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NVIDIA RTX Neural Texture Compression ändert dieses Modell: Ein Teil der Texturdaten wird zu einer kleinen neuronalen Repräsentation, die von der GPU selbst dekodiert werden kann.\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\">Direkte Antwort\u003C\u002Fstrong>\u003Cdiv class=\"text-gray-700 dark:text-gray-200\">\u003Cstrong>RTX Neural Texture Compression ist kein Textur-Upscaling.\u003C\u002Fstrong> Es komprimiert mehrere Materialtexturen in neuronale Netzwerkgewichte plus kompakte latente Daten und rekonstruiert dann die angeforderten Texturwerte mit einem kleinen neuronalen Netzwerk. Je nach Integrationsmodus kann das Spiel Speicherplatz und VRAM-Nutzung gegen zusätzliche GPU-Inferenzarbeit eintauschen.\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\">Aktueller Status\u003C\u002Fstrong>\u003Cdiv class=\"text-gray-700 dark:text-gray-200\">RTX Neural Texture Compression ist noch ein \u003Cstrong>Beta-SDK\u003C\u002Fstrong>. Die aktuelle Beta v0.10.0 fügte Unterstützung für DirectX 12 Linear Algebra Inferenz hinzu und verbindet NTC direkt mit der neuen Neural-Shader-Infrastruktur, die an anderer Stelle auf Figure Rocks diskutiert wird.\u003C\u002Fdiv>\u003C\u002Faside>\n\u003Cnav class=\"editorjs-toc\" data-editorjs-toc=\"true\" aria-label=\"Inhalt\">\u003Cstrong class=\"editorjs-toc__title\">Inhalt\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\">Warum normale Texturkomprimierung immer noch viel Speicher verbraucht\u003C\u002Fa>\u003C\u002Fli>\u003Cli class=\"editorjs-toc__item\">\u003Ca href=\"#section-9\" class=\"editorjs-toc__link\">Was RTX Neural Texture Compression tatsächlich speichert\u003C\u002Fa>\u003C\u002Fli>\u003Cli class=\"editorjs-toc__item\">\u003Ca href=\"#section-13\" class=\"editorjs-toc__link\">Warum das gemeinsame Komprimieren von Kanälen helfen kann\u003C\u002Fa>\u003C\u002Fli>\u003Cli class=\"editorjs-toc__item\">\u003Ca href=\"#section-17\" class=\"editorjs-toc__link\">Die drei NTC-Laufzeitmodi sind der Schlüssel zum Verständnis der Technologie\u003C\u002Fa>\u003C\u002Fli>\u003Cli class=\"editorjs-toc__item\">\u003Ca href=\"#section-20\" class=\"editorjs-toc__link\">Inferenz beim Laden: neuronale Kompression als Speicherformat\u003C\u002Fa>\u003C\u002Fli>\u003Cli class=\"editorjs-toc__item\">\u003Ca href=\"#section-25\" class=\"editorjs-toc__link\">Inferenz beim Sampling: Die Textur im VRAM neuronal halten\u003C\u002Fa>\u003C\u002Fli>\u003Cli class=\"editorjs-toc__item\">\u003Ca href=\"#section-32\" class=\"editorjs-toc__link\">Inferenz beim Feedback: Nur dekodieren, was der Spieler tatsächlich sieht\u003C\u002Fa>\u003C\u002Fli>\u003Cli class=\"editorjs-toc__item\">\u003Ca href=\"#section-37\" class=\"editorjs-toc__link\">Der Austausch zwischen Texturspeicher und Berechnung\u003C\u002Fa>\u003C\u002Fli>\u003Cli class=\"editorjs-toc__item\">\u003Ca href=\"#section-40\" class=\"editorjs-toc__link\">Warum spezielle Matrix-Hardware wichtig ist\u003C\u002Fa>\u003C\u002Fli>\u003Cli class=\"editorjs-toc__item\">\u003Ca href=\"#section-45\" class=\"editorjs-toc__link\">Warum neuronale Texturkompression kein Textur-Upscaling ist\u003C\u002Fa>\u003C\u002Fli>\u003Cli class=\"editorjs-toc__item\">\u003Ca href=\"#section-49\" class=\"editorjs-toc__link\">Der Qualitätsregler ist Bits pro Pixel\u003C\u002Fa>\u003C\u002Fli>\u003Cli class=\"editorjs-toc__item\">\u003Ca href=\"#section-54\" class=\"editorjs-toc__link\">Warum korrelierte Materialkanäle wichtig sind\u003C\u002Fa>\u003C\u002Fli>\u003Cli class=\"editorjs-toc__item\">\u003Ca href=\"#section-58\" class=\"editorjs-toc__link\">Der neuronale Texturwert-Test\u003C\u002Fa>\u003C\u002Fli>\u003Cli class=\"editorjs-toc__item\">\u003Ca href=\"#section-60\" class=\"editorjs-toc__link\">Warum „8× kleiner“ Kontext braucht\u003C\u002Fa>\u003C\u002Fli>\u003Cli class=\"editorjs-toc__item\">\u003Ca href=\"#section-64\" class=\"editorjs-toc__link\">Die Decoder-Größe ist ein weiterer Kompromiss zwischen Leistung und Qualität\u003C\u002Fa>\u003C\u002Fli>\u003Cli class=\"editorjs-toc__item\">\u003Ca href=\"#section-68\" class=\"editorjs-toc__link\">Warum das für zukünftige Spielinstallationen wichtig ist\u003C\u002Fa>\u003C\u002Fli>\u003Cli class=\"editorjs-toc__item\">\u003Ca href=\"#section-72\" class=\"editorjs-toc__link\">Es ändert auch, was 'Texturspeicher' bedeutet\u003C\u002Fa>\u003C\u002Fli>\u003Cli class=\"editorjs-toc__item\">\u003Ca href=\"#section-76\" class=\"editorjs-toc__link\">Herstellerübergreifende Unterstützung ist differenzierter, als der Name RTX vermuten lässt\u003C\u002Fa>\u003C\u002Fli>\u003Cli class=\"editorjs-toc__item\">\u003Ca href=\"#section-80\" class=\"editorjs-toc__link\">Was würde diese Antwort ändern?\u003C\u002Fa>\u003C\u002Fli>\u003Cli class=\"editorjs-toc__item\">\u003Ca href=\"#section-83\" class=\"editorjs-toc__link\">Einschränkungen\u003C\u002Fa>\u003C\u002Fli>\u003Cli class=\"editorjs-toc__item\">\u003Ca href=\"#section-86\" class=\"editorjs-toc__link\">Fazit\u003C\u002Fa>\u003C\u002Fli>\u003Cli class=\"editorjs-toc__item\">\u003Ca href=\"#section-91\" class=\"editorjs-toc__link\">FAQ\u003C\u002Fa>\u003C\u002Fli>\u003Cli class=\"editorjs-toc__item\">\u003Ca href=\"#section-93\" class=\"editorjs-toc__link\">Glossar\u003C\u002Fa>\u003C\u002Fli>\u003Cli class=\"editorjs-toc__item\">\u003Ca href=\"#section-95\" class=\"editorjs-toc__link\">Primärquellen\u003C\u002Fa>\u003C\u002Fli>\u003C\u002Fol>\u003C\u002Fnav>\n\u003Ch2 id=\"section-5\">Warum normale Texturkomprimierung immer noch viel Speicher verbraucht\u003C\u002Fh2>\n\u003Cp>Ein modernes physikalisch basiertes Material besteht selten aus einem einzigen Bild. Eine einzelne Oberfläche kann Albedo, Normal, Rauheit, Metallizität, Umgebungsverdeckung, Opazität und andere Kanäle verwenden.\u003C\u002Fp>\n\u003Cp>Traditionelle GPU-Blockkomprimierungsformate wie BC1 bis BC7 reduzieren die Kosten, aber die GPU speichert am Ende immer noch konventionelle Texturblöcke für das Material.\u003C\u002Fp>\n\u003Cp>Mit zunehmender Texturauflösung und Materialkomplexität verbrauchen diese Kanäle Speicherplatz, Streaming-Bandbreite und GPU-Speicher.\u003C\u002Fp>\n\u003Ch2 id=\"section-9\">Was RTX Neural Texture Compression tatsächlich speichert\u003C\u002Fh2>\n\u003Cp>NVIDIAs RTXNTC SDK komprimiert die zu einem Material gehörenden Kanäle gemeinsam. Das aktuelle SDK unterstützt bis zu 16 Texturkanäle in einem NTC-Texturset.\u003C\u002Fp>\n\u003Cp>Anstatt nur konventionelle komprimierte Texel zu behalten, erzeugt der Komprimierungsprozess zwei Hauptdinge: Gewichte für einen kleinen neuronalen Decoder und kompakte latente Feature-Daten.\u003C\u002Fp>\n\u003Csection class=\"editorjs-process my-6\">\u003Ch3 class=\"mb-3 text-lg font-semibold\">Die neuronale Textur-Pipeline\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. Original-Materialtexturen\u003C\u002Fdiv>\u003Cdiv class=\"mt-1 text-sm text-gray-600 dark:text-gray-300\">Albedo, Normal, Rauheit, Metallizität und andere Materialkanäle werden zusammen bereitgestellt.\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. Offline-Komprimierung\u003C\u002Fdiv>\u003Cdiv class=\"mt-1 text-sm text-gray-600 dark:text-gray-300\">Das SDK lernt eine kompakte Repräsentation des Materials.\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. Neuronale Gewichte\u003C\u002Fdiv>\u003Cdiv class=\"mt-1 text-sm text-gray-600 dark:text-gray-300\">Ein kleines Decoder-Netzwerk speichert einen Teil dessen, was zur Rekonstruktion des Materials benötigt wird.\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. Latente Daten\u003C\u002Fdiv>\u003Cdiv class=\"mt-1 text-sm text-gray-600 dark:text-gray-300\">Kompakte Feature-Tensoren speichern materialspezifische Informationen.\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. GPU-Inferenz\u003C\u002Fdiv>\u003Cdiv class=\"mt-1 text-sm text-gray-600 dark:text-gray-300\">Zur Laufzeit kombiniert der Decoder die latenten Daten und neuronalen Gewichte, um Texturwerte zu rekonstruieren.\u003C\u002Fdiv>\u003C\u002Fdiv>\u003C\u002Fdiv>\u003C\u002Fsection>\n\u003Ch2 id=\"section-13\">Warum das gemeinsame Komprimieren von Kanälen helfen kann\u003C\u002Fh2>\n\u003Cp>Materialkanäle sind oft miteinander verbunden. Ein in der Grundfarbe sichtbarer Kratzer kann auch in der Normal- oder Rauheitsmap erscheinen. Ein Stoffmuster kann mehrere Kanäle an derselben räumlichen Position beeinflussen.\u003C\u002Fp>\n\u003Cp>NVIDIA hat NTC entwickelt, um diese Korrelationen auszunutzen, anstatt jede Textur unabhängig zu komprimieren.\u003C\u002Fp>\n\u003Cp>Das ist einer der Gründe, warum die Technologie als materialorientierte Komprimierung beschrieben wird und nicht nur als ein weiteres Bildformat.\u003C\u002Fp>\n\u003Ch2 id=\"section-17\">Die drei NTC-Laufzeitmodi sind der Schlüssel zum Verständnis der Technologie\u003C\u002Fh2>\n\u003Cp>Der wichtigste Teil von RTXNTC ist nicht nur, wie das Material komprimiert wird. Es ist, wann das Spiel sich entscheidet, es zu dekomprimieren.\u003C\u002Fp>\n\u003Csection class=\"editorjs-comparison my-6\">\u003Ch3 class=\"mb-3 text-lg font-semibold\">Inferenz beim Laden vs. Inferenz beim Sampling vs. Inferenz beim Feedback\u003C\u002Fh3>\u003Cdiv class=\"overflow-x-auto\">\u003Ctable class=\"w-full border-collapse\">\u003Cthead>\u003Ctr>\u003Cth class=\"border border-gray-300 bg-gray-50 px-4 py-3 text-left dark:border-gray-700 dark:bg-gray-900\">\u003C\u002Fth>\u003Cth class=\"border border-gray-300 bg-gray-50 px-4 py-3 text-left font-semibold dark:border-gray-700 dark:bg-gray-900\">Wann die neuronale Dekodierung stattfindet\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\">Laufzeitverhalten des Texturspeichers\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\">Hauptkompromiss\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\">Inferenz beim Laden\u003C\u002Fth>\u003Ctd class=\"border border-gray-300 px-4 py-3 dark:border-gray-700\">\u003C\u002Ftd>\u003Ctd class=\"border border-gray-300 px-4 py-3 dark:border-gray-700\">\u003C\u002Ftd>\u003Ctd class=\"border border-gray-300 px-4 py-3 dark:border-gray-700\">\u003C\u002Ftd>\u003C\u002Ftr>\u003Ctr>\u003Cth class=\"border border-gray-300 px-4 py-3 text-left font-semibold dark:border-gray-700\">Inferenz beim Sampling\u003C\u002Fth>\u003Ctd class=\"border border-gray-300 px-4 py-3 dark:border-gray-700\">\u003C\u002Ftd>\u003Ctd class=\"border border-gray-300 px-4 py-3 dark:border-gray-700\">\u003C\u002Ftd>\u003Ctd class=\"border border-gray-300 px-4 py-3 dark:border-gray-700\">\u003C\u002Ftd>\u003C\u002Ftr>\u003Ctr>\u003Cth class=\"border border-gray-300 px-4 py-3 text-left font-semibold dark:border-gray-700\">Inferenz beim Feedback\u003C\u002Fth>\u003Ctd class=\"border border-gray-300 px-4 py-3 dark:border-gray-700\">\u003C\u002Ftd>\u003Ctd class=\"border border-gray-300 px-4 py-3 dark:border-gray-700\">\u003C\u002Ftd>\u003Ctd class=\"border border-gray-300 px-4 py-3 dark:border-gray-700\">\u003C\u002Ftd>\u003C\u002Ftr>\u003C\u002Ftbody>\u003C\u002Ftable>\u003C\u002Fdiv>\u003C\u002Fsection>\n\u003Ch2 id=\"section-20\">Inferenz beim Laden: neuronale Kompression als Speicherformat\u003C\u002Fh2>\n\u003Cp>Inferenz beim Laden ist der am einfachsten zu verstehende Modus.\u003C\u002Fp>\n\u003Cp>Das Spiel speichert das Material in kompakter NTC-Form. Wenn das Asset geladen wird, rekonstruiert die GPU die Texturdaten und kann sie in gewöhnliche BCn-Texturformate transkodieren.\u003C\u002Fp>\n\u003Cp>Danach kann das Rendering normales Textursampling verwenden. Die wesentliche Einsparung liegt vor allem vor der Dekompression: Paketgröße des Spiels, Downloadgröße oder Bandbreite für Asset-Streaming.\u003C\u002Fp>\n\u003Cp>Doch sobald das Material vollständig in konventionelle Texturen expandiert ist, nähert sich sein VRAM-Fußabdruck zur Laufzeit wieder der konventionellen Darstellung an.\u003C\u002Fp>\n\u003Ch2 id=\"section-25\">Inferenz beim Sampling: Die Textur im VRAM neuronal halten\u003C\u002Fh2>\n\u003Cp>Inferenz beim Sampling ist der radikalere Modus.\u003C\u002Fp>\n\u003Cp>Anstatt das Material vor dem Rendering in konventionelle Texturen zu expandieren, liest der Shader kompakte latente Daten und führt den neuronalen Decoder aus, wenn er Texturwerte benötigt.\u003C\u002Fp>\n\u003Cp>NVIDIAs eigenes SDK-Beispiel vergleicht eine 12 MB große BCn-Materialdarstellung mit einer 2,5 MB großen NTC-Darstellung bei Verwendung von Inferenz beim Sampling.\u003C\u002Fp>\n\u003Cp>Die Einsparung ist real, weil die konventionellen Texturdaten nicht vollständig resident bleiben müssen. Aber die Kosten verlagern sich woandershin: Der Pixel- oder Hit-Shader führt nun die neuronale Inferenz durch.\u003C\u002Fp>\n\u003Caside class=\"editorjs-callout editorjs-callout--warning my-6 rounded-xl border p-5 border-amber-300 bg-amber-50 dark:border-amber-900 dark:bg-amber-950\u002F20\" role=\"note\">\u003Cstrong class=\"block mb-2 text-gray-900 dark:text-gray-100\">Kompression lässt die Arbeit nicht verschwinden\u003C\u002Fstrong>\u003Cdiv class=\"text-gray-700 dark:text-gray-200\">Inferenz beim Sampling tauscht \u003Cstrong>Speicher und Bandbreite\u003C\u002Fstrong> gegen \u003Cstrong>GPU-Berechnung\u003C\u002Fstrong>. Die richtige Frage ist nicht „Wie viel kleiner ist die Textur?“, sondern „Ist die Speichereinsparung die zusätzlichen Inferenzkosten für diese Arbeitslast wert?“\u003C\u002Fdiv>\u003C\u002Faside>\n\u003Caside class=\"editorjs-referral my-6\">\u003Ca href=\"https:\u002F\u002Ffigure.rocks\u002Fde\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\">VRAM-Nutzung ist nicht VRAM-Bedarf: Warum ein voller Speicheranzeiger nicht die ganze Geschichte erzählt\u003C\u002Fstrong>\u003Cp class=\"mt-2 text-sm text-gray-600 dark:text-gray-300\">Warum VRAM-Kapazität, Budgets, Residenz und tatsächlicher Speicherdruck verschiedene Dinge sind.\u003C\u002Fp>\u003Cspan class=\"mt-3 inline-flex text-sm font-medium text-primary-600 dark:text-primary-400\">VRAM-Leitfaden lesen →\u003C\u002Fspan>\u003C\u002Fdiv>\u003C\u002Fa>\u003C\u002Faside>\n\u003Ch2 id=\"section-32\">Inferenz beim Feedback: Nur dekodieren, was der Spieler tatsächlich sieht\u003C\u002Fh2>\n\u003Cp>Inferenz beim Feedback liegt zwischen den beiden Extremen.\u003C\u002Fp>\n\u003Cp>Der Renderer verfolgt, welche Texturkacheln tatsächlich angefordert werden. Anstatt das vollständige Material sofort zu expandieren, kann das System angeforderte Kacheln in Stapeln dekodieren und einen Arbeitscache beibehalten.\u003C\u002Fp>\n\u003Cp>Konzeptionell kombiniert dies neuronale Kompression mit Textur-Streaming: Das System zahlt die Dekodierungskosten nur für Regionen, die relevant werden.\u003C\u002Fp>\n\u003Cp>Die aktuelle Beispielimplementierung ist spezialisierter als die anderen Modi und ihre Unterstützungsbedingungen unterscheiden sich, daher sollte sie als Integrationsstrategie und nicht als universeller Ersatz für gewöhnliches Textur-Streaming betrachtet werden.\u003C\u002Fp>\n\u003Ch2 id=\"section-37\">Der Austausch zwischen Texturspeicher und Berechnung\u003C\u002Fh2>\n\u003Cp>Der einfachste Weg, neuronale Texturkompression zu verstehen, ist sie als einen Austausch zwischen Ressourcen zu betrachten.\u003C\u002Fp>\n\u003Csection class=\"editorjs-comparison my-6\">\u003Ch3 class=\"mb-3 text-lg font-semibold\">Was sich verschiebt, wenn Texturspeicher neuronal wird\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\">Traditionelle komprimierte Textur\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\">Neuronale Texturrepräsentation\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\">Datenträger\u002FSpeicher\u003C\u002Fth>\u003Ctd class=\"border border-gray-300 px-4 py-3 dark:border-gray-700\">\u003C\u002Ftd>\u003Ctd class=\"border border-gray-300 px-4 py-3 dark:border-gray-700\">\u003C\u002Ftd>\u003C\u002Ftr>\u003Ctr>\u003Cth class=\"border border-gray-300 px-4 py-3 text-left font-semibold dark:border-gray-700\">VRAM\u003C\u002Fth>\u003Ctd class=\"border border-gray-300 px-4 py-3 dark:border-gray-700\">\u003C\u002Ftd>\u003Ctd class=\"border border-gray-300 px-4 py-3 dark:border-gray-700\">\u003C\u002Ftd>\u003C\u002Ftr>\u003Ctr>\u003Cth class=\"border border-gray-300 px-4 py-3 text-left font-semibold dark:border-gray-700\">Sampling-Kosten\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\">Qualitätskontrolle\u003C\u002Fth>\u003Ctd class=\"border border-gray-300 px-4 py-3 dark:border-gray-700\">\u003C\u002Ftd>\u003Ctd class=\"border border-gray-300 px-4 py-3 dark:border-gray-700\">\u003C\u002Ftd>\u003C\u002Ftr>\u003C\u002Ftbody>\u003C\u002Ftable>\u003C\u002Fdiv>\u003C\u002Fsection>\n\u003Ch2 id=\"section-40\">Warum spezielle Matrix-Hardware wichtig ist\u003C\u002Fh2>\n\u003Cp>Ein neuronales Netzwerk für das Texture-Sampling auszuführen, wäre zu teuer, wenn jede Multiplikation wie gewöhnliche skalare Shader-Arbeit behandelt werden müsste.\u003C\u002Fp>\n\u003Cp>RTXNTC profitiert daher von Cooperative Vector und nun von DirectX 12 Linear Algebra-Pfaden, die Shadern die Nutzung von GPU-Matrixbeschleunigungs-Hardware ermöglichen.\u003C\u002Fp>\n\u003Cp>Die Beta v0.10.0 fügte explizit Inferenz über die DirectX 12 Linear Algebra API hinzu, die mit Shader Model 6.10 eingeführt wurde.\u003C\u002Fp>\n\u003Caside class=\"editorjs-referral my-6\">\u003Ca href=\"https:\u002F\u002Ffigure.rocks\u002Fde\u002Fblog\u002Fdirectx-is-becoming-an-ml-platform-what-linear-algebra-and-neural-shaders-mean-for-future-games\" class=\"flex flex-col sm:flex-row gap-4 rounded-xl border border-gray-200 dark:border-gray-700 p-4 transition hover:border-primary-500\">\u003Cdiv class=\"min-w-0 flex-1\">\u003Cstrong class=\"block text-lg text-gray-900 dark:text-gray-100\">DirectX wird zu einer ML-Plattform: Was Lineare Algebra und neuronale Shader für zukünftige Spiele bedeuten\u003C\u002Fstrong>\u003Cp class=\"mt-2 text-sm text-gray-600 dark:text-gray-300\">Wie DirectX Matrix- und neuronale Operationen direkt in HLSL und die Grafikpipeline verlagert.\u003C\u002Fp>\u003Cspan class=\"mt-3 inline-flex text-sm font-medium text-primary-600 dark:text-primary-400\">Lies den DirectX-Leitfaden für neuronale Shader →\u003C\u002Fspan>\u003C\u002Fdiv>\u003C\u002Fa>\u003C\u002Faside>\n\u003Ch2 id=\"section-45\">Warum neuronale Texturkompression kein Textur-Upscaling ist\u003C\u002Fh2>\n\u003Cp>Beide Techniken können maschinelles Lernen nutzen, aber sie lösen unterschiedliche Probleme.\u003C\u002Fp>\n\u003Csection class=\"editorjs-comparison my-6\">\u003Ch3 class=\"mb-3 text-lg font-semibold\">Neuronale Texturkompression vs. Super Resolution\u003C\u002Fh3>\u003Cdiv class=\"overflow-x-auto\">\u003Ctable class=\"w-full border-collapse\">\u003Cthead>\u003Ctr>\u003Cth class=\"border border-gray-300 bg-gray-50 px-4 py-3 text-left dark:border-gray-700 dark:bg-gray-900\">\u003C\u002Fth>\u003Cth class=\"border border-gray-300 bg-gray-50 px-4 py-3 text-left font-semibold dark:border-gray-700 dark:bg-gray-900\">Neuronale Texturkompression\u003C\u002Fth>\u003Cth class=\"border border-gray-300 bg-gray-50 px-4 py-3 text-left font-semibold dark:border-gray-700 dark:bg-gray-900\">Super Resolution\u003C\u002Fth>\u003C\u002Ftr>\u003C\u002Fthead>\u003Ctbody>\u003Ctr>\u003Cth class=\"border border-gray-300 px-4 py-3 text-left font-semibold dark:border-gray-700\">Eingabe\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\">Ziel\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\">Wann es läuft\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\">Ausgabe\u003C\u002Fth>\u003Ctd class=\"border border-gray-300 px-4 py-3 dark:border-gray-700\">\u003C\u002Ftd>\u003Ctd class=\"border border-gray-300 px-4 py-3 dark:border-gray-700\">\u003C\u002Ftd>\u003C\u002Ftr>\u003C\u002Ftbody>\u003C\u002Ftable>\u003C\u002Fdiv>\u003C\u002Fsection>\n\u003Cp>Neuronale Texturkompression kann daher unterhalb von DLSS, FSR, XeSS oder nativem Rendering existieren. Sie ändert, wie Materialdaten gespeichert und rekonstruiert werden, nicht die endgültige Anzeigeauflösung.\u003C\u002Fp>\n\u003Ch2 id=\"section-49\">Der Qualitätsregler ist Bits pro Pixel\u003C\u002Fh2>\n\u003Cp>NTC ist verlustbehaftete Kompression. Die Menge der komprimierten Informationen wird weitgehend über die Einstellung Bits pro Pixel gesteuert.\u003C\u002Fp>\n\u003Cp>Eine höhere Bitrate gibt dem Modell mehr Informationen und verbessert im Allgemeinen die Rekonstruktionsqualität. Eine niedrigere Bitrate verbessert die Kompression, erhöht aber das Risiko sichtbarer Fehler.\u003C\u002Fp>\n\u003Cp>Da mehrere Kanäle dieselbe Repräsentation teilen, kann das Hinzufügen weiterer Materialkanäle ohne Erhöhung der Bitrate die für jeden Kanal verfügbare Qualität verringern.\u003C\u002Fp>\n\u003Caside class=\"editorjs-callout editorjs-callout--note my-6 rounded-xl border p-5 border-gray-300 bg-gray-50 dark:border-gray-700 dark:bg-gray-900\u002F40\" role=\"note\">\u003Cstrong class=\"block mb-2 text-gray-900 dark:text-gray-100\">Neuronal bedeutet nicht verlustfrei\u003C\u002Fstrong>\u003Cdiv class=\"text-gray-700 dark:text-gray-200\">Die SDK-Dokumentation weist ausdrücklich darauf hin, dass Kompressionsfehler normal sind. Das praktische Ziel ist \u003Cstrong>akzeptabler visueller Fehler bei nützlichem Speicher- und Laufzeitaufwand\u003C\u002Fstrong>.\u003C\u002Fdiv>\u003C\u002Faside>\n\u003Ch2 id=\"section-54\">Warum korrelierte Materialkanäle wichtig sind\u003C\u002Fh2>\n\u003Cp>Eine neuronale Repräsentation wird wertvoller, wenn mehrere Materialkanäle verwandte Struktur beschreiben.\u003C\u002Fp>\n\u003Cp>Wenn Albedo, Normale und Rauheit alle dieselben Kratzer, Nähte oder Gewebestrukturen enthalten, kann der Decoder gemeinsame räumliche Informationen nutzen.\u003C\u002Fp>\n\u003Cp>Wenn die Kanäle unzusammenhängendes Rauschen sind, gibt es weniger gemeinsame Struktur zu nutzen und das Kompressionsproblem wird schwieriger.\u003C\u002Fp>\n\u003Ch2 id=\"section-58\">Der neuronale Texturwert-Test\u003C\u002Fh2>\n\u003Csection class=\"editorjs-process my-6\">\u003Ch3 class=\"mb-3 text-lg font-semibold\">Wann ist neuronale Texturkompression tatsächlich nützlich?\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. Konventionelle Texturkosten messen\u003C\u002Fdiv>\u003Cdiv class=\"mt-1 text-sm text-gray-600 dark:text-gray-300\">Wie viel Speicherplatz, Streaming-Bandbreite und VRAM verbrauchen die vorhandenen Materialtexturen?\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. Den Laufzeitmodus wählen\u003C\u002Fdiv>\u003Cdiv class=\"mt-1 text-sm text-gray-600 dark:text-gray-300\">Möchten Sie nur Speichereinsparungen, dauerhafte VRAM-Einsparungen oder gestreamte Kachelrekonstruktion?\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. Ein akzeptables Qualitätsziel festlegen\u003C\u002Fdiv>\u003Cdiv class=\"mt-1 text-sm text-gray-600 dark:text-gray-300\">Vergleichen Sie rekonstruierte Kanäle mit dem Originalmaterial, nicht nur mit dem finalen Beauty-Bild.\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. Inferenzkosten messen\u003C\u002Fdiv>\u003Cdiv class=\"mt-1 text-sm text-gray-600 dark:text-gray-300\">Erfassen Sie zusätzliche Shader- oder Dekompressionszeit auf der tatsächlichen Ziel-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\">5. Speichereinsparungen messen\u003C\u002Fdiv>\u003Cdiv class=\"mt-1 text-sm text-gray-600 dark:text-gray-300\">Prüfen Sie den tatsächlichen Laufzeit-Working-Set und nicht nur die komprimierte Dateigröße.\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. Schwierige Materialien testen\u003C\u002Fdiv>\u003Cdiv class=\"mt-1 text-sm text-gray-600 dark:text-gray-300\">Feine Normalen, scharfe Masken, Opazität, Text und unzusammenhängende Kanäle können Kompressionsfehler aufdecken.\u003C\u002Fdiv>\u003C\u002Fdiv>\u003Cdiv class=\"editorjs-process__step min-w-0  rounded-xl border border-gray-200 dark:border-gray-700 p-4\">\u003Cdiv class=\"text-xs font-semibold text-gray-500 dark:text-gray-400\">7\u003C\u002Fdiv>\u003Cdiv class=\"mt-1 font-semibold text-gray-900 dark:text-gray-100\">7. Nach Nettovorteil entscheiden\u003C\u002Fdiv>\u003Cdiv class=\"mt-1 text-sm text-gray-600 dark:text-gray-300\">Verwenden Sie NTC nur dort, wo der eingesparte Speicher\u002Fdie eingesparte Bandbreite die zusätzliche Inferenz- und Integrationskomplexität wert ist.\u003C\u002Fdiv>\u003C\u002Fdiv>\u003C\u002Fdiv>\u003C\u002Fsection>\n\u003Ch2 id=\"section-60\">Warum „8× kleiner“ Kontext braucht\u003C\u002Fh2>\n\u003Cp>NVIDIAs RTX Kit beschreibt RTX Neural Texture Compression als bis zu 8× Verbesserung des Speicherplatzbedarfs bei ähnlicher visueller Qualität wie traditionelle Blockkompression.\u003C\u002Fp>\n\u003Cp>Der Ausdruck „bis zu“ ist wichtig. Das Kompressionsverhältnis hängt vom Material, der Anzahl der Kanäle, der Ziel-Bitrate, der Decoder-Konfiguration und der Qualitätsschwelle ab.\u003C\u002Fp>\n\u003Cp>Dasselbe Verhältnis beschreibt auch nicht automatisch VRAM-Einsparungen. Inferenz beim Laden kann von einer kompakten Datei ausgehen und dennoch in konventionelle GPU-Texturen expandieren. Inferenz beim Sampling bewahrt die kompakte Repräsentation im GPU-Speicher, verbraucht aber mehr Rechenleistung während des Shadings.\u003C\u002Fp>\n\u003Ch2 id=\"section-64\">Die Decoder-Größe ist ein weiterer Kompromiss zwischen Leistung und Qualität\u003C\u002Fh2>\n\u003Cp>Die NTC-Laufzeit verwendet ein kleines mehrschichtiges Perzeptron, um Texturwerte zu dekodieren.\u003C\u002Fp>\n\u003Cp>NVIDIAs aktuelle Bibliothek verwendet eine konfigurierbare Decoder-Architektur. Größere Netzwerke können die Kompressionsqualität verbessern, kosten aber mehr Ausführungszeit; kleinere Netzwerke können schneller laufen, mit einigem Verlust an Rekonstruktionsqualität.\u003C\u002Fp>\n\u003Cp>Das gibt Engine-Entwicklern eine weitere Tuning-Dimension jenseits von Texturauflösung und Bitrate.\u003C\u002Fp>\n\u003Ch2 id=\"section-68\">Warum das für zukünftige Spielinstallationen wichtig ist\u003C\u002Fh2>\n\u003Cp>Moderne Spiele liefern zunehmend hochauflösende Material-Sets, die sowohl die Download-Größe als auch den Laufzeitspeicher beeinflussen.\u003C\u002Fp>\n\u003Cp>Neuronale Texturkompression schafft eine neue Option: eine kompakte gelernte Repräsentation ausliefern und später entscheiden, ob sie beim Laden expandiert, direkt während des Shadings rekonstruiert oder nur angeforderte Kacheln dekodiert wird.\u003C\u002Fp>\n\u003Cp>Das bedeutet, dass eine komprimierte Asset-Repräsentation an mehreren verschiedenen Laufzeitspeicher-Strategien teilnehmen kann.\u003C\u002Fp>\n\u003Ch2 id=\"section-72\">Es ändert auch, was 'Texturspeicher' bedeutet\u003C\u002Fh2>\n\u003Cp>Beim traditionellen Rendering geht es bei einem Texturspeicher-Budget hauptsächlich um Texturformate, Mip-Ebenen, Auflösung und Residenz.\u003C\u002Fp>\n\u003Cp>Bei neuronalen Texturen können Entwickler auch latente Daten, Decoder-Gewichte, Inferenzpuffer, transkodierte Caches und die Berechnungskosten einplanen, die zur Rekonstruktion der angeforderten Werte erforderlich sind.\u003C\u002Fp>\n\u003Cp>Das Asset hat also nicht mehr eine einzige einfache feste Speicheridentität.\u003C\u002Fp>\n\u003Ch2 id=\"section-76\">Herstellerübergreifende Unterstützung ist differenzierter, als der Name RTX vermuten lässt\u003C\u002Fh2>\n\u003Cp>RTXNTC ist ein NVIDIA-SDK, und die Komprimierung selbst erfordert derzeit laut den SDK-Anforderungen eine NVIDIA-GPU.\u003C\u002Fp>\n\u003Cp>Die Laufzeitdekomprimierung ist breiter aufgestellt. NVIDIA dokumentiert funktionale Pfade auf Shader-Model-6-Hardware und vermerkt Validierungen auf NVIDIA-, AMD- und Intel-GPUs, während fortgeschrittene Cooperative-Vector-\u002FLinear-Algebra-Pfade von API- und Treiberunterstützung abhängen.\u003C\u002Fp>\n\u003Cp>Leistung und Funktionsparität sollten daher nicht allein deshalb über Hersteller hinweg angenommen werden, weil der grundlegende Decoder ausgeführt werden kann.\u003C\u002Fp>\n\u003Ch2 id=\"section-80\">Was würde diese Antwort ändern?\u003C\u002Fh2>\n\u003Cp>NTC befindet sich noch in der Beta-Phase. Laufzeitmodi, Decoder-Architekturen, Treiberunterstützung und Integrationspfade können sich vor einer stabilen Produktionsversion ändern.\u003C\u002Fp>\n\u003Cp>Die größte langfristige Veränderung wäre eine breite Einführung standardisierter neuronaler Shader-Primitive über DirectX und Vulkan hinweg. Das würde die neuronale Texturdekodierung weniger abhängig von herstellerspezifischen Ausführungspfaden machen.\u003C\u002Fp>\n\u003Ch2 id=\"section-83\">Einschränkungen\u003C\u002Fh2>\n\u003Cp>Dieser Artikel beschreibt die Architektur und das aktuelle öffentliche Verhalten des RTXNTC-SDK. Die von NVIDIA angegebenen Komprimierungsversprechen stammen vom Hersteller und sollten nicht als garantierte Ergebnisse für jedes Material betrachtet werden.\u003C\u002Fp>\n\u003Cp>Das aktuelle SDK ist Beta-Software, und einige Vorschau-Pfade haben dokumentierte Treiber- und Plattformbeschränkungen.\u003C\u002Fp>\n\u003Ch2 id=\"section-86\">Fazit\u003C\u002Fh2>\n\u003Cp>RTX Neural Texture Compression ist interessant, weil es eine sehr alte Annahme verändert: Texturdetails müssen nicht immer als herkömmliche Texel im Speicher vorliegen.\u003C\u002Fp>\n\u003Cp>Ein Material kann stattdessen teilweise als kompakte erlernte Repräsentation gespeichert und bei Bedarf rekonstruiert werden.\u003C\u002Fp>\n\u003Cp>Das liefert weder kostenlose Qualität noch kostenlosen Speicher. Es entsteht ein neuer Austausch: weniger Speicher, Bandbreite und potenziell VRAM im Gegenzug für neuronale Inferenzarbeit.\u003C\u002Fp>\n\u003Cp>Die eigentliche Innovation ist nicht „KI macht Texturen schärfer“. Sie besteht darin, dass ein Teil der Materialdaten eines Spiels zu Berechnung werden kann.\u003C\u002Fp>\n\u003Ch2 id=\"section-91\">FAQ\u003C\u002Fh2>\n\u003Csection class=\"editorjs-faq my-6 rounded-xl border border-gray-200 p-5 dark:border-gray-700\">\u003Ch3 class=\"mb-3 text-lg font-semibold\">RTX Neural Texture Compression einfach erklärt\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\">Ist RTX Neural Texture Compression ein Upscaler?\u003C\u002Fh4>\u003Cdiv class=\"mt-2 text-gray-600 dark:text-gray-300\">Nein. Es komprimiert und rekonstruiert Materialtexturdaten. Super-Resolution-Technologien rekonstruieren das final gerenderte Bild in einer höheren Anzeigeauflösung.\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\">Kann NTC den VRAM-Verbrauch reduzieren?\u003C\u002Fh4>\u003Cdiv class=\"mt-2 text-gray-600 dark:text-gray-300\">Ja, insbesondere bei Inference on Sample, da die kompakte neuronale Repräsentation im GPU-Speicher verbleiben kann, anstatt vollständig expandierte konventionelle Texturen zu benötigen. Inference on Load bewahrt die Speichereinsparungen hauptsächlich vor der Expansion.\u003C\u002Fdiv>\u003C\u002Fdiv>\u003Cdiv id=\"faq3\" class=\"border-t border-gray-200 py-4 first:border-t-0 dark:border-gray-700\">\u003Ch4 class=\"font-semibold text-gray-900 dark:text-gray-100\">Was speichert das neuronale Netzwerk?\u003C\u002Fh4>\u003Cdiv class=\"mt-2 text-gray-600 dark:text-gray-300\">Das komprimierte Material enthält kompakte latente Feature-Daten sowie Gewichte für ein kleines Decoder-Netzwerk, das die Texturwerte rekonstruiert.\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\">Dekodiert NTC die gesamte Textur vor dem Rendern?\u003C\u002Fh4>\u003Cdiv class=\"mt-2 text-gray-600 dark:text-gray-300\">Nicht unbedingt. Inference on Load tut dies, Inference on Sample rekonstruiert Werte während des Shader-Samplings, und Inference on Feedback kann angeforderte Textur-Tiles dekodieren.\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\">Ist neuronale Texturkompression verlustfrei?\u003C\u002Fh4>\u003Cdiv class=\"mt-2 text-gray-600 dark:text-gray-300\">Nein. RTXNTC ist verlustbehaftete Kompression, und die Qualität hängt von Bitrate, Kanalanzahl, Decoder-Konfiguration und dem Material selbst ab.\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\">Ist RTXNTC produktionsreif?\u003C\u002Fh4>\u003Cdiv class=\"mt-2 text-gray-600 dark:text-gray-300\">Das aktuelle öffentliche SDK ist noch als Beta gekennzeichnet, daher können sich APIs, Support und Leistungsmerkmale weiterhin ändern.\u003C\u002Fdiv>\u003C\u002Fdiv>\u003C\u002Fsection>\n\u003Ch2 id=\"section-93\">Glossar\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\">Wichtige Begriffe der neuronalen Texturierung\u003C\u002Fh3>\u003Cdl>\u003Cdiv id=\"neural-texture-compression\" class=\"border-t border-gray-200 dark:border-gray-700 py-3 first:border-t-0\">\u003Cdt class=\"font-semibold text-gray-900 dark:text-gray-100\">Neural Texture Compression\u003C\u002Fdt>\u003Cdd class=\"mt-1 text-gray-600 dark:text-gray-300\">Eine Technik, die Texturinformationen als kompakte latente Daten plus neuronale Decoder-Gewichte speichert, anstatt nur konventionelle Texel-Blöcke zu verwenden.\u003C\u002Fdd>\u003C\u002Fdiv>\u003Cdiv id=\"latent-data\" class=\"border-t border-gray-200 dark:border-gray-700 py-3 first:border-t-0\">\u003Cdt class=\"font-semibold text-gray-900 dark:text-gray-100\">Latente Daten\u003C\u002Fdt>\u003Cdd class=\"mt-1 text-gray-600 dark:text-gray-300\">Kompakte erlernte Features, die der neuronale Decoder zur Rekonstruktion der Texturwerte verwendet.\u003C\u002Fdd>\u003C\u002Fdiv>\u003Cdiv id=\"decoder\" class=\"border-t border-gray-200 dark:border-gray-700 py-3 first:border-t-0\">\u003Cdt class=\"font-semibold text-gray-900 dark:text-gray-100\">Decoder\u003C\u002Fdt>\u003Cdd class=\"mt-1 text-gray-600 dark:text-gray-300\">Ein kleines neuronales Netzwerk, das latente Features in rekonstruierte Texturkanäle umwandelt.\u003C\u002Fdd>\u003C\u002Fdiv>\u003Cdiv id=\"inference-on-load\" class=\"border-t border-gray-200 dark:border-gray-700 py-3 first:border-t-0\">\u003Cdt class=\"font-semibold text-gray-900 dark:text-gray-100\">Inference on Load\u003C\u002Fdt>\u003Cdd class=\"mt-1 text-gray-600 dark:text-gray-300\">Laufzeitmodus, der die neuronale Textur beim Laden eines Assets dekodiert, üblicherweise in konventionelle Texturformate.\u003C\u002Fdd>\u003C\u002Fdiv>\u003Cdiv id=\"inference-on-sample\" class=\"border-t border-gray-200 dark:border-gray-700 py-3 first:border-t-0\">\u003Cdt class=\"font-semibold text-gray-900 dark:text-gray-100\">Inference on Sample\u003C\u002Fdt>\u003Cdd class=\"mt-1 text-gray-600 dark:text-gray-300\">Laufzeitmodus, der die neuronale Dekodierung direkt während des Texture-Samplings durchführt, sodass die komprimierte Repräsentation resident bleiben kann.\u003C\u002Fdd>\u003C\u002Fdiv>\u003Cdiv id=\"inference-on-feedback\" class=\"border-t border-gray-200 dark:border-gray-700 py-3 first:border-t-0\">\u003Cdt class=\"font-semibold text-gray-900 dark:text-gray-100\">Inference on Feedback\u003C\u002Fdt>\u003Cdd class=\"mt-1 text-gray-600 dark:text-gray-300\">Laufzeitstrategie, die Texture-Feedback nutzt, um nur angeforderte Tiles zu dekodieren und zu cachen.\u003C\u002Fdd>\u003C\u002Fdiv>\u003Cdiv id=\"texture-storage-compute-exchange\" class=\"border-t border-gray-200 dark:border-gray-700 py-3 first:border-t-0\">\u003Cdt class=\"font-semibold text-gray-900 dark:text-gray-100\">Texture Storage–Compute Exchange\u003C\u002Fdt>\u003Cdd class=\"mt-1 text-gray-600 dark:text-gray-300\">Ein Figure-Rocks-Modell, das den Tausch von Texturspeicher, Bandbreite und VRAM gegen zusätzliche neuronale GPU-Inferenzarbeit beschreibt.\u003C\u002Fdd>\u003C\u002Fdiv>\u003Cdiv id=\"neural-texture-value-test\" class=\"border-t border-gray-200 dark:border-gray-700 py-3 first:border-t-0\">\u003Cdt class=\"font-semibold text-gray-900 dark:text-gray-100\">Neural Texture Value Test\u003C\u002Fdt>\u003Cdd class=\"mt-1 text-gray-600 dark:text-gray-300\">Ein Figure-Rocks-Workflow zur Entscheidung, ob neuronale Texturkompression für ein bestimmtes Material und eine Ziel-GPU einen Nettonutzen bringt.\u003C\u002Fdd>\u003C\u002Fdiv>\u003C\u002Fdl>\u003C\u002Fsection>\n\u003Ch2 id=\"section-95\">Primärquellen\u003C\u002Fh2>\n\u003Ca href=\"https:\u002F\u002Fdeveloper.nvidia.com\u002Frtx-kit\" target=\"_blank\" rel=\"noopener noreferrer\" class=\"editorjs-link-tool block border border-gray-200 dark:border-gray-700 rounded-lg p-4 transition text-gray-900 dark:text-gray-100 hover:border-primary-500 hover:bg-primary-50 dark:hover:bg-gray-900 hover:text-gray-900 dark:hover:text-gray-100\">\u003Cstrong class=\"block font-semibold\">NVIDIA Developer — RTX Kit\u003C\u002Fstrong>\u003Cp class=\"text-sm text-gray-600 dark:text-gray-400\">Offizielle Übersicht über RTX Neural Texture Compression und NVIDIAs veröffentlichte Positionierung zur Speicherreduzierung.\u003C\u002Fp>\u003C\u002Fa>\n\u003Ca href=\"https:\u002F\u002Fgithub.com\u002FNVIDIA-RTX\u002FRTXNTC\u002Fblob\u002Fmain\u002FREADME.md\" target=\"_blank\" rel=\"noopener noreferrer\" class=\"editorjs-link-tool block border border-gray-200 dark:border-gray-700 rounded-lg p-4 transition text-gray-900 dark:text-gray-100 hover:border-primary-500 hover:bg-primary-50 dark:hover:bg-gray-900 hover:text-gray-900 dark:hover:text-gray-100\">\u003Cstrong class=\"block font-semibold\">NVIDIA RTXNTC — SDK README\u003C\u002Fstrong>\u003Cp class=\"text-sm text-gray-600 dark:text-gray-400\">Offizielle SDK-Dokumentation, die Materialkanal-Kompression, Decoder-\u002FLatent-Repräsentation, Laufzeitmodi, Speicherbeispiele, Systemanforderungen und Cooperative-Vector-Unterstützung beschreibt.\u003C\u002Fp>\u003C\u002Fa>\n\u003Ca href=\"https:\u002F\u002Fgithub.com\u002FNVIDIA-RTX\u002FRTXNTC\u002Freleases\" target=\"_blank\" rel=\"noopener noreferrer\" class=\"editorjs-link-tool block border border-gray-200 dark:border-gray-700 rounded-lg p-4 transition text-gray-900 dark:text-gray-100 hover:border-primary-500 hover:bg-primary-50 dark:hover:bg-gray-900 hover:text-gray-900 dark:hover:text-gray-100\">\u003Cstrong class=\"block font-semibold\">NVIDIA RTXNTC — Releases\u003C\u002Fstrong>\u003Cp class=\"text-sm text-gray-600 dark:text-gray-400\">Offizielle Release-Historie, einschließlich v0.10.0 Beta und DirectX 12 Linear Algebra Inferenz-Unterstützung.\u003C\u002Fp>\u003C\u002Fa>\n\u003Ca href=\"https:\u002F\u002Fgithub.com\u002FNVIDIA-RTX\u002FRTXNTC\u002Fblob\u002Fmain\u002Fdocs\u002FSettingsAndQuality.md\" target=\"_blank\" rel=\"noopener noreferrer\" class=\"editorjs-link-tool block border border-gray-200 dark:border-gray-700 rounded-lg p-4 transition text-gray-900 dark:text-gray-100 hover:border-primary-500 hover:bg-primary-50 dark:hover:bg-gray-900 hover:text-gray-900 dark:hover:text-gray-100\">\u003Cstrong class=\"block font-semibold\">NVIDIA RTXNTC — Kompressionseinstellungen und Bildqualität\u003C\u002Fstrong>\u003Cp class=\"text-sm text-gray-600 dark:text-gray-400\">Offizielle Dokumentation zu Bitrate, Kanalinteraktionen, Qualitätsmessung und verlustbehaftetem Kompressionsverhalten.\u003C\u002Fp>\u003C\u002Fa>\n\u003Ca href=\"https:\u002F\u002Fgithub.com\u002FNVIDIA-RTX\u002FRTXNTC-Library\" target=\"_blank\" rel=\"noopener noreferrer\" class=\"editorjs-link-tool block border border-gray-200 dark:border-gray-700 rounded-lg p-4 transition text-gray-900 dark:text-gray-100 hover:border-primary-500 hover:bg-primary-50 dark:hover:bg-gray-900 hover:text-gray-900 dark:hover:text-gray-100\">\u003Cstrong class=\"block font-semibold\">NVIDIA — LibNTC\u003C\u002Fstrong>\u003Cp class=\"text-sm text-gray-600 dark:text-gray-400\">Offizielle Laufzeitbibliotheksdokumentation, die Decoder-Konfiguration und den Kompromiss zwischen Qualität und Leistung verschiedener neuronaler Netzwerkgrößen beschreibt.\u003C\u002Fp>\u003C\u002Fa>",{"time":538,"blocks":539,"version":1249},1790378959105,[540,546,554,561,568,574,579,584,589,594,599,604,627,632,637,642,647,652,657,687,692,697,702,707,712,717,722,727,732,737,743,752,757,762,767,772,777,782,787,816,821,826,831,836,844,849,854,882,887,892,897,902,907,913,918,923,928,933,938,965,970,975,980,985,990,995,1000,1005,1010,1015,1020,1025,1030,1035,1040,1045,1050,1055,1060,1065,1070,1075,1080,1085,1090,1095,1100,1105,1110,1115,1120,1125,1155,1160,1198,1203,1213,1222,1231,1240],{"id":541,"data":542,"type":544,"tunes":545},"intro",{"text":543},"Texturkomprimierung bedeutet normalerweise, eine kleinere Version der Texturdaten zu speichern und sie vor oder während der Nutzung in ein konventionelles GPU-Format zu expandieren. NVIDIA RTX Neural Texture Compression ändert dieses Modell: Ein Teil der Texturdaten wird zu einer kleinen neuronalen Repräsentation, die von der GPU selbst dekodiert werden kann.","paragraph",{},{"id":547,"data":548,"type":552,"tunes":553},"direct",{"body":549,"title":550,"variant":551},"\u003Cstrong>RTX Neural Texture Compression ist kein Textur-Upscaling.\u003C\u002Fstrong> Es komprimiert mehrere Materialtexturen in neuronale Netzwerkgewichte plus kompakte latente Daten und rekonstruiert dann die angeforderten Texturwerte mit einem kleinen neuronalen Netzwerk. Je nach Integrationsmodus kann das Spiel Speicherplatz und VRAM-Nutzung gegen zusätzliche GPU-Inferenzarbeit eintauschen.","Direkte Antwort","info","callout",{},{"id":555,"data":556,"type":552,"tunes":560},"status",{"body":557,"title":558,"variant":559},"RTX Neural Texture Compression ist noch ein \u003Cstrong>Beta-SDK\u003C\u002Fstrong>. Die aktuelle Beta v0.10.0 fügte Unterstützung für DirectX 12 Linear Algebra Inferenz hinzu und verbindet NTC direkt mit der neuen Neural-Shader-Infrastruktur, die an anderer Stelle auf Figure Rocks diskutiert wird.","Aktueller Status","note",{},{"id":562,"data":563,"type":566,"tunes":567},"toc",{"title":564,"maxLevel":565,"minLevel":47},"Inhalt",3,"tableOfContents",{},{"id":569,"data":570,"type":572,"tunes":573},"h-normal",{"text":571,"level":47},"Warum normale Texturkomprimierung immer noch viel Speicher verbraucht","header",{},{"id":575,"data":576,"type":544,"tunes":578},"p-normal-1",{"text":577},"Ein modernes physikalisch basiertes Material besteht selten aus einem einzigen Bild. Eine einzelne Oberfläche kann Albedo, Normal, Rauheit, Metallizität, Umgebungsverdeckung, Opazität und andere Kanäle verwenden.",{},{"id":580,"data":581,"type":544,"tunes":583},"p-normal-2",{"text":582},"Traditionelle GPU-Blockkomprimierungsformate wie BC1 bis BC7 reduzieren die Kosten, aber die GPU speichert am Ende immer noch konventionelle Texturblöcke für das Material.",{},{"id":585,"data":586,"type":544,"tunes":588},"p-normal-3",{"text":587},"Mit zunehmender Texturauflösung und Materialkomplexität verbrauchen diese Kanäle Speicherplatz, Streaming-Bandbreite und GPU-Speicher.",{},{"id":590,"data":591,"type":572,"tunes":593},"h-store",{"text":592,"level":47},"Was RTX Neural Texture Compression tatsächlich speichert",{},{"id":595,"data":596,"type":544,"tunes":598},"p-store-1",{"text":597},"NVIDIAs RTXNTC SDK komprimiert die zu einem Material gehörenden Kanäle gemeinsam. Das aktuelle SDK unterstützt bis zu 16 Texturkanäle in einem NTC-Texturset.",{},{"id":600,"data":601,"type":544,"tunes":603},"p-store-2",{"text":602},"Anstatt nur konventionelle komprimierte Texel zu behalten, erzeugt der Komprimierungsprozess zwei Hauptdinge: Gewichte für einen kleinen neuronalen Decoder und kompakte latente Feature-Daten.",{},{"id":605,"data":606,"type":625,"tunes":626},"pipeline",{"steps":607,"title":623,"orientation":624},[608,611,614,617,620],{"label":609,"description":610},"1. Original-Materialtexturen","Albedo, Normal, Rauheit, Metallizität und andere Materialkanäle werden zusammen bereitgestellt.",{"label":612,"description":613},"2. Offline-Komprimierung","Das SDK lernt eine kompakte Repräsentation des Materials.",{"label":615,"description":616},"3. Neuronale Gewichte","Ein kleines Decoder-Netzwerk speichert einen Teil dessen, was zur Rekonstruktion des Materials benötigt wird.",{"label":618,"description":619},"4. Latente Daten","Kompakte Feature-Tensoren speichern materialspezifische Informationen.",{"label":621,"description":622},"5. GPU-Inferenz","Zur Laufzeit kombiniert der Decoder die latenten Daten und neuronalen Gewichte, um Texturwerte zu rekonstruieren.","Die neuronale Textur-Pipeline","auto","processFlow",{},{"id":628,"data":629,"type":572,"tunes":631},"h-correlation",{"text":630,"level":47},"Warum das gemeinsame Komprimieren von Kanälen helfen kann",{},{"id":633,"data":634,"type":544,"tunes":636},"p-cor-1",{"text":635},"Materialkanäle sind oft miteinander verbunden. Ein in der Grundfarbe sichtbarer Kratzer kann auch in der Normal- oder Rauheitsmap erscheinen. Ein Stoffmuster kann mehrere Kanäle an derselben räumlichen Position beeinflussen.",{},{"id":638,"data":639,"type":544,"tunes":641},"p-cor-2",{"text":640},"NVIDIA hat NTC entwickelt, um diese Korrelationen auszunutzen, anstatt jede Textur unabhängig zu komprimieren.",{},{"id":643,"data":644,"type":544,"tunes":646},"p-cor-3",{"text":645},"Das ist einer der Gründe, warum die Technologie als materialorientierte Komprimierung beschrieben wird und nicht nur als ein weiteres Bildformat.",{},{"id":648,"data":649,"type":572,"tunes":651},"h-modes",{"text":650,"level":47},"Die drei NTC-Laufzeitmodi sind der Schlüssel zum Verständnis der Technologie",{},{"id":653,"data":654,"type":544,"tunes":656},"p-modes-1",{"text":655},"Der wichtigste Teil von RTXNTC ist nicht nur, wie das Material komprimiert wird. Es ist, wann das Spiel sich entscheidet, es zu dekomprimieren.",{},{"id":658,"data":659,"type":685,"tunes":686},"modes-table",{"rows":660,"title":673,"layout":674,"columns":675},[661,665,669],{"id":662,"label":663,"values":664},"load","Inferenz beim Laden",[13,13,13],{"id":666,"label":667,"values":668},"sample","Inferenz beim Sampling",[13,13,13],{"id":670,"label":671,"values":672},"feedback","Inferenz beim Feedback",[13,13,13],"Inferenz beim Laden vs. Inferenz beim Sampling vs. Inferenz beim Feedback","table",[676,679,682],{"id":677,"label":678},"when","Wann die neuronale Dekodierung stattfindet",{"id":680,"label":681},"memory","Laufzeitverhalten des Texturspeichers",{"id":683,"label":684},"tradeoff","Hauptkompromiss","comparison",{},{"id":688,"data":689,"type":572,"tunes":691},"h-load",{"text":690,"level":47},"Inferenz beim Laden: neuronale Kompression als Speicherformat",{},{"id":693,"data":694,"type":544,"tunes":696},"p-load-1",{"text":695},"Inferenz beim Laden ist der am einfachsten zu verstehende Modus.",{},{"id":698,"data":699,"type":544,"tunes":701},"p-load-2",{"text":700},"Das Spiel speichert das Material in kompakter NTC-Form. Wenn das Asset geladen wird, rekonstruiert die GPU die Texturdaten und kann sie in gewöhnliche BCn-Texturformate transkodieren.",{},{"id":703,"data":704,"type":544,"tunes":706},"p-load-3",{"text":705},"Danach kann das Rendering normales Textursampling verwenden. Die wesentliche Einsparung liegt vor allem vor der Dekompression: Paketgröße des Spiels, Downloadgröße oder Bandbreite für Asset-Streaming.",{},{"id":708,"data":709,"type":544,"tunes":711},"p-load-4",{"text":710},"Doch sobald das Material vollständig in konventionelle Texturen expandiert ist, nähert sich sein VRAM-Fußabdruck zur Laufzeit wieder der konventionellen Darstellung an.",{},{"id":713,"data":714,"type":572,"tunes":716},"h-sample",{"text":715,"level":47},"Inferenz beim Sampling: Die Textur im VRAM neuronal halten",{},{"id":718,"data":719,"type":544,"tunes":721},"p-sample-1",{"text":720},"Inferenz beim Sampling ist der radikalere Modus.",{},{"id":723,"data":724,"type":544,"tunes":726},"p-sample-2",{"text":725},"Anstatt das Material vor dem Rendering in konventionelle Texturen zu expandieren, liest der Shader kompakte latente Daten und führt den neuronalen Decoder aus, wenn er Texturwerte benötigt.",{},{"id":728,"data":729,"type":544,"tunes":731},"p-sample-3",{"text":730},"NVIDIAs eigenes SDK-Beispiel vergleicht eine 12 MB große BCn-Materialdarstellung mit einer 2,5 MB großen NTC-Darstellung bei Verwendung von Inferenz beim Sampling.",{},{"id":733,"data":734,"type":544,"tunes":736},"p-sample-4",{"text":735},"Die Einsparung ist real, weil die konventionellen Texturdaten nicht vollständig resident bleiben müssen. Aber die Kosten verlagern sich woandershin: Der Pixel- oder Hit-Shader führt nun die neuronale Inferenz durch.",{},{"id":683,"data":738,"type":552,"tunes":742},{"body":739,"title":740,"variant":741},"Inferenz beim Sampling tauscht \u003Cstrong>Speicher und Bandbreite\u003C\u002Fstrong> gegen \u003Cstrong>GPU-Berechnung\u003C\u002Fstrong>. Die richtige Frage ist nicht „Wie viel kleiner ist die Textur?“, sondern „Ist die Speichereinsparung die zusätzlichen Inferenzkosten für diese Arbeitslast wert?“","Kompression lässt die Arbeit nicht verschwinden","warning",{},{"id":744,"data":745,"type":750,"tunes":751},"ref-vram",{"url":746,"title":747,"excerpt":748,"ctaLabel":749},"https:\u002F\u002Ffigure.rocks\u002Fde\u002Fblog\u002Fvram-usage-is-not-vram-requirement-why-a-full-memory-meter-does-not-tell-the-whole-story","VRAM-Nutzung ist nicht VRAM-Bedarf: Warum ein voller Speicheranzeiger nicht die ganze Geschichte erzählt","Warum VRAM-Kapazität, Budgets, Residenz und tatsächlicher Speicherdruck verschiedene Dinge sind.","VRAM-Leitfaden lesen","referralArticle",{},{"id":753,"data":754,"type":572,"tunes":756},"h-feedback",{"text":755,"level":47},"Inferenz beim Feedback: Nur dekodieren, was der Spieler tatsächlich sieht",{},{"id":758,"data":759,"type":544,"tunes":761},"p-feed-1",{"text":760},"Inferenz beim Feedback liegt zwischen den beiden Extremen.",{},{"id":763,"data":764,"type":544,"tunes":766},"p-feed-2",{"text":765},"Der Renderer verfolgt, welche Texturkacheln tatsächlich angefordert werden. Anstatt das vollständige Material sofort zu expandieren, kann das System angeforderte Kacheln in Stapeln dekodieren und einen Arbeitscache beibehalten.",{},{"id":768,"data":769,"type":544,"tunes":771},"p-feed-3",{"text":770},"Konzeptionell kombiniert dies neuronale Kompression mit Textur-Streaming: Das System zahlt die Dekodierungskosten nur für Regionen, die relevant werden.",{},{"id":773,"data":774,"type":544,"tunes":776},"p-feed-4",{"text":775},"Die aktuelle Beispielimplementierung ist spezialisierter als die anderen Modi und ihre Unterstützungsbedingungen unterscheiden sich, daher sollte sie als Integrationsstrategie und nicht als universeller Ersatz für gewöhnliches Textur-Streaming betrachtet werden.",{},{"id":778,"data":779,"type":572,"tunes":781},"h-exchange",{"text":780,"level":47},"Der Austausch zwischen Texturspeicher und Berechnung",{},{"id":783,"data":784,"type":544,"tunes":786},"p-exchange-1",{"text":785},"Der einfachste Weg, neuronale Texturkompression zu verstehen, ist sie als einen Austausch zwischen Ressourcen zu betrachten.",{},{"id":788,"data":789,"type":685,"tunes":815},"exchange-table",{"rows":790,"title":807,"layout":674,"columns":808},[791,795,799,803],{"id":792,"label":793,"values":794},"storage","Datenträger\u002FSpeicher",[13,13],{"id":796,"label":797,"values":798},"vram","VRAM",[13,13],{"id":800,"label":801,"values":802},"sampling","Sampling-Kosten",[13,13],{"id":804,"label":805,"values":806},"quality","Qualitätskontrolle",[13,13],"Was sich verschiebt, wenn Texturspeicher neuronal wird",[809,812],{"id":810,"label":811},"traditional","Traditionelle komprimierte Textur",{"id":813,"label":814},"neural","Neuronale Texturrepräsentation",{},{"id":817,"data":818,"type":572,"tunes":820},"h-matrix",{"text":819,"level":47},"Warum spezielle Matrix-Hardware wichtig ist",{},{"id":822,"data":823,"type":544,"tunes":825},"p-matrix-1",{"text":824},"Ein neuronales Netzwerk für das Texture-Sampling auszuführen, wäre zu teuer, wenn jede Multiplikation wie gewöhnliche skalare Shader-Arbeit behandelt werden müsste.",{},{"id":827,"data":828,"type":544,"tunes":830},"p-matrix-2",{"text":829},"RTXNTC profitiert daher von Cooperative Vector und nun von DirectX 12 Linear Algebra-Pfaden, die Shadern die Nutzung von GPU-Matrixbeschleunigungs-Hardware ermöglichen.",{},{"id":832,"data":833,"type":544,"tunes":835},"p-matrix-3",{"text":834},"Die Beta v0.10.0 fügte explizit Inferenz über die DirectX 12 Linear Algebra API hinzu, die mit Shader Model 6.10 eingeführt wurde.",{},{"id":837,"data":838,"type":750,"tunes":843},"ref-directx",{"url":839,"title":840,"excerpt":841,"ctaLabel":842},"https:\u002F\u002Ffigure.rocks\u002Fde\u002Fblog\u002Fdirectx-is-becoming-an-ml-platform-what-linear-algebra-and-neural-shaders-mean-for-future-games","DirectX wird zu einer ML-Plattform: Was Lineare Algebra und neuronale Shader für zukünftige Spiele bedeuten","Wie DirectX Matrix- und neuronale Operationen direkt in HLSL und die Grafikpipeline verlagert.","Lies den DirectX-Leitfaden für neuronale Shader",{},{"id":845,"data":846,"type":572,"tunes":848},"h-not-upscale",{"text":847,"level":47},"Warum neuronale Texturkompression kein Textur-Upscaling ist",{},{"id":850,"data":851,"type":544,"tunes":853},"p-notup-1",{"text":852},"Beide Techniken können maschinelles Lernen nutzen, aber sie lösen unterschiedliche Probleme.",{},{"id":855,"data":856,"type":685,"tunes":881},"ntc-vs-sr",{"rows":857,"title":873,"layout":674,"columns":874},[858,862,866,869],{"id":859,"label":860,"values":861},"input","Eingabe",[13,13],{"id":863,"label":864,"values":865},"goal","Ziel",[13,13],{"id":677,"label":867,"values":868},"Wann es läuft",[13,13],{"id":870,"label":871,"values":872},"output","Ausgabe",[13,13],"Neuronale Texturkompression vs. Super Resolution",[875,878],{"id":876,"label":877},"ntc","Neuronale Texturkompression",{"id":879,"label":880},"sr","Super Resolution",{},{"id":883,"data":884,"type":544,"tunes":886},"p-notup-2",{"text":885},"Neuronale Texturkompression kann daher unterhalb von DLSS, FSR, XeSS oder nativem Rendering existieren. Sie ändert, wie Materialdaten gespeichert und rekonstruiert werden, nicht die endgültige Anzeigeauflösung.",{},{"id":888,"data":889,"type":572,"tunes":891},"h-bpp",{"text":890,"level":47},"Der Qualitätsregler ist Bits pro Pixel",{},{"id":893,"data":894,"type":544,"tunes":896},"p-bpp-1",{"text":895},"NTC ist verlustbehaftete Kompression. Die Menge der komprimierten Informationen wird weitgehend über die Einstellung Bits pro Pixel gesteuert.",{},{"id":898,"data":899,"type":544,"tunes":901},"p-bpp-2",{"text":900},"Eine höhere Bitrate gibt dem Modell mehr Informationen und verbessert im Allgemeinen die Rekonstruktionsqualität. Eine niedrigere Bitrate verbessert die Kompression, erhöht aber das Risiko sichtbarer Fehler.",{},{"id":903,"data":904,"type":544,"tunes":906},"p-bpp-3",{"text":905},"Da mehrere Kanäle dieselbe Repräsentation teilen, kann das Hinzufügen weiterer Materialkanäle ohne Erhöhung der Bitrate die für jeden Kanal verfügbare Qualität verringern.",{},{"id":908,"data":909,"type":552,"tunes":912},"lossy-note",{"body":910,"title":911,"variant":559},"Die SDK-Dokumentation weist ausdrücklich darauf hin, dass Kompressionsfehler normal sind. Das praktische Ziel ist \u003Cstrong>akzeptabler visueller Fehler bei nützlichem Speicher- und Laufzeitaufwand\u003C\u002Fstrong>.","Neuronal bedeutet nicht verlustfrei",{},{"id":914,"data":915,"type":572,"tunes":917},"h-channels",{"text":916,"level":47},"Warum korrelierte Materialkanäle wichtig sind",{},{"id":919,"data":920,"type":544,"tunes":922},"p-channels-1",{"text":921},"Eine neuronale Repräsentation wird wertvoller, wenn mehrere Materialkanäle verwandte Struktur beschreiben.",{},{"id":924,"data":925,"type":544,"tunes":927},"p-channels-2",{"text":926},"Wenn Albedo, Normale und Rauheit alle dieselben Kratzer, Nähte oder Gewebestrukturen enthalten, kann der Decoder gemeinsame räumliche Informationen nutzen.",{},{"id":929,"data":930,"type":544,"tunes":932},"p-channels-3",{"text":931},"Wenn die Kanäle unzusammenhängendes Rauschen sind, gibt es weniger gemeinsame Struktur zu nutzen und das Kompressionsproblem wird schwieriger.",{},{"id":934,"data":935,"type":572,"tunes":937},"h-test",{"text":936,"level":47},"Der neuronale Texturwert-Test",{},{"id":939,"data":940,"type":625,"tunes":964},"value-test",{"steps":941,"title":963,"orientation":624},[942,945,948,951,954,957,960],{"label":943,"description":944},"1. Konventionelle Texturkosten messen","Wie viel Speicherplatz, Streaming-Bandbreite und VRAM verbrauchen die vorhandenen Materialtexturen?",{"label":946,"description":947},"2. Den Laufzeitmodus wählen","Möchten Sie nur Speichereinsparungen, dauerhafte VRAM-Einsparungen oder gestreamte Kachelrekonstruktion?",{"label":949,"description":950},"3. Ein akzeptables Qualitätsziel festlegen","Vergleichen Sie rekonstruierte Kanäle mit dem Originalmaterial, nicht nur mit dem finalen Beauty-Bild.",{"label":952,"description":953},"4. Inferenzkosten messen","Erfassen Sie zusätzliche Shader- oder Dekompressionszeit auf der tatsächlichen Ziel-GPU.",{"label":955,"description":956},"5. Speichereinsparungen messen","Prüfen Sie den tatsächlichen Laufzeit-Working-Set und nicht nur die komprimierte Dateigröße.",{"label":958,"description":959},"6. Schwierige Materialien testen","Feine Normalen, scharfe Masken, Opazität, Text und unzusammenhängende Kanäle können Kompressionsfehler aufdecken.",{"label":961,"description":962},"7. Nach Nettovorteil entscheiden","Verwenden Sie NTC nur dort, wo der eingesparte Speicher\u002Fdie eingesparte Bandbreite die zusätzliche Inferenz- und Integrationskomplexität wert ist.","Wann ist neuronale Texturkompression tatsächlich nützlich?",{},{"id":966,"data":967,"type":572,"tunes":969},"h-8x",{"text":968,"level":47},"Warum „8× kleiner“ Kontext braucht",{},{"id":971,"data":972,"type":544,"tunes":974},"p-8x-1",{"text":973},"NVIDIAs RTX Kit beschreibt RTX Neural Texture Compression als bis zu 8× Verbesserung des Speicherplatzbedarfs bei ähnlicher visueller Qualität wie traditionelle Blockkompression.",{},{"id":976,"data":977,"type":544,"tunes":979},"p-8x-2",{"text":978},"Der Ausdruck „bis zu“ ist wichtig. Das Kompressionsverhältnis hängt vom Material, der Anzahl der Kanäle, der Ziel-Bitrate, der Decoder-Konfiguration und der Qualitätsschwelle ab.",{},{"id":981,"data":982,"type":544,"tunes":984},"p-8x-3",{"text":983},"Dasselbe Verhältnis beschreibt auch nicht automatisch VRAM-Einsparungen. Inferenz beim Laden kann von einer kompakten Datei ausgehen und dennoch in konventionelle GPU-Texturen expandieren. Inferenz beim Sampling bewahrt die kompakte Repräsentation im GPU-Speicher, verbraucht aber mehr Rechenleistung während des Shadings.",{},{"id":986,"data":987,"type":572,"tunes":989},"h-decoder",{"text":988,"level":47},"Die Decoder-Größe ist ein weiterer Kompromiss zwischen Leistung und Qualität",{},{"id":991,"data":992,"type":544,"tunes":994},"p-dec-1",{"text":993},"Die NTC-Laufzeit verwendet ein kleines mehrschichtiges Perzeptron, um Texturwerte zu dekodieren.",{},{"id":996,"data":997,"type":544,"tunes":999},"p-dec-2",{"text":998},"NVIDIAs aktuelle Bibliothek verwendet eine konfigurierbare Decoder-Architektur. Größere Netzwerke können die Kompressionsqualität verbessern, kosten aber mehr Ausführungszeit; kleinere Netzwerke können schneller laufen, mit einigem Verlust an Rekonstruktionsqualität.",{},{"id":1001,"data":1002,"type":544,"tunes":1004},"p-dec-3",{"text":1003},"Das gibt Engine-Entwicklern eine weitere Tuning-Dimension jenseits von Texturauflösung und Bitrate.",{},{"id":1006,"data":1007,"type":572,"tunes":1009},"h-install",{"text":1008,"level":47},"Warum das für zukünftige Spielinstallationen wichtig ist",{},{"id":1011,"data":1012,"type":544,"tunes":1014},"p-install-1",{"text":1013},"Moderne Spiele liefern zunehmend hochauflösende Material-Sets, die sowohl die Download-Größe als auch den Laufzeitspeicher beeinflussen.",{},{"id":1016,"data":1017,"type":544,"tunes":1019},"p-install-2",{"text":1018},"Neuronale Texturkompression schafft eine neue Option: eine kompakte gelernte Repräsentation ausliefern und später entscheiden, ob sie beim Laden expandiert, direkt während des Shadings rekonstruiert oder nur angeforderte Kacheln dekodiert wird.",{},{"id":1021,"data":1022,"type":544,"tunes":1024},"p-install-3",{"text":1023},"Das bedeutet, dass eine komprimierte Asset-Repräsentation an mehreren verschiedenen Laufzeitspeicher-Strategien teilnehmen kann.",{},{"id":1026,"data":1027,"type":572,"tunes":1029},"h-meaning",{"text":1028,"level":47},"Es ändert auch, was 'Texturspeicher' bedeutet",{},{"id":1031,"data":1032,"type":544,"tunes":1034},"p-meaning-1",{"text":1033},"Beim traditionellen Rendering geht es bei einem Texturspeicher-Budget hauptsächlich um Texturformate, Mip-Ebenen, Auflösung und Residenz.",{},{"id":1036,"data":1037,"type":544,"tunes":1039},"p-meaning-2",{"text":1038},"Bei neuronalen Texturen können Entwickler auch latente Daten, Decoder-Gewichte, Inferenzpuffer, transkodierte Caches und die Berechnungskosten einplanen, die zur Rekonstruktion der angeforderten Werte erforderlich sind.",{},{"id":1041,"data":1042,"type":544,"tunes":1044},"p-meaning-3",{"text":1043},"Das Asset hat also nicht mehr eine einzige einfache feste Speicheridentität.",{},{"id":1046,"data":1047,"type":572,"tunes":1049},"h-cross",{"text":1048,"level":47},"Herstellerübergreifende Unterstützung ist differenzierter, als der Name RTX vermuten lässt",{},{"id":1051,"data":1052,"type":544,"tunes":1054},"p-cross-1",{"text":1053},"RTXNTC ist ein NVIDIA-SDK, und die Komprimierung selbst erfordert derzeit laut den SDK-Anforderungen eine NVIDIA-GPU.",{},{"id":1056,"data":1057,"type":544,"tunes":1059},"p-cross-2",{"text":1058},"Die Laufzeitdekomprimierung ist breiter aufgestellt. NVIDIA dokumentiert funktionale Pfade auf Shader-Model-6-Hardware und vermerkt Validierungen auf NVIDIA-, AMD- und Intel-GPUs, während fortgeschrittene Cooperative-Vector-\u002FLinear-Algebra-Pfade von API- und Treiberunterstützung abhängen.",{},{"id":1061,"data":1062,"type":544,"tunes":1064},"p-cross-3",{"text":1063},"Leistung und Funktionsparität sollten daher nicht allein deshalb über Hersteller hinweg angenommen werden, weil der grundlegende Decoder ausgeführt werden kann.",{},{"id":1066,"data":1067,"type":572,"tunes":1069},"h-change",{"text":1068,"level":47},"Was würde diese Antwort ändern?",{},{"id":1071,"data":1072,"type":544,"tunes":1074},"p-change-1",{"text":1073},"NTC befindet sich noch in der Beta-Phase. Laufzeitmodi, Decoder-Architekturen, Treiberunterstützung und Integrationspfade können sich vor einer stabilen Produktionsversion ändern.",{},{"id":1076,"data":1077,"type":544,"tunes":1079},"p-change-2",{"text":1078},"Die größte langfristige Veränderung wäre eine breite Einführung standardisierter neuronaler Shader-Primitive über DirectX und Vulkan hinweg. Das würde die neuronale Texturdekodierung weniger abhängig von herstellerspezifischen Ausführungspfaden machen.",{},{"id":1081,"data":1082,"type":572,"tunes":1084},"h-limit",{"text":1083,"level":47},"Einschränkungen",{},{"id":1086,"data":1087,"type":544,"tunes":1089},"p-limit-1",{"text":1088},"Dieser Artikel beschreibt die Architektur und das aktuelle öffentliche Verhalten des RTXNTC-SDK. Die von NVIDIA angegebenen Komprimierungsversprechen stammen vom Hersteller und sollten nicht als garantierte Ergebnisse für jedes Material betrachtet werden.",{},{"id":1091,"data":1092,"type":544,"tunes":1094},"p-limit-2",{"text":1093},"Das aktuelle SDK ist Beta-Software, und einige Vorschau-Pfade haben dokumentierte Treiber- und Plattformbeschränkungen.",{},{"id":1096,"data":1097,"type":572,"tunes":1099},"h-conclusion",{"text":1098,"level":47},"Fazit",{},{"id":1101,"data":1102,"type":544,"tunes":1104},"p-conc-1",{"text":1103},"RTX Neural Texture Compression ist interessant, weil es eine sehr alte Annahme verändert: Texturdetails müssen nicht immer als herkömmliche Texel im Speicher vorliegen.",{},{"id":1106,"data":1107,"type":544,"tunes":1109},"p-conc-2",{"text":1108},"Ein Material kann stattdessen teilweise als kompakte erlernte Repräsentation gespeichert und bei Bedarf rekonstruiert werden.",{},{"id":1111,"data":1112,"type":544,"tunes":1114},"p-conc-3",{"text":1113},"Das liefert weder kostenlose Qualität noch kostenlosen Speicher. Es entsteht ein neuer Austausch: weniger Speicher, Bandbreite und potenziell VRAM im Gegenzug für neuronale Inferenzarbeit.",{},{"id":1116,"data":1117,"type":544,"tunes":1119},"p-conc-4",{"text":1118},"Die eigentliche Innovation ist nicht „KI macht Texturen schärfer“. Sie besteht darin, dass ein Teil der Materialdaten eines Spiels zu Berechnung werden kann.",{},{"id":1121,"data":1122,"type":572,"tunes":1124},"h-faq",{"text":1123,"level":47},"FAQ",{},{"id":1126,"data":1127,"type":1126,"tunes":1154},"faq",{"items":1128,"title":1153},[1129,1133,1137,1141,1145,1149],{"id":1130,"answer":1131,"question":1132},"faq1","Nein. Es komprimiert und rekonstruiert Materialtexturdaten. Super-Resolution-Technologien rekonstruieren das final gerenderte Bild in einer höheren Anzeigeauflösung.","Ist RTX Neural Texture Compression ein Upscaler?",{"id":1134,"answer":1135,"question":1136},"faq2","Ja, insbesondere bei Inference on Sample, da die kompakte neuronale Repräsentation im GPU-Speicher verbleiben kann, anstatt vollständig expandierte konventionelle Texturen zu benötigen. Inference on Load bewahrt die Speichereinsparungen hauptsächlich vor der Expansion.","Kann NTC den VRAM-Verbrauch reduzieren?",{"id":1138,"answer":1139,"question":1140},"faq3","Das komprimierte Material enthält kompakte latente Feature-Daten sowie Gewichte für ein kleines Decoder-Netzwerk, das die Texturwerte rekonstruiert.","Was speichert das neuronale Netzwerk?",{"id":1142,"answer":1143,"question":1144},"faq4","Nicht unbedingt. Inference on Load tut dies, Inference on Sample rekonstruiert Werte während des Shader-Samplings, und Inference on Feedback kann angeforderte Textur-Tiles dekodieren.","Dekodiert NTC die gesamte Textur vor dem Rendern?",{"id":1146,"answer":1147,"question":1148},"faq5","Nein. RTXNTC ist verlustbehaftete Kompression, und die Qualität hängt von Bitrate, Kanalanzahl, Decoder-Konfiguration und dem Material selbst ab.","Ist neuronale Texturkompression verlustfrei?",{"id":1150,"answer":1151,"question":1152},"faq6","Das aktuelle öffentliche SDK ist noch als Beta gekennzeichnet, daher können sich APIs, Support und Leistungsmerkmale weiterhin ändern.","Ist RTXNTC produktionsreif?","RTX Neural Texture Compression einfach erklärt",{},{"id":1156,"data":1157,"type":572,"tunes":1159},"h-glossary",{"text":1158,"level":47},"Glossar",{},{"id":1161,"data":1162,"type":1161,"tunes":1197},"glossary",{"title":1163,"entries":1164},"Wichtige Begriffe der neuronalen Texturierung",[1165,1169,1173,1177,1181,1185,1189,1193],{"term":1166,"anchor":1167,"definition":1168},"Neural Texture Compression","neural-texture-compression","Eine Technik, die Texturinformationen als kompakte latente Daten plus neuronale Decoder-Gewichte speichert, anstatt nur konventionelle Texel-Blöcke zu verwenden.",{"term":1170,"anchor":1171,"definition":1172},"Latente Daten","latent-data","Kompakte erlernte Features, die der neuronale Decoder zur Rekonstruktion der Texturwerte verwendet.",{"term":1174,"anchor":1175,"definition":1176},"Decoder","decoder","Ein kleines neuronales Netzwerk, das latente Features in rekonstruierte Texturkanäle umwandelt.",{"term":1178,"anchor":1179,"definition":1180},"Inference on Load","inference-on-load","Laufzeitmodus, der die neuronale Textur beim Laden eines Assets dekodiert, üblicherweise in konventionelle Texturformate.",{"term":1182,"anchor":1183,"definition":1184},"Inference on Sample","inference-on-sample","Laufzeitmodus, der die neuronale Dekodierung direkt während des Texture-Samplings durchführt, sodass die komprimierte Repräsentation resident bleiben kann.",{"term":1186,"anchor":1187,"definition":1188},"Inference on Feedback","inference-on-feedback","Laufzeitstrategie, die Texture-Feedback nutzt, um nur angeforderte Tiles zu dekodieren und zu cachen.",{"term":1190,"anchor":1191,"definition":1192},"Texture Storage–Compute Exchange","texture-storage-compute-exchange","Ein Figure-Rocks-Modell, das den Tausch von Texturspeicher, Bandbreite und VRAM gegen zusätzliche neuronale GPU-Inferenzarbeit beschreibt.",{"term":1194,"anchor":1195,"definition":1196},"Neural Texture Value Test","neural-texture-value-test","Ein Figure-Rocks-Workflow zur Entscheidung, ob neuronale Texturkompression für ein bestimmtes Material und eine Ziel-GPU einen Nettonutzen bringt.",{},{"id":1199,"data":1200,"type":572,"tunes":1202},"h-sources",{"text":1201,"level":47},"Primärquellen",{},{"id":1204,"data":1205,"type":1211,"tunes":1212},"src-rtxkit",{"link":1206,"meta":1207},"https:\u002F\u002Fdeveloper.nvidia.com\u002Frtx-kit",{"image":1208,"title":1209,"description":1210},{"url":13},"NVIDIA Developer — RTX Kit","Offizielle Übersicht über RTX Neural Texture Compression und NVIDIAs veröffentlichte Positionierung zur Speicherreduzierung.","linkTool",{},{"id":1214,"data":1215,"type":1211,"tunes":1221},"src-rtxntc-readme",{"link":1216,"meta":1217},"https:\u002F\u002Fgithub.com\u002FNVIDIA-RTX\u002FRTXNTC\u002Fblob\u002Fmain\u002FREADME.md",{"image":1218,"title":1219,"description":1220},{"url":13},"NVIDIA RTXNTC — SDK README","Offizielle SDK-Dokumentation, die Materialkanal-Kompression, Decoder-\u002FLatent-Repräsentation, Laufzeitmodi, Speicherbeispiele, Systemanforderungen und Cooperative-Vector-Unterstützung beschreibt.",{},{"id":1223,"data":1224,"type":1211,"tunes":1230},"src-rtxntc-release",{"link":1225,"meta":1226},"https:\u002F\u002Fgithub.com\u002FNVIDIA-RTX\u002FRTXNTC\u002Freleases",{"image":1227,"title":1228,"description":1229},{"url":13},"NVIDIA RTXNTC — Releases","Offizielle Release-Historie, einschließlich v0.10.0 Beta und DirectX 12 Linear Algebra Inferenz-Unterstützung.",{},{"id":1232,"data":1233,"type":1211,"tunes":1239},"src-rtxntc-quality",{"link":1234,"meta":1235},"https:\u002F\u002Fgithub.com\u002FNVIDIA-RTX\u002FRTXNTC\u002Fblob\u002Fmain\u002Fdocs\u002FSettingsAndQuality.md",{"image":1236,"title":1237,"description":1238},{"url":13},"NVIDIA RTXNTC — Kompressionseinstellungen und Bildqualität","Offizielle Dokumentation zu Bitrate, Kanalinteraktionen, Qualitätsmessung und verlustbehaftetem Kompressionsverhalten.",{},{"id":1241,"data":1242,"type":1211,"tunes":1248},"src-libntc",{"link":1243,"meta":1244},"https:\u002F\u002Fgithub.com\u002FNVIDIA-RTX\u002FRTXNTC-Library",{"image":1245,"title":1246,"description":1247},{"url":13},"NVIDIA — LibNTC","Offizielle Laufzeitbibliotheksdokumentation, die Decoder-Konfiguration und den Kompromiss zwischen Qualität und Leistung verschiedener neuronaler Netzwerkgrößen beschreibt.",{},"2.31","NVIDIA RTX Neural Texture Compression verändert die Art und Weise, wie Spielmaterialien gespeichert werden können. Anstatt jeden Texturkanal nur als herkömmliche Texel zu speichern, kann ein Material in kompakte latente Daten und einen kleinen neuronalen Decoder komprimiert und bei Bedarf von der GPU rekonstruiert werden.","\u002Fuploads\u002F2026\u002F09\u002Frtx-neural-texture-compression-is-not-upscaling-how-ai-can-trade-texture-memory-for-gpu-compute-1790378933528-ul75hf.webp","rtx-neural-texture-compression-is-not-upscaling-how-ai-can-trade-texture-memory-for-gpu-compute-1790378933528-ul75hf",false,"PUBLISHED","2026-09-25T21:27:00.000Z","2026-09-25T23:27:34.869Z","2026-09-25T23:32:35.051Z",{"en":1259,"de":1260,"sr":1261,"es":1262,"fr":1263,"it":1264,"ru":1265,"zh":1266},"\u002Fblog\u002Frtx-neural-texture-compression-is-not-upscaling-how-ai-can-trade-texture-memory-for-gpu-compute","\u002Fde\u002Fblog\u002Frtx-neural-texture-compression-is-not-upscaling-how-ai-can-trade-texture-memory-for-gpu-compute","\u002Fsr\u002Fblog\u002Frtx-neural-texture-compression-is-not-upscaling-how-ai-can-trade-texture-memory-for-gpu-compute","\u002Fes\u002Fblog\u002Frtx-neural-texture-compression-is-not-upscaling-how-ai-can-trade-texture-memory-for-gpu-compute","\u002Ffr\u002Fblog\u002Frtx-neural-texture-compression-is-not-upscaling-how-ai-can-trade-texture-memory-for-gpu-compute","\u002Fit\u002Fblog\u002Frtx-neural-texture-compression-is-not-upscaling-how-ai-can-trade-texture-memory-for-gpu-compute","\u002Fru\u002Fblog\u002Frtx-neural-texture-compression-is-not-upscaling-how-ai-can-trade-texture-memory-for-gpu-compute","\u002Fzh\u002Fblog\u002Frtx-neural-texture-compression-is-not-upscaling-how-ai-can-trade-texture-memory-for-gpu-compute",[1268,1272,1276,1280,1284],{"id":1269,"name":1270,"slug":1271},152,"VRAM und Streaming","vram-and-streaming",{"id":1273,"name":1274,"slug":1275},330,"Streaming- und IO-Fehlerbehebungen","streaming-and-io-fixes",{"id":1277,"name":1278,"slug":1279},173,"CPUs, Arbeitsspeicher, Speicher","cpus-memory-storage",{"id":1281,"name":1282,"slug":1283},210,"Was Qualität bedeutet","what-quality-means",{"id":1285,"name":1286,"slug":1287},214,"Marketing vs. Realität","marketing-vs-reality",{"id":283,"login":1289,"email":1290,"displayName":1291},"aleksandar","aleksandar@stajic.de","Aleksandar Stajic",[1293,1843],{"lang":8,"title":1294,"content":1295,"contentJson":1296,"excerpt":1842},"RTX Neural Texture Compression Is Not Upscaling: How AI Can Trade Texture Memory for GPU Compute","{\"time\":1790378899383,\"blocks\":[{\"id\":\"intro\",\"type\":\"paragraph\",\"data\":{\"text\":\"Texture compression normally means storing a smaller version of texture data and expanding it into a conventional GPU format before or during use. NVIDIA RTX Neural Texture Compression changes that model: part of the texture data becomes a small neural representation that can be decoded by the GPU itself.\"},\"tunes\":{}},{\"id\":\"direct\",\"type\":\"callout\",\"data\":{\"variant\":\"info\",\"title\":\"Direct answer\",\"body\":\"\u003Cstrong>RTX Neural Texture Compression is not texture upscaling.\u003C\u002Fstrong> It compresses multiple material textures into neural-network weights plus compact latent data, then reconstructs the requested texture values with a small neural network. Depending on the integration mode, the game can trade storage and VRAM usage for additional GPU inference work.\"},\"tunes\":{}},{\"id\":\"status\",\"type\":\"callout\",\"data\":{\"variant\":\"note\",\"title\":\"Current status\",\"body\":\"RTX Neural Texture Compression is still a \u003Cstrong>beta SDK\u003C\u002Fstrong>. The current v0.10.0 beta added DirectX 12 Linear Algebra inference support, connecting NTC directly to the new neural-shader infrastructure discussed elsewhere on Figure Rocks.\"},\"tunes\":{}},{\"id\":\"toc\",\"type\":\"tableOfContents\",\"data\":{\"title\":\"Contents\",\"minLevel\":2,\"maxLevel\":3},\"tunes\":{}},{\"id\":\"h-normal\",\"type\":\"header\",\"data\":{\"text\":\"Why normal texture compression still uses a lot of memory\",\"level\":2},\"tunes\":{}},{\"id\":\"p-normal-1\",\"type\":\"paragraph\",\"data\":{\"text\":\"A modern physically based material rarely consists of one image. A single surface may use albedo, normal, roughness, metalness, ambient occlusion, opacity and other channels.\"},\"tunes\":{}},{\"id\":\"p-normal-2\",\"type\":\"paragraph\",\"data\":{\"text\":\"Traditional GPU block-compression formats such as BC1 through BC7 reduce the cost, but the GPU still ends up storing conventional texture blocks for the material.\"},\"tunes\":{}},{\"id\":\"p-normal-3\",\"type\":\"paragraph\",\"data\":{\"text\":\"As texture resolution and material complexity increase, those channels consume disk space, streaming bandwidth and GPU memory.\"},\"tunes\":{}},{\"id\":\"h-store\",\"type\":\"header\",\"data\":{\"text\":\"What RTX Neural Texture Compression actually stores\",\"level\":2},\"tunes\":{}},{\"id\":\"p-store-1\",\"type\":\"paragraph\",\"data\":{\"text\":\"NVIDIA's RTXNTC SDK compresses the channels belonging to one material together. The current SDK supports up to 16 texture channels in one NTC texture set.\"},\"tunes\":{}},{\"id\":\"p-store-2\",\"type\":\"paragraph\",\"data\":{\"text\":\"Instead of keeping only conventional compressed texels, the compression process produces two main things: weights for a small neural decoder and compact latent feature data.\"},\"tunes\":{}},{\"id\":\"pipeline\",\"type\":\"processFlow\",\"data\":{\"title\":\"The neural texture pipeline\",\"orientation\":\"auto\",\"steps\":[{\"label\":\"1. Original material textures\",\"description\":\"Albedo, normal, roughness, metalness and other material channels are provided together.\"},{\"label\":\"2. Offline compression\",\"description\":\"The SDK learns a compact representation of the material.\"},{\"label\":\"3. Neural weights\",\"description\":\"A small decoder network stores part of what is needed to reconstruct the material.\"},{\"label\":\"4. Latent data\",\"description\":\"Compact feature tensors store material-specific information.\"},{\"label\":\"5. GPU inference\",\"description\":\"At runtime, the decoder combines the latent data and neural weights to reconstruct texture values.\"}]},\"tunes\":{}},{\"id\":\"h-correlation\",\"type\":\"header\",\"data\":{\"text\":\"Why compressing channels together can help\",\"level\":2},\"tunes\":{}},{\"id\":\"p-cor-1\",\"type\":\"paragraph\",\"data\":{\"text\":\"Material channels are often related. A scratch visible in the base color may also appear in the normal or roughness map. A fabric pattern can influence several channels at the same spatial location.\"},\"tunes\":{}},{\"id\":\"p-cor-2\",\"type\":\"paragraph\",\"data\":{\"text\":\"NVIDIA designed NTC to exploit those correlations instead of compressing every texture independently.\"},\"tunes\":{}},{\"id\":\"p-cor-3\",\"type\":\"paragraph\",\"data\":{\"text\":\"That is one reason the technology is described as material-oriented compression rather than merely another image format.\"},\"tunes\":{}},{\"id\":\"h-modes\",\"type\":\"header\",\"data\":{\"text\":\"The three NTC runtime modes are the key to understanding the technology\",\"level\":2},\"tunes\":{}},{\"id\":\"p-modes-1\",\"type\":\"paragraph\",\"data\":{\"text\":\"The most important part of RTXNTC is not only how the material is compressed. It is when the game chooses to decompress it.\"},\"tunes\":{}},{\"id\":\"modes-table\",\"type\":\"comparison\",\"data\":{\"title\":\"Inference on Load vs Inference on Sample vs Inference on Feedback\",\"layout\":\"table\",\"columns\":[{\"id\":\"when\",\"label\":\"When neural decoding happens\"},{\"id\":\"memory\",\"label\":\"Runtime texture-memory behavior\"},{\"id\":\"tradeoff\",\"label\":\"Main trade-off\"}],\"rows\":[{\"id\":\"load\",\"label\":\"Inference on Load\",\"values\":[\"\",\"\",\"\"]},{\"id\":\"sample\",\"label\":\"Inference on Sample\",\"values\":[\"\",\"\",\"\"]},{\"id\":\"feedback\",\"label\":\"Inference on Feedback\",\"values\":[\"\",\"\",\"\"]}]},\"tunes\":{}},{\"id\":\"h-load\",\"type\":\"header\",\"data\":{\"text\":\"Inference on Load: neural compression as a storage format\",\"level\":2},\"tunes\":{}},{\"id\":\"p-load-1\",\"type\":\"paragraph\",\"data\":{\"text\":\"Inference on Load is the easiest mode to understand.\"},\"tunes\":{}},{\"id\":\"p-load-2\",\"type\":\"paragraph\",\"data\":{\"text\":\"The game stores the material in compact NTC form. When the asset is loaded, the GPU reconstructs the texture data and can transcode it into ordinary BCn texture formats.\"},\"tunes\":{}},{\"id\":\"p-load-3\",\"type\":\"paragraph\",\"data\":{\"text\":\"After that step, rendering can use normal texture sampling. The important saving is primarily before decompression: packaged game size, download size or asset-streaming bandwidth.\"},\"tunes\":{}},{\"id\":\"p-load-4\",\"type\":\"paragraph\",\"data\":{\"text\":\"But once the material is fully expanded into conventional textures, its runtime VRAM footprint approaches the conventional representation again.\"},\"tunes\":{}},{\"id\":\"h-sample\",\"type\":\"header\",\"data\":{\"text\":\"Inference on Sample: keep the texture neural in VRAM\",\"level\":2},\"tunes\":{}},{\"id\":\"p-sample-1\",\"type\":\"paragraph\",\"data\":{\"text\":\"Inference on Sample is the more radical mode.\"},\"tunes\":{}},{\"id\":\"p-sample-2\",\"type\":\"paragraph\",\"data\":{\"text\":\"Instead of expanding the material into conventional textures before rendering, the shader reads compact latent data and runs the neural decoder when it needs texture values.\"},\"tunes\":{}},{\"id\":\"p-sample-3\",\"type\":\"paragraph\",\"data\":{\"text\":\"NVIDIA's own SDK example compares a 12 MB BCn material representation with a 2.5 MB NTC representation when using Inference on Sample.\"},\"tunes\":{}},{\"id\":\"p-sample-4\",\"type\":\"paragraph\",\"data\":{\"text\":\"The saving is real because the conventional texture data does not need to remain fully resident. But the cost moves somewhere else: the pixel or hit shader now performs neural inference.\"},\"tunes\":{}},{\"id\":\"tradeoff\",\"type\":\"callout\",\"data\":{\"variant\":\"warning\",\"title\":\"Compression does not make the work disappear\",\"body\":\"Inference on Sample trades \u003Cstrong>memory and bandwidth\u003C\u002Fstrong> for \u003Cstrong>GPU computation\u003C\u002Fstrong>. The right question is not “How much smaller is the texture?” but “Is the memory saving worth the added inference cost on this workload?”\"},\"tunes\":{}},{\"id\":\"ref-vram\",\"type\":\"referralArticle\",\"data\":{\"url\":\"https:\u002F\u002Ffigure.rocks\u002Fblog\u002Fvram-usage-is-not-vram-requirement-why-a-full-memory-meter-does-not-tell-the-whole-story\",\"title\":\"VRAM Usage Is Not VRAM Requirement: Why a Full Memory Meter Does Not Tell the Whole Story\",\"excerpt\":\"Why VRAM capacity, budgets, residency and actual memory pressure are different things.\",\"ctaLabel\":\"Read the VRAM guide\"},\"tunes\":{}},{\"id\":\"h-feedback\",\"type\":\"header\",\"data\":{\"text\":\"Inference on Feedback: decode only what the player actually sees\",\"level\":2},\"tunes\":{}},{\"id\":\"p-feed-1\",\"type\":\"paragraph\",\"data\":{\"text\":\"Inference on Feedback sits between the two extremes.\"},\"tunes\":{}},{\"id\":\"p-feed-2\",\"type\":\"paragraph\",\"data\":{\"text\":\"The renderer tracks which texture tiles are actually requested. Instead of expanding the full material immediately, the system can decode requested tiles in batches and keep a working cache.\"},\"tunes\":{}},{\"id\":\"p-feed-3\",\"type\":\"paragraph\",\"data\":{\"text\":\"Conceptually, this combines neural compression with texture streaming: the system pays the decoding cost only for regions that become relevant.\"},\"tunes\":{}},{\"id\":\"p-feed-4\",\"type\":\"paragraph\",\"data\":{\"text\":\"The current sample implementation is more specialized than the other modes and its support constraints differ, so it should be treated as an integration strategy rather than a universal replacement for ordinary texture streaming.\"},\"tunes\":{}},{\"id\":\"h-exchange\",\"type\":\"header\",\"data\":{\"text\":\"The Texture Storage–Compute Exchange\",\"level\":2},\"tunes\":{}},{\"id\":\"p-exchange-1\",\"type\":\"paragraph\",\"data\":{\"text\":\"The easiest way to understand neural texture compression is as an exchange between resources.\"},\"tunes\":{}},{\"id\":\"exchange-table\",\"type\":\"comparison\",\"data\":{\"title\":\"What moves when texture storage becomes neural\",\"layout\":\"table\",\"columns\":[{\"id\":\"traditional\",\"label\":\"Traditional compressed texture\"},{\"id\":\"neural\",\"label\":\"Neural texture representation\"}],\"rows\":[{\"id\":\"storage\",\"label\":\"Disk\u002Fstorage\",\"values\":[\"\",\"\"]},{\"id\":\"vram\",\"label\":\"VRAM\",\"values\":[\"\",\"\"]},{\"id\":\"sampling\",\"label\":\"Sampling cost\",\"values\":[\"\",\"\"]},{\"id\":\"quality\",\"label\":\"Quality control\",\"values\":[\"\",\"\"]}]},\"tunes\":{}},{\"id\":\"h-matrix\",\"type\":\"header\",\"data\":{\"text\":\"Why special matrix hardware matters\",\"level\":2},\"tunes\":{}},{\"id\":\"p-matrix-1\",\"type\":\"paragraph\",\"data\":{\"text\":\"Running a neural network for texture sampling would be too expensive if every multiply had to be handled like ordinary scalar shader work.\"},\"tunes\":{}},{\"id\":\"p-matrix-2\",\"type\":\"paragraph\",\"data\":{\"text\":\"RTXNTC therefore benefits from Cooperative Vector and now DirectX 12 Linear Algebra paths that let shaders use GPU matrix-acceleration hardware.\"},\"tunes\":{}},{\"id\":\"p-matrix-3\",\"type\":\"paragraph\",\"data\":{\"text\":\"The v0.10.0 beta explicitly added inference through the DirectX 12 Linear Algebra API introduced with Shader Model 6.10.\"},\"tunes\":{}},{\"id\":\"ref-directx\",\"type\":\"referralArticle\",\"data\":{\"url\":\"https:\u002F\u002Ffigure.rocks\u002Fblog\u002Fdirectx-is-becoming-an-ml-platform-what-linear-algebra-and-neural-shaders-mean-for-future-games\",\"title\":\"DirectX Is Becoming an ML Platform: What Linear Algebra and Neural Shaders Mean for Future Games\",\"excerpt\":\"How DirectX is moving matrix and neural operations directly into HLSL and the graphics pipeline.\",\"ctaLabel\":\"Read the DirectX neural-shader guide\"},\"tunes\":{}},{\"id\":\"h-not-upscale\",\"type\":\"header\",\"data\":{\"text\":\"Why neural texture compression is not texture upscaling\",\"level\":2},\"tunes\":{}},{\"id\":\"p-notup-1\",\"type\":\"paragraph\",\"data\":{\"text\":\"The two techniques can both use machine learning, but they solve different problems.\"},\"tunes\":{}},{\"id\":\"ntc-vs-sr\",\"type\":\"comparison\",\"data\":{\"title\":\"Neural Texture Compression vs Super Resolution\",\"layout\":\"table\",\"columns\":[{\"id\":\"ntc\",\"label\":\"Neural Texture Compression\"},{\"id\":\"sr\",\"label\":\"Super Resolution\"}],\"rows\":[{\"id\":\"input\",\"label\":\"Input\",\"values\":[\"\",\"\"]},{\"id\":\"goal\",\"label\":\"Goal\",\"values\":[\"\",\"\"]},{\"id\":\"when\",\"label\":\"When it runs\",\"values\":[\"\",\"\"]},{\"id\":\"output\",\"label\":\"Output\",\"values\":[\"\",\"\"]}]},\"tunes\":{}},{\"id\":\"p-notup-2\",\"type\":\"paragraph\",\"data\":{\"text\":\"Neural Texture Compression can therefore exist underneath DLSS, FSR, XeSS or native rendering. It changes how material data is stored and reconstructed, not the final display resolution.\"},\"tunes\":{}},{\"id\":\"h-bpp\",\"type\":\"header\",\"data\":{\"text\":\"The quality knob is bits per pixel\",\"level\":2},\"tunes\":{}},{\"id\":\"p-bpp-1\",\"type\":\"paragraph\",\"data\":{\"text\":\"NTC is lossy compression. The amount of compressed information is controlled largely through the bits-per-pixel setting.\"},\"tunes\":{}},{\"id\":\"p-bpp-2\",\"type\":\"paragraph\",\"data\":{\"text\":\"Higher bitrate gives the model more information and generally improves reconstruction quality. Lower bitrate improves compression but increases the risk of visible error.\"},\"tunes\":{}},{\"id\":\"p-bpp-3\",\"type\":\"paragraph\",\"data\":{\"text\":\"Because multiple channels share the same representation, adding more material channels without increasing the bitrate can reduce the quality available to each channel.\"},\"tunes\":{}},{\"id\":\"lossy-note\",\"type\":\"callout\",\"data\":{\"variant\":\"note\",\"title\":\"Neural does not mean lossless\",\"body\":\"The SDK documentation explicitly notes that compression error is normal. The practical target is \u003Cstrong>acceptable visual error at a useful storage and runtime cost\u003C\u002Fstrong>.\"},\"tunes\":{}},{\"id\":\"h-channels\",\"type\":\"header\",\"data\":{\"text\":\"Why correlated material channels are important\",\"level\":2},\"tunes\":{}},{\"id\":\"p-channels-1\",\"type\":\"paragraph\",\"data\":{\"text\":\"A neural representation becomes more valuable when several material channels describe related structure.\"},\"tunes\":{}},{\"id\":\"p-channels-2\",\"type\":\"paragraph\",\"data\":{\"text\":\"If albedo, normal and roughness all contain the same scratches, seams or fabric weave, the decoder can exploit shared spatial information.\"},\"tunes\":{}},{\"id\":\"p-channels-3\",\"type\":\"paragraph\",\"data\":{\"text\":\"If the channels are unrelated noise, there is less common structure to exploit and the compression problem becomes harder.\"},\"tunes\":{}},{\"id\":\"h-test\",\"type\":\"header\",\"data\":{\"text\":\"The Neural Texture Value Test\",\"level\":2},\"tunes\":{}},{\"id\":\"value-test\",\"type\":\"processFlow\",\"data\":{\"title\":\"When is neural texture compression actually useful?\",\"orientation\":\"auto\",\"steps\":[{\"label\":\"1. Measure conventional texture cost\",\"description\":\"How much disk space, streaming bandwidth and VRAM do the existing material textures consume?\"},{\"label\":\"2. Choose the runtime mode\",\"description\":\"Do you want storage savings only, persistent VRAM savings or streamed tile reconstruction?\"},{\"label\":\"3. Set an acceptable quality target\",\"description\":\"Compare reconstructed channels against the original material, not only the final beauty image.\"},{\"label\":\"4. Measure inference cost\",\"description\":\"Record added shader or decompression time on the actual target GPU.\"},{\"label\":\"5. Measure memory savings\",\"description\":\"Check the real runtime working set rather than only the compressed file size.\"},{\"label\":\"6. Test difficult materials\",\"description\":\"Fine normals, sharp masks, opacity, text and unrelated channels can expose compression failures.\"},{\"label\":\"7. Decide by net benefit\",\"description\":\"Use NTC only where the saved memory\u002Fbandwidth is worth the extra inference and integration complexity.\"}]},\"tunes\":{}},{\"id\":\"h-8x\",\"type\":\"header\",\"data\":{\"text\":\"Why “8× smaller” needs context\",\"level\":2},\"tunes\":{}},{\"id\":\"p-8x-1\",\"type\":\"paragraph\",\"data\":{\"text\":\"NVIDIA's RTX Kit describes RTX Neural Texture Compression as offering up to 8× disk-memory improvement at similar visual fidelity to traditional block compression.\"},\"tunes\":{}},{\"id\":\"p-8x-2\",\"type\":\"paragraph\",\"data\":{\"text\":\"The phrase “up to” matters. Compression ratio depends on the material, number of channels, target bitrate, decoder configuration and quality threshold.\"},\"tunes\":{}},{\"id\":\"p-8x-3\",\"type\":\"paragraph\",\"data\":{\"text\":\"The same ratio also does not automatically describe VRAM savings. Inference on Load can start from a compact file and still expand into conventional GPU textures. Inference on Sample preserves the compact representation in GPU memory but spends more compute during shading.\"},\"tunes\":{}},{\"id\":\"h-decoder\",\"type\":\"header\",\"data\":{\"text\":\"Decoder size is another performance-quality trade-off\",\"level\":2},\"tunes\":{}},{\"id\":\"p-dec-1\",\"type\":\"paragraph\",\"data\":{\"text\":\"The NTC runtime uses a small multilayer perceptron to decode texture values.\"},\"tunes\":{}},{\"id\":\"p-dec-2\",\"type\":\"paragraph\",\"data\":{\"text\":\"NVIDIA's current library uses a configurable decoder architecture. Larger networks can improve compression quality but cost more to execute; smaller networks can run faster with some loss in reconstruction quality.\"},\"tunes\":{}},{\"id\":\"p-dec-3\",\"type\":\"paragraph\",\"data\":{\"text\":\"That gives engine developers another tuning dimension beyond texture resolution and bitrate.\"},\"tunes\":{}},{\"id\":\"h-install\",\"type\":\"header\",\"data\":{\"text\":\"Why this matters for future game installations\",\"level\":2},\"tunes\":{}},{\"id\":\"p-install-1\",\"type\":\"paragraph\",\"data\":{\"text\":\"Modern games increasingly ship high-resolution material sets that affect download size as well as runtime memory.\"},\"tunes\":{}},{\"id\":\"p-install-2\",\"type\":\"paragraph\",\"data\":{\"text\":\"Neural texture compression creates a new option: ship a compact learned representation and decide later whether to expand it on load, reconstruct it directly during shading or decode only requested tiles.\"},\"tunes\":{}},{\"id\":\"p-install-3\",\"type\":\"paragraph\",\"data\":{\"text\":\"That means one compressed asset representation can participate in several different runtime memory strategies.\"},\"tunes\":{}},{\"id\":\"h-meaning\",\"type\":\"header\",\"data\":{\"text\":\"It also changes what 'texture memory' means\",\"level\":2},\"tunes\":{}},{\"id\":\"p-meaning-1\",\"type\":\"paragraph\",\"data\":{\"text\":\"With traditional rendering, a texture-memory budget is mostly about texture formats, mip levels, resolution and residency.\"},\"tunes\":{}},{\"id\":\"p-meaning-2\",\"type\":\"paragraph\",\"data\":{\"text\":\"With neural textures, developers may also budget latent data, decoder weights, inference buffers, transcoded caches and the compute needed to reconstruct requested values.\"},\"tunes\":{}},{\"id\":\"p-meaning-3\",\"type\":\"paragraph\",\"data\":{\"text\":\"So the asset no longer has one simple fixed memory identity.\"},\"tunes\":{}},{\"id\":\"h-cross\",\"type\":\"header\",\"data\":{\"text\":\"Cross-vendor support is more nuanced than the RTX name suggests\",\"level\":2},\"tunes\":{}},{\"id\":\"p-cross-1\",\"type\":\"paragraph\",\"data\":{\"text\":\"RTXNTC is an NVIDIA SDK, and compression itself currently requires an NVIDIA GPU according to the SDK requirements.\"},\"tunes\":{}},{\"id\":\"p-cross-2\",\"type\":\"paragraph\",\"data\":{\"text\":\"Runtime decompression is broader. NVIDIA documents functional paths on Shader Model 6 hardware and notes validation on NVIDIA, AMD and Intel GPUs, while advanced Cooperative Vector \u002F Linear Algebra paths depend on API and driver support.\"},\"tunes\":{}},{\"id\":\"p-cross-3\",\"type\":\"paragraph\",\"data\":{\"text\":\"Performance and feature parity therefore should not be assumed across vendors merely because the basic decoder can run.\"},\"tunes\":{}},{\"id\":\"h-change\",\"type\":\"header\",\"data\":{\"text\":\"What would change this answer?\",\"level\":2},\"tunes\":{}},{\"id\":\"p-change-1\",\"type\":\"paragraph\",\"data\":{\"text\":\"NTC is still beta. Runtime modes, decoder architectures, driver support and integration paths can change before a stable production release.\"},\"tunes\":{}},{\"id\":\"p-change-2\",\"type\":\"paragraph\",\"data\":{\"text\":\"The biggest long-term change would be broad adoption of standardized neural-shader primitives across DirectX and Vulkan. That would make neural texture decoding less dependent on custom vendor-specific execution paths.\"},\"tunes\":{}},{\"id\":\"h-limit\",\"type\":\"header\",\"data\":{\"text\":\"Limitations\",\"level\":2},\"tunes\":{}},{\"id\":\"p-limit-1\",\"type\":\"paragraph\",\"data\":{\"text\":\"This article describes the architecture and current public RTXNTC SDK behavior. NVIDIA's quoted compression claims are vendor-provided and should not be treated as guaranteed results for every material.\"},\"tunes\":{}},{\"id\":\"p-limit-2\",\"type\":\"paragraph\",\"data\":{\"text\":\"The current SDK is beta software, and some preview paths have documented driver and platform limitations.\"},\"tunes\":{}},{\"id\":\"h-conclusion\",\"type\":\"header\",\"data\":{\"text\":\"Conclusion\",\"level\":2},\"tunes\":{}},{\"id\":\"p-conc-1\",\"type\":\"paragraph\",\"data\":{\"text\":\"RTX Neural Texture Compression is interesting because it changes a very old assumption: texture detail does not always have to exist in memory as conventional texels.\"},\"tunes\":{}},{\"id\":\"p-conc-2\",\"type\":\"paragraph\",\"data\":{\"text\":\"A material can instead be stored partly as a compact learned representation and reconstructed when needed.\"},\"tunes\":{}},{\"id\":\"p-conc-3\",\"type\":\"paragraph\",\"data\":{\"text\":\"That does not give free quality or free memory. It creates a new exchange: less storage, bandwidth and potentially VRAM in return for neural inference work.\"},\"tunes\":{}},{\"id\":\"p-conc-4\",\"type\":\"paragraph\",\"data\":{\"text\":\"The real innovation is not “AI makes textures sharper.” It is that part of a game's material data can become computation.\"},\"tunes\":{}},{\"id\":\"h-faq\",\"type\":\"header\",\"data\":{\"text\":\"FAQ\",\"level\":2},\"tunes\":{}},{\"id\":\"faq\",\"type\":\"faq\",\"data\":{\"title\":\"RTX Neural Texture Compression in plain English\",\"items\":[{\"id\":\"faq1\",\"question\":\"Is RTX Neural Texture Compression an upscaler?\",\"answer\":\"No. It compresses and reconstructs material texture data. Super-resolution technologies reconstruct the final rendered image at a higher display resolution.\"},{\"id\":\"faq2\",\"question\":\"Can NTC reduce VRAM usage?\",\"answer\":\"Yes, particularly with Inference on Sample because the compact neural representation can remain in GPU memory instead of fully expanded conventional textures. Inference on Load mainly preserves storage savings before expansion.\"},{\"id\":\"faq3\",\"question\":\"What does the neural network store?\",\"answer\":\"The compressed material contains compact latent feature data plus weights for a small decoder network that reconstructs texture values.\"},{\"id\":\"faq4\",\"question\":\"Does NTC decode the whole texture before rendering?\",\"answer\":\"Not necessarily. Inference on Load does, Inference on Sample reconstructs values during shader sampling, and Inference on Feedback can decode requested texture tiles.\"},{\"id\":\"faq5\",\"question\":\"Is neural texture compression lossless?\",\"answer\":\"No. RTXNTC is lossy compression and quality depends on bitrate, channel count, decoder configuration and the material itself.\"},{\"id\":\"faq6\",\"question\":\"Is RTXNTC production-ready?\",\"answer\":\"The current public SDK is still labeled beta, so APIs, support and performance characteristics may continue to change.\"}]},\"tunes\":{}},{\"id\":\"h-glossary\",\"type\":\"header\",\"data\":{\"text\":\"Glossary\",\"level\":2},\"tunes\":{}},{\"id\":\"glossary\",\"type\":\"glossary\",\"data\":{\"title\":\"Key neural texture terms\",\"entries\":[{\"term\":\"Neural Texture Compression\",\"definition\":\"A technique that stores texture information as compact latent data plus neural decoder weights instead of only conventional texel blocks.\",\"anchor\":\"neural-texture-compression\"},{\"term\":\"Latent data\",\"definition\":\"Compact learned features that the neural decoder uses to reconstruct texture values.\",\"anchor\":\"latent-data\"},{\"term\":\"Decoder\",\"definition\":\"A small neural network that converts latent features into reconstructed texture channels.\",\"anchor\":\"decoder\"},{\"term\":\"Inference on Load\",\"definition\":\"Runtime mode that decodes the neural texture when an asset is loaded, usually into conventional texture formats.\",\"anchor\":\"inference-on-load\"},{\"term\":\"Inference on Sample\",\"definition\":\"Runtime mode that performs neural decoding directly during texture sampling so the compressed representation can remain resident.\",\"anchor\":\"inference-on-sample\"},{\"term\":\"Inference on Feedback\",\"definition\":\"Runtime strategy that uses texture feedback to decode and cache only requested tiles.\",\"anchor\":\"inference-on-feedback\"},{\"term\":\"Texture Storage–Compute Exchange\",\"definition\":\"A Figure Rocks model describing the trade of texture storage, bandwidth and VRAM for additional GPU neural-inference work.\",\"anchor\":\"texture-storage-compute-exchange\"},{\"term\":\"Neural Texture Value Test\",\"definition\":\"A Figure Rocks workflow for deciding whether neural texture compression creates a net benefit for a particular material and target GPU.\",\"anchor\":\"neural-texture-value-test\"}]},\"tunes\":{}},{\"id\":\"h-sources\",\"type\":\"header\",\"data\":{\"text\":\"Primary sources\",\"level\":2},\"tunes\":{}},{\"id\":\"src-rtxkit\",\"type\":\"linkTool\",\"data\":{\"link\":\"https:\u002F\u002Fdeveloper.nvidia.com\u002Frtx-kit\",\"meta\":{\"title\":\"NVIDIA Developer — RTX Kit\",\"description\":\"Official overview of RTX Neural Texture Compression and NVIDIA's published storage-reduction positioning.\",\"image\":{\"url\":\"\"}}},\"tunes\":{}},{\"id\":\"src-rtxntc-readme\",\"type\":\"linkTool\",\"data\":{\"link\":\"https:\u002F\u002Fgithub.com\u002FNVIDIA-RTX\u002FRTXNTC\u002Fblob\u002Fmain\u002FREADME.md\",\"meta\":{\"title\":\"NVIDIA RTXNTC — SDK README\",\"description\":\"Official SDK documentation describing material-channel compression, decoder\u002Flatent representation, runtime modes, memory examples, system requirements and Cooperative Vector support.\",\"image\":{\"url\":\"\"}}},\"tunes\":{}},{\"id\":\"src-rtxntc-release\",\"type\":\"linkTool\",\"data\":{\"link\":\"https:\u002F\u002Fgithub.com\u002FNVIDIA-RTX\u002FRTXNTC\u002Freleases\",\"meta\":{\"title\":\"NVIDIA RTXNTC — Releases\",\"description\":\"Official release history, including v0.10.0 beta and DirectX 12 Linear Algebra inference support.\",\"image\":{\"url\":\"\"}}},\"tunes\":{}},{\"id\":\"src-rtxntc-quality\",\"type\":\"linkTool\",\"data\":{\"link\":\"https:\u002F\u002Fgithub.com\u002FNVIDIA-RTX\u002FRTXNTC\u002Fblob\u002Fmain\u002Fdocs\u002FSettingsAndQuality.md\",\"meta\":{\"title\":\"NVIDIA RTXNTC — Compression Settings and Image Quality\",\"description\":\"Official documentation covering bitrate, channel interactions, quality measurement and lossy compression behavior.\",\"image\":{\"url\":\"\"}}},\"tunes\":{}},{\"id\":\"src-libntc\",\"type\":\"linkTool\",\"data\":{\"link\":\"https:\u002F\u002Fgithub.com\u002FNVIDIA-RTX\u002FRTXNTC-Library\",\"meta\":{\"title\":\"NVIDIA — LibNTC\",\"description\":\"Official runtime library documentation describing decoder configuration and the quality\u002Fperformance trade-off of different neural-network sizes.\",\"image\":{\"url\":\"\"}}},\"tunes\":{}}],\"version\":\"2.31.6\"}",{"time":1297,"blocks":1298,"version":1841},1790378899383,[1299,1303,1308,1313,1317,1321,1325,1329,1333,1337,1341,1345,1365,1369,1373,1377,1381,1385,1389,1407,1411,1415,1419,1423,1427,1431,1435,1439,1443,1447,1452,1459,1463,1467,1471,1475,1479,1483,1487,1508,1512,1516,1520,1524,1531,1535,1539,1559,1563,1567,1571,1575,1579,1584,1588,1592,1596,1600,1604,1630,1634,1638,1642,1646,1650,1654,1658,1662,1666,1670,1674,1678,1682,1686,1690,1694,1698,1702,1706,1710,1714,1718,1722,1726,1730,1734,1738,1742,1746,1750,1754,1757,1780,1784,1806,1810,1816,1822,1828,1835],{"id":541,"data":1300,"type":544,"tunes":1302},{"text":1301},"Texture compression normally means storing a smaller version of texture data and expanding it into a conventional GPU format before or during use. NVIDIA RTX Neural Texture Compression changes that model: part of the texture data becomes a small neural representation that can be decoded by the GPU itself.",{},{"id":547,"data":1304,"type":552,"tunes":1307},{"body":1305,"title":1306,"variant":551},"\u003Cstrong>RTX Neural Texture Compression is not texture upscaling.\u003C\u002Fstrong> It compresses multiple material textures into neural-network weights plus compact latent data, then reconstructs the requested texture values with a small neural network. Depending on the integration mode, the game can trade storage and VRAM usage for additional GPU inference work.","Direct answer",{},{"id":555,"data":1309,"type":552,"tunes":1312},{"body":1310,"title":1311,"variant":559},"RTX Neural Texture Compression is still a \u003Cstrong>beta SDK\u003C\u002Fstrong>. The current v0.10.0 beta added DirectX 12 Linear Algebra inference support, connecting NTC directly to the new neural-shader infrastructure discussed elsewhere on Figure Rocks.","Current status",{},{"id":562,"data":1314,"type":566,"tunes":1316},{"title":1315,"maxLevel":565,"minLevel":47},"Contents",{},{"id":569,"data":1318,"type":572,"tunes":1320},{"text":1319,"level":47},"Why normal texture compression still uses a lot of memory",{},{"id":575,"data":1322,"type":544,"tunes":1324},{"text":1323},"A modern physically based material rarely consists of one image. A single surface may use albedo, normal, roughness, metalness, ambient occlusion, opacity and other channels.",{},{"id":580,"data":1326,"type":544,"tunes":1328},{"text":1327},"Traditional GPU block-compression formats such as BC1 through BC7 reduce the cost, but the GPU still ends up storing conventional texture blocks for the material.",{},{"id":585,"data":1330,"type":544,"tunes":1332},{"text":1331},"As texture resolution and material complexity increase, those channels consume disk space, streaming bandwidth and GPU memory.",{},{"id":590,"data":1334,"type":572,"tunes":1336},{"text":1335,"level":47},"What RTX Neural Texture Compression actually stores",{},{"id":595,"data":1338,"type":544,"tunes":1340},{"text":1339},"NVIDIA's RTXNTC SDK compresses the channels belonging to one material together. The current SDK supports up to 16 texture channels in one NTC texture set.",{},{"id":600,"data":1342,"type":544,"tunes":1344},{"text":1343},"Instead of keeping only conventional compressed texels, the compression process produces two main things: weights for a small neural decoder and compact latent feature data.",{},{"id":605,"data":1346,"type":625,"tunes":1364},{"steps":1347,"title":1363,"orientation":624},[1348,1351,1354,1357,1360],{"label":1349,"description":1350},"1. Original material textures","Albedo, normal, roughness, metalness and other material channels are provided together.",{"label":1352,"description":1353},"2. Offline compression","The SDK learns a compact representation of the material.",{"label":1355,"description":1356},"3. Neural weights","A small decoder network stores part of what is needed to reconstruct the material.",{"label":1358,"description":1359},"4. Latent data","Compact feature tensors store material-specific information.",{"label":1361,"description":1362},"5. GPU inference","At runtime, the decoder combines the latent data and neural weights to reconstruct texture values.","The neural texture pipeline",{},{"id":628,"data":1366,"type":572,"tunes":1368},{"text":1367,"level":47},"Why compressing channels together can help",{},{"id":633,"data":1370,"type":544,"tunes":1372},{"text":1371},"Material channels are often related. A scratch visible in the base color may also appear in the normal or roughness map. A fabric pattern can influence several channels at the same spatial location.",{},{"id":638,"data":1374,"type":544,"tunes":1376},{"text":1375},"NVIDIA designed NTC to exploit those correlations instead of compressing every texture independently.",{},{"id":643,"data":1378,"type":544,"tunes":1380},{"text":1379},"That is one reason the technology is described as material-oriented compression rather than merely another image format.",{},{"id":648,"data":1382,"type":572,"tunes":1384},{"text":1383,"level":47},"The three NTC runtime modes are the key to understanding the technology",{},{"id":653,"data":1386,"type":544,"tunes":1388},{"text":1387},"The most important part of RTXNTC is not only how the material is compressed. It is when the game chooses to decompress it.",{},{"id":658,"data":1390,"type":685,"tunes":1406},{"rows":1391,"title":1398,"layout":674,"columns":1399},[1392,1394,1396],{"id":662,"label":1178,"values":1393},[13,13,13],{"id":666,"label":1182,"values":1395},[13,13,13],{"id":670,"label":1186,"values":1397},[13,13,13],"Inference on Load vs Inference on Sample vs Inference on Feedback",[1400,1402,1404],{"id":677,"label":1401},"When neural decoding happens",{"id":680,"label":1403},"Runtime texture-memory behavior",{"id":683,"label":1405},"Main trade-off",{},{"id":688,"data":1408,"type":572,"tunes":1410},{"text":1409,"level":47},"Inference on Load: neural compression as a storage format",{},{"id":693,"data":1412,"type":544,"tunes":1414},{"text":1413},"Inference on Load is the easiest mode to understand.",{},{"id":698,"data":1416,"type":544,"tunes":1418},{"text":1417},"The game stores the material in compact NTC form. When the asset is loaded, the GPU reconstructs the texture data and can transcode it into ordinary BCn texture formats.",{},{"id":703,"data":1420,"type":544,"tunes":1422},{"text":1421},"After that step, rendering can use normal texture sampling. The important saving is primarily before decompression: packaged game size, download size or asset-streaming bandwidth.",{},{"id":708,"data":1424,"type":544,"tunes":1426},{"text":1425},"But once the material is fully expanded into conventional textures, its runtime VRAM footprint approaches the conventional representation again.",{},{"id":713,"data":1428,"type":572,"tunes":1430},{"text":1429,"level":47},"Inference on Sample: keep the texture neural in VRAM",{},{"id":718,"data":1432,"type":544,"tunes":1434},{"text":1433},"Inference on Sample is the more radical mode.",{},{"id":723,"data":1436,"type":544,"tunes":1438},{"text":1437},"Instead of expanding the material into conventional textures before rendering, the shader reads compact latent data and runs the neural decoder when it needs texture values.",{},{"id":728,"data":1440,"type":544,"tunes":1442},{"text":1441},"NVIDIA's own SDK example compares a 12 MB BCn material representation with a 2.5 MB NTC representation when using Inference on Sample.",{},{"id":733,"data":1444,"type":544,"tunes":1446},{"text":1445},"The saving is real because the conventional texture data does not need to remain fully resident. But the cost moves somewhere else: the pixel or hit shader now performs neural inference.",{},{"id":683,"data":1448,"type":552,"tunes":1451},{"body":1449,"title":1450,"variant":741},"Inference on Sample trades \u003Cstrong>memory and bandwidth\u003C\u002Fstrong> for \u003Cstrong>GPU computation\u003C\u002Fstrong>. The right question is not “How much smaller is the texture?” but “Is the memory saving worth the added inference cost on this workload?”","Compression does not make the work disappear",{},{"id":744,"data":1453,"type":750,"tunes":1458},{"url":1454,"title":1455,"excerpt":1456,"ctaLabel":1457},"https:\u002F\u002Ffigure.rocks\u002Fblog\u002Fvram-usage-is-not-vram-requirement-why-a-full-memory-meter-does-not-tell-the-whole-story","VRAM Usage Is Not VRAM Requirement: Why a Full Memory Meter Does Not Tell the Whole Story","Why VRAM capacity, budgets, residency and actual memory pressure are different things.","Read the VRAM guide",{},{"id":753,"data":1460,"type":572,"tunes":1462},{"text":1461,"level":47},"Inference on Feedback: decode only what the player actually sees",{},{"id":758,"data":1464,"type":544,"tunes":1466},{"text":1465},"Inference on Feedback sits between the two extremes.",{},{"id":763,"data":1468,"type":544,"tunes":1470},{"text":1469},"The renderer tracks which texture tiles are actually requested. Instead of expanding the full material immediately, the system can decode requested tiles in batches and keep a working cache.",{},{"id":768,"data":1472,"type":544,"tunes":1474},{"text":1473},"Conceptually, this combines neural compression with texture streaming: the system pays the decoding cost only for regions that become relevant.",{},{"id":773,"data":1476,"type":544,"tunes":1478},{"text":1477},"The current sample implementation is more specialized than the other modes and its support constraints differ, so it should be treated as an integration strategy rather than a universal replacement for ordinary texture streaming.",{},{"id":778,"data":1480,"type":572,"tunes":1482},{"text":1481,"level":47},"The Texture Storage–Compute Exchange",{},{"id":783,"data":1484,"type":544,"tunes":1486},{"text":1485},"The easiest way to understand neural texture compression is as an exchange between resources.",{},{"id":788,"data":1488,"type":685,"tunes":1507},{"rows":1489,"title":1501,"layout":674,"columns":1502},[1490,1493,1495,1498],{"id":792,"label":1491,"values":1492},"Disk\u002Fstorage",[13,13],{"id":796,"label":797,"values":1494},[13,13],{"id":800,"label":1496,"values":1497},"Sampling cost",[13,13],{"id":804,"label":1499,"values":1500},"Quality control",[13,13],"What moves when texture storage becomes neural",[1503,1505],{"id":810,"label":1504},"Traditional compressed texture",{"id":813,"label":1506},"Neural texture representation",{},{"id":817,"data":1509,"type":572,"tunes":1511},{"text":1510,"level":47},"Why special matrix hardware matters",{},{"id":822,"data":1513,"type":544,"tunes":1515},{"text":1514},"Running a neural network for texture sampling would be too expensive if every multiply had to be handled like ordinary scalar shader work.",{},{"id":827,"data":1517,"type":544,"tunes":1519},{"text":1518},"RTXNTC therefore benefits from Cooperative Vector and now DirectX 12 Linear Algebra paths that let shaders use GPU matrix-acceleration hardware.",{},{"id":832,"data":1521,"type":544,"tunes":1523},{"text":1522},"The v0.10.0 beta explicitly added inference through the DirectX 12 Linear Algebra API introduced with Shader Model 6.10.",{},{"id":837,"data":1525,"type":750,"tunes":1530},{"url":1526,"title":1527,"excerpt":1528,"ctaLabel":1529},"https:\u002F\u002Ffigure.rocks\u002Fblog\u002Fdirectx-is-becoming-an-ml-platform-what-linear-algebra-and-neural-shaders-mean-for-future-games","DirectX Is Becoming an ML Platform: What Linear Algebra and Neural Shaders Mean for Future Games","How DirectX is moving matrix and neural operations directly into HLSL and the graphics pipeline.","Read the DirectX neural-shader guide",{},{"id":845,"data":1532,"type":572,"tunes":1534},{"text":1533,"level":47},"Why neural texture compression is not texture upscaling",{},{"id":850,"data":1536,"type":544,"tunes":1538},{"text":1537},"The two techniques can both use machine learning, but they solve different problems.",{},{"id":855,"data":1540,"type":685,"tunes":1558},{"rows":1541,"title":1554,"layout":674,"columns":1555},[1542,1545,1548,1551],{"id":859,"label":1543,"values":1544},"Input",[13,13],{"id":863,"label":1546,"values":1547},"Goal",[13,13],{"id":677,"label":1549,"values":1550},"When it runs",[13,13],{"id":870,"label":1552,"values":1553},"Output",[13,13],"Neural Texture Compression vs Super Resolution",[1556,1557],{"id":876,"label":1166},{"id":879,"label":880},{},{"id":883,"data":1560,"type":544,"tunes":1562},{"text":1561},"Neural Texture Compression can therefore exist underneath DLSS, FSR, XeSS or native rendering. It changes how material data is stored and reconstructed, not the final display resolution.",{},{"id":888,"data":1564,"type":572,"tunes":1566},{"text":1565,"level":47},"The quality knob is bits per pixel",{},{"id":893,"data":1568,"type":544,"tunes":1570},{"text":1569},"NTC is lossy compression. The amount of compressed information is controlled largely through the bits-per-pixel setting.",{},{"id":898,"data":1572,"type":544,"tunes":1574},{"text":1573},"Higher bitrate gives the model more information and generally improves reconstruction quality. Lower bitrate improves compression but increases the risk of visible error.",{},{"id":903,"data":1576,"type":544,"tunes":1578},{"text":1577},"Because multiple channels share the same representation, adding more material channels without increasing the bitrate can reduce the quality available to each channel.",{},{"id":908,"data":1580,"type":552,"tunes":1583},{"body":1581,"title":1582,"variant":559},"The SDK documentation explicitly notes that compression error is normal. The practical target is \u003Cstrong>acceptable visual error at a useful storage and runtime cost\u003C\u002Fstrong>.","Neural does not mean lossless",{},{"id":914,"data":1585,"type":572,"tunes":1587},{"text":1586,"level":47},"Why correlated material channels are important",{},{"id":919,"data":1589,"type":544,"tunes":1591},{"text":1590},"A neural representation becomes more valuable when several material channels describe related structure.",{},{"id":924,"data":1593,"type":544,"tunes":1595},{"text":1594},"If albedo, normal and roughness all contain the same scratches, seams or fabric weave, the decoder can exploit shared spatial information.",{},{"id":929,"data":1597,"type":544,"tunes":1599},{"text":1598},"If the channels are unrelated noise, there is less common structure to exploit and the compression problem becomes harder.",{},{"id":934,"data":1601,"type":572,"tunes":1603},{"text":1602,"level":47},"The Neural Texture Value Test",{},{"id":939,"data":1605,"type":625,"tunes":1629},{"steps":1606,"title":1628,"orientation":624},[1607,1610,1613,1616,1619,1622,1625],{"label":1608,"description":1609},"1. Measure conventional texture cost","How much disk space, streaming bandwidth and VRAM do the existing material textures consume?",{"label":1611,"description":1612},"2. Choose the runtime mode","Do you want storage savings only, persistent VRAM savings or streamed tile reconstruction?",{"label":1614,"description":1615},"3. Set an acceptable quality target","Compare reconstructed channels against the original material, not only the final beauty image.",{"label":1617,"description":1618},"4. Measure inference cost","Record added shader or decompression time on the actual target GPU.",{"label":1620,"description":1621},"5. Measure memory savings","Check the real runtime working set rather than only the compressed file size.",{"label":1623,"description":1624},"6. Test difficult materials","Fine normals, sharp masks, opacity, text and unrelated channels can expose compression failures.",{"label":1626,"description":1627},"7. Decide by net benefit","Use NTC only where the saved memory\u002Fbandwidth is worth the extra inference and integration complexity.","When is neural texture compression actually useful?",{},{"id":966,"data":1631,"type":572,"tunes":1633},{"text":1632,"level":47},"Why “8× smaller” needs context",{},{"id":971,"data":1635,"type":544,"tunes":1637},{"text":1636},"NVIDIA's RTX Kit describes RTX Neural Texture Compression as offering up to 8× disk-memory improvement at similar visual fidelity to traditional block compression.",{},{"id":976,"data":1639,"type":544,"tunes":1641},{"text":1640},"The phrase “up to” matters. Compression ratio depends on the material, number of channels, target bitrate, decoder configuration and quality threshold.",{},{"id":981,"data":1643,"type":544,"tunes":1645},{"text":1644},"The same ratio also does not automatically describe VRAM savings. Inference on Load can start from a compact file and still expand into conventional GPU textures. Inference on Sample preserves the compact representation in GPU memory but spends more compute during shading.",{},{"id":986,"data":1647,"type":572,"tunes":1649},{"text":1648,"level":47},"Decoder size is another performance-quality trade-off",{},{"id":991,"data":1651,"type":544,"tunes":1653},{"text":1652},"The NTC runtime uses a small multilayer perceptron to decode texture values.",{},{"id":996,"data":1655,"type":544,"tunes":1657},{"text":1656},"NVIDIA's current library uses a configurable decoder architecture. Larger networks can improve compression quality but cost more to execute; smaller networks can run faster with some loss in reconstruction quality.",{},{"id":1001,"data":1659,"type":544,"tunes":1661},{"text":1660},"That gives engine developers another tuning dimension beyond texture resolution and bitrate.",{},{"id":1006,"data":1663,"type":572,"tunes":1665},{"text":1664,"level":47},"Why this matters for future game installations",{},{"id":1011,"data":1667,"type":544,"tunes":1669},{"text":1668},"Modern games increasingly ship high-resolution material sets that affect download size as well as runtime memory.",{},{"id":1016,"data":1671,"type":544,"tunes":1673},{"text":1672},"Neural texture compression creates a new option: ship a compact learned representation and decide later whether to expand it on load, reconstruct it directly during shading or decode only requested tiles.",{},{"id":1021,"data":1675,"type":544,"tunes":1677},{"text":1676},"That means one compressed asset representation can participate in several different runtime memory strategies.",{},{"id":1026,"data":1679,"type":572,"tunes":1681},{"text":1680,"level":47},"It also changes what 'texture memory' means",{},{"id":1031,"data":1683,"type":544,"tunes":1685},{"text":1684},"With traditional rendering, a texture-memory budget is mostly about texture formats, mip levels, resolution and residency.",{},{"id":1036,"data":1687,"type":544,"tunes":1689},{"text":1688},"With neural textures, developers may also budget latent data, decoder weights, inference buffers, transcoded caches and the compute needed to reconstruct requested values.",{},{"id":1041,"data":1691,"type":544,"tunes":1693},{"text":1692},"So the asset no longer has one simple fixed memory identity.",{},{"id":1046,"data":1695,"type":572,"tunes":1697},{"text":1696,"level":47},"Cross-vendor support is more nuanced than the RTX name suggests",{},{"id":1051,"data":1699,"type":544,"tunes":1701},{"text":1700},"RTXNTC is an NVIDIA SDK, and compression itself currently requires an NVIDIA GPU according to the SDK requirements.",{},{"id":1056,"data":1703,"type":544,"tunes":1705},{"text":1704},"Runtime decompression is broader. NVIDIA documents functional paths on Shader Model 6 hardware and notes validation on NVIDIA, AMD and Intel GPUs, while advanced Cooperative Vector \u002F Linear Algebra paths depend on API and driver support.",{},{"id":1061,"data":1707,"type":544,"tunes":1709},{"text":1708},"Performance and feature parity therefore should not be assumed across vendors merely because the basic decoder can run.",{},{"id":1066,"data":1711,"type":572,"tunes":1713},{"text":1712,"level":47},"What would change this answer?",{},{"id":1071,"data":1715,"type":544,"tunes":1717},{"text":1716},"NTC is still beta. Runtime modes, decoder architectures, driver support and integration paths can change before a stable production release.",{},{"id":1076,"data":1719,"type":544,"tunes":1721},{"text":1720},"The biggest long-term change would be broad adoption of standardized neural-shader primitives across DirectX and Vulkan. That would make neural texture decoding less dependent on custom vendor-specific execution paths.",{},{"id":1081,"data":1723,"type":572,"tunes":1725},{"text":1724,"level":47},"Limitations",{},{"id":1086,"data":1727,"type":544,"tunes":1729},{"text":1728},"This article describes the architecture and current public RTXNTC SDK behavior. NVIDIA's quoted compression claims are vendor-provided and should not be treated as guaranteed results for every material.",{},{"id":1091,"data":1731,"type":544,"tunes":1733},{"text":1732},"The current SDK is beta software, and some preview paths have documented driver and platform limitations.",{},{"id":1096,"data":1735,"type":572,"tunes":1737},{"text":1736,"level":47},"Conclusion",{},{"id":1101,"data":1739,"type":544,"tunes":1741},{"text":1740},"RTX Neural Texture Compression is interesting because it changes a very old assumption: texture detail does not always have to exist in memory as conventional texels.",{},{"id":1106,"data":1743,"type":544,"tunes":1745},{"text":1744},"A material can instead be stored partly as a compact learned representation and reconstructed when needed.",{},{"id":1111,"data":1747,"type":544,"tunes":1749},{"text":1748},"That does not give free quality or free memory. It creates a new exchange: less storage, bandwidth and potentially VRAM in return for neural inference work.",{},{"id":1116,"data":1751,"type":544,"tunes":1753},{"text":1752},"The real innovation is not “AI makes textures sharper.” It is that part of a game's material data can become computation.",{},{"id":1121,"data":1755,"type":572,"tunes":1756},{"text":1123,"level":47},{},{"id":1126,"data":1758,"type":1126,"tunes":1779},{"items":1759,"title":1778},[1760,1763,1766,1769,1772,1775],{"id":1130,"answer":1761,"question":1762},"No. It compresses and reconstructs material texture data. Super-resolution technologies reconstruct the final rendered image at a higher display resolution.","Is RTX Neural Texture Compression an upscaler?",{"id":1134,"answer":1764,"question":1765},"Yes, particularly with Inference on Sample because the compact neural representation can remain in GPU memory instead of fully expanded conventional textures. Inference on Load mainly preserves storage savings before expansion.","Can NTC reduce VRAM usage?",{"id":1138,"answer":1767,"question":1768},"The compressed material contains compact latent feature data plus weights for a small decoder network that reconstructs texture values.","What does the neural network store?",{"id":1142,"answer":1770,"question":1771},"Not necessarily. Inference on Load does, Inference on Sample reconstructs values during shader sampling, and Inference on Feedback can decode requested texture tiles.","Does NTC decode the whole texture before rendering?",{"id":1146,"answer":1773,"question":1774},"No. RTXNTC is lossy compression and quality depends on bitrate, channel count, decoder configuration and the material itself.","Is neural texture compression lossless?",{"id":1150,"answer":1776,"question":1777},"The current public SDK is still labeled beta, so APIs, support and performance characteristics may continue to change.","Is RTXNTC production-ready?","RTX Neural Texture Compression in plain English",{},{"id":1156,"data":1781,"type":572,"tunes":1783},{"text":1782,"level":47},"Glossary",{},{"id":1161,"data":1785,"type":1161,"tunes":1805},{"title":1786,"entries":1787},"Key neural texture terms",[1788,1790,1793,1795,1797,1799,1801,1803],{"term":1166,"anchor":1167,"definition":1789},"A technique that stores texture information as compact latent data plus neural decoder weights instead of only conventional texel blocks.",{"term":1791,"anchor":1171,"definition":1792},"Latent data","Compact learned features that the neural decoder uses to reconstruct texture values.",{"term":1174,"anchor":1175,"definition":1794},"A small neural network that converts latent features into reconstructed texture channels.",{"term":1178,"anchor":1179,"definition":1796},"Runtime mode that decodes the neural texture when an asset is loaded, usually into conventional texture formats.",{"term":1182,"anchor":1183,"definition":1798},"Runtime mode that performs neural decoding directly during texture sampling so the compressed representation can remain resident.",{"term":1186,"anchor":1187,"definition":1800},"Runtime strategy that uses texture feedback to decode and cache only requested tiles.",{"term":1190,"anchor":1191,"definition":1802},"A Figure Rocks model describing the trade of texture storage, bandwidth and VRAM for additional GPU neural-inference work.",{"term":1194,"anchor":1195,"definition":1804},"A Figure Rocks workflow for deciding whether neural texture compression creates a net benefit for a particular material and target GPU.",{},{"id":1199,"data":1807,"type":572,"tunes":1809},{"text":1808,"level":47},"Primary sources",{},{"id":1204,"data":1811,"type":1211,"tunes":1815},{"link":1206,"meta":1812},{"image":1813,"title":1209,"description":1814},{"url":13},"Official overview of RTX Neural Texture Compression and NVIDIA's published storage-reduction positioning.",{},{"id":1214,"data":1817,"type":1211,"tunes":1821},{"link":1216,"meta":1818},{"image":1819,"title":1219,"description":1820},{"url":13},"Official SDK documentation describing material-channel compression, decoder\u002Flatent representation, runtime modes, memory examples, system requirements and Cooperative Vector support.",{},{"id":1223,"data":1823,"type":1211,"tunes":1827},{"link":1225,"meta":1824},{"image":1825,"title":1228,"description":1826},{"url":13},"Official release history, including v0.10.0 beta and DirectX 12 Linear Algebra inference support.",{},{"id":1232,"data":1829,"type":1211,"tunes":1834},{"link":1234,"meta":1830},{"image":1831,"title":1832,"description":1833},{"url":13},"NVIDIA RTXNTC — Compression Settings and Image Quality","Official documentation covering bitrate, channel interactions, quality measurement and lossy compression behavior.",{},{"id":1241,"data":1836,"type":1211,"tunes":1840},{"link":1243,"meta":1837},{"image":1838,"title":1246,"description":1839},{"url":13},"Official runtime library documentation describing decoder configuration and the quality\u002Fperformance trade-off of different neural-network sizes.",{},"2.31.6","NVIDIA RTX Neural Texture Compression changes how game materials can be stored. 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erfolgreich abgerufen",{"items":2223,"source":2277,"manualIds":2278,"manualMatchedIds":2279},[2224,2231,2238,2244,2250,2257,2264,2271],{"id":2225,"slug":2226,"title":2227,"excerpt":2228,"featuredImage":2229,"publishedAt":2230},"451","windows-auto-sr-is-not-dlss-how-npu-upscaling-works-without-game-integration","Windows Auto SR ist nicht DLSS: Wie NPU-Upscaling ohne Spielintegration funktioniert","Windows Auto SR kann unterstützte Spiele ohne DLSS-, FSR- oder XeSS-Integration hochskalieren. Anstatt das Rekonstruktionsmodell im Spiel auf der GPU auszuführen, verwendet Windows die NPU, um aus einem niedriger aufgelösten Render ein Bild mit höherer Auflösung neu aufzubauen.","\u002Fuploads\u002F2026\u002F09\u002Fwindows-auto-sr-is-not-dlss-how-npu-upscaling-works-without-game-integration-1790406942266-77ihme.webp","2026-09-26T03:14:00.000Z",{"id":2232,"slug":2233,"title":2234,"excerpt":2235,"featuredImage":2236,"publishedAt":2237},"449","directstorage-1-4-does-not-make-your-ssd-decompress-games-what-zstd-and-gpu-decompression-actually-do","DirectStorage 1.4 lässt deine SSD keine Spiele dekomprimieren: Was Zstd und GPU-Dekompression tatsächlich bewirken","DirectStorage 1.4 fügt Zstandard-Komprimierung, GPU-Dekomprimierung und eine neue Game Asset Conditioning Library hinzu, aber die SSD selbst ist nach wie vor nur ein Teil der Ladepipeline. Dieser Leitfaden erklärt, was die SSD, DirectStorage, CPU, GPU und Game Engine jeweils tatsächlich tun.","\u002Fuploads\u002F2026\u002F09\u002Fdirectstorage-1-4-does-not-make-your-ssd-decompress-games-what-zstd-and-gpu-decompression-actually-do-1790405481526-fwnzz4.webp","2026-09-26T02:49:00.000Z",{"id":2239,"slug":2240,"title":2241,"excerpt":2242,"featuredImage":14,"publishedAt":2243},"24","storage-and-streaming-reduce-load-times-without-creating-stutter","Storage und Streaming: Ladezeiten verkürzen, ohne Ruckler zu verursachen","Schneller Speicher hilft nur, wenn das Streaming-Verhalten stabil ist. Dieser Leitfaden erklärt, wie sich IO auf Ruckler auswirkt und was man zuerst ändern sollte.","2026-02-19T11:00:00.000Z",{"id":2245,"slug":2246,"title":2247,"excerpt":2248,"featuredImage":14,"publishedAt":2249},"221","storage-streaming-stutter-fixes-when-assets-cant-keep-up","Speicher-Streaming-Ruckler-Fixes: Wenn Assets nicht hinterherkommen","Streaming-Ruckler treten auf, wenn neue Bereiche geladen werden: Speicher-, Dekompressions- oder Asset-Streaming-Limits. Nutzen Sie diese Fix-Reihenfolge, bevor Sie jede Grafikeinstellung herabsetzen.","2026-02-20T21:00:00.000Z",{"id":2251,"slug":2252,"title":2253,"excerpt":2254,"featuredImage":2255,"publishedAt":2256},"443","dlss-5-is-not-just-upscaling-what-3d-guided-neural-rendering-actually-changes","DLSS 5 ist nicht nur Upscaling: Was 3D-geführtes neuronales Rendering tatsächlich verändert","DLSS 5 verlagert KI in einen neuen Teil der Grafikpipeline. Statt nur die Auflösung zu rekonstruieren oder zusätzliche Frames zu generieren, verwendet 3D-Guided Neural Rendering das eigene Frame der Spiel-Engine als Grundlage und verbessert Beleuchtung und Materialdetails unter Entwicklerkontrolle.","\u002Fuploads\u002F2026\u002F09\u002Fdlss-5-is-not-just-upscaling-what-3d-guided-neural-rendering-actually-changes-1790376457301-ytk4sx.webp","2026-09-25T18:46:00.000Z",{"id":2258,"slug":2259,"title":2260,"excerpt":2261,"featuredImage":2262,"publishedAt":2263},"440","lowered-graphics-settings-but-fps-didn-t-improve-you-re-probably-tuning-the-wrong-bottleneck","Grafikeinstellungen verringert, aber die FPS haben sich nicht verbessert? Wahrscheinlich optimierst du den falschen Engpass","Du reduzierst Schatten, Effekte und Auflösung, aber die FPS ändern sich kaum. Dieser Leitfaden erklärt, warum Grafikoptionen nur dann helfen, wenn sie die Arbeitslast verringern, die tatsächlich die Bildrate begrenzt – und wie man CPU-, GPU-, Speicher-, Streaming- und Frame-Cap-Engpässe erkennt.","\u002Fuploads\u002F2026\u002F09\u002Flowered-graphics-settings-but-fps-didn-t-improve-you-re-probably-tuning-the-wrong-bottleneck-1790375130830-i10hb3.webp","2026-09-25T18:23:00.000Z",{"id":2265,"slug":2266,"title":2267,"excerpt":2268,"featuredImage":2269,"publishedAt":2270},"441","vram-usage-is-not-vram-requirement-why-a-full-memory-meter-does-not-tell-the-whole-story","VRAM-Nutzung ist nicht VRAM-Anforderung: Warum eine vollständige Speicheranzeige nicht die ganze Geschichte erzählt","Wenn 7,8 GB auf einer 8-GB-Grafikkarte belegt sind, kann das wie ein Beweis dafür aussehen, dass einem Spiel der VRAM ausgegangen ist. So einfach ist es nicht. Dieser Leitfaden erklärt VRAM-Kapazität, Residenzbudgets, Working Sets, Shared Memory und wie man feststellt, ob Speicherdruck tatsächlich Ruckeln verursacht.","\u002Fuploads\u002F2026\u002F09\u002Fvram-usage-is-not-vram-requirement-why-a-full-memory-meter-does-not-tell-the-whole-story-1790375650647-k854hg.webp","2026-09-25T18:33:00.000Z",{"id":2272,"slug":2273,"title":2274,"excerpt":2275,"featuredImage":14,"publishedAt":2276},"302","amiibo-faq-the-20-questions-everyone-asks-and-the-straight-answers","amiibo FAQ: Die 20 Fragen, die jeder stellt (und die direkten Antworten)","Ein schnörkelloses Amiibo-FAQ: Kompatibilität, Scannen, Regionen, Neuauflagen, Wert und Sammelregeln — klar beantwortet, damit Anfänger aufhören, Geld zu verschwenden.","2026-02-21T17:00:00.000Z","fallback",[],[]]