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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\">直接回答\u003C\u002Fstrong>\u003Cdiv class=\"text-gray-700 dark:text-gray-200\">\u003Cstrong>RTX 神经纹理压缩不是纹理超分辨率。\u003C\u002Fstrong>它将多个材质纹理压缩为神经网络权重加上紧凑的潜在数据，然后用一个小型神经网络重建所请求的纹理值。根据集成模式，游戏可以用存储和显存占用来换取额外的 GPU 推理工作。\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\">当前状态\u003C\u002Fstrong>\u003Cdiv class=\"text-gray-700 dark:text-gray-200\">RTX 神经纹理压缩仍是一个\u003Cstrong>测试版 SDK\u003C\u002Fstrong>。当前的 v0.10.0 测试版增加了 DirectX 12 线性代数推理支持，将 NTC 直接连接到 Figure Rocks 上其他地方讨论的新神经着色器基础设施。\u003C\u002Fdiv>\u003C\u002Faside>\n\u003Cnav class=\"editorjs-toc\" data-editorjs-toc=\"true\" aria-label=\"目录\">\u003Cstrong class=\"editorjs-toc__title\">目录\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\">为什么普通纹理压缩仍然占用大量内存\u003C\u002Fa>\u003C\u002Fli>\u003Cli class=\"editorjs-toc__item\">\u003Ca href=\"#section-9\" class=\"editorjs-toc__link\">RTX 神经纹理压缩实际存储什么\u003C\u002Fa>\u003C\u002Fli>\u003Cli class=\"editorjs-toc__item\">\u003Ca href=\"#section-13\" class=\"editorjs-toc__link\">为什么一起压缩通道会有帮助\u003C\u002Fa>\u003C\u002Fli>\u003Cli class=\"editorjs-toc__item\">\u003Ca href=\"#section-17\" class=\"editorjs-toc__link\">三种 NTC 运行时模式是理解该技术的关键\u003C\u002Fa>\u003C\u002Fli>\u003Cli class=\"editorjs-toc__item\">\u003Ca href=\"#section-20\" class=\"editorjs-toc__link\">加载时推理：将神经压缩作为存储格式\u003C\u002Fa>\u003C\u002Fli>\u003Cli class=\"editorjs-toc__item\">\u003Ca href=\"#section-25\" class=\"editorjs-toc__link\">采样时推理：让纹理在显存中保持神经形式\u003C\u002Fa>\u003C\u002Fli>\u003Cli class=\"editorjs-toc__item\">\u003Ca href=\"#section-32\" class=\"editorjs-toc__link\">反馈时推理：只解码玩家实际看到的内容\u003C\u002Fa>\u003C\u002Fli>\u003Cli class=\"editorjs-toc__item\">\u003Ca href=\"#section-37\" class=\"editorjs-toc__link\">纹理存储与计算的交换\u003C\u002Fa>\u003C\u002Fli>\u003Cli class=\"editorjs-toc__item\">\u003Ca href=\"#section-40\" class=\"editorjs-toc__link\">为什么专用矩阵硬件很重要\u003C\u002Fa>\u003C\u002Fli>\u003Cli class=\"editorjs-toc__item\">\u003Ca href=\"#section-45\" class=\"editorjs-toc__link\">为什么神经纹理压缩不是纹理超分辨率\u003C\u002Fa>\u003C\u002Fli>\u003Cli class=\"editorjs-toc__item\">\u003Ca href=\"#section-49\" class=\"editorjs-toc__link\">质量调节旋钮是每像素比特数\u003C\u002Fa>\u003C\u002Fli>\u003Cli class=\"editorjs-toc__item\">\u003Ca href=\"#section-54\" class=\"editorjs-toc__link\">为什么相关材质通道很重要\u003C\u002Fa>\u003C\u002Fli>\u003Cli class=\"editorjs-toc__item\">\u003Ca href=\"#section-58\" class=\"editorjs-toc__link\">神经纹理价值测试\u003C\u002Fa>\u003C\u002Fli>\u003Cli class=\"editorjs-toc__item\">\u003Ca href=\"#section-60\" class=\"editorjs-toc__link\">为什么“小 8 倍”需要结合语境\u003C\u002Fa>\u003C\u002Fli>\u003Cli class=\"editorjs-toc__item\">\u003Ca href=\"#section-64\" class=\"editorjs-toc__link\">解码器大小是另一个性能与质量的权衡\u003C\u002Fa>\u003C\u002Fli>\u003Cli class=\"editorjs-toc__item\">\u003Ca href=\"#section-68\" class=\"editorjs-toc__link\">为什么这对未来的游戏安装很重要\u003C\u002Fa>\u003C\u002Fli>\u003Cli class=\"editorjs-toc__item\">\u003Ca href=\"#section-72\" class=\"editorjs-toc__link\">它也改变了“纹理内存”的含义\u003C\u002Fa>\u003C\u002Fli>\u003Cli class=\"editorjs-toc__item\">\u003Ca href=\"#section-76\" class=\"editorjs-toc__link\">跨厂商支持比 RTX 名称所暗示的更为微妙\u003C\u002Fa>\u003C\u002Fli>\u003Cli class=\"editorjs-toc__item\">\u003Ca href=\"#section-80\" class=\"editorjs-toc__link\">什么会改变这个答案？\u003C\u002Fa>\u003C\u002Fli>\u003Cli class=\"editorjs-toc__item\">\u003Ca href=\"#section-83\" class=\"editorjs-toc__link\">局限性\u003C\u002Fa>\u003C\u002Fli>\u003Cli class=\"editorjs-toc__item\">\u003Ca href=\"#section-86\" class=\"editorjs-toc__link\">结论\u003C\u002Fa>\u003C\u002Fli>\u003Cli class=\"editorjs-toc__item\">\u003Ca href=\"#section-91\" class=\"editorjs-toc__link\">常见问题\u003C\u002Fa>\u003C\u002Fli>\u003Cli class=\"editorjs-toc__item\">\u003Ca href=\"#section-93\" class=\"editorjs-toc__link\">术语表\u003C\u002Fa>\u003C\u002Fli>\u003Cli class=\"editorjs-toc__item\">\u003Ca href=\"#section-95\" class=\"editorjs-toc__link\">主要来源\u003C\u002Fa>\u003C\u002Fli>\u003C\u002Fol>\u003C\u002Fnav>\n\u003Ch2 id=\"section-5\">为什么普通纹理压缩仍然占用大量内存\u003C\u002Fh2>\n\u003Cp>现代基于物理的材质很少由一张图像组成。单个表面可能使用反照率、法线、粗糙度、金属度、环境光遮蔽、不透明度和其他通道。\u003C\u002Fp>\n\u003Cp>传统的 GPU 块压缩格式（如 BC1 到 BC7）降低了成本，但 GPU 最终仍要为材质存储常规纹理块。\u003C\u002Fp>\n\u003Cp>随着纹理分辨率和材质复杂度的增加，这些通道会消耗磁盘空间、流式传输带宽和 GPU 内存。\u003C\u002Fp>\n\u003Ch2 id=\"section-9\">RTX 神经纹理压缩实际存储什么\u003C\u002Fh2>\n\u003Cp>NVIDIA 的 RTXNTC SDK 将属于同一材质的通道一起压缩。当前 SDK 在一个 NTC 纹理集中最多支持 16 个纹理通道。\u003C\u002Fp>\n\u003Cp>压缩过程不是只保留传统的压缩纹素，而是产生两个主要部分：一个小型神经解码器的权重和紧凑的潜在特征数据。\u003C\u002Fp>\n\u003Csection class=\"editorjs-process my-6\">\u003Ch3 class=\"mb-3 text-lg font-semibold\">神经纹理管线\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. 原始材质纹理\u003C\u002Fdiv>\u003Cdiv class=\"mt-1 text-sm text-gray-600 dark:text-gray-300\">反照率、法线、粗糙度、金属度和其他材质通道一起提供。\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. 离线压缩\u003C\u002Fdiv>\u003Cdiv class=\"mt-1 text-sm text-gray-600 dark:text-gray-300\">SDK 学习材质的紧凑表示。\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. 神经权重\u003C\u002Fdiv>\u003Cdiv class=\"mt-1 text-sm text-gray-600 dark:text-gray-300\">一个小型解码器网络存储重建材质所需的部分信息。\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. 潜在数据\u003C\u002Fdiv>\u003Cdiv class=\"mt-1 text-sm text-gray-600 dark:text-gray-300\">紧凑的特征张量存储材质特定的信息。\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 推理\u003C\u002Fdiv>\u003Cdiv class=\"mt-1 text-sm text-gray-600 dark:text-gray-300\">在运行时，解码器结合潜在数据和神经权重来重建纹理值。\u003C\u002Fdiv>\u003C\u002Fdiv>\u003C\u002Fdiv>\u003C\u002Fsection>\n\u003Ch2 id=\"section-13\">为什么一起压缩通道会有帮助\u003C\u002Fh2>\n\u003Cp>材质通道通常是相关的。基础颜色中可见的划痕也可能出现在法线或粗糙度贴图中。织物图案可以影响同一空间位置上的多个通道。\u003C\u002Fp>\n\u003Cp>NVIDIA 设计 NTC 就是为了利用这些相关性，而不是独立压缩每个纹理。\u003C\u002Fp>\n\u003Cp>这就是该技术被描述为面向材质的压缩，而不仅仅是另一种图像格式的原因之一。\u003C\u002Fp>\n\u003Ch2 id=\"section-17\">三种 NTC 运行时模式是理解该技术的关键\u003C\u002Fh2>\n\u003Cp>RTXNTC 最重要的部分不仅在于材质如何被压缩，还在于游戏选择何时解压它。\u003C\u002Fp>\n\u003Csection class=\"editorjs-comparison my-6\">\u003Ch3 class=\"mb-3 text-lg font-semibold\">加载时推理 vs 采样时推理 vs 反馈时推理\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\">神经解码发生的时间\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\">运行时纹理内存行为\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\">主要权衡\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\">加载时推理\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\">采样时推理\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\">反馈时推理\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\">加载时推理：将神经压缩作为存储格式\u003C\u002Fh2>\n\u003Cp>加载时推理是最容易理解的一种模式。\u003C\u002Fp>\n\u003Cp>游戏以紧凑的 NTC 形式存储材质。当资源被加载时，GPU 重建纹理数据，并可将其转码为普通的 BCn 纹理格式。\u003C\u002Fp>\n\u003Cp>完成该步骤后，渲染可以使用常规的纹理采样。重要的节省主要发生在解压之前：打包后的游戏体积、下载体积或资源流式传输带宽。\u003C\u002Fp>\n\u003Cp>但一旦材质被完全展开为常规纹理，其运行时显存占用又会接近常规表示。\u003C\u002Fp>\n\u003Ch2 id=\"section-25\">采样时推理：让纹理在显存中保持神经形式\u003C\u002Fh2>\n\u003Cp>采样时推理是更激进的一种模式。\u003C\u002Fp>\n\u003Cp>着色器不会在渲染前将材质展开为常规纹理，而是读取紧凑的潜在数据，并在需要纹理值时运行神经解码器。\u003C\u002Fp>\n\u003Cp>NVIDIA 自家的 SDK 示例在使用采样时推理时，将 12 MB 的 BCn 材质表示与 2.5 MB 的 NTC 表示进行了对比。\u003C\u002Fp>\n\u003Cp>这种节省是真实的，因为常规纹理数据不需要完全常驻。但代价转移到了别处：像素着色器或命中着色器现在要执行神经推理。\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\">压缩并不会让工作消失\u003C\u002Fstrong>\u003Cdiv class=\"text-gray-700 dark:text-gray-200\">采样时推理用\u003Cstrong>显存和带宽\u003C\u002Fstrong>换取\u003Cstrong>GPU 计算\u003C\u002Fstrong>。正确的问题不是“纹理小了多少？”，而是“在这种工作负载下，节省的显存是否值得增加的推理成本？”\u003C\u002Fdiv>\u003C\u002Faside>\n\u003Caside class=\"editorjs-referral my-6\">\u003Ca href=\"https:\u002F\u002Ffigure.rocks\u002Fzh\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\">显存使用量不等于显存需求：为什么显存占用满并不说明全部问题\u003C\u002Fstrong>\u003Cp class=\"mt-2 text-sm text-gray-600 dark:text-gray-300\">为什么显存容量、预算、常驻和实际显存压力是不同的概念。\u003C\u002Fp>\u003Cspan class=\"mt-3 inline-flex text-sm font-medium text-primary-600 dark:text-primary-400\">阅读显存指南 →\u003C\u002Fspan>\u003C\u002Fdiv>\u003C\u002Fa>\u003C\u002Faside>\n\u003Ch2 id=\"section-32\">反馈时推理：只解码玩家实际看到的内容\u003C\u002Fh2>\n\u003Cp>反馈时推理介于两个极端之间。\u003C\u002Fp>\n\u003Cp>渲染器会跟踪实际请求了哪些纹理瓦片。系统不会立即展开完整材质，而是可以批量解码被请求的瓦片，并保留一个工作缓存。\u003C\u002Fp>\n\u003Cp>从概念上讲，这结合了神经压缩与纹理流式传输：系统只为变得相关的区域支付解码成本。\u003C\u002Fp>\n\u003Cp>当前的示例实现比其他模式更专门化，其支持限制也不同，因此应将其视为一种集成策略，而不是对普通纹理流式传输的通用替代方案。\u003C\u002Fp>\n\u003Ch2 id=\"section-37\">纹理存储与计算的交换\u003C\u002Fh2>\n\u003Cp>理解神经纹理压缩最简单的方式，是将其视为资源之间的交换。\u003C\u002Fp>\n\u003Csection class=\"editorjs-comparison my-6\">\u003Ch3 class=\"mb-3 text-lg font-semibold\">当纹理存储变为神经化时，什么发生了转移\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\">传统压缩纹理\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\">神经纹理表示\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\">磁盘\u002F存储\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\">显存\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\">采样开销\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\">质量控制\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\">为什么专用矩阵硬件很重要\u003C\u002Fh2>\n\u003Cp>如果每次乘法都必须像普通的标量着色器工作那样处理，那么为纹理采样运行神经网络将会过于昂贵。\u003C\u002Fp>\n\u003Cp>因此，RTXNTC 受益于 Cooperative Vector 以及现在的 DirectX 12 线性代数路径，这些路径允许着色器使用 GPU 矩阵加速硬件。\u003C\u002Fp>\n\u003Cp>v0.10.0 测试版明确添加了通过随 Shader Model 6.10 引入的 DirectX 12 线性代数 API 进行推理的功能。\u003C\u002Fp>\n\u003Caside class=\"editorjs-referral my-6\">\u003Ca href=\"https:\u002F\u002Ffigure.rocks\u002Fzh\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 正在成为机器学习平台：线性代数和神经着色器对未来游戏意味着什么\u003C\u002Fstrong>\u003Cp class=\"mt-2 text-sm text-gray-600 dark:text-gray-300\">DirectX 如何将矩阵和神经操作直接移入 HLSL 和图形管线。\u003C\u002Fp>\u003Cspan class=\"mt-3 inline-flex text-sm font-medium text-primary-600 dark:text-primary-400\">阅读 DirectX 神经着色器指南 →\u003C\u002Fspan>\u003C\u002Fdiv>\u003C\u002Fa>\u003C\u002Faside>\n\u003Ch2 id=\"section-45\">为什么神经纹理压缩不是纹理超分辨率\u003C\u002Fh2>\n\u003Cp>这两种技术都可以使用机器学习，但它们解决的是不同的问题。\u003C\u002Fp>\n\u003Csection class=\"editorjs-comparison my-6\">\u003Ch3 class=\"mb-3 text-lg font-semibold\">神经纹理压缩与超分辨率\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\">神经纹理压缩\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\">超分辨率\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\">输入\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\">目标\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\">运行时机\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\">输出\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>因此，神经纹理压缩可以存在于 DLSS、FSR、XeSS 或原生渲染之下。它改变的是材质数据的存储和重建方式，而不是最终显示分辨率。\u003C\u002Fp>\n\u003Ch2 id=\"section-49\">质量调节旋钮是每像素比特数\u003C\u002Fh2>\n\u003Cp>NTC 是有损压缩。压缩信息量主要通过每像素比特数设置来控制。\u003C\u002Fp>\n\u003Cp>更高的比特率给模型更多信息，通常能改善重建质量。更低的比特率提高压缩率，但增加可见错误的风险。\u003C\u002Fp>\n\u003Cp>由于多个通道共享同一表示，在不增加比特率的情况下添加更多材质通道，可能会降低每个通道可用的质量。\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\">神经化并不意味着无损\u003C\u002Fstrong>\u003Cdiv class=\"text-gray-700 dark:text-gray-200\">SDK 文档明确指出压缩误差是正常的。实际目标是在\u003Cstrong>可接受的视觉误差下，实现有用的存储和运行时开销\u003C\u002Fstrong>。\u003C\u002Fdiv>\u003C\u002Faside>\n\u003Ch2 id=\"section-54\">为什么相关材质通道很重要\u003C\u002Fh2>\n\u003Cp>当多个材质通道描述相关结构时，神经表示会变得更有价值。\u003C\u002Fp>\n\u003Cp>如果反照率、法线和粗糙度都包含相同的划痕、接缝或织物纹理，解码器就可以利用共享的空间信息。\u003C\u002Fp>\n\u003Cp>如果这些通道是不相关的噪声，可利用的公共结构就更少，压缩问题也会变得更困难。\u003C\u002Fp>\n\u003Ch2 id=\"section-58\">神经纹理价值测试\u003C\u002Fh2>\n\u003Csection class=\"editorjs-process my-6\">\u003Ch3 class=\"mb-3 text-lg font-semibold\">神经纹理压缩什么时候才真正有用？\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. 测量传统纹理成本\u003C\u002Fdiv>\u003Cdiv class=\"mt-1 text-sm text-gray-600 dark:text-gray-300\">现有材质纹理会占用多少磁盘空间、流式带宽和显存？\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. 选择运行时模式\u003C\u002Fdiv>\u003Cdiv class=\"mt-1 text-sm text-gray-600 dark:text-gray-300\">你只想要节省存储、持续节省显存，还是流式瓦片重建？\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. 设定可接受的质量目标\u003C\u002Fdiv>\u003Cdiv class=\"mt-1 text-sm text-gray-600 dark:text-gray-300\">将重建后的通道与原始材质进行比较，而不仅仅是最终的美术效果图。\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. 测量推理成本\u003C\u002Fdiv>\u003Cdiv class=\"mt-1 text-sm text-gray-600 dark:text-gray-300\">在实际目标 GPU 上记录增加的着色器或解压时间。\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. 测量内存节省\u003C\u002Fdiv>\u003Cdiv class=\"mt-1 text-sm text-gray-600 dark:text-gray-300\">检查真实的运行时工作集，而不仅仅是压缩后的文件大小。\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. 测试困难材质\u003C\u002Fdiv>\u003Cdiv class=\"mt-1 text-sm text-gray-600 dark:text-gray-300\">精细法线、锐利遮罩、不透明度、文字和不相关通道都可能暴露压缩失败。\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. 根据净收益决定\u003C\u002Fdiv>\u003Cdiv class=\"mt-1 text-sm text-gray-600 dark:text-gray-300\">仅在节省的内存\u002F带宽值得额外推理和集成复杂度时使用 NTC。\u003C\u002Fdiv>\u003C\u002Fdiv>\u003C\u002Fdiv>\u003C\u002Fsection>\n\u003Ch2 id=\"section-60\">为什么“小 8 倍”需要结合语境\u003C\u002Fh2>\n\u003Cp>NVIDIA 的 RTX Kit 将 RTX 神经纹理压缩描述为：在视觉保真度与传统块压缩相似的情况下，可提供最高 8 倍的磁盘内存改进。\u003C\u002Fp>\n\u003Cp>“最高”这个词很重要。压缩比取决于材质、通道数量、目标比特率、解码器配置和质量阈值。\u003C\u002Fp>\n\u003Cp>同样的比例也并不自动代表显存节省。加载时推理可以从紧凑文件开始，但仍会扩展为传统 GPU 纹理。采样时推理会在 GPU 内存中保留紧凑表示，但会在着色期间消耗更多计算。\u003C\u002Fp>\n\u003Ch2 id=\"section-64\">解码器大小是另一个性能与质量的权衡\u003C\u002Fh2>\n\u003Cp>NTC 运行时使用一个小型多层感知机来解码纹理值。\u003C\u002Fp>\n\u003Cp>NVIDIA 当前的库使用可配置的解码器架构。更大的网络可以提高压缩质量，但执行成本更高；更小的网络可以运行得更快，但重建质量会有所损失。\u003C\u002Fp>\n\u003Cp>这为引擎开发者提供了纹理分辨率和比特率之外的另一个调优维度。\u003C\u002Fp>\n\u003Ch2 id=\"section-68\">为什么这对未来的游戏安装很重要\u003C\u002Fh2>\n\u003Cp>现代游戏越来越多地附带高分辨率材质集，这既影响下载大小，也影响运行时内存。\u003C\u002Fp>\n\u003Cp>神经纹理压缩创造了一个新选项：发布紧凑的学习表示，然后稍后决定是在加载时扩展它、在着色期间直接重建它，还是只解码请求的瓦片。\u003C\u002Fp>\n\u003Cp>这意味着一种压缩资产表示可以参与多种不同的运行时内存策略。\u003C\u002Fp>\n\u003Ch2 id=\"section-72\">它也改变了“纹理内存”的含义\u003C\u002Fh2>\n\u003Cp>在传统渲染中，纹理内存预算主要涉及纹理格式、mip 级别、分辨率和驻留。\u003C\u002Fp>\n\u003Cp>在神经纹理中，开发者还可能需要为潜在数据、解码器权重、推理缓冲区、转码缓存以及重建所请求值所需的计算进行预算。\u003C\u002Fp>\n\u003Cp>因此，资产不再具有一个简单的固定内存标识。\u003C\u002Fp>\n\u003Ch2 id=\"section-76\">跨厂商支持比 RTX 名称所暗示的更为微妙\u003C\u002Fh2>\n\u003Cp>RTXNTC 是 NVIDIA 的 SDK，根据 SDK 要求，压缩本身目前需要 NVIDIA GPU。\u003C\u002Fp>\n\u003Cp>运行时解压缩范围更广。NVIDIA 记录了在 Shader Model 6 硬件上的功能路径，并指出在 NVIDIA、AMD 和 Intel GPU 上的验证，而高级 Cooperative Vector \u002F Linear Algebra 路径取决于 API 和驱动程序支持。\u003C\u002Fp>\n\u003Cp>因此，不应仅仅因为基本解码器可以运行就假设各厂商之间的性能和功能对等。\u003C\u002Fp>\n\u003Ch2 id=\"section-80\">什么会改变这个答案？\u003C\u002Fh2>\n\u003Cp>NTC 仍处于测试阶段。在稳定生产版本发布之前，运行时模式、解码器架构、驱动程序支持和集成路径可能会发生变化。\u003C\u002Fp>\n\u003Cp>最大的长期变化将是 DirectX 和 Vulkan 中标准化神经着色器原语的广泛采用。这将使神经纹理解码不再那么依赖于特定厂商的自定义执行路径。\u003C\u002Fp>\n\u003Ch2 id=\"section-83\">局限性\u003C\u002Fh2>\n\u003Cp>本文描述了架构和当前公开的 RTXNTC SDK 行为。NVIDIA 引用的压缩声明由厂商提供，不应被视为对每种材质都有保证的结果。\u003C\u002Fp>\n\u003Cp>当前 SDK 是测试版软件，一些预览路径有记录的驱动程序和平台限制。\u003C\u002Fp>\n\u003Ch2 id=\"section-86\">结论\u003C\u002Fh2>\n\u003Cp>RTX 神经纹理压缩之所以有趣，是因为它改变了一个非常古老的假设：纹理细节并不总是必须以传统纹素的形式存在于内存中。\u003C\u002Fp>\n\u003Cp>材质可以部分存储为紧凑的学习表示，并在需要时重建。\u003C\u002Fp>\n\u003Cp>这并不会带来免费的质量或免费的内存。它创造了一种新的交换：以更少的存储、带宽和潜在的 VRAM 换取神经推理工作。\u003C\u002Fp>\n\u003Cp>真正的创新不是“AI 让纹理更清晰”。而是游戏材质数据的一部分可以变成计算。\u003C\u002Fp>\n\u003Ch2 id=\"section-91\">常见问题\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\">通俗解读 RTX 神经纹理压缩\u003C\u002Fh3>\u003Cdiv id=\"faq1\" class=\"border-t border-gray-200 py-4 first:border-t-0 dark:border-gray-700\">\u003Ch4 class=\"font-semibold text-gray-900 dark:text-gray-100\">RTX 神经纹理压缩是超分辨率技术吗？\u003C\u002Fh4>\u003Cdiv class=\"mt-2 text-gray-600 dark:text-gray-300\">不是。它压缩并重建材质纹理数据。超分辨率技术则以更高的显示分辨率重建最终渲染图像。\u003C\u002Fdiv>\u003C\u002Fdiv>\u003Cdiv id=\"faq2\" class=\"border-t border-gray-200 py-4 first:border-t-0 dark:border-gray-700\">\u003Ch4 class=\"font-semibold text-gray-900 dark:text-gray-100\">NTC 能减少显存占用吗？\u003C\u002Fh4>\u003Cdiv class=\"mt-2 text-gray-600 dark:text-gray-300\">能，尤其是在采样时推理模式下，因为紧凑的神经表示可以保留在 GPU 内存中，而无需完全展开为传统纹理。加载时推理主要是在展开前保留存储节省。\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\">神经网络存储了什么？\u003C\u002Fh4>\u003Cdiv class=\"mt-2 text-gray-600 dark:text-gray-300\">压缩材质包含紧凑的潜在特征数据，以及用于重建纹理值的小型解码器网络的权重。\u003C\u002Fdiv>\u003C\u002Fdiv>\u003Cdiv id=\"faq4\" class=\"border-t border-gray-200 py-4 first:border-t-0 dark:border-gray-700\">\u003Ch4 class=\"font-semibold text-gray-900 dark:text-gray-100\">NTC 会在渲染前解码整个纹理吗？\u003C\u002Fh4>\u003Cdiv class=\"mt-2 text-gray-600 dark:text-gray-300\">不一定。加载时推理会这样做，采样时推理会在着色器采样期间重建值，而反馈时推理可以解码所请求的纹理块。\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\">神经纹理压缩是无损的吗？\u003C\u002Fh4>\u003Cdiv class=\"mt-2 text-gray-600 dark:text-gray-300\">不是。RTXNTC 是有损压缩，质量取决于比特率、通道数、解码器配置以及材质本身。\u003C\u002Fdiv>\u003C\u002Fdiv>\u003Cdiv id=\"faq6\" class=\"border-t border-gray-200 py-4 first:border-t-0 dark:border-gray-700\">\u003Ch4 class=\"font-semibold text-gray-900 dark:text-gray-100\">RTXNTC 已可用于生产环境吗？\u003C\u002Fh4>\u003Cdiv class=\"mt-2 text-gray-600 dark:text-gray-300\">当前公开的 SDK 仍标记为测试版，因此 API、支持和性能特征可能仍会变化。\u003C\u002Fdiv>\u003C\u002Fdiv>\u003C\u002Fsection>\n\u003Ch2 id=\"section-93\">术语表\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\">关键神经纹理术语\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\">神经纹理压缩\u003C\u002Fdt>\u003Cdd class=\"mt-1 text-gray-600 dark:text-gray-300\">一种将纹理信息存储为紧凑潜在数据加神经解码器权重，而不仅仅是传统纹素块的技术。\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\">潜在数据\u003C\u002Fdt>\u003Cdd class=\"mt-1 text-gray-600 dark:text-gray-300\">紧凑的学习特征，神经解码器用其重建纹理值。\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\">解码器\u003C\u002Fdt>\u003Cdd class=\"mt-1 text-gray-600 dark:text-gray-300\">一个小型神经网络，将潜在特征转换为重建的纹理通道。\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\">加载时推理\u003C\u002Fdt>\u003Cdd class=\"mt-1 text-gray-600 dark:text-gray-300\">一种运行时模式，在加载资产时解码神经纹理，通常转换为传统纹理格式。\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\">采样时推理\u003C\u002Fdt>\u003Cdd class=\"mt-1 text-gray-600 dark:text-gray-300\">一种运行时模式，在纹理采样期间直接执行神经解码，因此压缩表示可以保持驻留。\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\">反馈时推理\u003C\u002Fdt>\u003Cdd class=\"mt-1 text-gray-600 dark:text-gray-300\">一种运行时策略，利用纹理反馈仅解码和缓存所请求的纹理块。\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\">纹理存储与计算交换\u003C\u002Fdt>\u003Cdd class=\"mt-1 text-gray-600 dark:text-gray-300\">Figure Rocks 模型，描述以纹理存储、带宽和显存换取额外 GPU 神经推理工作。\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\">神经纹理价值测试\u003C\u002Fdt>\u003Cdd class=\"mt-1 text-gray-600 dark:text-gray-300\">Figure Rocks 工作流程，用于判断神经纹理压缩是否对特定材质和目标 GPU 产生净收益。\u003C\u002Fdd>\u003C\u002Fdiv>\u003C\u002Fdl>\u003C\u002Fsection>\n\u003Ch2 id=\"section-95\">主要来源\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 开发者 — RTX Kit\u003C\u002Fstrong>\u003Cp class=\"text-sm text-gray-600 dark:text-gray-400\">RTX 神经纹理压缩的官方概述，以及 NVIDIA 公布的存储减少定位。\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\">官方 SDK 文档，描述材质通道压缩、解码器\u002F潜在表示、运行时模式、内存示例、系统要求和 Cooperative Vector 支持。\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 — 版本发布\u003C\u002Fstrong>\u003Cp class=\"text-sm text-gray-600 dark:text-gray-400\">官方发布历史，包括 v0.10.0 测试版和 DirectX 12 线性代数推理支持。\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 — 压缩设置与图像质量\u003C\u002Fstrong>\u003Cp class=\"text-sm text-gray-600 dark:text-gray-400\">官方文档，涵盖比特率、通道交互、质量测量和有损压缩行为。\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\">官方运行时库文档，描述解码器配置以及不同神经网络规模的质量\u002F性能权衡。\u003C\u002Fp>\u003C\u002Fa>",{"time":538,"blocks":539,"version":1244},1790379155018,[540,546,554,561,568,574,579,584,589,594,599,604,627,632,637,642,647,652,657,687,692,697,702,707,712,717,722,727,732,737,743,752,757,762,767,772,777,782,787,816,821,826,831,836,844,849,854,882,887,892,897,902,907,913,918,923,928,933,938,965,970,975,980,985,990,995,1000,1005,1010,1015,1020,1025,1030,1035,1040,1045,1050,1055,1060,1065,1070,1075,1080,1085,1090,1095,1100,1105,1110,1115,1120,1125,1155,1160,1193,1198,1208,1217,1226,1235],{"id":541,"data":542,"type":544,"tunes":545},"intro",{"text":543},"纹理压缩通常意味着存储纹理数据的较小版本，并在使用前或使用期间将其扩展为常规 GPU 格式。NVIDIA RTX 神经纹理压缩改变了这一模式：部分纹理数据变成了一种可由 GPU 自身解码的小型神经表示。","paragraph",{},{"id":547,"data":548,"type":552,"tunes":553},"direct",{"body":549,"title":550,"variant":551},"\u003Cstrong>RTX 神经纹理压缩不是纹理超分辨率。\u003C\u002Fstrong>它将多个材质纹理压缩为神经网络权重加上紧凑的潜在数据，然后用一个小型神经网络重建所请求的纹理值。根据集成模式，游戏可以用存储和显存占用来换取额外的 GPU 推理工作。","直接回答","info","callout",{},{"id":555,"data":556,"type":552,"tunes":560},"status",{"body":557,"title":558,"variant":559},"RTX 神经纹理压缩仍是一个\u003Cstrong>测试版 SDK\u003C\u002Fstrong>。当前的 v0.10.0 测试版增加了 DirectX 12 线性代数推理支持，将 NTC 直接连接到 Figure Rocks 上其他地方讨论的新神经着色器基础设施。","当前状态","note",{},{"id":562,"data":563,"type":566,"tunes":567},"toc",{"title":564,"maxLevel":565,"minLevel":47},"目录",3,"tableOfContents",{},{"id":569,"data":570,"type":572,"tunes":573},"h-normal",{"text":571,"level":47},"为什么普通纹理压缩仍然占用大量内存","header",{},{"id":575,"data":576,"type":544,"tunes":578},"p-normal-1",{"text":577},"现代基于物理的材质很少由一张图像组成。单个表面可能使用反照率、法线、粗糙度、金属度、环境光遮蔽、不透明度和其他通道。",{},{"id":580,"data":581,"type":544,"tunes":583},"p-normal-2",{"text":582},"传统的 GPU 块压缩格式（如 BC1 到 BC7）降低了成本，但 GPU 最终仍要为材质存储常规纹理块。",{},{"id":585,"data":586,"type":544,"tunes":588},"p-normal-3",{"text":587},"随着纹理分辨率和材质复杂度的增加，这些通道会消耗磁盘空间、流式传输带宽和 GPU 内存。",{},{"id":590,"data":591,"type":572,"tunes":593},"h-store",{"text":592,"level":47},"RTX 神经纹理压缩实际存储什么",{},{"id":595,"data":596,"type":544,"tunes":598},"p-store-1",{"text":597},"NVIDIA 的 RTXNTC SDK 将属于同一材质的通道一起压缩。当前 SDK 在一个 NTC 纹理集中最多支持 16 个纹理通道。",{},{"id":600,"data":601,"type":544,"tunes":603},"p-store-2",{"text":602},"压缩过程不是只保留传统的压缩纹素，而是产生两个主要部分：一个小型神经解码器的权重和紧凑的潜在特征数据。",{},{"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. 原始材质纹理","反照率、法线、粗糙度、金属度和其他材质通道一起提供。",{"label":612,"description":613},"2. 离线压缩","SDK 学习材质的紧凑表示。",{"label":615,"description":616},"3. 神经权重","一个小型解码器网络存储重建材质所需的部分信息。",{"label":618,"description":619},"4. 潜在数据","紧凑的特征张量存储材质特定的信息。",{"label":621,"description":622},"5. GPU 推理","在运行时，解码器结合潜在数据和神经权重来重建纹理值。","神经纹理管线","auto","processFlow",{},{"id":628,"data":629,"type":572,"tunes":631},"h-correlation",{"text":630,"level":47},"为什么一起压缩通道会有帮助",{},{"id":633,"data":634,"type":544,"tunes":636},"p-cor-1",{"text":635},"材质通道通常是相关的。基础颜色中可见的划痕也可能出现在法线或粗糙度贴图中。织物图案可以影响同一空间位置上的多个通道。",{},{"id":638,"data":639,"type":544,"tunes":641},"p-cor-2",{"text":640},"NVIDIA 设计 NTC 就是为了利用这些相关性，而不是独立压缩每个纹理。",{},{"id":643,"data":644,"type":544,"tunes":646},"p-cor-3",{"text":645},"这就是该技术被描述为面向材质的压缩，而不仅仅是另一种图像格式的原因之一。",{},{"id":648,"data":649,"type":572,"tunes":651},"h-modes",{"text":650,"level":47},"三种 NTC 运行时模式是理解该技术的关键",{},{"id":653,"data":654,"type":544,"tunes":656},"p-modes-1",{"text":655},"RTXNTC 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是测试版软件，一些预览路径有记录的驱动程序和平台限制。",{},{"id":1096,"data":1097,"type":572,"tunes":1099},"h-conclusion",{"text":1098,"level":47},"结论",{},{"id":1101,"data":1102,"type":544,"tunes":1104},"p-conc-1",{"text":1103},"RTX 神经纹理压缩之所以有趣，是因为它改变了一个非常古老的假设：纹理细节并不总是必须以传统纹素的形式存在于内存中。",{},{"id":1106,"data":1107,"type":544,"tunes":1109},"p-conc-2",{"text":1108},"材质可以部分存储为紧凑的学习表示，并在需要时重建。",{},{"id":1111,"data":1112,"type":544,"tunes":1114},"p-conc-3",{"text":1113},"这并不会带来免费的质量或免费的内存。它创造了一种新的交换：以更少的存储、带宽和潜在的 VRAM 换取神经推理工作。",{},{"id":1116,"data":1117,"type":544,"tunes":1119},"p-conc-4",{"text":1118},"真正的创新不是“AI 让纹理更清晰”。而是游戏材质数据的一部分可以变成计算。",{},{"id":1121,"data":1122,"type":572,"tunes":1124},"h-faq",{"text":1123,"level":47},"常见问题",{},{"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","不是。它压缩并重建材质纹理数据。超分辨率技术则以更高的显示分辨率重建最终渲染图像。","RTX 神经纹理压缩是超分辨率技术吗？",{"id":1134,"answer":1135,"question":1136},"faq2","能，尤其是在采样时推理模式下，因为紧凑的神经表示可以保留在 GPU 内存中，而无需完全展开为传统纹理。加载时推理主要是在展开前保留存储节省。","NTC 能减少显存占用吗？",{"id":1138,"answer":1139,"question":1140},"faq3","压缩材质包含紧凑的潜在特征数据，以及用于重建纹理值的小型解码器网络的权重。","神经网络存储了什么？",{"id":1142,"answer":1143,"question":1144},"faq4","不一定。加载时推理会这样做，采样时推理会在着色器采样期间重建值，而反馈时推理可以解码所请求的纹理块。","NTC 会在渲染前解码整个纹理吗？",{"id":1146,"answer":1147,"question":1148},"faq5","不是。RTXNTC 是有损压缩，质量取决于比特率、通道数、解码器配置以及材质本身。","神经纹理压缩是无损的吗？",{"id":1150,"answer":1151,"question":1152},"faq6","当前公开的 SDK 仍标记为测试版，因此 API、支持和性能特征可能仍会变化。","RTXNTC 已可用于生产环境吗？","通俗解读 RTX 神经纹理压缩",{},{"id":1156,"data":1157,"type":572,"tunes":1159},"h-glossary",{"text":1158,"level":47},"术语表",{},{"id":1161,"data":1162,"type":1161,"tunes":1192},"glossary",{"title":1163,"entries":1164},"关键神经纹理术语",[1165,1168,1172,1176,1179,1182,1185,1189],{"term":877,"anchor":1166,"definition":1167},"neural-texture-compression","一种将纹理信息存储为紧凑潜在数据加神经解码器权重，而不仅仅是传统纹素块的技术。",{"term":1169,"anchor":1170,"definition":1171},"潜在数据","latent-data","紧凑的学习特征，神经解码器用其重建纹理值。",{"term":1173,"anchor":1174,"definition":1175},"解码器","decoder","一个小型神经网络，将潜在特征转换为重建的纹理通道。",{"term":663,"anchor":1177,"definition":1178},"inference-on-load","一种运行时模式，在加载资产时解码神经纹理，通常转换为传统纹理格式。",{"term":667,"anchor":1180,"definition":1181},"inference-on-sample","一种运行时模式，在纹理采样期间直接执行神经解码，因此压缩表示可以保持驻留。",{"term":671,"anchor":1183,"definition":1184},"inference-on-feedback","一种运行时策略，利用纹理反馈仅解码和缓存所请求的纹理块。",{"term":1186,"anchor":1187,"definition":1188},"纹理存储与计算交换","texture-storage-compute-exchange","Figure Rocks 模型，描述以纹理存储、带宽和显存换取额外 GPU 神经推理工作。",{"term":936,"anchor":1190,"definition":1191},"neural-texture-value-test","Figure Rocks 工作流程，用于判断神经纹理压缩是否对特定材质和目标 GPU 产生净收益。",{},{"id":1194,"data":1195,"type":572,"tunes":1197},"h-sources",{"text":1196,"level":47},"主要来源",{},{"id":1199,"data":1200,"type":1206,"tunes":1207},"src-rtxkit",{"link":1201,"meta":1202},"https:\u002F\u002Fdeveloper.nvidia.com\u002Frtx-kit",{"image":1203,"title":1204,"description":1205},{"url":13},"NVIDIA 开发者 — RTX Kit","RTX 神经纹理压缩的官方概述，以及 NVIDIA 公布的存储减少定位。","linkTool",{},{"id":1209,"data":1210,"type":1206,"tunes":1216},"src-rtxntc-readme",{"link":1211,"meta":1212},"https:\u002F\u002Fgithub.com\u002FNVIDIA-RTX\u002FRTXNTC\u002Fblob\u002Fmain\u002FREADME.md",{"image":1213,"title":1214,"description":1215},{"url":13},"NVIDIA RTXNTC — SDK README","官方 SDK 文档，描述材质通道压缩、解码器\u002F潜在表示、运行时模式、内存示例、系统要求和 Cooperative Vector 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神经纹理压缩改变了游戏材质的存储方式。材质不再仅以传统纹素的形式保存每个纹理通道，而是可以压缩为紧凑的潜在数据和一个小型神经解码器，然后在需要时由 GPU 重建。","\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":1254,"de":1255,"sr":1256,"es":1257,"fr":1258,"it":1259,"ru":1260,"zh":1261},"\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",[1263,1267,1271,1275,1279],{"id":1264,"name":1265,"slug":1266},152,"VRAM 与串流","vram-and-streaming",{"id":1268,"name":1269,"slug":1270},330,"串流与 IO 修复","streaming-and-io-fixes",{"id":1272,"name":1273,"slug":1274},173,"CPU、内存、存储","cpus-memory-storage",{"id":1276,"name":1277,"slug":1278},210,"质量的含义","what-quality-means",{"id":1280,"name":1281,"slug":1282},214,"营销与现实","marketing-vs-reality",{"id":283,"login":1284,"email":1285,"displayName":1286},"aleksandar","aleksandar@stajic.de","Aleksandar Stajic",[1288,1850],{"lang":8,"title":1289,"content":1290,"contentJson":1291,"excerpt":1849},"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":1292,"blocks":1293,"version":1848},1790378899383,[1294,1298,1303,1308,1312,1316,1320,1324,1328,1332,1336,1340,1360,1364,1368,1372,1376,1380,1384,1405,1409,1413,1417,1421,1425,1429,1433,1437,1441,1445,1450,1457,1461,1465,1469,1473,1477,1481,1485,1507,1511,1515,1519,1523,1530,1534,1538,1560,1564,1568,1572,1576,1580,1585,1589,1593,1597,1601,1605,1631,1635,1639,1643,1647,1651,1655,1659,1663,1667,1671,1675,1679,1683,1687,1691,1695,1699,1703,1707,1711,1715,1719,1723,1727,1731,1735,1739,1743,1747,1751,1755,1759,1782,1786,1811,1815,1822,1828,1835,1842],{"id":541,"data":1295,"type":544,"tunes":1297},{"text":1296},"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":1299,"type":552,"tunes":1302},{"body":1300,"title":1301,"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":1304,"type":552,"tunes":1307},{"body":1305,"title":1306,"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":1309,"type":566,"tunes":1311},{"title":1310,"maxLevel":565,"minLevel":47},"Contents",{},{"id":569,"data":1313,"type":572,"tunes":1315},{"text":1314,"level":47},"Why normal texture compression still uses a lot of memory",{},{"id":575,"data":1317,"type":544,"tunes":1319},{"text":1318},"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":1321,"type":544,"tunes":1323},{"text":1322},"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":1325,"type":544,"tunes":1327},{"text":1326},"As texture resolution and material complexity increase, those channels consume disk space, streaming bandwidth and GPU memory.",{},{"id":590,"data":1329,"type":572,"tunes":1331},{"text":1330,"level":47},"What RTX Neural Texture Compression actually stores",{},{"id":595,"data":1333,"type":544,"tunes":1335},{"text":1334},"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":1337,"type":544,"tunes":1339},{"text":1338},"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":1341,"type":625,"tunes":1359},{"steps":1342,"title":1358,"orientation":624},[1343,1346,1349,1352,1355],{"label":1344,"description":1345},"1. Original material textures","Albedo, normal, roughness, metalness and other material channels are provided together.",{"label":1347,"description":1348},"2. Offline compression","The SDK learns a compact representation of the material.",{"label":1350,"description":1351},"3. Neural weights","A small decoder network stores part of what is needed to reconstruct the material.",{"label":1353,"description":1354},"4. Latent data","Compact feature tensors store material-specific information.",{"label":1356,"description":1357},"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":1361,"type":572,"tunes":1363},{"text":1362,"level":47},"Why compressing channels together can help",{},{"id":633,"data":1365,"type":544,"tunes":1367},{"text":1366},"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":1369,"type":544,"tunes":1371},{"text":1370},"NVIDIA designed NTC to exploit those correlations instead of compressing every texture independently.",{},{"id":643,"data":1373,"type":544,"tunes":1375},{"text":1374},"That is one reason the technology is described as material-oriented compression rather than merely another image format.",{},{"id":648,"data":1377,"type":572,"tunes":1379},{"text":1378,"level":47},"The three NTC runtime modes are the key to understanding the technology",{},{"id":653,"data":1381,"type":544,"tunes":1383},{"text":1382},"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":1385,"type":685,"tunes":1404},{"rows":1386,"title":1396,"layout":674,"columns":1397},[1387,1390,1393],{"id":662,"label":1388,"values":1389},"Inference on Load",[13,13,13],{"id":666,"label":1391,"values":1392},"Inference on Sample",[13,13,13],{"id":670,"label":1394,"values":1395},"Inference on Feedback",[13,13,13],"Inference on Load vs Inference on Sample vs Inference on Feedback",[1398,1400,1402],{"id":677,"label":1399},"When neural decoding happens",{"id":680,"label":1401},"Runtime texture-memory behavior",{"id":683,"label":1403},"Main trade-off",{},{"id":688,"data":1406,"type":572,"tunes":1408},{"text":1407,"level":47},"Inference on Load: neural compression as a storage format",{},{"id":693,"data":1410,"type":544,"tunes":1412},{"text":1411},"Inference on Load is the easiest mode to understand.",{},{"id":698,"data":1414,"type":544,"tunes":1416},{"text":1415},"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":1418,"type":544,"tunes":1420},{"text":1419},"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":1422,"type":544,"tunes":1424},{"text":1423},"But once the material is fully expanded into conventional textures, its runtime VRAM footprint approaches the conventional representation again.",{},{"id":713,"data":1426,"type":572,"tunes":1428},{"text":1427,"level":47},"Inference on Sample: keep the texture neural in VRAM",{},{"id":718,"data":1430,"type":544,"tunes":1432},{"text":1431},"Inference on Sample is the more radical mode.",{},{"id":723,"data":1434,"type":544,"tunes":1436},{"text":1435},"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":1438,"type":544,"tunes":1440},{"text":1439},"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":1442,"type":544,"tunes":1444},{"text":1443},"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":1446,"type":552,"tunes":1449},{"body":1447,"title":1448,"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":1451,"type":750,"tunes":1456},{"url":1452,"title":1453,"excerpt":1454,"ctaLabel":1455},"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":1458,"type":572,"tunes":1460},{"text":1459,"level":47},"Inference on Feedback: decode only what the player actually sees",{},{"id":758,"data":1462,"type":544,"tunes":1464},{"text":1463},"Inference on Feedback sits between the two extremes.",{},{"id":763,"data":1466,"type":544,"tunes":1468},{"text":1467},"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":1470,"type":544,"tunes":1472},{"text":1471},"Conceptually, this combines neural compression with texture streaming: the system pays the decoding cost only for regions that become relevant.",{},{"id":773,"data":1474,"type":544,"tunes":1476},{"text":1475},"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":1478,"type":572,"tunes":1480},{"text":1479,"level":47},"The Texture Storage–Compute Exchange",{},{"id":783,"data":1482,"type":544,"tunes":1484},{"text":1483},"The easiest way to understand neural texture compression is as an exchange between resources.",{},{"id":788,"data":1486,"type":685,"tunes":1506},{"rows":1487,"title":1500,"layout":674,"columns":1501},[1488,1491,1494,1497],{"id":792,"label":1489,"values":1490},"Disk\u002Fstorage",[13,13],{"id":796,"label":1492,"values":1493},"VRAM",[13,13],{"id":800,"label":1495,"values":1496},"Sampling cost",[13,13],{"id":804,"label":1498,"values":1499},"Quality control",[13,13],"What moves when texture storage becomes neural",[1502,1504],{"id":810,"label":1503},"Traditional compressed texture",{"id":813,"label":1505},"Neural texture representation",{},{"id":817,"data":1508,"type":572,"tunes":1510},{"text":1509,"level":47},"Why special matrix hardware matters",{},{"id":822,"data":1512,"type":544,"tunes":1514},{"text":1513},"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":1516,"type":544,"tunes":1518},{"text":1517},"RTXNTC therefore benefits from Cooperative Vector and now DirectX 12 Linear Algebra paths that let shaders use GPU matrix-acceleration hardware.",{},{"id":832,"data":1520,"type":544,"tunes":1522},{"text":1521},"The v0.10.0 beta explicitly added inference through the DirectX 12 Linear Algebra API introduced with Shader Model 6.10.",{},{"id":837,"data":1524,"type":750,"tunes":1529},{"url":1525,"title":1526,"excerpt":1527,"ctaLabel":1528},"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":1531,"type":572,"tunes":1533},{"text":1532,"level":47},"Why neural texture compression is not texture upscaling",{},{"id":850,"data":1535,"type":544,"tunes":1537},{"text":1536},"The two techniques can both use machine learning, but they solve different problems.",{},{"id":855,"data":1539,"type":685,"tunes":1559},{"rows":1540,"title":1553,"layout":674,"columns":1554},[1541,1544,1547,1550],{"id":859,"label":1542,"values":1543},"Input",[13,13],{"id":863,"label":1545,"values":1546},"Goal",[13,13],{"id":677,"label":1548,"values":1549},"When it runs",[13,13],{"id":870,"label":1551,"values":1552},"Output",[13,13],"Neural Texture Compression vs Super Resolution",[1555,1557],{"id":876,"label":1556},"Neural Texture Compression",{"id":879,"label":1558},"Super Resolution",{},{"id":883,"data":1561,"type":544,"tunes":1563},{"text":1562},"Neural Texture Compression can therefore exist underneath DLSS, FSR, XeSS or native rendering. It changes how material data is stored and reconstructed, not the final display resolution.",{},{"id":888,"data":1565,"type":572,"tunes":1567},{"text":1566,"level":47},"The quality knob is bits per pixel",{},{"id":893,"data":1569,"type":544,"tunes":1571},{"text":1570},"NTC is lossy compression. The amount of compressed information is controlled largely through the bits-per-pixel setting.",{},{"id":898,"data":1573,"type":544,"tunes":1575},{"text":1574},"Higher bitrate gives the model more information and generally improves reconstruction quality. Lower bitrate improves compression but increases the risk of visible error.",{},{"id":903,"data":1577,"type":544,"tunes":1579},{"text":1578},"Because multiple channels share the same representation, adding more material channels without increasing the bitrate can reduce the quality available to each channel.",{},{"id":908,"data":1581,"type":552,"tunes":1584},{"body":1582,"title":1583,"variant":559},"The SDK documentation explicitly notes that compression error is normal. The practical target is \u003Cstrong>acceptable visual error at a useful storage and runtime cost\u003C\u002Fstrong>.","Neural does not mean lossless",{},{"id":914,"data":1586,"type":572,"tunes":1588},{"text":1587,"level":47},"Why correlated material channels are important",{},{"id":919,"data":1590,"type":544,"tunes":1592},{"text":1591},"A neural representation becomes more valuable when several material channels describe related structure.",{},{"id":924,"data":1594,"type":544,"tunes":1596},{"text":1595},"If albedo, normal and roughness all contain the same scratches, seams or fabric weave, the decoder can exploit shared spatial information.",{},{"id":929,"data":1598,"type":544,"tunes":1600},{"text":1599},"If the channels are unrelated noise, there is less common structure to exploit and the compression problem becomes harder.",{},{"id":934,"data":1602,"type":572,"tunes":1604},{"text":1603,"level":47},"The Neural Texture Value Test",{},{"id":939,"data":1606,"type":625,"tunes":1630},{"steps":1607,"title":1629,"orientation":624},[1608,1611,1614,1617,1620,1623,1626],{"label":1609,"description":1610},"1. Measure conventional texture cost","How much disk space, streaming bandwidth and VRAM do the existing material textures consume?",{"label":1612,"description":1613},"2. Choose the runtime mode","Do you want storage savings only, persistent VRAM savings or streamed tile reconstruction?",{"label":1615,"description":1616},"3. Set an acceptable quality target","Compare reconstructed channels against the original material, not only the final beauty image.",{"label":1618,"description":1619},"4. Measure inference cost","Record added shader or decompression time on the actual target GPU.",{"label":1621,"description":1622},"5. Measure memory savings","Check the real runtime working set rather than only the compressed file size.",{"label":1624,"description":1625},"6. Test difficult materials","Fine normals, sharp masks, opacity, text and unrelated channels can expose compression failures.",{"label":1627,"description":1628},"7. Decide by net benefit","Use NTC only where the saved memory\u002Fbandwidth is worth the extra inference and integration complexity.","When is neural texture compression actually useful?",{},{"id":966,"data":1632,"type":572,"tunes":1634},{"text":1633,"level":47},"Why “8× smaller” needs context",{},{"id":971,"data":1636,"type":544,"tunes":1638},{"text":1637},"NVIDIA's RTX Kit describes RTX Neural Texture Compression as offering up to 8× disk-memory improvement at similar visual fidelity to traditional block compression.",{},{"id":976,"data":1640,"type":544,"tunes":1642},{"text":1641},"The phrase “up to” matters. Compression ratio depends on the material, number of channels, target bitrate, decoder configuration and quality threshold.",{},{"id":981,"data":1644,"type":544,"tunes":1646},{"text":1645},"The same ratio also does not automatically describe VRAM savings. Inference on Load can start from a compact file and still expand into conventional GPU textures. Inference on Sample preserves the compact representation in GPU memory but spends more compute during shading.",{},{"id":986,"data":1648,"type":572,"tunes":1650},{"text":1649,"level":47},"Decoder size is another performance-quality trade-off",{},{"id":991,"data":1652,"type":544,"tunes":1654},{"text":1653},"The NTC runtime uses a small multilayer perceptron to decode texture values.",{},{"id":996,"data":1656,"type":544,"tunes":1658},{"text":1657},"NVIDIA's current library uses a configurable decoder architecture. Larger networks can improve compression quality but cost more to execute; smaller networks can run faster with some loss in reconstruction quality.",{},{"id":1001,"data":1660,"type":544,"tunes":1662},{"text":1661},"That gives engine developers another tuning dimension beyond texture resolution and bitrate.",{},{"id":1006,"data":1664,"type":572,"tunes":1666},{"text":1665,"level":47},"Why this matters for future game installations",{},{"id":1011,"data":1668,"type":544,"tunes":1670},{"text":1669},"Modern games increasingly ship high-resolution material sets that affect download size as well as runtime memory.",{},{"id":1016,"data":1672,"type":544,"tunes":1674},{"text":1673},"Neural texture compression creates a new option: ship a compact learned representation and decide later whether to expand it on load, reconstruct it directly during shading or decode only requested tiles.",{},{"id":1021,"data":1676,"type":544,"tunes":1678},{"text":1677},"That means one compressed asset representation can participate in several different runtime memory strategies.",{},{"id":1026,"data":1680,"type":572,"tunes":1682},{"text":1681,"level":47},"It also changes what 'texture memory' means",{},{"id":1031,"data":1684,"type":544,"tunes":1686},{"text":1685},"With traditional rendering, a texture-memory budget is mostly about texture formats, mip levels, resolution and residency.",{},{"id":1036,"data":1688,"type":544,"tunes":1690},{"text":1689},"With neural textures, developers may also budget latent data, decoder weights, inference buffers, transcoded caches and the compute needed to reconstruct requested values.",{},{"id":1041,"data":1692,"type":544,"tunes":1694},{"text":1693},"So the asset no longer has one simple fixed memory identity.",{},{"id":1046,"data":1696,"type":572,"tunes":1698},{"text":1697,"level":47},"Cross-vendor support is more nuanced than the RTX name suggests",{},{"id":1051,"data":1700,"type":544,"tunes":1702},{"text":1701},"RTXNTC is an NVIDIA SDK, and compression itself currently requires an NVIDIA GPU according to the SDK requirements.",{},{"id":1056,"data":1704,"type":544,"tunes":1706},{"text":1705},"Runtime decompression is broader. NVIDIA documents functional paths on Shader Model 6 hardware and notes validation on NVIDIA, AMD and Intel GPUs, while advanced Cooperative Vector \u002F Linear Algebra paths depend on API and driver support.",{},{"id":1061,"data":1708,"type":544,"tunes":1710},{"text":1709},"Performance and feature parity therefore should not be assumed across vendors merely because the basic decoder can run.",{},{"id":1066,"data":1712,"type":572,"tunes":1714},{"text":1713,"level":47},"What would change this answer?",{},{"id":1071,"data":1716,"type":544,"tunes":1718},{"text":1717},"NTC is still beta. Runtime modes, decoder architectures, driver support and integration paths can change before a stable production release.",{},{"id":1076,"data":1720,"type":544,"tunes":1722},{"text":1721},"The biggest long-term change would be broad adoption of standardized neural-shader primitives across DirectX and Vulkan. That would make neural texture decoding less dependent on custom vendor-specific execution paths.",{},{"id":1081,"data":1724,"type":572,"tunes":1726},{"text":1725,"level":47},"Limitations",{},{"id":1086,"data":1728,"type":544,"tunes":1730},{"text":1729},"This article describes the architecture and current public RTXNTC SDK behavior. NVIDIA's quoted compression claims are vendor-provided and should not be treated as guaranteed results for every material.",{},{"id":1091,"data":1732,"type":544,"tunes":1734},{"text":1733},"The current SDK is beta software, and some preview paths have documented driver and platform limitations.",{},{"id":1096,"data":1736,"type":572,"tunes":1738},{"text":1737,"level":47},"Conclusion",{},{"id":1101,"data":1740,"type":544,"tunes":1742},{"text":1741},"RTX Neural Texture Compression is interesting because it changes a very old assumption: texture detail does not always have to exist in memory as conventional texels.",{},{"id":1106,"data":1744,"type":544,"tunes":1746},{"text":1745},"A material can instead be stored partly as a compact learned representation and reconstructed when needed.",{},{"id":1111,"data":1748,"type":544,"tunes":1750},{"text":1749},"That does not give free quality or free memory. It creates a new exchange: less storage, bandwidth and potentially VRAM in return for neural inference work.",{},{"id":1116,"data":1752,"type":544,"tunes":1754},{"text":1753},"The real innovation is not “AI makes textures sharper.” It is that part of a game's material data can become computation.",{},{"id":1121,"data":1756,"type":572,"tunes":1758},{"text":1757,"level":47},"FAQ",{},{"id":1126,"data":1760,"type":1126,"tunes":1781},{"items":1761,"title":1780},[1762,1765,1768,1771,1774,1777],{"id":1130,"answer":1763,"question":1764},"No. It compresses and reconstructs material texture data. Super-resolution technologies reconstruct the final rendered image at a higher display resolution.","Is RTX Neural Texture Compression an upscaler?",{"id":1134,"answer":1766,"question":1767},"Yes, particularly with Inference on Sample because the compact neural representation can remain in GPU memory instead of fully expanded conventional textures. Inference on Load mainly preserves storage savings before expansion.","Can NTC reduce VRAM usage?",{"id":1138,"answer":1769,"question":1770},"The compressed material contains compact latent feature data plus weights for a small decoder network that reconstructs texture values.","What does the neural network store?",{"id":1142,"answer":1772,"question":1773},"Not necessarily. Inference on Load does, Inference on Sample reconstructs values during shader sampling, and Inference on Feedback can decode requested texture tiles.","Does NTC decode the whole texture before rendering?",{"id":1146,"answer":1775,"question":1776},"No. RTXNTC is lossy compression and quality depends on bitrate, channel count, decoder configuration and the material itself.","Is neural texture compression lossless?",{"id":1150,"answer":1778,"question":1779},"The current public SDK is still labeled beta, so APIs, support and performance characteristics may continue to change.","Is RTXNTC production-ready?","RTX Neural Texture Compression in plain English",{},{"id":1156,"data":1783,"type":572,"tunes":1785},{"text":1784,"level":47},"Glossary",{},{"id":1161,"data":1787,"type":1161,"tunes":1810},{"title":1788,"entries":1789},"Key neural texture terms",[1790,1792,1795,1798,1800,1802,1804,1807],{"term":1556,"anchor":1166,"definition":1791},"A technique that stores texture information as compact latent data plus neural decoder weights instead of only conventional texel blocks.",{"term":1793,"anchor":1170,"definition":1794},"Latent data","Compact learned features that the neural decoder uses to reconstruct texture values.",{"term":1796,"anchor":1174,"definition":1797},"Decoder","A small neural network that converts latent features into reconstructed texture channels.",{"term":1388,"anchor":1177,"definition":1799},"Runtime mode that decodes the neural texture when an asset is loaded, usually into conventional texture formats.",{"term":1391,"anchor":1180,"definition":1801},"Runtime mode that performs neural decoding directly during texture sampling so the compressed representation can remain resident.",{"term":1394,"anchor":1183,"definition":1803},"Runtime strategy that uses texture feedback to decode and cache only requested tiles.",{"term":1805,"anchor":1187,"definition":1806},"Texture Storage–Compute Exchange","A Figure Rocks model describing the trade of texture storage, bandwidth and VRAM for additional GPU neural-inference work.",{"term":1808,"anchor":1190,"definition":1809},"Neural Texture Value Test","A Figure Rocks workflow for deciding whether neural texture compression creates a net benefit for a particular material and target GPU.",{},{"id":1194,"data":1812,"type":572,"tunes":1814},{"text":1813,"level":47},"Primary sources",{},{"id":1199,"data":1816,"type":1206,"tunes":1821},{"link":1201,"meta":1817},{"image":1818,"title":1819,"description":1820},{"url":13},"NVIDIA Developer — RTX Kit","Official overview of RTX Neural Texture Compression and NVIDIA's published storage-reduction positioning.",{},{"id":1209,"data":1823,"type":1206,"tunes":1827},{"link":1211,"meta":1824},{"image":1825,"title":1214,"description":1826},{"url":13},"Official SDK documentation describing material-channel compression, decoder\u002Flatent representation, runtime modes, memory examples, system requirements and Cooperative Vector support.",{},{"id":1218,"data":1829,"type":1206,"tunes":1834},{"link":1220,"meta":1830},{"image":1831,"title":1832,"description":1833},{"url":13},"NVIDIA RTXNTC — Releases","Official release history, including v0.10.0 beta and DirectX 12 Linear Algebra inference support.",{},{"id":1227,"data":1836,"type":1206,"tunes":1841},{"link":1229,"meta":1837},{"image":1838,"title":1839,"description":1840},{"url":13},"NVIDIA RTXNTC — Compression Settings and Image Quality","Official documentation covering bitrate, channel interactions, quality measurement and lossy compression behavior.",{},{"id":1236,"data":1843,"type":1206,"tunes":1847},{"link":1238,"meta":1844},{"image":1845,"title":1241,"description":1846},{"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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