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Microsoft sta aggiungendo primitive di machine learning direttamente a HLSL e un secondo percorso per eseguire grafi ML più grandi all'interno dell'ecosistema DirectX. Questo cambia dove il rendering neurale può risiedere nei futuri giochi per 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\">Risposta diretta\u003C\u002Fstrong>\u003Cdiv class=\"text-gray-700 dark:text-gray-200\">\u003Cstrong>DirectX sta diventando una piattaforma grafica con capacità ML.\u003C\u002Fstrong> Piccoli carichi di lavoro neurali possono essere eseguiti direttamente all'interno degli shader tramite DX Linear Algebra, mentre modelli più grandi sono presi di mira dal DirectX Compute Graph Compiler. L'obiettivo è consentire ai motori di gioco di utilizzare l'hardware AI della GPU senza costruire un percorso separato specifico per ogni fornitore per ogni tecnica.\u003C\u002Fdiv>\u003C\u002Faside>\n\u003Caside class=\"editorjs-callout editorjs-callout--note my-6 rounded-xl border p-5 border-gray-300 bg-gray-50 dark:border-gray-700 dark:bg-gray-900\u002F40\" role=\"note\">\u003Cstrong class=\"block mb-2 text-gray-900 dark:text-gray-100\">Stato attuale\u003C\u002Fstrong>\u003Cdiv class=\"text-gray-700 dark:text-gray-200\">A settembre 2026, \u003Cstrong>DX Linear Algebra è ancora una tecnologia in anteprima\u003C\u002Fstrong> nel percorso di anteprima Shader Model 6.10 \u002F Agility SDK. Microsoft ha annunciato il DirectX Compute Graph Compiler per l'anteprima privata piuttosto che come una funzionalità retail ampiamente distribuita. Questo articolo spiega l'architettura e la direzione, non afferma che ogni gioco attuale possa usarla oggi.\u003C\u002Fdiv>\u003C\u002Faside>\n\u003Cnav class=\"editorjs-toc\" data-editorjs-toc=\"true\" aria-label=\"Contenuti\">\u003Cstrong class=\"editorjs-toc__title\">Contenuti\u003C\u002Fstrong>\u003Col class=\"editorjs-toc__list editorjs-toc__list--depth-0\">\u003Cli class=\"editorjs-toc__item\">\u003Ca href=\"#section-5\" class=\"editorjs-toc__link\">Perché il rendering neurale necessita di nuove primitive DirectX\u003C\u002Fa>\u003C\u002Fli>\u003Cli class=\"editorjs-toc__item\">\u003Ca href=\"#section-9\" class=\"editorjs-toc__link\">Il modello ML DirectX a due livelli\u003C\u002Fa>\u003C\u002Fli>\u003Cli class=\"editorjs-toc__item\">\u003Ca href=\"#section-12\" class=\"editorjs-toc__link\">Cosa dà effettivamente DX Linear Algebra a uno shader\u003C\u002Fa>\u003C\u002Fli>\u003Cli class=\"editorjs-toc__item\">\u003Ca href=\"#section-17\" class=\"editorjs-toc__link\">Perché Cooperative Vector era solo l'inizio\u003C\u002Fa>\u003C\u002Fli>\u003Cli class=\"editorjs-toc__item\">\u003Ca href=\"#section-21\" class=\"editorjs-toc__link\">Cosa può effettivamente essere eseguito all'interno di uno shader?\u003C\u002Fa>\u003C\u002Fli>\u003Cli class=\"editorjs-toc__item\">\u003Ca href=\"#section-24\" class=\"editorjs-toc__link\">Perché questo è importante per la memoria delle texture\u003C\u002Fa>\u003C\u002Fli>\u003Cli class=\"editorjs-toc__item\">\u003Ca href=\"#section-29\" class=\"editorjs-toc__link\">Perché i modelli completi necessitano di un percorso diverso\u003C\u002Fa>\u003C\u002Fli>\u003Cli class=\"editorjs-toc__item\">\u003Ca href=\"#section-33\" class=\"editorjs-toc__link\">Il confine tra Shader e Grafo\u003C\u002Fa>\u003C\u002Fli>\u003Cli class=\"editorjs-toc__item\">\u003Ca href=\"#section-35\" class=\"editorjs-toc__link\">Perché il supporto cross-vendor conta più di un'altra funzionalità AI\u003C\u002Fa>\u003C\u002Fli>\u003Cli class=\"editorjs-toc__item\">\u003Ca href=\"#section-40\" class=\"editorjs-toc__link\">Quale hardware supporta l'anteprima attuale?\u003C\u002Fa>\u003C\u002Fli>\u003Cli class=\"editorjs-toc__item\">\u003Ca href=\"#section-44\" class=\"editorjs-toc__link\">Il rendering neurale sta diventando infrastruttura\u003C\u002Fa>\u003C\u002Fli>\u003Cli class=\"editorjs-toc__item\">\u003Ca href=\"#section-49\" class=\"editorjs-toc__link\">Lo stack di rendering neurale sta diventando stratificato\u003C\u002Fa>\u003C\u002Fli>\u003Cli class=\"editorjs-toc__item\">\u003Ca href=\"#section-51\" class=\"editorjs-toc__link\">Perché la profilazione unificata è importante\u003C\u002Fa>\u003C\u002Fli>\u003Cli class=\"editorjs-toc__item\">\u003Ca href=\"#section-55\" class=\"editorjs-toc__link\">Questo non significa che ogni shader diventerà una rete neurale\u003C\u002Fa>\u003C\u002Fli>\u003Cli class=\"editorjs-toc__item\">\u003Ca href=\"#section-59\" class=\"editorjs-toc__link\">Il test del valore dello shader neurale\u003C\u002Fa>\u003C\u002Fli>\u003Cli class=\"editorjs-toc__item\">\u003Ca href=\"#section-61\" class=\"editorjs-toc__link\">Cosa significa questo per i giocatori\u003C\u002Fa>\u003C\u002Fli>\u003Cli class=\"editorjs-toc__item\">\u003Ca href=\"#section-65\" class=\"editorjs-toc__link\">Cosa cambierebbe questa risposta?\u003C\u002Fa>\u003C\u002Fli>\u003Cli class=\"editorjs-toc__item\">\u003Ca href=\"#section-69\" class=\"editorjs-toc__link\">Limitazioni\u003C\u002Fa>\u003C\u002Fli>\u003Cli class=\"editorjs-toc__item\">\u003Ca href=\"#section-72\" class=\"editorjs-toc__link\">Conclusione\u003C\u002Fa>\u003C\u002Fli>\u003Cli class=\"editorjs-toc__item\">\u003Ca href=\"#section-76\" class=\"editorjs-toc__link\">FAQ\u003C\u002Fa>\u003C\u002Fli>\u003Cli class=\"editorjs-toc__item\">\u003Ca href=\"#section-78\" class=\"editorjs-toc__link\">Glossario\u003C\u002Fa>\u003C\u002Fli>\u003Cli class=\"editorjs-toc__item\">\u003Ca href=\"#section-80\" class=\"editorjs-toc__link\">Fonti primarie\u003C\u002Fa>\u003C\u002Fli>\u003C\u002Fol>\u003C\u002Fnav>\n\u003Ch2 id=\"section-5\">Perché il rendering neurale necessita di nuove primitive DirectX\u003C\u002Fh2>\n\u003Cp>Le GPU moderne contengono già hardware specializzato per operazioni matriciali utilizzate dal machine learning. Il problema per uno sviluppatore di giochi non è solo se quell'hardware esiste, ma come accedervi in modo efficiente da una pipeline grafica in tempo reale.\u003C\u002Fp>\n\u003Cp>Microsoft ha esplorato questo per la prima volta con il supporto Cooperative Vector. Nel 2026, quel lavoro si è evoluto in un design DirectX Linear Algebra più ampio che supporta sia operazioni vettore-matrice che matrice-matrice.\u003C\u002Fp>\n\u003Cp>Questo è importante perché diversi lavori di rendering neurale hanno forme diverse. Un piccolo modello che valuta il comportamento del materiale per pixel non è lo stesso carico di lavoro di un grande grafo di super-risoluzione o denoising.\u003C\u002Fp>\n\u003Ch2 id=\"section-9\">Il modello ML DirectX a due livelli\u003C\u002Fh2>\n\u003Csection class=\"editorjs-process my-6\">\u003Ch3 class=\"mb-3 text-lg font-semibold\">Due modi in cui il ML può entrare nella pipeline grafica 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 livello di shader\u003C\u002Fdiv>\u003Cdiv class=\"mt-1 text-sm text-gray-600 dark:text-gray-300\">Piccoli carichi di lavoro neurali o di algebra lineare vengono eseguiti direttamente da HLSL insieme al codice shader tradizionale.\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\">Lo shader può richiedere operazioni vettoriali e matriciali accelerate dall'hardware invece di implementare manualmente ogni primitiva 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 livello di modello\u003C\u002Fdiv>\u003Cdiv class=\"mt-1 text-sm text-gray-600 dark:text-gray-300\">Reti neurali più grandi sono rappresentate come grafi di calcolo completi piuttosto che come frammenti di shader scritti 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\">Il percorso del compilatore pianificato da Microsoft analizza e abbassa quei grafi in carichi di lavoro GPU ottimizzati integrati con code e liste di comandi 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 semplice differenza\u003C\u002Fstrong>\u003Cdiv class=\"text-gray-700 dark:text-gray-200\">\u003Cstrong>DX Linear Algebra:\u003C\u002Fstrong> inserisci piccola matematica ML all'interno dello shader.\u003Cbr>\u003Cstrong>Compute Graph Compiler:\u003C\u002Fstrong> porta un modello ML più grande nel motore come grafo.\u003C\u002Fdiv>\u003C\u002Faside>\n\u003Ch2 id=\"section-12\">Cosa dà effettivamente DX Linear Algebra a uno shader\u003C\u002Fh2>\n\u003Cp>L'HLSL tradizionale è costruito attorno a operazioni grafiche e di calcolo. I carichi di lavoro neurali si basano fortemente sull'algebra lineare: vettori, matrici, moltiplicazione, accumulo e layout di dati ottimizzati per quelle operazioni.\u003C\u002Fp>\n\u003Cp>L'anteprima di Shader Model 6.10 aggiunge API matriciali di prima classe in modo che gli sviluppatori possano esprimere quei carichi di lavoro più direttamente e lasciare che il driver li mappi su hardware specializzato.\u003C\u002Fp>\n\u003Cp>L'anteprima di Microsoft di aprile 2026 descrive esplicitamente questo come un percorso unificato per il rendering neurale, il ML e i carichi di lavoro di elaborazione delle immagini piuttosto che una funzionalità solo grafica.\u003C\u002Fp>\n\u003Csection class=\"editorjs-comparison my-6\">\u003Ch3 class=\"mb-3 text-lg font-semibold\">Matematica shader tradizionale vs matematica shader orientata al 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\">Focus shader tradizionale\u003C\u002Fth>\u003Cth class=\"border border-gray-300 bg-gray-50 px-4 py-3 text-left font-semibold dark:border-gray-700 dark:bg-gray-900\">Focus orientato al 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\">Lavoro tipico\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\">Percorso 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\">Espressione dello sviluppatore\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\">Perché Cooperative Vector era solo l'inizio\u003C\u002Fh2>\n\u003Cp>Cooperative Vector consentiva ai thread degli shader di richiedere lavoro vettore-matrice che i driver potevano mappare su hardware specializzato. Ciò era utile per carichi di lavoro altamente paralleli per pixel.\u003C\u002Fp>\n\u003Cp>Microsoft in seguito ha concluso che molti carichi di lavoro ML importanti richiedono più delle operazioni vettore-matrice. Super risoluzione, denoising, ricostruzione temporale, modelli di immagini più grandi e inferenza generale possono richiedere operazioni matrice-matrice e lavoro condiviso tra molti thread.\u003C\u002Fp>\n\u003Cp>DX Linear Algebra quindi amplia il modello invece di trattare Cooperative Vector come l'astrazione finale.\u003C\u002Fp>\n\u003Ch2 id=\"section-21\">Cosa può effettivamente essere eseguito all'interno di uno shader?\u003C\u002Fh2>\n\u003Cp>I carichi di lavoro ML a livello di shader più interessanti sono abbastanza piccoli da essere eseguiti vicino ai dati grafici su cui operano.\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\">Carico di lavoro\u003C\u002Fth>\u003Cth class=\"border border-gray-300 px-4 py-2 text-left font-semibold\">Perché l'ML a livello di shader è adatto\u003C\u002Fth>\u003C\u002Ftr>\u003C\u002Fthead>\u003Ctbody>\u003Ctr>\u003Ctd class=\"border border-gray-300 px-4 py-2\">Compressione neurale delle texture\u003C\u002Ftd>\u003Ctd class=\"border border-gray-300 px-4 py-2\">Una piccola rete può ricostruire le informazioni della texture vicino al punto in cui lo shader ne ha bisogno\u003C\u002Ftd>\u003C\u002Ftr>\u003Ctr>\u003Ctd class=\"border border-gray-300 px-4 py-2\">Valutazione neurale dei materiali\u003C\u002Ftd>\u003Ctd class=\"border border-gray-300 px-4 py-2\">Una funzione appresa può sostituire o integrare calcoli costosi sui materiali scritti manualmente\u003C\u002Ftd>\u003C\u002Ftr>\u003Ctr>\u003Ctd class=\"border border-gray-300 px-4 py-2\">Caching neurale della radianza\u003C\u002Ftd>\u003Ctd class=\"border border-gray-300 px-4 py-2\">L'inferenza per pixel o locale può stimare le informazioni di illuminazione dal comportamento appreso della scena\u003C\u002Ftd>\u003C\u002Ftr>\u003Ctr>\u003Ctd class=\"border border-gray-300 px-4 py-2\">Piccoli kernel di denoising\u002Fricostruzione\u003C\u002Ftd>\u003Ctd class=\"border border-gray-300 px-4 py-2\">Le operazioni ML possono essere posizionate direttamente accanto alla fase di rendering che migliorano\u003C\u002Ftd>\u003C\u002Ftr>\u003Ctr>\u003Ctd class=\"border border-gray-300 px-4 py-2\">Inferenza per l'elaborazione delle immagini\u003C\u002Ftd>\u003Ctd class=\"border border-gray-300 px-4 py-2\">Le operazioni matriciali possono essere incorporate nell'elaborazione GPU senza un runtime esterno separato\u003C\u002Ftd>\u003C\u002Ftr>\u003C\u002Ftbody>\u003C\u002Ftable>\u003C\u002Fdiv>\n\u003Ch2 id=\"section-24\">Perché questo è importante per la memoria delle texture\u003C\u002Fh2>\n\u003Cp>Uno degli esempi ricorrenti di Microsoft è la compressione neurale delle texture.\u003C\u002Fp>\n\u003Cp>Invece di memorizzare ogni canale della texture a fedeltà convenzionale, un gioco può memorizzare una rappresentazione più compatta e utilizzare una piccola rete neurale per ricostruire i dettagli durante il rendering.\u003C\u002Fp>\n\u003Cp>Questo scambia un po' di lavoro di inferenza GPU con una minore pressione di archiviazione o memoria. Il beneficio esatto dipende dalla tecnica e dall'hardware, ma il cambiamento architetturale è importante: alcuni dettagli visivi possono diventare calcolo invece che dati memorizzati.\u003C\u002Fp>\n\u003Caside class=\"editorjs-referral my-6\">\u003Ca href=\"https:\u002F\u002Ffigure.rocks\u002Fit\u002Fblog\u002Fvram-usage-is-not-vram-requirement-why-a-full-memory-meter-does-not-tell-the-whole-story\" class=\"flex flex-col sm:flex-row gap-4 rounded-xl border border-gray-200 dark:border-gray-700 p-4 transition hover:border-primary-500\">\u003Cdiv class=\"min-w-0 flex-1\">\u003Cstrong class=\"block text-lg text-gray-900 dark:text-gray-100\">L'utilizzo della VRAM non è il requisito di VRAM: perché un misuratore di memoria completo non racconta tutta la storia\u003C\u002Fstrong>\u003Cp class=\"mt-2 text-sm text-gray-600 dark:text-gray-300\">Una guida pratica alla capacità della VRAM, ai budget di residenza, ai working set e perché la pressione di memoria è più complicata di un singolo numero di utilizzo.\u003C\u002Fp>\u003Cspan class=\"mt-3 inline-flex text-sm font-medium text-primary-600 dark:text-primary-400\">Leggi la guida alla VRAM →\u003C\u002Fspan>\u003C\u002Fdiv>\u003C\u002Fa>\u003C\u002Faside>\n\u003Ch2 id=\"section-29\">Perché i modelli completi necessitano di un percorso diverso\u003C\u002Fh2>\n\u003Cp>Scrivere a mano alcune operazioni matriciali in HLSL è pratico per piccole funzioni neurali. Diventa molto meno pratico quando il carico di lavoro è un modello moderno completo con molti livelli, dipendenze e tensori intermedi.\u003C\u002Fp>\n\u003Cp>Il DirectX Compute Graph Compiler di Microsoft è progettato per questa classe più ampia di carichi di lavoro.\u003C\u002Fp>\n\u003Cp>Invece di riscrivere il modello come codice shader personalizzato, il compilatore può accettare un grafo di calcolo, analizzare l'intero grafo, pianificare la memoria, fondere le operazioni e trasformare il risultato in lavoro GPU che si integra con DirectX 12.\u003C\u002Fp>\n\u003Ch2 id=\"section-33\">Il confine tra Shader e Grafo\u003C\u002Fh2>\n\u003Csection class=\"editorjs-comparison my-6\">\u003Ch3 class=\"mb-3 text-lg font-semibold\">Quando il carico di lavoro ML appartiene a HLSL rispetto a un compilatore di modelli\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\">Percorso a livello di 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\">Percorso a livello di modello\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\">Dimensione del modello\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\">Stile di esecuzione\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\">Authoring\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\">Ambito di ottimizzazione\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 tipico\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\">Perché il supporto cross-vendor conta più di un'altra funzionalità AI\u003C\u002Fh2>\n\u003Cp>AMD, Intel, NVIDIA e Qualcomm espongono architetture GPU diverse e diverse forme di accelerazione matriciale dedicata.\u003C\u002Fp>\n\u003Cp>Un'astrazione DirectX offre a Microsoft e ai fornitori di driver un punto in cui tradurre HLSL comune o ML a livello di grafo nel percorso hardware corretto.\u003C\u002Fp>\n\u003Cp>Ciò non rende ogni GPU ugualmente veloce, ma può ridurre la necessità per un motore di gioco di implementare un'API di rendering neurale completamente diversa per ogni fornitore.\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 portabilità è la vera storia della piattaforma\u003C\u002Fstrong>\u003Cdiv class=\"text-gray-700 dark:text-gray-200\">La parte interessante non è che DirectX possa "eseguire AI". Le GPU già potevano. Il cambiamento della piattaforma è che \u003Cstrong>il ML diventa esprimibile attraverso un'API grafica e una toolchain comuni\u003C\u002Fstrong>.\u003C\u002Fdiv>\u003C\u002Faside>\n\u003Ch2 id=\"section-40\">Quale hardware supporta l'anteprima attuale?\u003C\u002Fh2>\n\u003Cp>La risposta dipende dall'operazione specifica di Algebra Lineare e dal driver di anteprima.\u003C\u002Fp>\n\u003Cp>La tabella di supporto dell'anteprima di Agility SDK 1.721 di Microsoft elenca LinAlg VectorAccumulate per l'hardware AMD Radeon RX 9000 Series, l'hardware Intel Xe2 o successivo tramite un driver imminente e l'hardware NVIDIA RTX tramite il percorso di anteprima supportato.\u003C\u002Fp>\n\u003Cp>Questo è un supporto in era di anteprima, non una garanzia universale al dettaglio. Il supporto hardware e driver può cambiare prima della finalizzazione.\u003C\u002Fp>\n\u003Ch2 id=\"section-44\">Il rendering neurale sta diventando infrastruttura\u003C\u002Fh2>\n\u003Cp>DLSS, FSR e altre tecnologie grafiche neurali sono spesso discusse come funzionalità di marca visibili nel menu delle impostazioni di un gioco.\u003C\u002Fp>\n\u003Cp>DirectX Linear Algebra indica un cambiamento più profondo. L'operazione neurale può diventare un dettaglio implementativo interno al renderer piuttosto che una singola funzionalità opzionale di post-elaborazione.\u003C\u002Fp>\n\u003Cp>Uno sviluppatore potrebbe usare il ML per texture, materiali, illuminazione, ricostruzione o altre funzioni locali senza esporre ciascuna come un interruttore AI rivolto al consumatore.\u003C\u002Fp>\n\u003Caside class=\"editorjs-referral my-6\">\u003Ca href=\"https:\u002F\u002Ffigure.rocks\u002Fit\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 non è solo upscaling: cosa cambia realmente il rendering neurale guidato in 3D\u003C\u002Fstrong>\u003Cp class=\"mt-2 text-sm text-gray-600 dark:text-gray-300\">Come il rendering neurale si sta spostando oltre l'upscaling e i frame generati verso la ricostruzione di materiali e illuminazione all'interno della pipeline grafica.\u003C\u002Fp>\u003Cspan class=\"mt-3 inline-flex text-sm font-medium text-primary-600 dark:text-primary-400\">Leggi la guida al rendering neurale di DLSS 5 →\u003C\u002Fspan>\u003C\u002Fdiv>\u003C\u002Fa>\u003C\u002Faside>\n\u003Ch2 id=\"section-49\">Lo stack di rendering neurale sta diventando stratificato\u003C\u002Fh2>\n\u003Csection class=\"editorjs-process my-6\">\u003Ch3 class=\"mb-3 text-lg font-semibold\">Una possibile futura pipeline di gioco 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\">Lavoro tradizionale del motore\u003C\u002Fdiv>\u003Cdiv class=\"mt-1 text-sm text-gray-600 dark:text-gray-300\">Simulazione, geometria, visibilità e rendering di base rimangono responsabilità standard del motore.\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\">Shader neurali inline\u003C\u002Fdiv>\u003Cdiv class=\"mt-1 text-sm text-gray-600 dark:text-gray-300\">Piccole funzioni apprese ricostruiscono texture, materiali o illuminazione all'interno di 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\">Grafici ML più grandi\u003C\u002Fdiv>\u003Cdiv class=\"mt-1 text-sm text-gray-600 dark:text-gray-300\">Modelli completi di ricostruzione o inferenza vengono eseguiti attraverso un percorso DirectX a livello di 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\">Mappatura hardware del fornitore\u003C\u002Fdiv>\u003Cdiv class=\"mt-1 text-sm text-gray-600 dark:text-gray-300\">I driver mappano le operazioni DirectX comuni sulle unità AI e matriciali specializzate della 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\">Visibilità PIX\u003C\u002Fdiv>\u003Cdiv class=\"mt-1 text-sm text-gray-600 dark:text-gray-300\">Il lavoro grafico e ML può essere profilato insieme invece di risiedere in runtime opachi separati.\u003C\u002Fdiv>\u003C\u002Fdiv>\u003C\u002Fdiv>\u003C\u002Fsection>\n\u003Ch2 id=\"section-51\">Perché la profilazione unificata è importante\u003C\u002Fh2>\n\u003Cp>Un carico di lavoro neurale che migliora la qualità dell'immagine può comunque danneggiare un gioco se consuma inaspettatamente tempo di frame, larghezza di banda della memoria o VRAM.\u003C\u002Fp>\n\u003Cp>Microsoft include esplicitamente la visibilità unificata di PIX come parte della direzione del Compute Graph Compiler. Ciò è importante perché gli sviluppatori devono vedere il lavoro grafico e ML nella stessa cattura di frame.\u003C\u002Fp>\n\u003Cp>Se il rendering neurale diventa infrastruttura, deve essere misurabile come qualsiasi altra fase di rendering.\u003C\u002Fp>\n\u003Ch2 id=\"section-55\">Questo non significa che ogni shader diventerà una rete neurale\u003C\u002Fh2>\n\u003Cp>La matematica tradizionale degli shader rimane efficiente, deterministica e facile da ragionare per molti carichi di lavoro.\u003C\u002Fp>\n\u003Cp>Una funzione neurale ha senso quando può approssimare o ricostruire qualcosa di costoso in modo più efficiente, comprimere dati o produrre una qualità che altrimenti richiederebbe troppo calcolo o memoria convenzionale.\u003C\u002Fp>\n\u003Cp>L'architettura giusta rimarrà ibrida.\u003C\u002Fp>\n\u003Ch2 id=\"section-59\">Il test del valore dello shader neurale\u003C\u002Fh2>\n\u003Csection class=\"editorjs-process my-6\">\u003Ch3 class=\"mb-3 text-lg font-semibold\">Quando ha davvero senso inserire il ML nella pipeline di rendering?\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. Identifica l'operazione tradizionale costosa\u003C\u002Fdiv>\u003Cdiv class=\"mt-1 text-sm text-gray-600 dark:text-gray-300\">Quale costo di calcolo, larghezza di banda, memoria o archiviazione stai cercando di sostituire?\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. Definisci il sostituto neurale\u003C\u002Fdiv>\u003Cdiv class=\"mt-1 text-sm text-gray-600 dark:text-gray-300\">Cosa può ricostruire, prevedere o comprimere un piccolo modello?\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. Misura il costo di inferenza\u003C\u002Fdiv>\u003Cdiv class=\"mt-1 text-sm text-gray-600 dark:text-gray-300\">Il modello ML stesso consuma tempo GPU, memoria e larghezza di 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. Misura la stabilità della qualità\u003C\u002Fdiv>\u003Cdiv class=\"mt-1 text-sm text-gray-600 dark:text-gray-300\">Cerca artefatti temporali, errori di ricostruzione e casi di fallimento.\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. Misura il risparmio netto\u003C\u002Fdiv>\u003Cdiv class=\"mt-1 text-sm text-gray-600 dark:text-gray-300\">La tecnica è utile solo se il costo tradizionale evitato vale il costo ML aggiunto.\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. Testa su diversi fornitori\u003C\u002Fdiv>\u003Cdiv class=\"mt-1 text-sm text-gray-600 dark:text-gray-300\">Un'astrazione DirectX aiuta la portabilità, ma le prestazioni hardware reali differiscono comunque.\u003C\u002Fdiv>\u003C\u002Fdiv>\u003C\u002Fdiv>\u003C\u002Fsection>\n\u003Ch2 id=\"section-61\">Cosa significa questo per i giocatori\u003C\u002Fh2>\n\u003Cp>I giocatori potrebbero non vedere mai un interruttore \"DX Linear Algebra\" in un menu grafico.\u003C\u002Fp>\n\u003Cp>L'impatto probabile è indiretto: texture più leggere, migliore ricostruzione dell'illuminazione, grafica neurale più efficiente o nuove tecniche visive che diventano praticabili perché il motore può accedere all'accelerazione matriciale attraverso un percorso standard.\u003C\u002Fp>\n\u003Cp>La funzionalità è più importante come infrastruttura che come marchio.\u003C\u002Fp>\n\u003Ch2 id=\"section-65\">Cosa cambierebbe questa risposta?\u003C\u002Fh2>\n\u003Cp>La maggiore incertezza è la forma finale al dettaglio di queste API. DX Linear Algebra rimane in anteprima, e il Compute Graph Compiler non ha ancora raggiunto un'ampia disponibilità al dettaglio.\u003C\u002Fp>\n\u003Cp>Il supporto hardware finale, i dettagli delle API, il comportamento del compilatore e l'adozione da parte dei motori potrebbero cambiare prima che questi sistemi diventino infrastruttura normale per i giochi pubblicati.\u003C\u002Fp>\n\u003Cp>Se i principali motori adottassero direttamente le astrazioni, gli shader neurali potrebbero diventare molto più comuni senza che i singoli team di sviluppo implementino ogni tecnica da zero.\u003C\u002Fp>\n\u003Ch2 id=\"section-69\">Limitazioni\u003C\u002Fh2>\n\u003Cp>Questo articolo spiega l'architettura DirectX documentata da Microsoft e le API in anteprima. Non afferma che DX Linear Algebra migliori attualmente le prestazioni in ogni gioco o che il Compute Graph Compiler sia un prodotto finito al dettaglio.\u003C\u002Fp>\n\u003Cp>Gli esempi di Microsoft descrivono capacità e uso previsto. I benefici reali dipendono dal modello, dall'integrazione nel motore, dall'architettura GPU, dai driver e dal carico di lavoro.\u003C\u002Fp>\n\u003Ch2 id=\"section-72\">Conclusione\u003C\u002Fh2>\n\u003Cp>Il cambiamento importante di DirectX non è un'altra casella di controllo chiamata AI.\u003C\u002Fp>\n\u003Cp>È che la matematica del machine learning si sta spostando nel modello di programmazione grafica stesso. Piccole funzioni neurali possono risiedere all'interno degli shader, modelli più grandi possono avvicinarsi alla compilazione a livello di grafo, e la stessa toolchain DirectX può esporre quei carichi di lavoro su più fornitori di GPU.\u003C\u002Fp>\n\u003Cp>Ecco come il rendering neurale smette di essere una singola funzionalità di marca e inizia a diventare parte dell'infrastruttura di rendering.\u003C\u002Fp>\n\u003Ch2 id=\"section-76\">FAQ\u003C\u002Fh2>\n\u003Csection class=\"editorjs-faq my-6 rounded-xl border border-gray-200 p-5 dark:border-gray-700\">\u003Ch3 class=\"mb-3 text-lg font-semibold\">DirectX Linear Algebra e shader neurali\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\">Cos'è DirectX Linear Algebra?\u003C\u002Fh4>\u003Cdiv class=\"mt-2 text-gray-600 dark:text-gray-300\">È un insieme di funzionalità DirectX\u002FHLSL per operazioni vettoriali e matriciali accelerate dall'hardware utilizzate da carichi di lavoro di machine learning, rendering neurale ed elaborazione di immagini.\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\">Cos'è uno shader neurale?\u003C\u002Fh4>\u003Cdiv class=\"mt-2 text-gray-600 dark:text-gray-300\">Una descrizione utile in parole semplici è uno shader che include una funzione neurale appresa o un passaggio di inferenza ML come parte del suo lavoro grafico.\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\">DX Linear Algebra è già una normale funzionalità DirectX disponibile al pubblico?\u003C\u002Fh4>\u003Cdiv class=\"mt-2 text-gray-600 dark:text-gray-300\">A settembre 2026 rimane nel percorso di anteprima 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\">Cos'è il DirectX Compute Graph Compiler?\u003C\u002Fh4>\u003Cdiv class=\"mt-2 text-gray-600 dark:text-gray-300\">È l'API del compilatore ML a livello di modello annunciata da Microsoft, pensata per prendere grafi di calcolo più grandi e abbassarli in carichi di lavoro GPU ottimizzati integrati 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\">Perché non eseguire ogni modello ML direttamente in HLSL?\u003C\u002Fh4>\u003Cdiv class=\"mt-2 text-gray-600 dark:text-gray-300\">Le piccole funzioni si adattano bene alla scrittura a livello di shader, mentre i modelli grandi traggono vantaggio dall'ottimizzazione dell'intero grafo, dalla pianificazione della memoria e dalla fusione degli operatori.\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\">Questo sostituirà DLSS o FSR?\u003C\u002Fh4>\u003Cdiv class=\"mt-2 text-gray-600 dark:text-gray-300\">Non direttamente. DirectX fornisce un'infrastruttura di livello inferiore che fornitori e sviluppatori possono utilizzare per la grafica neurale. Le tecnologie di marca possono comunque implementare i propri modelli e strategie di integrazione.\u003C\u002Fdiv>\u003C\u002Fdiv>\u003C\u002Fsection>\n\u003Ch2 id=\"section-78\">Glossario\u003C\u002Fh2>\n\u003Csection class=\"editorjs-glossary my-6 rounded-xl border border-gray-200 dark:border-gray-700 p-5\">\u003Ch3 class=\"mb-3 text-lg font-semibold\">Termini chiave di DirectX ML\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\">DX Linear Algebra\u003C\u002Fdt>\u003Cdd class=\"mt-1 text-gray-600 dark:text-gray-300\">API DirectX\u002FHLSL per operazioni vettoriali e matriciali accelerate destinate a carichi di lavoro di rendering neurale, ML ed elaborazione di immagini.\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\">Cooperative Vector\u003C\u002Fdt>\u003Cdd class=\"mt-1 text-gray-600 dark:text-gray-300\">Un precedente approccio DirectX per operazioni vettore-matrice accelerate all'interno degli shader che ha contribuito a stabilire il percorso di rendering neurale a livello di shader.\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 generazione di shader model in anteprima che contiene le attuali API matriciali di DirectX Linear Algebra.\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\">DirectX Compute Graph Compiler\u003C\u002Fdt>\u003Cdd class=\"mt-1 text-gray-600 dark:text-gray-300\">L'API del compilatore annunciata da Microsoft per ottimizzare ed eseguire grafi di calcolo ML più grandi come carichi di lavoro GPU DirectX nativi.\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\">Shader neurale\u003C\u002Fdt>\u003Cdd class=\"mt-1 text-gray-600 dark:text-gray-300\">Un termine pratico per uno shader che esegue inferenza appresa o calcolo neurale come parte dell'elaborazione grafica.\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\">Confine shader-o-grafo\u003C\u002Fdt>\u003Cdd class=\"mt-1 text-gray-600 dark:text-gray-300\">Un modello Figure Rocks per decidere se un carico di lavoro ML appartiene come piccola matematica inline nello shader o come grafo di calcolo più grande a livello di modello.\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\">Test del valore dello shader neurale\u003C\u002Fdt>\u003Cdd class=\"mt-1 text-gray-600 dark:text-gray-300\">Un flusso di lavoro Figure Rocks per decidere se il costo tradizionale di calcolo o memoria evitato da una tecnica neurale vale il suo costo di inferenza e i compromessi sulla qualità.\u003C\u002Fdd>\u003C\u002Fdiv>\u003C\u002Fdl>\u003C\u002Fsection>\n\u003Ch2 id=\"section-80\">Fonti primarie\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 — Evolving DirectX for the ML Era on Windows\u003C\u002Fstrong>\u003Cp class=\"text-sm text-gray-600 dark:text-gray-400\">Panoramica ufficiale dell'architettura GDC 2026 che copre ML a livello di shader, DX Linear Algebra e il DirectX Compute Graph Compiler.\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 — D3D12 LinAlg Matrix Preview\u003C\u002Fstrong>\u003Cp class=\"text-sm text-gray-600 dark:text-gray-400\">Anteprima ufficiale di aprile 2026 che spiega le API unificate di Linear Algebra, le operazioni matriciali e la motivazione del rendering neurale.\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 — Agility SDK 1.721 Preview\u003C\u002Fstrong>\u003Cp class=\"text-sm text-gray-600 dark:text-gray-400\">Rilascio ufficiale di maggio 2026 che documenta gli aggiornamenti di Linear Algebra di Shader Model 6.10 e il supporto hardware in anteprima.\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: Evolving DirectX for the ML Era\u003C\u002Fstrong>\u003Cp class=\"text-sm text-gray-600 dark:text-gray-400\">Riepilogo ufficiale di Microsoft Game Dev che spiega il ML a livello di shader e a livello di modello e il ruolo del Compute Graph Compiler.\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 — D3D12 Cooperative Vector\u003C\u002Fstrong>\u003Cp class=\"text-sm text-gray-600 dark:text-gray-400\">Contesto ufficiale sulle operazioni vettoriali\u002Fmatriciali accelerate dall'hardware e sul rendering neurale direttamente dai thread degli shader.\u003C\u002Fp>\u003C\u002Fa>",{"time":538,"blocks":539,"version":1178},1790378184026,[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 non è più solo un'API grafica che invia shader tradizionali alla GPU. Microsoft sta aggiungendo primitive di machine learning direttamente a HLSL e un secondo percorso per eseguire grafi ML più grandi all'interno dell'ecosistema DirectX. Questo cambia dove il rendering neurale può risiedere nei futuri giochi per PC.","paragraph",{},{"id":547,"data":548,"type":552,"tunes":553},"direct",{"body":549,"title":550,"variant":551},"\u003Cstrong>DirectX sta diventando una piattaforma grafica con capacità ML.\u003C\u002Fstrong> Piccoli carichi di lavoro neurali possono essere eseguiti direttamente all'interno degli shader tramite DX Linear Algebra, mentre modelli più grandi sono presi di mira dal DirectX Compute Graph Compiler. L'obiettivo è consentire ai motori di gioco di utilizzare l'hardware AI della GPU senza costruire un percorso separato specifico per ogni fornitore per ogni tecnica.","Risposta diretta","info","callout",{},{"id":555,"data":556,"type":552,"tunes":560},"status-note",{"body":557,"title":558,"variant":559},"A settembre 2026, \u003Cstrong>DX Linear Algebra è ancora una tecnologia in anteprima\u003C\u002Fstrong> nel percorso di anteprima Shader Model 6.10 \u002F Agility SDK. Microsoft ha annunciato il DirectX Compute Graph Compiler per l'anteprima privata piuttosto che come una funzionalità retail ampiamente distribuita. Questo articolo spiega l'architettura e la direzione, non afferma che ogni gioco attuale possa usarla oggi.","Stato attuale","note",{},{"id":562,"data":563,"type":566,"tunes":567},"toc",{"title":564,"maxLevel":565,"minLevel":47},"Contenuti",3,"tableOfContents",{},{"id":569,"data":570,"type":572,"tunes":573},"h-why",{"text":571,"level":47},"Perché il rendering neurale necessita di nuove primitive DirectX","header",{},{"id":575,"data":576,"type":544,"tunes":578},"p-why-1",{"text":577},"Le GPU moderne contengono già hardware specializzato per operazioni matriciali utilizzate dal machine learning. Il problema per uno sviluppatore di giochi non è solo se quell'hardware esiste, ma come accedervi in modo efficiente da una pipeline grafica in tempo reale.",{},{"id":580,"data":581,"type":544,"tunes":583},"p-why-2",{"text":582},"Microsoft ha esplorato questo per la prima volta con il supporto Cooperative Vector. Nel 2026, quel lavoro si è evoluto in un design DirectX Linear Algebra più ampio che supporta sia operazioni vettore-matrice che matrice-matrice.",{},{"id":585,"data":586,"type":544,"tunes":588},"p-why-3",{"text":587},"Questo è importante perché diversi lavori di rendering neurale hanno forme diverse. Un piccolo modello che valuta il comportamento del materiale per pixel non è lo stesso carico di lavoro di un grande grafo di super-risoluzione o denoising.",{},{"id":590,"data":591,"type":572,"tunes":593},"h-two-level",{"text":592,"level":47},"Il modello ML DirectX a due livelli",{},{"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 livello di shader","Piccoli carichi di lavoro neurali o di algebra lineare vengono eseguiti direttamente da HLSL insieme al codice shader tradizionale.",{"label":602,"description":603},"2. DX Linear Algebra","Lo shader può richiedere operazioni vettoriali e matriciali accelerate dall'hardware invece di implementare manualmente ogni primitiva ML.",{"label":605,"description":606},"3. ML a livello di modello","Reti neurali più grandi sono rappresentate come grafi di calcolo completi piuttosto che come frammenti di shader scritti a mano.",{"label":608,"description":609},"4. DirectX Compute Graph Compiler","Il percorso del compilatore pianificato da Microsoft analizza e abbassa quei grafi in carichi di lavoro GPU ottimizzati integrati con code e liste di comandi D3D12.","Due modi in cui il ML può entrare nella pipeline grafica 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> inserisci piccola matematica ML all'interno dello shader.\u003Cbr>\u003Cstrong>Compute Graph Compiler:\u003C\u002Fstrong> porta un modello ML più grande nel motore come grafo.","La semplice differenza","success",{},{"id":622,"data":623,"type":572,"tunes":625},"h-linalg",{"text":624,"level":47},"Cosa dà effettivamente DX Linear Algebra a uno shader",{},{"id":627,"data":628,"type":544,"tunes":630},"p-linalg-1",{"text":629},"L'HLSL tradizionale è costruito attorno a operazioni grafiche e di calcolo. I carichi di lavoro neurali si basano fortemente sull'algebra lineare: vettori, matrici, moltiplicazione, accumulo e layout di dati ottimizzati per quelle operazioni.",{},{"id":632,"data":633,"type":544,"tunes":635},"p-linalg-2",{"text":634},"L'anteprima di Shader Model 6.10 aggiunge API matriciali di prima classe in modo che gli sviluppatori possano esprimere quei carichi di lavoro più direttamente e lasciare che il driver li mappi su hardware specializzato.",{},{"id":637,"data":638,"type":544,"tunes":640},"p-linalg-3",{"text":639},"L'anteprima di Microsoft di aprile 2026 descrive esplicitamente questo come un percorso unificato per il rendering neurale, il ML e i carichi di lavoro di elaborazione delle immagini piuttosto che una funzionalità solo grafica.",{},{"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","Lavoro tipico",[13,13],{"id":650,"label":651,"values":652},"hardware","Percorso hardware",[13,13],{"id":654,"label":655,"values":656},"code","Espressione dello sviluppatore",[13,13],"Matematica shader tradizionale vs matematica shader orientata al ML","table",[660,663],{"id":661,"label":662},"traditional","Focus shader tradizionale",{"id":664,"label":665},"ml","Focus orientato al ML","comparison",{},{"id":669,"data":670,"type":572,"tunes":672},"h-coop",{"text":671,"level":47},"Perché Cooperative Vector era solo l'inizio",{},{"id":674,"data":675,"type":544,"tunes":677},"p-coop-1",{"text":676},"Cooperative Vector consentiva ai thread degli shader di richiedere lavoro vettore-matrice che i driver potevano mappare su hardware specializzato. Ciò era utile per carichi di lavoro altamente paralleli per pixel.",{},{"id":679,"data":680,"type":544,"tunes":682},"p-coop-2",{"text":681},"Microsoft in seguito ha concluso che molti carichi di lavoro ML importanti richiedono più delle operazioni vettore-matrice. Super risoluzione, denoising, ricostruzione temporale, modelli di immagini più grandi e inferenza generale possono richiedere operazioni matrice-matrice e lavoro condiviso tra molti thread.",{},{"id":684,"data":685,"type":544,"tunes":687},"p-coop-3",{"text":686},"DX Linear Algebra quindi amplia il modello invece di trattare Cooperative Vector come l'astrazione finale.",{},{"id":689,"data":690,"type":572,"tunes":692},"h-usecases",{"text":691,"level":47},"Cosa può effettivamente essere eseguito all'interno di uno shader?",{},{"id":694,"data":695,"type":544,"tunes":697},"p-use-1",{"text":696},"I carichi di lavoro ML a livello di shader più interessanti sono abbastanza piccoli da essere eseguiti vicino ai dati grafici su cui operano.",{},{"id":699,"data":700,"type":658,"tunes":721},"usecases-table",{"content":701,"stretched":720,"withHeadings":15},[702,705,708,711,714,717],[703,704],"Carico di lavoro","Perché l'ML a livello di shader è adatto",[706,707],"Compressione neurale delle texture","Una piccola rete può ricostruire le informazioni della texture vicino al punto in cui lo shader ne ha bisogno",[709,710],"Valutazione neurale dei materiali","Una funzione appresa può sostituire o integrare calcoli costosi sui materiali scritti manualmente",[712,713],"Caching neurale della radianza","L'inferenza per pixel o locale può stimare le informazioni di illuminazione dal comportamento appreso della scena",[715,716],"Piccoli kernel di denoising\u002Fricostruzione","Le operazioni ML possono essere posizionate direttamente accanto alla fase di rendering che migliorano",[718,719],"Inferenza per l'elaborazione delle immagini","Le operazioni matriciali possono essere incorporate nell'elaborazione GPU senza un runtime esterno separato",false,{},{"id":723,"data":724,"type":572,"tunes":726},"h-texture",{"text":725,"level":47},"Perché questo è importante per la memoria delle texture",{},{"id":728,"data":729,"type":544,"tunes":731},"p-tex-1",{"text":730},"Uno degli esempi ricorrenti di Microsoft è la compressione neurale delle texture.",{},{"id":733,"data":734,"type":544,"tunes":736},"p-tex-2",{"text":735},"Invece di memorizzare ogni canale della texture a fedeltà convenzionale, un gioco può memorizzare una rappresentazione più compatta e utilizzare una piccola rete neurale per ricostruire i dettagli durante il rendering.",{},{"id":738,"data":739,"type":544,"tunes":741},"p-tex-3",{"text":740},"Questo scambia un po' di lavoro di inferenza GPU con una minore pressione di archiviazione o memoria. Il beneficio esatto dipende dalla tecnica e dall'hardware, ma il cambiamento architetturale è importante: alcuni dettagli visivi possono diventare calcolo invece che dati memorizzati.",{},{"id":743,"data":744,"type":749,"tunes":750},"ref-vram",{"url":745,"title":746,"excerpt":747,"ctaLabel":748},"https:\u002F\u002Ffigure.rocks\u002Fit\u002Fblog\u002Fvram-usage-is-not-vram-requirement-why-a-full-memory-meter-does-not-tell-the-whole-story","L'utilizzo della VRAM non è il requisito di VRAM: perché un misuratore di memoria completo non racconta tutta la storia","Una guida pratica alla capacità della VRAM, ai budget di residenza, ai working set e perché la pressione di memoria è più complicata di un singolo numero di utilizzo.","Leggi la guida alla VRAM","referralArticle",{},{"id":752,"data":753,"type":572,"tunes":755},"h-models",{"text":754,"level":47},"Perché i modelli completi necessitano di un percorso diverso",{},{"id":757,"data":758,"type":544,"tunes":760},"p-models-1",{"text":759},"Scrivere a mano alcune operazioni matriciali in HLSL è pratico per piccole funzioni neurali. Diventa molto meno pratico quando il carico di lavoro è un modello moderno completo con molti livelli, dipendenze e tensori intermedi.",{},{"id":762,"data":763,"type":544,"tunes":765},"p-models-2",{"text":764},"Il DirectX Compute Graph Compiler di Microsoft è progettato per questa classe più ampia di carichi di lavoro.",{},{"id":767,"data":768,"type":544,"tunes":770},"p-models-3",{"text":769},"Invece di riscrivere il modello come codice shader personalizzato, il compilatore può accettare un grafo di calcolo, analizzare l'intero grafo, pianificare la memoria, fondere le operazioni e trasformare il risultato in lavoro GPU che si integra con DirectX 12.",{},{"id":772,"data":773,"type":572,"tunes":775},"h-boundary",{"text":774,"level":47},"Il confine tra Shader e 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","Dimensione del modello",[13,13],{"id":785,"label":786,"values":787},"location","Stile di esecuzione",[13,13],{"id":789,"label":790,"values":791},"authoring","Authoring",[13,13],{"id":793,"label":794,"values":795},"optimization","Ambito di ottimizzazione",[13,13],{"id":797,"label":798,"values":799},"use","Uso tipico",[13,13],"Quando il carico di lavoro ML appartiene a HLSL rispetto a un compilatore di modelli",[802,805],{"id":803,"label":804},"shader","Percorso a livello di shader",{"id":806,"label":807},"graph","Percorso a livello di modello",{},{"id":810,"data":811,"type":572,"tunes":813},"h-crossvendor",{"text":812,"level":47},"Perché il supporto cross-vendor conta più di un'altra funzionalità AI",{},{"id":815,"data":816,"type":544,"tunes":818},"p-cross-1",{"text":817},"AMD, Intel, NVIDIA e Qualcomm espongono architetture GPU diverse e diverse forme di accelerazione matriciale dedicata.",{},{"id":820,"data":821,"type":544,"tunes":823},"p-cross-2",{"text":822},"Un'astrazione DirectX offre a Microsoft e ai fornitori di driver un punto in cui tradurre HLSL comune o ML a livello di grafo nel percorso hardware corretto.",{},{"id":825,"data":826,"type":544,"tunes":828},"p-cross-3",{"text":827},"Ciò non rende ogni GPU ugualmente veloce, ma può ridurre la necessità per un motore di gioco di implementare un'API di rendering neurale completamente diversa per ogni fornitore.",{},{"id":830,"data":831,"type":552,"tunes":834},"portability-note",{"body":832,"title":833,"variant":559},"La parte interessante non è che DirectX possa \"eseguire AI\". Le GPU già potevano. Il cambiamento della piattaforma è che \u003Cstrong>il ML diventa esprimibile attraverso un'API grafica e una toolchain comuni\u003C\u002Fstrong>.","La portabilità è la vera storia della piattaforma",{},{"id":836,"data":837,"type":572,"tunes":839},"h-support",{"text":838,"level":47},"Quale hardware supporta l'anteprima attuale?",{},{"id":841,"data":842,"type":544,"tunes":844},"p-support-1",{"text":843},"La risposta dipende dall'operazione specifica di Algebra Lineare e dal driver di anteprima.",{},{"id":846,"data":847,"type":544,"tunes":849},"p-support-2",{"text":848},"La tabella di supporto dell'anteprima di Agility SDK 1.721 di Microsoft elenca LinAlg VectorAccumulate per l'hardware AMD Radeon RX 9000 Series, l'hardware Intel Xe2 o successivo tramite un driver imminente e l'hardware NVIDIA RTX tramite il percorso di anteprima supportato.",{},{"id":851,"data":852,"type":544,"tunes":854},"p-support-3",{"text":853},"Questo è un supporto in era di anteprima, non una garanzia universale al dettaglio. Il supporto hardware e driver può cambiare prima della finalizzazione.",{},{"id":856,"data":857,"type":572,"tunes":859},"h-infra",{"text":858,"level":47},"Il rendering neurale sta diventando infrastruttura",{},{"id":861,"data":862,"type":544,"tunes":864},"p-infra-1",{"text":863},"DLSS, FSR e altre tecnologie grafiche neurali sono spesso discusse come funzionalità di marca visibili nel menu delle impostazioni di un gioco.",{},{"id":866,"data":867,"type":544,"tunes":869},"p-infra-2",{"text":868},"DirectX Linear Algebra indica un cambiamento più profondo. L'operazione neurale può diventare un dettaglio implementativo interno al renderer piuttosto che una singola funzionalità opzionale di post-elaborazione.",{},{"id":871,"data":872,"type":544,"tunes":874},"p-infra-3",{"text":873},"Uno sviluppatore potrebbe usare il ML per texture, materiali, illuminazione, ricostruzione o altre funzioni locali senza esporre ciascuna come un interruttore AI rivolto al consumatore.",{},{"id":876,"data":877,"type":749,"tunes":882},"ref-dlss5",{"url":878,"title":879,"excerpt":880,"ctaLabel":881},"https:\u002F\u002Ffigure.rocks\u002Fit\u002Fblog\u002Fdlss-5-is-not-just-upscaling-what-3d-guided-neural-rendering-actually-changes","DLSS 5 non è solo upscaling: cosa cambia realmente il rendering neurale guidato in 3D","Come il rendering neurale si sta spostando oltre l'upscaling e i frame generati verso la ricostruzione di materiali e illuminazione all'interno della pipeline grafica.","Leggi la guida al rendering neurale di DLSS 5",{},{"id":884,"data":885,"type":572,"tunes":887},"h-stack",{"text":886,"level":47},"Lo stack di rendering neurale sta diventando stratificato",{},{"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},"Lavoro tradizionale del motore","Simulazione, geometria, visibilità e rendering di base rimangono responsabilità standard del motore.",{"label":896,"description":897},"Shader neurali inline","Piccole funzioni apprese ricostruiscono texture, materiali o illuminazione all'interno di HLSL.",{"label":899,"description":900},"Grafici ML più grandi","Modelli completi di ricostruzione o inferenza vengono eseguiti attraverso un percorso DirectX a livello di grafo.",{"label":902,"description":903},"Mappatura hardware del fornitore","I driver mappano le operazioni DirectX comuni sulle unità AI e matriciali specializzate della GPU.",{"label":905,"description":906},"Visibilità PIX","Il lavoro grafico e ML può essere profilato insieme invece di risiedere in runtime opachi separati.","Una possibile futura pipeline di gioco DirectX",{},{"id":910,"data":911,"type":572,"tunes":913},"h-pix",{"text":912,"level":47},"Perché la profilazione unificata è importante",{},{"id":915,"data":916,"type":544,"tunes":918},"p-pix-1",{"text":917},"Un carico di lavoro neurale che migliora la qualità dell'immagine può comunque danneggiare un gioco se consuma inaspettatamente tempo di frame, larghezza di banda della memoria o VRAM.",{},{"id":920,"data":921,"type":544,"tunes":923},"p-pix-2",{"text":922},"Microsoft include esplicitamente la visibilità unificata di PIX come parte della direzione del Compute Graph Compiler. Ciò è importante perché gli sviluppatori devono vedere il lavoro grafico e ML nella stessa cattura di frame.",{},{"id":925,"data":926,"type":544,"tunes":928},"p-pix-3",{"text":927},"Se il rendering neurale diventa infrastruttura, deve essere misurabile come qualsiasi altra fase di rendering.",{},{"id":930,"data":931,"type":572,"tunes":933},"h-not-everything",{"text":932,"level":47},"Questo non significa che ogni shader diventerà una rete neurale",{},{"id":935,"data":936,"type":544,"tunes":938},"p-not-1",{"text":937},"La matematica tradizionale degli shader rimane efficiente, deterministica e facile da ragionare per molti carichi di lavoro.",{},{"id":940,"data":941,"type":544,"tunes":943},"p-not-2",{"text":942},"Una funzione neurale ha senso quando può approssimare o ricostruire qualcosa di costoso in modo più efficiente, comprimere dati o produrre una qualità che altrimenti richiederebbe troppo calcolo o memoria convenzionale.",{},{"id":945,"data":946,"type":544,"tunes":948},"p-not-3",{"text":947},"L'architettura giusta rimarrà ibrida.",{},{"id":950,"data":951,"type":572,"tunes":953},"h-test",{"text":952,"level":47},"Il test del valore dello shader neurale",{},{"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. Identifica l'operazione tradizionale costosa","Quale costo di calcolo, larghezza di banda, memoria o archiviazione stai cercando di sostituire?",{"label":962,"description":963},"2. Definisci il sostituto neurale","Cosa può ricostruire, prevedere o comprimere un piccolo modello?",{"label":965,"description":966},"3. Misura il costo di inferenza","Il modello ML stesso consuma tempo GPU, memoria e larghezza di banda.",{"label":968,"description":969},"4. Misura la stabilità della qualità","Cerca artefatti temporali, errori di ricostruzione e casi di fallimento.",{"label":971,"description":972},"5. Misura il risparmio netto","La tecnica è utile solo se il costo tradizionale evitato vale il costo ML aggiunto.",{"label":974,"description":975},"6. Testa su diversi fornitori","Un'astrazione DirectX aiuta la portabilità, ma le prestazioni hardware reali differiscono comunque.","Quando ha davvero senso inserire il ML nella pipeline di rendering?",{},{"id":979,"data":980,"type":572,"tunes":982},"h-gamers",{"text":981,"level":47},"Cosa significa questo per i giocatori",{},{"id":984,"data":985,"type":544,"tunes":987},"p-gamers-1",{"text":986},"I giocatori potrebbero non vedere mai un interruttore \"DX Linear Algebra\" in un menu grafico.",{},{"id":989,"data":990,"type":544,"tunes":992},"p-gamers-2",{"text":991},"L'impatto probabile è indiretto: texture più leggere, migliore ricostruzione dell'illuminazione, grafica neurale più efficiente o nuove tecniche visive che diventano praticabili perché il motore può accedere all'accelerazione matriciale attraverso un percorso standard.",{},{"id":994,"data":995,"type":544,"tunes":997},"p-gamers-3",{"text":996},"La funzionalità è più importante come infrastruttura che come marchio.",{},{"id":999,"data":1000,"type":572,"tunes":1002},"h-change",{"text":1001,"level":47},"Cosa cambierebbe questa risposta?",{},{"id":1004,"data":1005,"type":544,"tunes":1007},"p-change-1",{"text":1006},"La maggiore incertezza è la forma finale al dettaglio di queste API. DX Linear Algebra rimane in anteprima, e il Compute Graph Compiler non ha ancora raggiunto un'ampia disponibilità al dettaglio.",{},{"id":1009,"data":1010,"type":544,"tunes":1012},"p-change-2",{"text":1011},"Il supporto hardware finale, i dettagli delle API, il comportamento del compilatore e l'adozione da parte dei motori potrebbero cambiare prima che questi sistemi diventino infrastruttura normale per i giochi pubblicati.",{},{"id":1014,"data":1015,"type":544,"tunes":1017},"p-change-3",{"text":1016},"Se i principali motori adottassero direttamente le astrazioni, gli shader neurali potrebbero diventare molto più comuni senza che i singoli team di sviluppo implementino ogni tecnica da zero.",{},{"id":1019,"data":1020,"type":572,"tunes":1022},"h-limit",{"text":1021,"level":47},"Limitazioni",{},{"id":1024,"data":1025,"type":544,"tunes":1027},"p-limit-1",{"text":1026},"Questo articolo spiega l'architettura DirectX documentata da Microsoft e le API in anteprima. Non afferma che DX Linear Algebra migliori attualmente le prestazioni in ogni gioco o che il Compute Graph Compiler sia un prodotto finito al dettaglio.",{},{"id":1029,"data":1030,"type":544,"tunes":1032},"p-limit-2",{"text":1031},"Gli esempi di Microsoft descrivono capacità e uso previsto. I benefici reali dipendono dal modello, dall'integrazione nel motore, dall'architettura GPU, dai driver e dal carico di lavoro.",{},{"id":1034,"data":1035,"type":572,"tunes":1037},"h-conclusion",{"text":1036,"level":47},"Conclusione",{},{"id":1039,"data":1040,"type":544,"tunes":1042},"p-conc-1",{"text":1041},"Il cambiamento importante di DirectX non è un'altra casella di controllo chiamata AI.",{},{"id":1044,"data":1045,"type":544,"tunes":1047},"p-conc-2",{"text":1046},"È che la matematica del machine learning si sta spostando nel modello di programmazione grafica stesso. Piccole funzioni neurali possono risiedere all'interno degli shader, modelli più grandi possono avvicinarsi alla compilazione a livello di grafo, e la stessa toolchain DirectX può esporre quei carichi di lavoro su più fornitori di GPU.",{},{"id":1049,"data":1050,"type":544,"tunes":1052},"p-conc-3",{"text":1051},"Ecco come il rendering neurale smette di essere una singola funzionalità di marca e inizia a diventare parte dell'infrastruttura di rendering.",{},{"id":1054,"data":1055,"type":572,"tunes":1057},"h-faq",{"text":1056,"level":47},"FAQ",{},{"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","È un insieme di funzionalità DirectX\u002FHLSL per operazioni vettoriali e matriciali accelerate dall'hardware utilizzate da carichi di lavoro di machine learning, rendering neurale ed elaborazione di immagini.","Cos'è DirectX Linear Algebra?",{"id":1067,"answer":1068,"question":1069},"faq2","Una descrizione utile in parole semplici è uno shader che include una funzione neurale appresa o un passaggio di inferenza ML come parte del suo lavoro grafico.","Cos'è uno shader neurale?",{"id":1071,"answer":1072,"question":1073},"faq3","A settembre 2026 rimane nel percorso di anteprima Shader Model 6.10 \u002F Agility SDK.","DX Linear Algebra è già una normale funzionalità DirectX disponibile al pubblico?",{"id":1075,"answer":1076,"question":1077},"faq4","È l'API del compilatore ML a livello di modello annunciata da Microsoft, pensata per prendere grafi di calcolo più grandi e abbassarli in carichi di lavoro GPU ottimizzati integrati con D3D12.","Cos'è il DirectX Compute Graph Compiler?",{"id":1079,"answer":1080,"question":1081},"faq5","Le piccole funzioni si adattano bene alla scrittura a livello di shader, mentre i modelli grandi traggono vantaggio dall'ottimizzazione dell'intero grafo, dalla pianificazione della memoria e dalla fusione degli operatori.","Perché non eseguire ogni modello ML direttamente in HLSL?",{"id":1083,"answer":1084,"question":1085},"faq6","Non direttamente. DirectX fornisce un'infrastruttura di livello inferiore che fornitori e sviluppatori possono utilizzare per la grafica neurale. Le tecnologie di marca possono comunque implementare i propri modelli e strategie di integrazione.","Questo sostituirà DLSS o FSR?","DirectX Linear Algebra e shader neurali",{},{"id":1089,"data":1090,"type":572,"tunes":1092},"h-glossary",{"text":1091,"level":47},"Glossario",{},{"id":1094,"data":1095,"type":1094,"tunes":1126},"glossary",{"title":1096,"entries":1097},"Termini chiave di DirectX ML",[1098,1102,1106,1110,1114,1118,1122],{"term":1099,"anchor":1100,"definition":1101},"DX Linear Algebra","dx-linear-algebra","API DirectX\u002FHLSL per operazioni vettoriali e matriciali accelerate destinate a carichi di lavoro di rendering neurale, ML ed elaborazione di immagini.",{"term":1103,"anchor":1104,"definition":1105},"Cooperative Vector","cooperative-vector","Un precedente approccio DirectX per operazioni vettore-matrice accelerate all'interno degli shader che ha contribuito a stabilire il percorso di rendering neurale a livello di shader.",{"term":1107,"anchor":1108,"definition":1109},"Shader Model 6.10","shader-model-6-10","La generazione di shader model in anteprima che contiene le attuali API matriciali di DirectX Linear Algebra.",{"term":1111,"anchor":1112,"definition":1113},"DirectX Compute Graph Compiler","compute-graph-compiler","L'API del compilatore annunciata da Microsoft per ottimizzare ed eseguire grafi di calcolo ML più grandi come carichi di lavoro GPU DirectX nativi.",{"term":1115,"anchor":1116,"definition":1117},"Shader neurale","neural-shader","Un termine pratico per uno shader che esegue inferenza appresa o calcolo neurale come parte dell'elaborazione grafica.",{"term":1119,"anchor":1120,"definition":1121},"Confine shader-o-grafo","shader-or-graph-boundary","Un modello Figure Rocks per decidere se un carico di lavoro ML appartiene come piccola matematica inline nello shader o come grafo di calcolo più grande a livello di modello.",{"term":1123,"anchor":1124,"definition":1125},"Test del valore dello shader neurale","neural-shader-value-test","Un flusso di lavoro Figure Rocks per decidere se il costo tradizionale di calcolo o memoria evitato da una tecnica neurale vale il suo costo di inferenza e i compromessi sulla qualità.",{},{"id":1128,"data":1129,"type":572,"tunes":1131},"h-sources",{"text":1130,"level":47},"Fonti primarie",{},{"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 — Evolving DirectX for the ML Era on Windows","Panoramica ufficiale dell'architettura GDC 2026 che copre ML a livello di shader, DX Linear Algebra e il DirectX Compute Graph Compiler.","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 — D3D12 LinAlg Matrix Preview","Anteprima ufficiale di aprile 2026 che spiega le API unificate di Linear Algebra, le operazioni matriciali e la motivazione del rendering neurale.",{},{"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 — Agility SDK 1.721 Preview","Rilascio ufficiale di maggio 2026 che documenta gli aggiornamenti di Linear Algebra di Shader Model 6.10 e il supporto hardware in anteprima.",{},{"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: Evolving DirectX for the ML Era","Riepilogo ufficiale di Microsoft Game Dev che spiega il ML a livello di shader e a livello di modello e il ruolo del Compute Graph Compiler.",{},{"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 — D3D12 Cooperative Vector","Contesto ufficiale sulle operazioni vettoriali\u002Fmatriciali accelerate dall'hardware e sul rendering neurale direttamente dai thread degli shader.",{},"2.31","DirectX sta andando oltre i tradizionali shader grafici. Microsoft sta aggiungendo l'algebra lineare accelerata via hardware direttamente a HLSL e un percorso separato per modelli ML più grandi, gettando le basi per texture neurali, materiali appresi, illuminazione neurale e altre tecniche di rendering guidate dall'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,"Sfocatura e persistenza","blur-and-persistence",{"id":1201,"name":1202,"slug":1203},252,"Overdrive e Smearing","overdrive-and-smearing",{"id":1205,"name":1206,"slug":1207},253,"Frequenza di aggiornamento e chiarezza","refresh-and-clarity",{"id":1209,"name":1210,"slug":1211},220,"Design al servizio dell'uso","design-that-serves-use",{"id":283,"login":1213,"email":1214,"displayName":1215},"aleksandar","aleksandar@stajic.de","Aleksandar Stajic",[1217,1720],{"lang":8,"title":1218,"content":1219,"contentJson":1220,"excerpt":1719},"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":1718},1790408311441,[1223,1227,1232,1237,1241,1245,1249,1253,1257,1261,1276,1281,1285,1289,1293,1297,1316,1320,1324,1328,1332,1336,1340,1362,1366,1370,1374,1378,1385,1389,1393,1397,1401,1405,1429,1433,1437,1441,1445,1450,1454,1458,1462,1466,1470,1474,1478,1482,1489,1493,1513,1517,1521,1525,1529,1533,1537,1541,1545,1549,1572,1576,1580,1584,1588,1592,1596,1600,1604,1608,1612,1616,1620,1624,1628,1632,1635,1658,1662,1684,1688,1694,1700,1706,1712],{"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":1428},{"rows":1407,"title":1422,"layout":658,"columns":1423},[1408,1411,1414,1416,1419],{"id":781,"label":1409,"values":1410},"Model size",[13,13],{"id":785,"label":1412,"values":1413},"Execution style",[13,13],{"id":789,"label":790,"values":1415},[13,13],{"id":793,"label":1417,"values":1418},"Optimization scope",[13,13],{"id":797,"label":1420,"values":1421},"Typical use",[13,13],"When the ML workload belongs in HLSL vs a model compiler",[1424,1426],{"id":803,"label":1425},"Shader-level path",{"id":806,"label":1427},"Model-level path",{},{"id":810,"data":1430,"type":572,"tunes":1432},{"text":1431,"level":47},"Why cross-vendor support matters more than another AI feature",{},{"id":815,"data":1434,"type":544,"tunes":1436},{"text":1435},"AMD, Intel, NVIDIA and Qualcomm expose different GPU architectures and different forms of dedicated matrix acceleration.",{},{"id":820,"data":1438,"type":544,"tunes":1440},{"text":1439},"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":1442,"type":544,"tunes":1444},{"text":1443},"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":1446,"type":552,"tunes":1449},{"body":1447,"title":1448,"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":1451,"type":572,"tunes":1453},{"text":1452,"level":47},"What hardware supports the current preview?",{},{"id":841,"data":1455,"type":544,"tunes":1457},{"text":1456},"The answer depends on the specific Linear Algebra operation and preview driver.",{},{"id":846,"data":1459,"type":544,"tunes":1461},{"text":1460},"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":1463,"type":544,"tunes":1465},{"text":1464},"This is preview-era support, not a universal retail guarantee. Hardware and driver support can change before finalization.",{},{"id":856,"data":1467,"type":572,"tunes":1469},{"text":1468,"level":47},"Neural rendering is becoming infrastructure",{},{"id":861,"data":1471,"type":544,"tunes":1473},{"text":1472},"DLSS, FSR and other neural graphics technologies are often discussed as branded features visible in a game's settings menu.",{},{"id":866,"data":1475,"type":544,"tunes":1477},{"text":1476},"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":1479,"type":544,"tunes":1481},{"text":1480},"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":1483,"type":749,"tunes":1488},{"url":1484,"title":1485,"excerpt":1486,"ctaLabel":1487},"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":1490,"type":572,"tunes":1492},{"text":1491,"level":47},"The Neural Rendering Stack is becoming layered",{},{"id":889,"data":1494,"type":612,"tunes":1512},{"steps":1495,"title":1511,"orientation":611},[1496,1499,1502,1505,1508],{"label":1497,"description":1498},"Traditional engine work","Simulation, geometry, visibility and base rendering remain standard engine responsibilities.",{"label":1500,"description":1501},"Inline neural shaders","Small learned functions reconstruct textures, materials or lighting inside HLSL.",{"label":1503,"description":1504},"Larger ML graphs","Full reconstruction or inference models execute through a graph-level DirectX path.",{"label":1506,"description":1507},"Vendor hardware mapping","Drivers map common DirectX operations onto the GPU's specialized AI and matrix units.",{"label":1509,"description":1510},"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":1514,"type":572,"tunes":1516},{"text":1515,"level":47},"Why unified profiling matters",{},{"id":915,"data":1518,"type":544,"tunes":1520},{"text":1519},"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":1522,"type":544,"tunes":1524},{"text":1523},"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":1526,"type":544,"tunes":1528},{"text":1527},"If neural rendering becomes infrastructure, it must be measurable like any other rendering stage.",{},{"id":930,"data":1530,"type":572,"tunes":1532},{"text":1531,"level":47},"This does not mean every shader will become a neural network",{},{"id":935,"data":1534,"type":544,"tunes":1536},{"text":1535},"Traditional shader math remains efficient, deterministic and easy to reason about for many workloads.",{},{"id":940,"data":1538,"type":544,"tunes":1540},{"text":1539},"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":1542,"type":544,"tunes":1544},{"text":1543},"The right architecture will remain hybrid.",{},{"id":950,"data":1546,"type":572,"tunes":1548},{"text":1547,"level":47},"The Neural Shader Value Test",{},{"id":955,"data":1550,"type":612,"tunes":1571},{"steps":1551,"title":1570,"orientation":611},[1552,1555,1558,1561,1564,1567],{"label":1553,"description":1554},"1. Identify the expensive traditional operation","What compute, bandwidth, memory or storage cost are you trying to replace?",{"label":1556,"description":1557},"2. Define the neural substitute","What can a small model reconstruct, predict or compress?",{"label":1559,"description":1560},"3. Measure inference cost","The ML model itself consumes GPU time, memory and bandwidth.",{"label":1562,"description":1563},"4. Measure quality stability","Look for temporal artifacts, reconstruction errors and failure cases.",{"label":1565,"description":1566},"5. Measure net savings","The technique is useful only if the avoided traditional cost is worth the added ML cost.",{"label":1568,"description":1569},"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":1573,"type":572,"tunes":1575},{"text":1574,"level":47},"What this means for gamers",{},{"id":984,"data":1577,"type":544,"tunes":1579},{"text":1578},"Gamers may never see a “DX Linear Algebra” switch in a graphics menu.",{},{"id":989,"data":1581,"type":544,"tunes":1583},{"text":1582},"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":1585,"type":544,"tunes":1587},{"text":1586},"The feature is more important as infrastructure than as a brand.",{},{"id":999,"data":1589,"type":572,"tunes":1591},{"text":1590,"level":47},"What would change this answer?",{},{"id":1004,"data":1593,"type":544,"tunes":1595},{"text":1594},"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":1597,"type":544,"tunes":1599},{"text":1598},"Final hardware support, API details, compiler behavior and engine adoption may change before these systems become normal shipping-game infrastructure.",{},{"id":1014,"data":1601,"type":544,"tunes":1603},{"text":1602},"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":1605,"type":572,"tunes":1607},{"text":1606,"level":47},"Limitations",{},{"id":1024,"data":1609,"type":544,"tunes":1611},{"text":1610},"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":1613,"type":544,"tunes":1615},{"text":1614},"Microsoft's examples describe capability and intended use. Real benefits depend on the model, engine integration, GPU architecture, drivers and workload.",{},{"id":1034,"data":1617,"type":572,"tunes":1619},{"text":1618,"level":47},"Conclusion",{},{"id":1039,"data":1621,"type":544,"tunes":1623},{"text":1622},"The important DirectX change is not another checkbox called AI.",{},{"id":1044,"data":1625,"type":544,"tunes":1627},{"text":1626},"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":1629,"type":544,"tunes":1631},{"text":1630},"That is how neural rendering stops being one branded feature and starts becoming part of the rendering infrastructure.",{},{"id":1054,"data":1633,"type":572,"tunes":1634},{"text":1056,"level":47},{},{"id":1059,"data":1636,"type":1059,"tunes":1657},{"items":1637,"title":1656},[1638,1641,1644,1647,1650,1653],{"id":1063,"answer":1639,"question":1640},"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":1642,"question":1643},"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":1645,"question":1646},"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":1648,"question":1649},"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":1651,"question":1652},"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":1654,"question":1655},"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":1659,"type":572,"tunes":1661},{"text":1660,"level":47},"Glossary",{},{"id":1094,"data":1663,"type":1094,"tunes":1683},{"title":1664,"entries":1665},"Key DirectX ML terms",[1666,1668,1670,1672,1674,1677,1680],{"term":1099,"anchor":1100,"definition":1667},"DirectX\u002FHLSL APIs for accelerated vector and matrix operations intended for neural rendering, ML and image-processing workloads.",{"term":1103,"anchor":1104,"definition":1669},"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":1671},"The preview shader-model generation containing the current DirectX Linear Algebra matrix APIs.",{"term":1111,"anchor":1112,"definition":1673},"Microsoft's announced compiler API for optimizing and executing larger ML computation graphs as native DirectX GPU workloads.",{"term":1675,"anchor":1116,"definition":1676},"Neural shader","A practical term for a shader that performs learned inference or neural computation as part of graphics processing.",{"term":1678,"anchor":1120,"definition":1679},"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":1681,"anchor":1124,"definition":1682},"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":1685,"type":572,"tunes":1687},{"text":1686,"level":47},"Primary sources",{},{"id":1133,"data":1689,"type":1140,"tunes":1693},{"link":1135,"meta":1690},{"image":1691,"title":1138,"description":1692},{"url":13},"Official GDC 2026 architecture overview covering shader-level ML, DX Linear Algebra and the DirectX Compute Graph Compiler.",{},{"id":1143,"data":1695,"type":1140,"tunes":1699},{"link":1145,"meta":1696},{"image":1697,"title":1148,"description":1698},{"url":13},"Official April 2026 preview explaining the unified Linear Algebra APIs, matrix operations and neural-rendering motivation.",{},{"id":1152,"data":1701,"type":1140,"tunes":1705},{"link":1154,"meta":1702},{"image":1703,"title":1157,"description":1704},{"url":13},"Official May 2026 release documenting Shader Model 6.10 Linear Algebra updates and preview hardware support.",{},{"id":1161,"data":1707,"type":1140,"tunes":1711},{"link":1163,"meta":1708},{"image":1709,"title":1166,"description":1710},{"url":13},"Official Microsoft Game Dev summary explaining shader-level and model-level ML and the role of the Compute Graph Compiler.",{},{"id":1170,"data":1713,"type":1140,"tunes":1717},{"link":1172,"meta":1714},{"image":1715,"title":1175,"description":1716},{"url":13},"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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