"header\",\"data\":{\"text\":\"Neural rendering is becoming infrastructure\",\"level\":2},\"tunes\":{}},{\"id\":\"p-infra-1\",\"type\":\"paragraph\",\"data\":{\"text\":\"DLSS, FSR and other neural graphics technologies are often discussed as branded features visible in a game's settings menu.\"},\"tunes\":{}},{\"id\":\"p-infra-2\",\"type\":\"paragraph\",\"data\":{\"text\":\"DirectX Linear Algebra points to a deeper change. The neural operation can become an internal implementation detail inside the renderer rather than a single optional post-processing feature.\"},\"tunes\":{}},{\"id\":\"p-infra-3\",\"type\":\"paragraph\",\"data\":{\"text\":\"A developer could use ML for textures, materials, lighting, reconstruction or other local functions without exposing each one as a consumer-facing AI toggle.\"},\"tunes\":{}},{\"id\":\"ref-dlss5\",\"type\":\"referralArticle\",\"data\":{\"url\":\"https:\u002F\u002Ffigure.rocks\u002Fblog\u002Fdlss-5-is-not-just-upscaling-what-3d-guided-neural-rendering-actually-changes\",\"title\":\"DLSS 5 Is Not Just Upscaling: What 3D-Guided Neural Rendering Actually Changes\",\"excerpt\":\"How neural rendering is moving beyond upscaling and generated frames into material and lighting reconstruction inside the graphics pipeline.\",\"ctaLabel\":\"Read the DLSS 5 neural-rendering guide\"},\"tunes\":{}},{\"id\":\"h-stack\",\"type\":\"header\",\"data\":{\"text\":\"The Neural Rendering Stack is becoming layered\",\"level\":2},\"tunes\":{}},{\"id\":\"stack-flow\",\"type\":\"processFlow\",\"data\":{\"title\":\"A possible future DirectX game pipeline\",\"orientation\":\"auto\",\"steps\":[{\"label\":\"Traditional engine work\",\"description\":\"Simulation, geometry, visibility and base rendering remain standard engine responsibilities.\"},{\"label\":\"Inline neural shaders\",\"description\":\"Small learned functions reconstruct textures, materials or lighting inside HLSL.\"},{\"label\":\"Larger ML graphs\",\"description\":\"Full reconstruction or inference models execute through a graph-level DirectX path.\"},{\"label\":\"Vendor hardware mapping\",\"description\":\"Drivers map common DirectX operations onto the GPU's specialized AI and matrix units.\"},{\"label\":\"PIX visibility\",\"description\":\"Graphics and ML work can be profiled together instead of living in separate opaque runtimes.\"}]},\"tunes\":{}},{\"id\":\"h-pix\",\"type\":\"header\",\"data\":{\"text\":\"Why unified profiling matters\",\"level\":2},\"tunes\":{}},{\"id\":\"p-pix-1\",\"type\":\"paragraph\",\"data\":{\"text\":\"A neural workload that improves image quality can still damage a game if it unexpectedly consumes frame time, memory bandwidth or VRAM.\"},\"tunes\":{}},{\"id\":\"p-pix-2\",\"type\":\"paragraph\",\"data\":{\"text\":\"Microsoft explicitly includes unified PIX visibility as part of the Compute Graph Compiler direction. That matters because developers need to see graphics and ML work in the same frame capture.\"},\"tunes\":{}},{\"id\":\"p-pix-3\",\"type\":\"paragraph\",\"data\":{\"text\":\"If neural rendering becomes infrastructure, it must be measurable like any other rendering stage.\"},\"tunes\":{}},{\"id\":\"h-not-everything\",\"type\":\"header\",\"data\":{\"text\":\"This does not mean every shader will become a neural network\",\"level\":2},\"tunes\":{}},{\"id\":\"p-not-1\",\"type\":\"paragraph\",\"data\":{\"text\":\"Traditional shader math remains efficient, deterministic and easy to reason about for many workloads.\"},\"tunes\":{}},{\"id\":\"p-not-2\",\"type\":\"paragraph\",\"data\":{\"text\":\"A neural function makes sense when it can approximate or reconstruct something expensive more efficiently, compress data, or produce quality that would otherwise require too much conventional compute or memory.\"},\"tunes\":{}},{\"id\":\"p-not-3\",\"type\":\"paragraph\",\"data\":{\"text\":\"The right architecture will remain hybrid.\"},\"tunes\":{}},{\"id\":\"h-test\",\"type\":\"header\",\"data\":{\"text\":\"The Neural Shader Value Test\",\"level\":2},\"tunes\":{}},{\"id\":\"value-test\",\"type\":\"processFlow\",\"data\":{\"title\":\"When does putting ML into the rendering pipeline actually make sense?\",\"orientation\":\"auto\",\"steps\":[{\"label\":\"1. Identify the expensive traditional operation\",\"description\":\"What compute, bandwidth, memory or storage cost are you trying to replace?\"},{\"label\":\"2. Define the neural substitute\",\"description\":\"What can a small model reconstruct, predict or compress?\"},{\"label\":\"3. Measure inference cost\",\"description\":\"The ML model itself consumes GPU time, memory and bandwidth.\"},{\"label\":\"4. Measure quality stability\",\"description\":\"Look for temporal artifacts, reconstruction errors and failure cases.\"},{\"label\":\"5. Measure net savings\",\"description\":\"The technique is useful only if the avoided traditional cost is worth the added ML cost.\"},{\"label\":\"6. Test across vendors\",\"description\":\"A DirectX abstraction helps portability, but real hardware performance still differs.\"}]},\"tunes\":{}},{\"id\":\"h-gamers\",\"type\":\"header\",\"data\":{\"text\":\"What this means for gamers\",\"level\":2},\"tunes\":{}},{\"id\":\"p-gamers-1\",\"type\":\"paragraph\",\"data\":{\"text\":\"Gamers may never see a “DX Linear Algebra” switch in a graphics menu.\"},\"tunes\":{}},{\"id\":\"p-gamers-2\",\"type\":\"paragraph\",\"data\":{\"text\":\"The likely impact is indirect: smaller texture footprints, better lighting reconstruction, more efficient neural graphics, or new visual techniques that become practical because the engine can access matrix acceleration through a standard path.\"},\"tunes\":{}},{\"id\":\"p-gamers-3\",\"type\":\"paragraph\",\"data\":{\"text\":\"The feature is more important as infrastructure than as a brand.\"},\"tunes\":{}},{\"id\":\"h-change\",\"type\":\"header\",\"data\":{\"text\":\"What would change this answer?\",\"level\":2},\"tunes\":{}},{\"id\":\"p-change-1\",\"type\":\"paragraph\",\"data\":{\"text\":\"The biggest uncertainty is the final retail shape of these APIs. DX Linear Algebra remains in preview, and the Compute Graph Compiler has not yet reached broad retail availability.\"},\"tunes\":{}},{\"id\":\"p-change-2\",\"type\":\"paragraph\",\"data\":{\"text\":\"Final hardware support, API details, compiler behavior and engine adoption may change before these systems become normal shipping-game infrastructure.\"},\"tunes\":{}},{\"id\":\"p-change-3\",\"type\":\"paragraph\",\"data\":{\"text\":\"If major engines adopt the abstractions directly, neural shaders could become much more common without individual game teams implementing every technique from scratch.\"},\"tunes\":{}},{\"id\":\"h-limit\",\"type\":\"header\",\"data\":{\"text\":\"Limitations\",\"level\":2},\"tunes\":{}},{\"id\":\"p-limit-1\",\"type\":\"paragraph\",\"data\":{\"text\":\"This article explains Microsoft's documented DirectX architecture and preview APIs. It does not claim that DX Linear Algebra currently improves performance in every game or that the Compute Graph Compiler is a finished retail product.\"},\"tunes\":{}},{\"id\":\"p-limit-2\",\"type\":\"paragraph\",\"data\":{\"text\":\"Microsoft's examples describe capability and intended use. Real benefits depend on the model, engine integration, GPU architecture, drivers and workload.\"},\"tunes\":{}},{\"id\":\"h-conclusion\",\"type\":\"header\",\"data\":{\"text\":\"Conclusion\",\"level\":2},\"tunes\":{}},{\"id\":\"p-conc-1\",\"type\":\"paragraph\",\"data\":{\"text\":\"The important DirectX change is not another checkbox called AI.\"},\"tunes\":{}},{\"id\":\"p-conc-2\",\"type\":\"paragraph\",\"data\":{\"text\":\"It is that machine-learning math is moving into the graphics programming model itself. Small neural functions can sit inside shaders, larger models can move toward graph-level compilation, and the same DirectX toolchain can expose those workloads across multiple GPU vendors.\"},\"tunes\":{}},{\"id\":\"p-conc-3\",\"type\":\"paragraph\",\"data\":{\"text\":\"That is how neural rendering stops being one branded feature and starts becoming part of the rendering infrastructure.\"},\"tunes\":{}},{\"id\":\"h-faq\",\"type\":\"header\",\"data\":{\"text\":\"FAQ\",\"level\":2},\"tunes\":{}},{\"id\":\"faq\",\"type\":\"faq\",\"data\":{\"title\":\"DirectX Linear Algebra and neural shaders\",\"items\":[{\"id\":\"faq1\",\"question\":\"What is DirectX Linear Algebra?\",\"answer\":\"It is a DirectX\u002FHLSL feature set for hardware-accelerated vector and matrix operations used by machine learning, neural rendering and image-processing workloads.\"},{\"id\":\"faq2\",\"question\":\"What is a neural shader?\",\"answer\":\"A useful plain-English description is a shader that includes a learned neural function or ML inference step as part of its graphics work.\"},{\"id\":\"faq3\",\"question\":\"Is DX Linear Algebra already a normal retail DirectX feature?\",\"answer\":\"As of September 2026 it remains in the Shader Model 6.10 \u002F Agility SDK preview path.\"},{\"id\":\"faq4\",\"question\":\"What is the DirectX Compute Graph Compiler?\",\"answer\":\"It is Microsoft's announced model-level ML compiler API intended to take larger computation graphs and lower them into optimized GPU workloads integrated with D3D12.\"},{\"id\":\"faq5\",\"question\":\"Why not run every ML model directly in HLSL?\",\"answer\":\"Small functions fit shader-level authoring well, while large models benefit from whole-graph optimization, memory planning and operator fusion.\"},{\"id\":\"faq6\",\"question\":\"Will this replace DLSS or FSR?\",\"answer\":\"Not directly. DirectX provides lower-level infrastructure that vendors and developers can use for neural graphics. Branded technologies can still implement their own models and integration strategies.\"}]},\"tunes\":{}},{\"id\":\"h-glossary\",\"type\":\"header\",\"data\":{\"text\":\"Glossary\",\"level\":2},\"tunes\":{}},{\"id\":\"glossary\",\"type\":\"glossary\",\"data\":{\"title\":\"Key DirectX ML terms\",\"entries\":[{\"term\":\"DX Linear Algebra\",\"definition\":\"DirectX\u002FHLSL APIs for accelerated vector and matrix operations intended for neural rendering, ML and image-processing workloads.\",\"anchor\":\"dx-linear-algebra\"},{\"term\":\"Cooperative Vector\",\"definition\":\"An earlier DirectX approach for accelerated vector-matrix operations inside shaders that helped establish the shader-level neural-rendering path.\",\"anchor\":\"cooperative-vector\"},{\"term\":\"Shader Model 6.10\",\"definition\":\"The preview shader-model generation containing the current DirectX Linear Algebra matrix APIs.\",\"anchor\":\"shader-model-6-10\"},{\"term\":\"DirectX Compute Graph Compiler\",\"definition\":\"Microsoft's announced compiler API for optimizing and executing larger ML computation graphs as native DirectX GPU workloads.\",\"anchor\":\"compute-graph-compiler\"},{\"term\":\"Neural shader\",\"definition\":\"A practical term for a shader that performs learned inference or neural computation as part of graphics processing.\",\"anchor\":\"neural-shader\"},{\"term\":\"Shader-or-Graph Boundary\",\"definition\":\"A Figure Rocks model for deciding whether an ML workload belongs as small inline shader math or as a larger model-level computation graph.\",\"anchor\":\"shader-or-graph-boundary\"},{\"term\":\"Neural Shader Value Test\",\"definition\":\"A Figure Rocks workflow for deciding whether the traditional compute or memory cost avoided by a neural technique is worth its inference cost and quality trade-offs.\",\"anchor\":\"neural-shader-value-test\"}]},\"tunes\":{}},{\"id\":\"h-sources\",\"type\":\"header\",\"data\":{\"text\":\"Primary sources\",\"level\":2},\"tunes\":{}},{\"id\":\"src-ms-ml-era\",\"type\":\"linkTool\",\"data\":{\"link\":\"https:\u002F\u002Fdevblogs.microsoft.com\u002Fdirectx\u002Fevolving-directx-for-the-ml-era-on-windows\u002F\",\"meta\":{\"image\":{\"url\":\"\"},\"title\":\"Microsoft DirectX — Evolving DirectX for the ML Era on Windows\",\"description\":\"Official GDC 2026 architecture overview covering shader-level ML, DX Linear Algebra and the DirectX Compute Graph Compiler.\"}},\"tunes\":{}},{\"id\":\"src-ms-linalg\",\"type\":\"linkTool\",\"data\":{\"link\":\"https:\u002F\u002Fdevblogs.microsoft.com\u002Fdirectx\u002Fd3d12-linalg-preview\u002F\",\"meta\":{\"image\":{\"url\":\"\"},\"title\":\"Microsoft DirectX — D3D12 LinAlg Matrix Preview\",\"description\":\"Official April 2026 preview explaining the unified Linear Algebra APIs, matrix operations and neural-rendering motivation.\"}},\"tunes\":{}},{\"id\":\"src-ms-agility721\",\"type\":\"linkTool\",\"data\":{\"link\":\"https:\u002F\u002Fdevblogs.microsoft.com\u002Fdirectx\u002Fannouncing-agilitysdk-721-preview-and-more-shader-model-6-10-features\u002F\",\"meta\":{\"image\":{\"url\":\"\"},\"title\":\"Microsoft DirectX — Agility SDK 1.721 Preview\",\"description\":\"Official May 2026 release documenting Shader Model 6.10 Linear Algebra updates and preview hardware support.\"}},\"tunes\":{}},{\"id\":\"src-ms-gdc\",\"type\":\"linkTool\",\"data\":{\"link\":\"https:\u002F\u002Fdeveloper.microsoft.com\u002Fen-us\u002Fgames\u002Farticles\u002F2026\u002F03\u002Fgdc-2026-evolving-directx-for-ml-era-on-windows\u002F\",\"meta\":{\"image\":{\"url\":\"\"},\"title\":\"Microsoft Game Dev — GDC 2026: Evolving DirectX for the ML Era\",\"description\":\"Official Microsoft Game Dev summary explaining shader-level and model-level ML and the role of the Compute Graph Compiler.\"}},\"tunes\":{}},{\"id\":\"src-ms-coop\",\"type\":\"linkTool\",\"data\":{\"link\":\"https:\u002F\u002Fdevblogs.microsoft.com\u002Fdirectx\u002Fcooperative-vector\u002F\",\"meta\":{\"image\":{\"url\":\"\"},\"title\":\"Microsoft DirectX — D3D12 Cooperative Vector\",\"description\":\"Official background on hardware-accelerated vector\u002Fmatrix operations and neural rendering directly from shader threads.\"}},\"tunes\":{}}],\"version\":\"2.31.6\"}",{"time":1221,"blocks":1222,"version":1728},1790408311441,[1223,1227,1232,1237,1241,1245,1249,1253,1257,1261,1276,1281,1285,1289,1293,1297,1316,1320,1324,1328,1332,1336,1340,1362,1366,1370,1374,1378,1385,1389,1393,1397,1401,1405,1430,1434,1438,1442,1446,1451,1455,1459,1463,1467,1471,1475,1479,1483,1490,1494,1514,1518,1522,1526,1530,1534,1538,1542,1546,1550,1573,1577,1581,1585,1589,1593,1597,1601,1605,1609,1613,1617,1621,1625,1629,1633,1637,1660,1664,1689,1693,1700,1707,1714,1721],{"id":541,"data":1224,"type":544,"tunes":1226},{"text":1225},"DirectX is no longer only a graphics API that sends traditional shaders to the GPU. Microsoft is adding machine-learning primitives directly to HLSL and a second path for running larger ML graphs inside the DirectX ecosystem. That changes where neural rendering can live inside future PC games.",{},{"id":547,"data":1228,"type":552,"tunes":1231},{"body":1229,"title":1230,"variant":551},"\u003Cstrong>DirectX is becoming an ML-capable graphics platform.\u003C\u002Fstrong> Small neural workloads can run directly inside shaders through DX Linear Algebra, while larger models are being targeted by the DirectX Compute Graph Compiler. The goal is to let game engines use GPU AI hardware without building a separate vendor-specific path for every technique.","Direct answer",{},{"id":555,"data":1233,"type":552,"tunes":1236},{"body":1234,"title":1235,"variant":559},"As of September 2026, \u003Cstrong>DX Linear Algebra is still a preview technology\u003C\u002Fstrong> in the Shader Model 6.10 \u002F Agility SDK preview path. Microsoft announced the DirectX Compute Graph Compiler for private preview rather than as a broadly shipping retail feature. This article explains the architecture and direction, not a claim that every current game can use it today.","Current status",{},{"id":562,"data":1238,"type":566,"tunes":1240},{"title":1239,"maxLevel":565,"minLevel":47},"Contents",{},{"id":569,"data":1242,"type":572,"tunes":1244},{"text":1243,"level":47},"Why neural rendering needs new DirectX primitives",{},{"id":575,"data":1246,"type":544,"tunes":1248},{"text":1247},"Modern GPUs already contain specialized hardware for matrix operations used by machine learning. The problem for a game developer is not only whether that hardware exists, but how to access it efficiently from a real-time graphics pipeline.",{},{"id":580,"data":1250,"type":544,"tunes":1252},{"text":1251},"Microsoft first explored this with Cooperative Vector support. In 2026, that work evolved into a broader DirectX Linear Algebra design that supports both vector-matrix and matrix-matrix operations.",{},{"id":585,"data":1254,"type":544,"tunes":1256},{"text":1255},"That matters because different neural-rendering jobs have different shapes. A tiny model evaluating material behavior per pixel is not the same workload as a large super-resolution or denoising graph.",{},{"id":590,"data":1258,"type":572,"tunes":1260},{"text":1259,"level":47},"The Two-Level DirectX ML Model",{},{"id":595,"data":1262,"type":612,"tunes":1275},{"steps":1263,"title":1274,"orientation":611},[1264,1267,1269,1272],{"label":1265,"description":1266},"1. Shader-level ML","Small neural or linear-algebra workloads execute directly from HLSL alongside traditional shader code.",{"label":602,"description":1268},"The shader can request hardware-accelerated vector and matrix operations instead of manually implementing every ML primitive.",{"label":1270,"description":1271},"3. Model-level ML","Larger neural networks are represented as complete computation graphs rather than hand-written shader fragments.",{"label":608,"description":1273},"Microsoft's planned compiler path analyzes and lowers those graphs into optimized GPU workloads integrated with D3D12 queues and command lists.","Two ways ML can enter the DirectX graphics pipeline",{},{"id":615,"data":1277,"type":552,"tunes":1280},{"body":1278,"title":1279,"variant":619},"\u003Cstrong>DX Linear Algebra:\u003C\u002Fstrong> put small ML math inside the shader.\u003Cbr>\u003Cstrong>Compute Graph Compiler:\u003C\u002Fstrong> bring a larger ML model into the engine as a graph.","The simple difference",{},{"id":622,"data":1282,"type":572,"tunes":1284},{"text":1283,"level":47},"What DX Linear Algebra actually gives a shader",{},{"id":627,"data":1286,"type":544,"tunes":1288},{"text":1287},"Traditional HLSL is built around graphics and compute operations. Neural workloads rely heavily on linear algebra: vectors, matrices, multiplication, accumulation and data layouts optimized for those operations.",{},{"id":632,"data":1290,"type":544,"tunes":1292},{"text":1291},"The Shader Model 6.10 preview adds first-class matrix APIs so developers can express those workloads more directly and let the driver map them to specialized hardware.",{},{"id":637,"data":1294,"type":544,"tunes":1296},{"text":1295},"Microsoft's April 2026 preview explicitly describes this as a unified path for neural rendering, ML and image-processing workloads rather than a graphics-only feature.",{},{"id":642,"data":1298,"type":666,"tunes":1315},{"rows":1299,"title":1309,"layout":658,"columns":1310},[1300,1303,1306],{"id":646,"label":1301,"values":1302},"Typical work",[13,13],{"id":650,"label":1304,"values":1305},"Hardware path",[13,13],{"id":654,"label":1307,"values":1308},"Developer expression",[13,13],"Traditional shader math vs ML-oriented shader math",[1311,1313],{"id":661,"label":1312},"Traditional shader focus",{"id":664,"label":1314},"ML-oriented focus",{},{"id":669,"data":1317,"type":572,"tunes":1319},{"text":1318,"level":47},"Why Cooperative Vector was only the beginning",{},{"id":674,"data":1321,"type":544,"tunes":1323},{"text":1322},"Cooperative Vector allowed shader threads to request vector-matrix work that drivers could map to specialized hardware. That was useful for highly parallel per-pixel workloads.",{},{"id":679,"data":1325,"type":544,"tunes":1327},{"text":1326},"Microsoft later concluded that many important ML workloads need more than vector-matrix operations. Super resolution, denoising, temporal reconstruction, larger image models and general inference can require matrix-matrix operations and shared work across many threads.",{},{"id":684,"data":1329,"type":544,"tunes":1331},{"text":1330},"DX Linear Algebra therefore broadens the model instead of treating Cooperative Vector as the final abstraction.",{},{"id":689,"data":1333,"type":572,"tunes":1335},{"text":1334,"level":47},"What can actually run inside a shader?",{},{"id":694,"data":1337,"type":544,"tunes":1339},{"text":1338},"The most interesting shader-level ML workloads are small enough to execute close to the graphics data they operate on.",{},{"id":699,"data":1341,"type":658,"tunes":1361},{"content":1342,"stretched":720,"withHeadings":15},[1343,1346,1349,1352,1355,1358],[1344,1345],"Workload","Why shader-level ML fits",[1347,1348],"Neural texture compression","A small network can reconstruct texture information near the point where the shader needs it",[1350,1351],"Neural material evaluation","A learned function can replace or augment expensive hand-authored material math",[1353,1354],"Neural radiance caching","Per-pixel or local inference can estimate lighting information from learned scene behavior",[1356,1357],"Small denoising\u002Freconstruction kernels","ML operations can sit directly beside the rendering stage they improve",[1359,1360],"Image-processing inference","Matrix operations can be embedded into GPU processing without a separate external runtime",{},{"id":723,"data":1363,"type":572,"tunes":1365},{"text":1364,"level":47},"Why this matters for texture memory",{},{"id":728,"data":1367,"type":544,"tunes":1369},{"text":1368},"One of Microsoft's recurring examples is neural texture compression.",{},{"id":733,"data":1371,"type":544,"tunes":1373},{"text":1372},"Instead of storing every texture channel at conventional fidelity, a game can store a more compact representation and use a small neural network to reconstruct detail during rendering.",{},{"id":738,"data":1375,"type":544,"tunes":1377},{"text":1376},"That trades some GPU inference work for lower storage or memory pressure. The exact benefit depends on the technique and hardware, but the architectural change is important: some visual detail can become computation instead of stored data.",{},{"id":743,"data":1379,"type":749,"tunes":1384},{"url":1380,"title":1381,"excerpt":1382,"ctaLabel":1383},"https:\u002F\u002Ffigure.rocks\u002Fblog\u002Fvram-usage-is-not-vram-requirement-why-a-full-memory-meter-does-not-tell-the-whole-story","VRAM Usage Is Not VRAM Requirement: Why a Full Memory Meter Does Not Tell the Whole Story","A practical guide to VRAM capacity, residency budgets, working sets and why memory pressure is more complicated than one usage number.","Read the VRAM guide",{},{"id":752,"data":1386,"type":572,"tunes":1388},{"text":1387,"level":47},"Why full models need a different path",{},{"id":757,"data":1390,"type":544,"tunes":1392},{"text":1391},"Hand-writing a few matrix operations in HLSL is practical for small neural functions. It becomes much less practical when the workload is a complete modern model with many layers, dependencies and intermediate tensors.",{},{"id":762,"data":1394,"type":544,"tunes":1396},{"text":1395},"Microsoft's DirectX Compute Graph Compiler is designed for this larger class of workload.",{},{"id":767,"data":1398,"type":544,"tunes":1400},{"text":1399},"Instead of rewriting the model as custom shader code, the compiler can accept a computation graph, analyze the whole graph, plan memory, fuse operations and lower the result into GPU work that integrates with DirectX 12.",{},{"id":772,"data":1402,"type":572,"tunes":1404},{"text":1403,"level":47},"The Shader-or-Graph Boundary",{},{"id":777,"data":1406,"type":666,"tunes":1429},{"rows":1407,"title":1423,"layout":658,"columns":1424},[1408,1411,1414,1417,1420],{"id":781,"label":1409,"values":1410},"Model size",[13,13],{"id":785,"label":1412,"values":1413},"Execution style",[13,13],{"id":789,"label":1415,"values":1416},"Authoring",[13,13],{"id":793,"label":1418,"values":1419},"Optimization scope",[13,13],{"id":797,"label":1421,"values":1422},"Typical use",[13,13],"When the ML workload belongs in HLSL vs a model compiler",[1425,1427],{"id":803,"label":1426},"Shader-level path",{"id":806,"label":1428},"Model-level path",{},{"id":810,"data":1431,"type":572,"tunes":1433},{"text":1432,"level":47},"Why cross-vendor support matters more than another AI feature",{},{"id":815,"data":1435,"type":544,"tunes":1437},{"text":1436},"AMD, Intel, NVIDIA and Qualcomm expose different GPU architectures and different forms of dedicated matrix acceleration.",{},{"id":820,"data":1439,"type":544,"tunes":1441},{"text":1440},"A DirectX abstraction gives Microsoft and the driver vendors a place to translate common HLSL or graph-level ML into the correct hardware path.",{},{"id":825,"data":1443,"type":544,"tunes":1445},{"text":1444},"That does not make every GPU equally fast, but it can reduce the need for a game engine to implement a completely different neural-rendering API for every vendor.",{},{"id":830,"data":1447,"type":552,"tunes":1450},{"body":1448,"title":1449,"variant":559},"The interesting part is not that DirectX can “run AI.” GPUs already could. The platform change is that \u003Cstrong>ML becomes expressible through a common graphics API and toolchain\u003C\u002Fstrong>.","Portability is the real platform story",{},{"id":836,"data":1452,"type":572,"tunes":1454},{"text":1453,"level":47},"What hardware supports the current preview?",{},{"id":841,"data":1456,"type":544,"tunes":1458},{"text":1457},"The answer depends on the specific Linear Algebra operation and preview driver.",{},{"id":846,"data":1460,"type":544,"tunes":1462},{"text":1461},"Microsoft's Agility SDK 1.721 preview support table lists LinAlg VectorAccumulate for AMD Radeon RX 9000 Series hardware, Intel Xe2-or-newer hardware through an upcoming driver, and NVIDIA RTX hardware through the supported preview path.",{},{"id":851,"data":1464,"type":544,"tunes":1466},{"text":1465},"This is preview-era support, not a universal retail guarantee. Hardware and driver support can change before finalization.",{},{"id":856,"data":1468,"type":572,"tunes":1470},{"text":1469,"level":47},"Neural rendering is becoming infrastructure",{},{"id":861,"data":1472,"type":544,"tunes":1474},{"text":1473},"DLSS, FSR and other neural graphics technologies are often discussed as branded features visible in a game's settings menu.",{},{"id":866,"data":1476,"type":544,"tunes":1478},{"text":1477},"DirectX Linear Algebra points to a deeper change. The neural operation can become an internal implementation detail inside the renderer rather than a single optional post-processing feature.",{},{"id":871,"data":1480,"type":544,"tunes":1482},{"text":1481},"A developer could use ML for textures, materials, lighting, reconstruction or other local functions without exposing each one as a consumer-facing AI toggle.",{},{"id":876,"data":1484,"type":749,"tunes":1489},{"url":1485,"title":1486,"excerpt":1487,"ctaLabel":1488},"https:\u002F\u002Ffigure.rocks\u002Fblog\u002Fdlss-5-is-not-just-upscaling-what-3d-guided-neural-rendering-actually-changes","DLSS 5 Is Not Just Upscaling: What 3D-Guided Neural Rendering Actually Changes","How neural rendering is moving beyond upscaling and generated frames into material and lighting reconstruction inside the graphics pipeline.","Read the DLSS 5 neural-rendering guide",{},{"id":884,"data":1491,"type":572,"tunes":1493},{"text":1492,"level":47},"The Neural Rendering Stack is becoming layered",{},{"id":889,"data":1495,"type":612,"tunes":1513},{"steps":1496,"title":1512,"orientation":611},[1497,1500,1503,1506,1509],{"label":1498,"description":1499},"Traditional engine work","Simulation, geometry, visibility and base rendering remain standard engine responsibilities.",{"label":1501,"description":1502},"Inline neural shaders","Small learned functions reconstruct textures, materials or lighting inside HLSL.",{"label":1504,"description":1505},"Larger ML graphs","Full reconstruction or inference models execute through a graph-level DirectX path.",{"label":1507,"description":1508},"Vendor hardware mapping","Drivers map common DirectX operations onto the GPU's specialized AI and matrix units.",{"label":1510,"description":1511},"PIX visibility","Graphics and ML work can be profiled together instead of living in separate opaque runtimes.","A possible future DirectX game pipeline",{},{"id":910,"data":1515,"type":572,"tunes":1517},{"text":1516,"level":47},"Why unified profiling matters",{},{"id":915,"data":1519,"type":544,"tunes":1521},{"text":1520},"A neural workload that improves image quality can still damage a game if it unexpectedly consumes frame time, memory bandwidth or VRAM.",{},{"id":920,"data":1523,"type":544,"tunes":1525},{"text":1524},"Microsoft explicitly includes unified PIX visibility as part of the Compute Graph Compiler direction. That matters because developers need to see graphics and ML work in the same frame capture.",{},{"id":925,"data":1527,"type":544,"tunes":1529},{"text":1528},"If neural rendering becomes infrastructure, it must be measurable like any other rendering stage.",{},{"id":930,"data":1531,"type":572,"tunes":1533},{"text":1532,"level":47},"This does not mean every shader will become a neural network",{},{"id":935,"data":1535,"type":544,"tunes":1537},{"text":1536},"Traditional shader math remains efficient, deterministic and easy to reason about for many workloads.",{},{"id":940,"data":1539,"type":544,"tunes":1541},{"text":1540},"A neural function makes sense when it can approximate or reconstruct something expensive more efficiently, compress data, or produce quality that would otherwise require too much conventional compute or memory.",{},{"id":945,"data":1543,"type":544,"tunes":1545},{"text":1544},"The right architecture will remain hybrid.",{},{"id":950,"data":1547,"type":572,"tunes":1549},{"text":1548,"level":47},"The Neural Shader Value Test",{},{"id":955,"data":1551,"type":612,"tunes":1572},{"steps":1552,"title":1571,"orientation":611},[1553,1556,1559,1562,1565,1568],{"label":1554,"description":1555},"1. Identify the expensive traditional operation","What compute, bandwidth, memory or storage cost are you trying to replace?",{"label":1557,"description":1558},"2. Define the neural substitute","What can a small model reconstruct, predict or compress?",{"label":1560,"description":1561},"3. Measure inference cost","The ML model itself consumes GPU time, memory and bandwidth.",{"label":1563,"description":1564},"4. Measure quality stability","Look for temporal artifacts, reconstruction errors and failure cases.",{"label":1566,"description":1567},"5. Measure net savings","The technique is useful only if the avoided traditional cost is worth the added ML cost.",{"label":1569,"description":1570},"6. Test across vendors","A DirectX abstraction helps portability, but real hardware performance still differs.","When does putting ML into the rendering pipeline actually make sense?",{},{"id":979,"data":1574,"type":572,"tunes":1576},{"text":1575,"level":47},"What this means for gamers",{},{"id":984,"data":1578,"type":544,"tunes":1580},{"text":1579},"Gamers may never see a “DX Linear Algebra” switch in a graphics menu.",{},{"id":989,"data":1582,"type":544,"tunes":1584},{"text":1583},"The likely impact is indirect: smaller texture footprints, better lighting reconstruction, more efficient neural graphics, or new visual techniques that become practical because the engine can access matrix acceleration through a standard path.",{},{"id":994,"data":1586,"type":544,"tunes":1588},{"text":1587},"The feature is more important as infrastructure than as a brand.",{},{"id":999,"data":1590,"type":572,"tunes":1592},{"text":1591,"level":47},"What would change this answer?",{},{"id":1004,"data":1594,"type":544,"tunes":1596},{"text":1595},"The biggest uncertainty is the final retail shape of these APIs. DX Linear Algebra remains in preview, and the Compute Graph Compiler has not yet reached broad retail availability.",{},{"id":1009,"data":1598,"type":544,"tunes":1600},{"text":1599},"Final hardware support, API details, compiler behavior and engine adoption may change before these systems become normal shipping-game infrastructure.",{},{"id":1014,"data":1602,"type":544,"tunes":1604},{"text":1603},"If major engines adopt the abstractions directly, neural shaders could become much more common without individual game teams implementing every technique from scratch.",{},{"id":1019,"data":1606,"type":572,"tunes":1608},{"text":1607,"level":47},"Limitations",{},{"id":1024,"data":1610,"type":544,"tunes":1612},{"text":1611},"This article explains Microsoft's documented DirectX architecture and preview APIs. It does not claim that DX Linear Algebra currently improves performance in every game or that the Compute Graph Compiler is a finished retail product.",{},{"id":1029,"data":1614,"type":544,"tunes":1616},{"text":1615},"Microsoft's examples describe capability and intended use. Real benefits depend on the model, engine integration, GPU architecture, drivers and workload.",{},{"id":1034,"data":1618,"type":572,"tunes":1620},{"text":1619,"level":47},"Conclusion",{},{"id":1039,"data":1622,"type":544,"tunes":1624},{"text":1623},"The important DirectX change is not another checkbox called AI.",{},{"id":1044,"data":1626,"type":544,"tunes":1628},{"text":1627},"It is that machine-learning math is moving into the graphics programming model itself. Small neural functions can sit inside shaders, larger models can move toward graph-level compilation, and the same DirectX toolchain can expose those workloads across multiple GPU vendors.",{},{"id":1049,"data":1630,"type":544,"tunes":1632},{"text":1631},"That is how neural rendering stops being one branded feature and starts becoming part of the rendering infrastructure.",{},{"id":1054,"data":1634,"type":572,"tunes":1636},{"text":1635,"level":47},"FAQ",{},{"id":1059,"data":1638,"type":1059,"tunes":1659},{"items":1639,"title":1658},[1640,1643,1646,1649,1652,1655],{"id":1063,"answer":1641,"question":1642},"It is a DirectX\u002FHLSL feature set for hardware-accelerated vector and matrix operations used by machine learning, neural rendering and image-processing workloads.","What is DirectX Linear Algebra?",{"id":1067,"answer":1644,"question":1645},"A useful plain-English description is a shader that includes a learned neural function or ML inference step as part of its graphics work.","What is a neural shader?",{"id":1071,"answer":1647,"question":1648},"As of September 2026 it remains in the Shader Model 6.10 \u002F Agility SDK preview path.","Is DX Linear Algebra already a normal retail DirectX feature?",{"id":1075,"answer":1650,"question":1651},"It is Microsoft's announced model-level ML compiler API intended to take larger computation graphs and lower them into optimized GPU workloads integrated with D3D12.","What is the DirectX Compute Graph Compiler?",{"id":1079,"answer":1653,"question":1654},"Small functions fit shader-level authoring well, while large models benefit from whole-graph optimization, memory planning and operator fusion.","Why not run every ML model directly in HLSL?",{"id":1083,"answer":1656,"question":1657},"Not directly. DirectX provides lower-level infrastructure that vendors and developers can use for neural graphics. Branded technologies can still implement their own models and integration strategies.","Will this replace DLSS or FSR?","DirectX Linear Algebra and neural shaders",{},{"id":1089,"data":1661,"type":572,"tunes":1663},{"text":1662,"level":47},"Glossary",{},{"id":1094,"data":1665,"type":1094,"tunes":1688},{"title":1666,"entries":1667},"Key DirectX ML terms",[1668,1671,1674,1676,1679,1682,1685],{"term":1669,"anchor":1100,"definition":1670},"DX Linear Algebra","DirectX\u002FHLSL APIs for accelerated vector and matrix operations intended for neural rendering, ML and image-processing workloads.",{"term":1672,"anchor":1104,"definition":1673},"Cooperative Vector","An earlier DirectX approach for accelerated vector-matrix operations inside shaders that helped establish the shader-level neural-rendering path.",{"term":1107,"anchor":1108,"definition":1675},"The preview shader-model generation containing the current DirectX Linear Algebra matrix APIs.",{"term":1677,"anchor":1112,"definition":1678},"DirectX Compute Graph Compiler","Microsoft's announced compiler API for optimizing and executing larger ML computation graphs as native DirectX GPU workloads.",{"term":1680,"anchor":1116,"definition":1681},"Neural shader","A practical term for a shader that performs learned inference or neural computation as part of graphics processing.",{"term":1683,"anchor":1120,"definition":1684},"Shader-or-Graph Boundary","A Figure Rocks model for deciding whether an ML workload belongs as small inline shader math or as a larger model-level computation graph.",{"term":1686,"anchor":1124,"definition":1687},"Neural Shader Value Test","A Figure Rocks workflow for deciding whether the traditional compute or memory cost avoided by a neural technique is worth its inference cost and quality trade-offs.",{},{"id":1128,"data":1690,"type":572,"tunes":1692},{"text":1691,"level":47},"Primary sources",{},{"id":1133,"data":1694,"type":1140,"tunes":1699},{"link":1135,"meta":1695},{"image":1696,"title":1697,"description":1698},{"url":13},"Microsoft DirectX — Evolving DirectX for the ML Era on Windows","Official GDC 2026 architecture overview covering shader-level ML, DX Linear Algebra and the DirectX Compute Graph Compiler.",{},{"id":1143,"data":1701,"type":1140,"tunes":1706},{"link":1145,"meta":1702},{"image":1703,"title":1704,"description":1705},{"url":13},"Microsoft DirectX — D3D12 LinAlg Matrix Preview","Official April 2026 preview explaining the unified Linear Algebra APIs, matrix operations and neural-rendering motivation.",{},{"id":1152,"data":1708,"type":1140,"tunes":1713},{"link":1154,"meta":1709},{"image":1710,"title":1711,"description":1712},{"url":13},"Microsoft DirectX — Agility SDK 1.721 Preview","Official May 2026 release documenting Shader Model 6.10 Linear Algebra updates and preview hardware support.",{},{"id":1161,"data":1715,"type":1140,"tunes":1720},{"link":1163,"meta":1716},{"image":1717,"title":1718,"description":1719},{"url":13},"Microsoft Game Dev — GDC 2026: Evolving DirectX for the ML Era","Official Microsoft Game Dev summary explaining shader-level and model-level ML and the role of the Compute Graph Compiler.",{},{"id":1170,"data":1722,"type":1140,"tunes":1727},{"link":1172,"meta":1723},{"image":1724,"title":1725,"description":1726},{"url":13},"Microsoft DirectX — D3D12 Cooperative Vector","Official background on hardware-accelerated vector\u002Fmatrix operations and neural rendering directly from shader threads.",{},"2.31.6","DirectX is moving beyond traditional graphics shaders. Microsoft is adding hardware-accelerated linear algebra directly to HLSL and a separate path for larger ML models, laying the foundation for neural textures, learned materials, neural lighting and other AI-driven rendering 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Técnicamente, son pequeños soportes de datos dentro de figuras de plástico moldeado. En la práctica, se sitúan en algún punto entre juguete, objeto de colección y dispositivo de interfaz. Escanearlos en una consola es solo una parte de su ciclo de vida. Observado a lo largo de los años, muchos propietarios los tratan como objetos con su propio potencial creativo.","2026-02-27T16:36:00.000Z",{"id":2072,"slug":2073,"title":2074,"excerpt":2075,"featuredImage":14,"publishedAt":2076},"127","motion-clarity-why-blur-happens-and-the-fixes-that-actually-work","Claridad de movimiento: Por qué ocurre el desenfoque y las soluciones que realmente funcionan","El desenfoque de movimiento no es solo un ajuste — es sincronización más persistencia. Aprende el orden práctico: modo correcto, frecuencia de actualización correcta, luego los pocos arreglos de claridad que importan.","2026-02-20T11:00:00.000Z",{"id":2078,"slug":2079,"title":2080,"excerpt":2081,"featuredImage":14,"publishedAt":2082},"207","120hz-feels-worse-the-diagnosis-checklist-wrong-mode-vrr-range-caps","¿Los 120Hz se sienten peor? Lista de verificación de diagnóstico (Modo incorrecto, rango de VRR, límites)","Una mayor frecuencia de actualización puede exponer la inestabilidad. Usa esta lista de verificación para diagnosticar por qué los 120 Hz se sienten peor: modo incorrecto, ruta de actualización incorrecta, problemas de rango de VRR o falta de límites.","2026-02-20T20:30:00.000Z",{"id":2084,"slug":2085,"title":2086,"excerpt":2087,"featuredImage":2088,"publishedAt":2089},"444","pubg-ally-shows-why-ai-teammates-need-two-brains-fast-reflexes-and-slow-reasoning","PUBG Ally muestra por qué los compañeros de equipo de IA necesitan dos cerebros: reflejos rápidos y razonamiento lento","Un modelo de lenguaje puede entender tácticas y la intención del jugador, pero no debería controlar directamente cada movimiento y reacción de combate. PUBG Ally muestra una arquitectura más práctica: control rápido mediante árboles de comportamiento para acciones reflejas, combinado con un modelo de lenguaje pequeño para planificación, coordinación y conversación natural.","\u002Fuploads\u002F2026\u002F09\u002Fpubg-ally-shows-why-ai-teammates-need-two-brains-fast-reflexes-and-slow-reasoning-1790376777825-bi2zzb.webp","2026-09-25T14:51:00.000Z",{"id":2091,"slug":2092,"title":2093,"excerpt":2094,"featuredImage":2095,"publishedAt":2096},"451","windows-auto-sr-is-not-dlss-how-npu-upscaling-works-without-game-integration","Windows Auto SR no es DLSS: cómo funciona el escalado por NPU sin integración en el juego","Windows Auto SR puede reescalar juegos compatibles sin integración de DLSS, FSR o XeSS. En lugar de ejecutar el modelo de reconstrucción dentro del juego en la GPU, Windows utiliza la NPU para reconstruir una imagen de mayor resolución a partir de un renderizado de menor resolución.","\u002Fuploads\u002F2026\u002F09\u002Fwindows-auto-sr-is-not-dlss-how-npu-upscaling-works-without-game-integration-1790406942266-77ihme.webp","2026-09-26T03:14:00.000Z",{"id":2098,"slug":2099,"title":2100,"excerpt":2101,"featuredImage":2102,"publishedAt":2103},"445","amd-fsr-redstone-is-not-just-fsr-4-upscaling-frame-generation-ray-regeneration-and-radiance-caching-explained","AMD FSR Redstone no es solo FSR 4: escalado, generación de fotogramas, regeneración de rayos y caché de radiancia explicados","El nombre de FSR de AMD cambió porque FSR ya no es una sola función. Redstone es ahora una suite de renderizado neuronal con tecnologías separadas para el escalado, la generación de fotogramas, la reconstrucción de trazado de rayos y la iluminación global aprendida.","\u002Fuploads\u002F2026\u002F09\u002Famd-fsr-redstone-is-not-just-fsr-4-upscaling-frame-generation-ray-regeneration-and-radiance-caching-explained-1790377725715-arwmri.webp","2026-09-25T15:07:00.000Z",{"id":2105,"slug":2106,"title":2107,"excerpt":2108,"featuredImage":14,"publishedAt":2109},"241","overdrive-tuning-the-clean-way-to-reduce-blur-without-ghosting","Ajuste de Overdrive: la forma limpia de reducir el desenfoque sin ghosting","El Overdrive puede mejorar la claridad o añadir halos feos. Usa este método sencillo para elegir el ajuste intermedio limpio que reduce el desenfoque sin artefactos de ghosting.","2026-02-21T03:30:00.000Z","fallback",[],[]]