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设置、检查驱动程序、调整风扇配置文件或控制外设。\u003C\u002Fp>\n\u003Caside class=\"editorjs-callout editorjs-callout--info my-6 rounded-xl border p-5 border-blue-300 bg-blue-50 dark:border-blue-900 dark:bg-blue-950\u002F20\" role=\"note\">\u003Cstrong class=\"block mb-2 text-gray-900 dark:text-gray-100\">直接回答\u003C\u002Fstrong>\u003Cdiv class=\"text-gray-700 dark:text-gray-200\">\u003Cstrong>G-Assist 不仅仅是你的 GPU 的聊天机器人。\u003C\u002Fstrong>它是一个本地小型语言模型，连接到一组允许的工具。该模型解释你的需求，选择受支持的函数，将参数传递给该函数，然后 PC 端工具执行操作。\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\">最简单的思维模型\u003C\u002Fstrong>\u003Cdiv class=\"text-gray-700 dark:text-gray-200\">\u003Cstrong>SLM = 理解请求。\u003C\u002Fstrong>\u003Cbr>\u003Cstrong>知识 = 解释受支持的 NVIDIA\u002FPC 概念。\u003C\u002Fstrong>\u003Cbr>\u003Cstrong>工具 = 执行操作。\u003C\u002Fstrong>\u003Cbr>\u003Cstrong>插件 = 添加新工具。\u003C\u002Fstrong>\u003Cbr>\u003Cstrong>你的 PC = 被检查或更改的环境。\u003C\u002Fstrong>\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\">首先：AI 模型实际上在做什么？\u003C\u002Fa>\u003C\u002Fli>\u003Cli class=\"editorjs-toc__item\">\u003Ca href=\"#section-10\" class=\"editorjs-toc__link\">G-Assist 操作流程\u003C\u002Fa>\u003C\u002Fli>\u003Cli class=\"editorjs-toc__item\">\u003Ca href=\"#section-12\" class=\"editorjs-toc__link\">这是工具调用，不是魔法 PC 控制\u003C\u002Fa>\u003C\u002Fli>\u003Cli class=\"editorjs-toc__item\">\u003Ca href=\"#section-17\" class=\"editorjs-toc__link\">什么是知识层？\u003C\u002Fa>\u003C\u002Fli>\u003Cli class=\"editorjs-toc__item\">\u003Ca href=\"#section-24\" class=\"editorjs-toc__link\">G-Assist 使用 RAG 吗？\u003C\u002Fa>\u003C\u002Fli>\u003Cli class=\"editorjs-toc__item\">\u003Ca href=\"#section-28\" class=\"editorjs-toc__link\">为什么 G-Assist 可以离线工作\u003C\u002Fa>\u003C\u002Fli>\u003Cli class=\"editorjs-toc__item\">\u003Ca href=\"#section-33\" class=\"editorjs-toc__link\">本地 AI 的代价：G-Assist 会占用您的 GPU 和 VRAM\u003C\u002Fa>\u003C\u002Fli>\u003Cli class=\"editorjs-toc__item\">\u003Ca href=\"#section-40\" class=\"editorjs-toc__link\">推理模式与闪存模式\u003C\u002Fa>\u003C\u002Fli>\u003Cli class=\"editorjs-toc__item\">\u003Ca href=\"#section-44\" class=\"editorjs-toc__link\">插件实际添加了什么\u003C\u002Fa>\u003C\u002Fli>\u003Cli class=\"editorjs-toc__item\">\u003Ca href=\"#section-49\" class=\"editorjs-toc__link\">G-Assist 协议 V2 是 JSON-RPC 2.0\u003C\u002Fa>\u003C\u002Fli>\u003Cli class=\"editorjs-toc__item\">\u003Ca href=\"#section-54\" class=\"editorjs-toc__link\">MCP 的切入点\u003C\u002Fa>\u003C\u002Fli>\u003Cli class=\"editorjs-toc__item\">\u003Ca href=\"#section-59\" class=\"editorjs-toc__link\">为什么这不仅仅是 RGB 灯效\u003C\u002Fa>\u003C\u002Fli>\u003Cli class=\"editorjs-toc__item\">\u003Ca href=\"#section-63\" class=\"editorjs-toc__link\">本地代理栈\u003C\u002Fa>\u003C\u002Fli>\u003Cli class=\"editorjs-toc__item\">\u003Ca href=\"#section-65\" class=\"editorjs-toc__link\">为什么工具边界是一种安全特性\u003C\u002Fa>\u003C\u002Fli>\u003Cli class=\"editorjs-toc__item\">\u003Ca href=\"#section-70\" class=\"editorjs-toc__link\">工具权限边界\u003C\u002Fa>\u003C\u002Fli>\u003Cli class=\"editorjs-toc__item\">\u003Ca href=\"#section-72\" class=\"editorjs-toc__link\">为什么 G-Assist 可能会暂时降低游戏性能\u003C\u002Fa>\u003C\u002Fli>\u003Cli class=\"editorjs-toc__item\">\u003Ca href=\"#section-77\" class=\"editorjs-toc__link\">本地助手资源测试\u003C\u002Fa>\u003C\u002Fli>\u003Cli class=\"editorjs-toc__item\">\u003Ca href=\"#section-79\" class=\"editorjs-toc__link\">为什么插件应像其他软件一样受到严格审查\u003C\u002Fa>\u003C\u002Fli>\u003Cli class=\"editorjs-toc__item\">\u003Ca href=\"#section-84\" class=\"editorjs-toc__link\">G-Assist 更接近智能体而非聊天机器人\u003C\u002Fa>\u003C\u002Fli>\u003Cli class=\"editorjs-toc__item\">\u003Ca href=\"#section-89\" class=\"editorjs-toc__link\">G-Assist 仍然不是什么\u003C\u002Fa>\u003C\u002Fli>\u003Cli class=\"editorjs-toc__item\">\u003Ca href=\"#section-93\" class=\"editorjs-toc__link\">当前版本有哪些变化？\u003C\u002Fa>\u003C\u002Fli>\u003Cli class=\"editorjs-toc__item\">\u003Ca href=\"#section-96\" 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-100\" class=\"editorjs-toc__link\">局限性\u003C\u002Fa>\u003C\u002Fli>\u003Cli class=\"editorjs-toc__item\">\u003Ca href=\"#section-104\" class=\"editorjs-toc__link\">结论\u003C\u002Fa>\u003C\u002Fli>\u003Cli class=\"editorjs-toc__item\">\u003Ca href=\"#section-109\" class=\"editorjs-toc__link\">常见问题\u003C\u002Fa>\u003C\u002Fli>\u003Cli class=\"editorjs-toc__item\">\u003Ca href=\"#section-111\" class=\"editorjs-toc__link\">术语表\u003C\u002Fa>\u003C\u002Fli>\u003Cli class=\"editorjs-toc__item\">\u003Ca href=\"#section-113\" class=\"editorjs-toc__link\">主要来源\u003C\u002Fa>\u003C\u002Fli>\u003C\u002Fol>\u003C\u002Fnav>\n\u003Ch2 id=\"section-5\">首先：AI 模型实际上在做什么？\u003C\u002Fh2>\n\u003Cp>G-Assist 使用本地小型语言模型，即 SLM。NVIDIA 目前描述该系统使用基于 Llama 的 80 亿参数指令模型。\u003C\u002Fp>\n\u003Cp>该模型的主要工作不是渲染图形或直接更改硬件寄存器。它的工作是理解用户的语言，决定哪个受支持的能力与请求匹配，并准备对该能力的调用。\u003C\u002Fp>\n\u003Cp>例如，如果你说“将 DLSS 超分辨率设置为性能模式”，语言模型本身不会修改 DLSS。它解释命令并将其路由到可以更改该设置的受支持函数。\u003C\u002Fp>\n\u003Caside class=\"editorjs-callout editorjs-callout--success my-6 rounded-xl border p-5 border-emerald-300 bg-emerald-50 dark:border-emerald-900 dark:bg-emerald-950\u002F20\" role=\"note\">\u003Cstrong class=\"block mb-2 text-gray-900 dark:text-gray-100\">重要的区别\u003C\u002Fstrong>\u003Cdiv class=\"text-gray-700 dark:text-gray-200\">AI 模型\u003Cstrong>决定调用哪个工具\u003C\u002Fstrong>。工具\u003Cstrong>完成实际工作\u003C\u002Fstrong>。\u003C\u002Fdiv>\u003C\u002Faside>\n\u003Ch2 id=\"section-10\">G-Assist 操作流程\u003C\u002Fh2>\n\u003Csection class=\"editorjs-process my-6\">\u003Ch3 class=\"mb-3 text-lg font-semibold\">给 G-Assist 命令后会发生什么\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. 你给出自然语言请求\u003C\u002Fdiv>\u003Cdiv class=\"mt-1 text-sm text-gray-600 dark:text-gray-300\">例如：“优化此游戏的性能。”\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. 本地 SLM 解释请求\u003C\u002Fdiv>\u003Cdiv class=\"mt-1 text-sm text-gray-600 dark:text-gray-300\">它确定用户意图并识别哪个受支持的函数或插件相关。\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. 系统选择一个工具\u003C\u002Fdiv>\u003Cdiv class=\"mt-1 text-sm text-gray-600 dark:text-gray-300\">这可以是内置的图形设置函数、驱动程序检查、监控函数或社区插件。\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. 提取参数\u003C\u002Fdiv>\u003Cdiv class=\"mt-1 text-sm text-gray-600 dark:text-gray-300\">模型将请求转换为结构化值，如游戏名称、模式、风扇配置文件或 DLSS 设置。\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. 工具执行\u003C\u002Fdiv>\u003Cdiv class=\"mt-1 text-sm text-gray-600 dark:text-gray-300\">工具与 NVIDIA App、操作系统、外设软件或其他服务通信。\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. 返回结果\u003C\u002Fdiv>\u003Cdiv class=\"mt-1 text-sm text-gray-600 dark:text-gray-300\">G-Assist 报告发生了什么，或返回模型可以解释的数据。\u003C\u002Fdiv>\u003C\u002Fdiv>\u003C\u002Fdiv>\u003C\u002Fsection>\n\u003Ch2 id=\"section-12\">这是工具调用，不是魔法 PC 控制\u003C\u002Fh2>\n\u003Cp>语言模型不能仅仅因为理解英语就安全地控制任意计算机。\u003C\u002Fp>\n\u003Cp>它需要一个定义的接口，说明存在哪些操作、它们接受哪些参数以及工具返回什么。\u003C\u002Fp>\n\u003Cp>G-Assist 当前的内置能力包括优化图形设置、更改受支持的 RTX 选项、检查或下载驱动程序、显示性能信息、更改某些笔记本电脑电源功能、控制受支持的显示器功能以及使用受支持的外设插件等功能。\u003C\u002Fp>\n\u003Csection class=\"editorjs-comparison my-6\">\u003Ch3 class=\"mb-3 text-lg font-semibold\">语言模型 vs 工具\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\">语言模型\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\">工具 \u002F 插件\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\">理解“打开 DLSS”\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\">选择正确的函数\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\">实际更改设置\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\">解释结果\u003C\u002Fth>\u003Ctd class=\"border border-gray-300 px-4 py-3 dark:border-gray-700\">\u003C\u002Ftd>\u003Ctd class=\"border border-gray-300 px-4 py-3 dark:border-gray-700\">\u003C\u002Ftd>\u003C\u002Ftr>\u003C\u002Ftbody>\u003C\u002Ftable>\u003C\u002Fdiv>\u003C\u002Fsection>\n\u003Ch2 id=\"section-17\">什么是知识层？\u003C\u002Fh2>\n\u003Cp>G-Assist 还有一个知识层，用于回答问题并提供建议。\u003C\u002Fp>\n\u003Cp>NVIDIA 表示，0.2.1 版本引入了改进的知识系统，可帮助 G-Assist 提供更准确的设置建议。\u003C\u002Fp>\n\u003Cp>该知识层与您 PC 的实时状态不同。\u003C\u002Fp>\n\u003Csection class=\"editorjs-comparison my-6\">\u003Ch3 class=\"mb-3 text-lg font-semibold\">知识与实时 PC 状态对比\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\">知识\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\">实时状态\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\">GPU\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\">游戏设置\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\">性能\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\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\">不要将知识与状态混淆\u003C\u002Fstrong>\u003Cdiv class=\"text-gray-700 dark:text-gray-200\">\u003Cstrong>知识\u003C\u002Fstrong>告诉助手某个功能的含义或通常重要的内容。\u003Cbr>\u003Cstrong>状态\u003C\u002Fstrong>告诉助手您机器当前的实际情况。\u003C\u002Fdiv>\u003C\u002Faside>\n\u003Caside class=\"editorjs-referral my-6\">\u003Ca href=\"https:\u002F\u002Fstajic.de\u002Fblog\u002Fwhat-is-rag-the-simplest-explanation-of-how-it-works\" target=\"_blank\" rel=\"noopener noreferrer\" 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\">什么是 RAG？其工作原理的最简单解释\u003C\u002Fstrong>\u003Cp class=\"mt-2 text-sm text-gray-600 dark:text-gray-300\">用通俗易懂的语言解释知识检索、状态、记忆、上下文和语言模型。\u003C\u002Fp>\u003Cspan class=\"mt-3 inline-flex text-sm font-medium text-primary-600 dark:text-primary-400\">阅读简单的 RAG 指南 →\u003C\u002Fspan>\u003C\u002Fdiv>\u003C\u002Fa>\u003C\u002Faside>\n\u003Ch2 id=\"section-24\">G-Assist 使用 RAG 吗？\u003C\u002Fh2>\n\u003Cp>最稳妥的回答是：G-Assist 显然拥有知识系统，但 NVIDIA 的公开产品页面并未详细记录所有内部检索机制，因此无法断定每个知识回答都是由 RAG 专门生成的。\u003C\u002Fp>\n\u003Cp>架构上的区别仍然很重要。检索层可以提供相关知识，而系统工具则提供当前 PC 状态并执行操作。\u003C\u002Fp>\n\u003Cp>这与许多现代代理中使用的分离方式相同：知识是一种上下文来源，实时观察是另一种，工具执行则是第三种。\u003C\u002Fp>\n\u003Ch2 id=\"section-28\">为什么 G-Assist 可以离线工作\u003C\u002Fh2>\n\u003Cp>NVIDIA 在 GeForce RTX GPU 上本地运行语言模型。\u003C\u002Fp>\n\u003Cp>这意味着核心助手不需要为每个提示使用云端托管的语言模型。\u003C\u002Fp>\n\u003Cp>NVIDIA 明确表示，G-Assist 的本地功能可以离线运行。\u003C\u002Fp>\n\u003Cp>如果插件调用在线服务，它仍然可以使用互联网。本地模型执行和在线插件访问是两个独立的问题。\u003C\u002Fp>\n\u003Ch2 id=\"section-33\">本地 AI 的代价：G-Assist 会占用您的 GPU 和 VRAM\u003C\u002Fh2>\n\u003Cp>本地运行带来了隐私性和对云模型的独立性，但计算必须在某处执行。\u003C\u002Fp>\n\u003Cp>对于 G-Assist 来说，这个“某处”就是可能已经在渲染您游戏的同一块 GeForce RTX GPU。\u003C\u002Fp>\n\u003Cp>NVIDIA 警告称，当 G-Assist 响应时，GPU 会短暂地将计算资源分配给 AI 推理。如果同时运行要求较高的游戏，当模型处于活动状态时，游戏可能会暂时损失一些渲染性能。\u003C\u002Fp>\n\u003Cp>当前的要求还规定了游戏已使用内存之外的可用显存目标：推理模式大约需要 6 GB 可用显存，闪存模式大约需要 4.5 GB。\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\">8 GB 显存并不意味着 G-Assist 可用 8 GB\u003C\u002Fstrong>\u003Cdiv class=\"text-gray-700 dark:text-gray-200\">游戏、Windows、驱动和其他应用程序已经占用了显存。G-Assist 自身的要求是\u003Cstrong>在正在运行的工作负载已使用之外，还需要可用的显存\u003C\u002Fstrong>。\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：为什么内存条满了并不能说明全部问题\u003C\u002Fstrong>\u003Cp class=\"mt-2 text-sm text-gray-600 dark:text-gray-300\">为什么已安装显存、当前使用量、驻留预算和真实内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-40\">推理模式与闪存模式\u003C\u002Fh2>\n\u003Cp>当前的 G-Assist 版本将功能更强的推理模式与更快的闪存模式区分开来。\u003C\u002Fp>\n\u003Cp>推理模式旨在实现更高质量的决策，并且可以从一个提示中协调多个操作。闪存模式针对更快的响应和更低的资源需求进行了优化。\u003C\u002Fp>\n\u003Csection class=\"editorjs-comparison my-6\">\u003Ch3 class=\"mb-3 text-lg font-semibold\">推理模式与闪存模式\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\">推理模式\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\">闪存模式\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\">主要目标\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\">建议可用显存\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\">最适合\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-44\">插件实际添加了什么\u003C\u002Fh2>\n\u003Cp>插件不会替换语言模型。它为模型提供了一个允许调用的新操作。\u003C\u002Fp>\n\u003Cp>插件定义了函数、描述和参数。G-Assist 读取这些描述，将用户请求匹配到相应的函数，并向其发送结构化参数。\u003C\u002Fp>\n\u003Cp>这让助手能够超越 NVIDIA 的内置功能。\u003C\u002Fp>\n\u003Csection class=\"editorjs-process my-6\">\u003Ch3 class=\"mb-3 text-lg font-semibold\">G-Assist 插件如何工作\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. 插件声明一个函数\u003C\u002Fdiv>\u003Cdiv class=\"mt-1 text-sm text-gray-600 dark:text-gray-300\">例如：set_keyboard_color(color)。\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. 清单描述该函数\u003C\u002Fdiv>\u003Cdiv class=\"mt-1 text-sm text-gray-600 dark:text-gray-300\">清单告诉 G-Assist 该函数的作用以及需要哪些参数。\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. 用户自然提问\u003C\u002Fdiv>\u003Cdiv class=\"mt-1 text-sm text-gray-600 dark:text-gray-300\">例如：“把我的键盘变成绿色。”\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. SLM 选择函数\u003C\u002Fdiv>\u003Cdiv class=\"mt-1 text-sm text-gray-600 dark:text-gray-300\">模型将请求映射到插件函数，并提取绿色作为参数。\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. 插件执行\u003C\u002Fdiv>\u003Cdiv class=\"mt-1 text-sm text-gray-600 dark:text-gray-300\">插件与外设软件或外部服务通信。\u003C\u002Fdiv>\u003C\u002Fdiv>\u003C\u002Fdiv>\u003C\u002Fsection>\n\u003Ch2 id=\"section-49\">G-Assist 协议 V2 是 JSON-RPC 2.0\u003C\u002Fh2>\n\u003Cp>NVIDIA 当前的公共插件仓库使用协议 V2。\u003C\u002Fp>\n\u003Cp>协议 V2 在 G-Assist 引擎和插件之间使用带有长度前缀消息的 JSON-RPC 2.0。\u003C\u002Fp>\n\u003Cp>引擎可以初始化插件、检查其健康状况、执行函数、发送用户输入并关闭它。插件可以返回结果、流式传输进度或报告错误。\u003C\u002Fp>\n\u003Csection class=\"editorjs-comparison my-6\">\u003Ch3 class=\"mb-3 text-lg font-semibold\">重要的协议 V2 消息\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\">方向\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\">目的\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\">initialize\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\">ping\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\">execute\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\">stream\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\">complete\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-54\">MCP 的切入点\u003C\u002Fh2>\n\u003Cp>G-Assist 自己的插件协议不是 MCP。其当前的公开插件系统使用 JSON-RPC 2.0。\u003C\u002Fp>\n\u003Cp>然而，G-Assist 插件可以连接到 MCP 服务器。\u003C\u002Fp>\n\u003Cp>NVIDIA 当前的 0.2.2 版本包含一个 Elgato 集成，可以调用通过 Elgato Stream Deck MCP 服务器公开的操作。\u003C\u002Fp>\n\u003Caside class=\"editorjs-callout editorjs-callout--success my-6 rounded-xl border p-5 border-emerald-300 bg-emerald-50 dark:border-emerald-900 dark:bg-emerald-950\u002F20\" role=\"note\">\u003Cstrong class=\"block mb-2 text-gray-900 dark:text-gray-100\">重要的协议区别\u003C\u002Fstrong>\u003Cdiv class=\"text-gray-700 dark:text-gray-200\">\u003Cstrong>G-Assist ↔ 其插件：\u003C\u002Fstrong>NVIDIA Protocol V2 \u002F JSON-RPC 2.0。\u003Cbr>\u003Cstrong>插件 ↔ 另一个工具生态系统：\u003C\u002Fstrong>当该集成支持时，可以使用 MCP。\u003C\u002Fdiv>\u003C\u002Faside>\n\u003Ch2 id=\"section-59\">为什么这不仅仅是 RGB 灯效\u003C\u002Fh2>\n\u003Cp>插件架构将 G-Assist 从一个固定的助手转变为一个本地工具路由器。\u003C\u002Fp>\n\u003Cp>同样的模式可以将 SLM 连接到监控工具、外设、API、自动化系统、本地应用程序或外部服务。\u003C\u002Fp>\n\u003Cp>因此，重要的架构单元不是聊天窗口。它是自然语言意图与结构化工具执行之间的边界。\u003C\u002Fp>\n\u003Ch2 id=\"section-63\">本地代理栈\u003C\u002Fh2>\n\u003Csection class=\"editorjs-process my-6\">\u003Ch3 class=\"mb-3 text-lg font-semibold\">理解 G-Assist 的一个有用方式\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. 用户意图\u003C\u002Fdiv>\u003Cdiv class=\"mt-1 text-sm text-gray-600 dark:text-gray-300\">自然语言或语音命令。\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. 本地 SLM\u003C\u002Fdiv>\u003Cdiv class=\"mt-1 text-sm text-gray-600 dark:text-gray-300\">理解请求并选择一项能力。\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. 知识 \u002F 上下文\u003C\u002Fdiv>\u003Cdiv class=\"mt-1 text-sm text-gray-600 dark:text-gray-300\">提供产品知识、建议或决策所需的信息。\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\">4\u003C\u002Fdiv>\u003Cdiv class=\"mt-1 font-semibold text-gray-900 dark:text-gray-100\">4. 实时系统状态\u003C\u002Fdiv>\u003Cdiv class=\"mt-1 text-sm text-gray-600 dark:text-gray-300\">在支持时提供当前硬件、驱动、性能或配置信息。\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. 工具选择\u003C\u002Fdiv>\u003Cdiv class=\"mt-1 text-sm text-gray-600 dark:text-gray-300\">选择内置函数或插件。\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. 结构化执行\u003C\u002Fdiv>\u003Cdiv class=\"mt-1 text-sm text-gray-600 dark:text-gray-300\">函数接收参数并执行实际动作。\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. 结果验证\u003C\u002Fdiv>\u003Cdiv class=\"mt-1 text-sm text-gray-600 dark:text-gray-300\">工具返回状态或数据，模型解释发生了什么。\u003C\u002Fdiv>\u003C\u002Fdiv>\u003C\u002Fdiv>\u003C\u002Fsection>\n\u003Ch2 id=\"section-65\">为什么工具边界是一种安全特性\u003C\u002Fh2>\n\u003Cp>一个能够执行任意操作系统命令的 AI 助手会强大得多，但也更难约束。\u003C\u002Fp>\n\u003Cp>相反，G-Assist 公开具有已知参数的已定义函数。\u003C\u002Fp>\n\u003Cp>这意味着模型只能执行内置函数集或已安装插件所提供的操作。\u003C\u002Fp>\n\u003Cp>这并不能消除所有风险，尤其是对于第三方插件，但它比不受限制的 shell 访问创建了更清晰的权限和能力边界。\u003C\u002Fp>\n\u003Ch2 id=\"section-70\">工具权限边界\u003C\u002Fh2>\n\u003Csection class=\"editorjs-comparison my-6\">\u003Ch3 class=\"mb-3 text-lg font-semibold\">助手能理解什么 vs 它被允许做什么\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\">模型可能理解\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\">工具权限\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\">GPU 调优\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\">文件\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\">互联网\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\">外设控制\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-72\">为什么 G-Assist 可能会暂时降低游戏性能\u003C\u002Fh2>\n\u003Cp>本地模型架构有一个直接的后果：AI 和游戏可能会争用同一块 GPU。\u003C\u002Fp>\n\u003Cp>NVIDIA 明确警告，在 GPU 负载较高时，G-Assist 处理请求期间，渲染帧率或推理速度可能会短暂下降。\u003C\u002Fp>\n\u003Cp>一旦推理完成，这些 GPU 资源就会归还给游戏。\u003C\u002Fp>\n\u003Cp>这与云端助手不同，云端助手的模型推理大多发生在远程服务器上，不会占用游戏 GPU。\u003C\u002Fp>\n\u003Ch2 id=\"section-77\">本地助手资源测试\u003C\u002Fh2>\n\u003Csection class=\"editorjs-process my-6\">\u003Ch3 class=\"mb-3 text-lg font-semibold\">如何判断本地游戏助手是否适合你的电脑\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. 检查已安装的显存\u003C\u002Fdiv>\u003Cdiv class=\"mt-1 text-sm text-gray-600 dark:text-gray-300\">当前 G-Assist 的基准要求是至少 6 GB 显存的 RTX GPU。\u003C\u002Fdiv>\u003C\u002Fdiv>\u003Cdiv class=\"editorjs-process__step min-w-0  rounded-xl border border-gray-200 dark:border-gray-700 p-4\">\u003Cdiv class=\"text-xs font-semibold text-gray-500 dark:text-gray-400\">2\u003C\u002Fdiv>\u003Cdiv class=\"mt-1 font-semibold text-gray-900 dark:text-gray-100\">2. 在实际游戏中检查可用显存\u003C\u002Fdiv>\u003Cdiv class=\"mt-1 text-sm text-gray-600 dark:text-gray-300\">已安装容量不等于可用容量。\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. 合理选择推理模式或闪速模式\u003C\u002Fdiv>\u003Cdiv class=\"mt-1 text-sm text-gray-600 dark:text-gray-300\">当资源压力比深度推理更重要时，使用更轻量的模式。\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. 测量推理期间的帧率下降\u003C\u002Fdiv>\u003Cdiv class=\"mt-1 text-sm text-gray-600 dark:text-gray-300\">在让 G-Assist 执行任务时观察游戏表现。\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. 检查工具权限\u003C\u002Fdiv>\u003Cdiv class=\"mt-1 text-sm text-gray-600 dark:text-gray-300\">确切了解哪些内置功能和插件可以更改你的系统。\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. 将第三方插件视为软件\u003C\u002Fdiv>\u003Cdiv class=\"mt-1 text-sm text-gray-600 dark:text-gray-300\">审查其来源、权限、网络访问和存储的凭据。\u003C\u002Fdiv>\u003C\u002Fdiv>\u003C\u002Fdiv>\u003C\u002Fsection>\n\u003Ch2 id=\"section-79\">为什么插件应像其他软件一样受到严格审查\u003C\u002Fh2>\n\u003Cp>插件可以将助手连接到 API、外设应用程序和在线服务。\u003C\u002Fp>\n\u003Cp>这意味着插件可能会根据其构建目的处理配置值、凭据或外部请求。\u003C\u002Fp>\n\u003Cp>NVIDIA 的代码仓库明确提供了用于插件设置的 config.json 位置，并警告开发者不要提交凭据。\u003C\u002Fp>\n\u003Cp>因此，正确的安全问题不仅是“G-Assist 是本地运行的吗？”，还包括“每个已安装的插件连接到了什么？”\u003C\u002Fp>\n\u003Ch2 id=\"section-84\">G-Assist 更接近智能体而非聊天机器人\u003C\u002Fh2>\n\u003Cp>聊天机器人主要生成语言。\u003C\u002Fp>\n\u003Cp>智能体则解释目标、观察相关信息、选择行动、调用工具并评估结果。\u003C\u002Fp>\n\u003Cp>G-Assist 现在更符合第二种描述，因为它可以协调多个操作、检查支持的 PC 状态并调用真实工具。\u003C\u002Fp>\n\u003Caside class=\"editorjs-referral my-6\">\u003Ca href=\"https:\u002F\u002Ffigure.rocks\u002Fzh\u002Fblog\u002Fpubg-ally-shows-why-ai-teammates-need-two-brains-fast-reflexes-and-slow-reasoning\" 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\">PUBG Ally 表明为什么 AI 队友需要两个大脑：快速反射和慢速推理\u003C\u002Fstrong>\u003Cp class=\"mt-2 text-sm text-gray-600 dark:text-gray-300\">一个游戏智能体架构示例，展示了推理、实时状态、工具和确定性执行之间的分离。\u003C\u002Fp>\u003Cspan class=\"mt-3 inline-flex text-sm font-medium text-primary-600 dark:text-primary-400\">阅读 PUBG Ally 架构指南 →\u003C\u002Fspan>\u003C\u002Fdiv>\u003C\u002Fa>\u003C\u002Faside>\n\u003Ch2 id=\"section-89\">G-Assist 仍然不是什么\u003C\u002Fh2>\n\u003Cp>NVIDIA 明确将 G-Assist 描述为专用的本地助手，而非通用对话式 AI。\u003C\u002Fp>\n\u003Cp>它的价值来自于理解一组专注的PC和游戏任务，并拥有与这些任务相关的工具。\u003C\u002Fp>\n\u003Cp>这种专注很重要，因为当周围系统为较小的本地模型提供强大的工具和清晰的边界时，它就能发挥作用。\u003C\u002Fp>\n\u003Ch2 id=\"section-93\">当前版本有哪些变化？\u003C\u002Fh2>\n\u003Cp>当前的公开版本历史显示，G-Assist正稳步朝着更强大的本地代理方向发展。\u003C\u002Fp>\n\u003Cdiv class=\"overflow-x-auto\">\u003Ctable class=\"w-full border-collapse\">\u003Cthead>\u003Ctr>\u003Cth class=\"border border-gray-300 px-4 py-2 text-left font-semibold\">版本\u003C\u002Fth>\u003Cth class=\"border border-gray-300 px-4 py-2 text-left font-semibold\">重要变化\u003C\u002Fth>\u003C\u002Ftr>\u003C\u002Fthead>\u003Ctbody>\u003Ctr>\u003Ctd class=\"border border-gray-300 px-4 py-2\">0.1.17\u003C\u002Ftd>\u003Ctd class=\"border border-gray-300 px-4 py-2\">更轻量的模型，支持所有6GB以上显存的RTX GPU，社区插件\u003C\u002Ftd>\u003C\u002Ftr>\u003Ctr>\u003Ctd class=\"border border-gray-300 px-4 py-2\">0.1.18\u003C\u002Ftd>\u003Ctd class=\"border border-gray-300 px-4 py-2\">笔记本优化，BatteryBoost和WhisperMode控制\u003C\u002Ftd>\u003C\u002Ftr>\u003Ctr>\u003Ctd class=\"border border-gray-300 px-4 py-2\">0.2\u003C\u002Ftd>\u003Ctd class=\"border border-gray-300 px-4 py-2\">推理模式，闪速模式，多操作提示，新设备控制\u003C\u002Ftd>\u003C\u002Ftr>\u003Ctr>\u003Ctd class=\"border border-gray-300 px-4 py-2\">0.2.1\u003C\u002Ftd>\u003Ctd class=\"border border-gray-300 px-4 py-2\">改进的知识系统，更好的推荐，RTX功能控制\u003C\u002Ftd>\u003C\u002Ftr>\u003Ctr>\u003Ctd class=\"border border-gray-300 px-4 py-2\">0.2.2\u003C\u002Ftd>\u003Ctd class=\"border border-gray-300 px-4 py-2\">通过MCP服务器集成Elgato Stream Deck\u003C\u002Ftd>\u003C\u002Ftr>\u003C\u002Ftbody>\u003C\u002Ftable>\u003C\u002Fdiv>\n\u003Ch2 id=\"section-96\">什么会改变这个答案？\u003C\u002Fh2>\n\u003Cp>G-Assist仍处于实验阶段，其架构正在不断演进。\u003C\u002Fp>\n\u003Cp>模型大小、显存需求、工具集、插件协议和硬件支持都可能在未来的版本中发生变化。\u003C\u002Fp>\n\u003Cp>未来的版本还可能将部分推理转移到NPU或引入更广泛的MCP集成，但在NVIDIA正式文档说明之前，不应假设这一点。\u003C\u002Fp>\n\u003Ch2 id=\"section-100\">局限性\u003C\u002Fh2>\n\u003Cp>本文描述了截至2026年9月NVIDIA公开的G-Assist产品文档和公开插件架构。\u003C\u002Fp>\n\u003Cp>NVIDIA并未公开其知识和推荐管道的所有内部细节，因此本文并不声称每个知识回答都使用RAG或任何其他特定的检索实现。\u003C\u002Fp>\n\u003Cp>第三方插件可能具有超出NVIDIA内置功能的行为和网络访问权限，因此每个插件都应单独评估。\u003C\u002Fp>\n\u003Ch2 id=\"section-104\">结论\u003C\u002Fh2>\n\u003Cp>理解Project G-Assist的最简单方法是不要再把它当作聊天机器人。\u003C\u002Fp>\n\u003Cp>本地SLM解释你的请求。知识帮助它理解问题。实时系统信息告诉它你的PC上的真实情况。内置功能或插件定义了它实际被允许做什么。工具执行操作并返回结果。\u003C\u002Fp>\n\u003Cp>这就是本地代理架构。\u003C\u002Fp>\n\u003Cp>它最强大的理念不是80亿参数的模型可以谈论你的GPU，而是当相对较小的本地模型连接到定义明确的工具、当前状态和受控的操作边界时，它就能变得有用。\u003C\u002Fp>\n\u003Ch2 id=\"section-109\">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 NVIDIA Project G-Assist\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\">G-Assist 是云端聊天机器人吗？\u003C\u002Fh4>\u003Cdiv class=\"mt-2 text-gray-600 dark:text-gray-300\">不是。其核心语言模型在 GeForce RTX GPU 上本地运行，对于支持的本地功能可以离线工作。\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\">G-Assist 使用什么模型？\u003C\u002Fh4>\u003Cdiv class=\"mt-2 text-gray-600 dark:text-gray-300\">NVIDIA 目前将其描述为一个基于 Llama 的本地指令模型，拥有 80 亿参数。\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\">AI 模型会直接更改我的 GPU 设置吗？\u003C\u002Fh4>\u003Cdiv class=\"mt-2 text-gray-600 dark:text-gray-300\">不会。模型会选择受支持的内置功能或插件，由该工具执行实际更改。\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\">G-Assist 在游戏时会占用显存吗？\u003C\u002Fh4>\u003Cdiv class=\"mt-2 text-gray-600 dark:text-gray-300\">会。NVIDIA 建议为该助手预留额外的空闲显存，并警告推理过程可能会短暂降低游戏渲染性能。\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\">G-Assist 插件是 MCP 插件吗？\u003C\u002Fh4>\u003Cdiv class=\"mt-2 text-gray-600 dark:text-gray-300\">不直接是。G-Assist 当前自身的插件协议使用 JSON-RPC 2.0，不过插件可以连接到 MCP 服务器。\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\">G-Assist 可以离线工作吗？\u003C\u002Fh4>\u003Cdiv class=\"mt-2 text-gray-600 dark:text-gray-300\">其本地助手可以，但如果个别插件调用在线服务，则可能仍需要互联网访问。\u003C\u002Fdiv>\u003C\u002Fdiv>\u003Cdiv id=\"faq7\" 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\">G-Assist 是通用 AI 助手吗？\u003C\u002Fh4>\u003Cdiv class=\"mt-2 text-gray-600 dark:text-gray-300\">NVIDIA 将其描述为专用的 PC 和游戏助手，而非广泛的通用对话模型。\u003C\u002Fdiv>\u003C\u002Fdiv>\u003C\u002Fsection>\n\u003Ch2 id=\"section-111\">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\">G-Assist 关键术语\u003C\u002Fh3>\u003Cdl>\u003Cdiv id=\"slm\" 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\">SLM\u003C\u002Fdt>\u003Cdd class=\"mt-1 text-gray-600 dark:text-gray-300\">小型语言模型，一种紧凑的语言模型，设计为在本地运行，硬件要求低于大型云端模型。\u003C\u002Fdd>\u003C\u002Fdiv>\u003Cdiv id=\"tool-calling\" 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\">工具调用\u003C\u002Fdt>\u003Cdd class=\"mt-1 text-gray-600 dark:text-gray-300\">语言模型选择已定义函数并提供结构化参数，以便其他组件执行操作的过程。\u003C\u002Fdd>\u003C\u002Fdiv>\u003Cdiv id=\"plugin\" 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\">插件\u003C\u002Fdt>\u003Cdd class=\"mt-1 text-gray-600 dark:text-gray-300\">向 G-Assist 公开额外功能或集成的扩展。\u003C\u002Fdd>\u003C\u002Fdiv>\u003Cdiv id=\"json-rpc\" 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\">JSON-RPC 2.0\u003C\u002Fdt>\u003Cdd class=\"mt-1 text-gray-600 dark:text-gray-300\">当前 G-Assist Protocol V2 插件系统使用的结构化消息协议。\u003C\u002Fdd>\u003C\u002Fdiv>\u003Cdiv id=\"mcp\" 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\">MCP\u003C\u002Fdt>\u003Cdd class=\"mt-1 text-gray-600 dark:text-gray-300\">模型上下文协议，一种独立的工具和上下文互操作协议，某些外部集成可以公开该协议。\u003C\u002Fdd>\u003C\u002Fdiv>\u003Cdiv id=\"tool-authority-boundary\" class=\"border-t border-gray-200 dark:border-gray-700 py-3 first:border-t-0\">\u003Cdt class=\"font-semibold text-gray-900 dark:text-gray-100\">工具-权限边界\u003C\u002Fdt>\u003Cdd class=\"mt-1 text-gray-600 dark:text-gray-300\">一种 Figure Rocks 模型，用于区分 AI 模型能理解的内容与其连接工具实际允许它执行的操作。\u003C\u002Fdd>\u003C\u002Fdiv>\u003Cdiv id=\"local-agent-stack\" 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\">本地代理栈\u003C\u002Fdt>\u003Cdd class=\"mt-1 text-gray-600 dark:text-gray-300\">一种 Figure Rocks 模型，结合用户意图、本地 SLM、知识、实时状态、工具选择、执行和结果验证。\u003C\u002Fdd>\u003C\u002Fdiv>\u003Cdiv id=\"local-assistant-resource-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\">本地助手资源测试\u003C\u002Fdt>\u003Cdd class=\"mt-1 text-gray-600 dark:text-gray-300\">一种 Figure Rocks 工作流程，用于在使用本地游戏助手前评估显存、推理成本、工具权限和插件风险。\u003C\u002Fdd>\u003C\u002Fdiv>\u003C\u002Fdl>\u003C\u002Fsection>\n\u003Ch2 id=\"section-113\">主要来源\u003C\u002Fh2>\n\u003Ca href=\"https:\u002F\u002Fwww.nvidia.com\u002Fen-us\u002Fsoftware\u002Fnvidia-app\u002Fg-assist\u002F\" target=\"_blank\" rel=\"noopener noreferrer\" class=\"editorjs-link-tool block border border-gray-200 dark:border-gray-700 rounded-lg p-4 transition text-gray-900 dark:text-gray-100 hover:border-primary-500 hover:bg-primary-50 dark:hover:bg-gray-900 hover:text-gray-900 dark:hover:text-gray-100\">\u003Cstrong class=\"block font-semibold\">NVIDIA — Project G-Assist\u003C\u002Fstrong>\u003Cp class=\"text-sm text-gray-600 dark:text-gray-400\">官方当前产品页面，涵盖本地模型、支持的功能、推理和闪速模式、显存要求、RTX 控制、插件以及基于 MCP 的 0.2.2 版 Elgato 集成。\u003C\u002Fp>\u003C\u002Fa>\n\u003Ca href=\"https:\u002F\u002Fgithub.com\u002FNVIDIA\u002FG-Assist\" 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 — G-Assist GitHub\u003C\u002Fstrong>\u003Cp class=\"text-sm text-gray-600 dark:text-gray-400\">官方公共插件仓库，记录 G-Assist 的模块架构、插件发现和 Protocol V2。\u003C\u002Fp>\u003C\u002Fa>\n\u003Ca href=\"https:\u002F\u002Fgithub.com\u002FNVIDIA\u002FG-Assist\u002Fblob\u002Fmain\u002FPLUGIN_MIGRATION_GUIDE_V2.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 — G-Assist Protocol V2 迁移指南\u003C\u002Fstrong>\u003Cp class=\"text-sm text-gray-600 dark:text-gray-400\">官方协议文档，涵盖 JSON-RPC 2.0、初始化、健康检查、执行、流式传输、完成和插件 SDK 行为。\u003C\u002Fp>\u003C\u002Fa>\n\u003Ca href=\"https:\u002F\u002Fblogs.nvidia.com\u002Fblog\u002Frtx-ai-garage-gamescom-g-assist-rtx-remix\u002F\" target=\"_blank\" rel=\"noopener noreferrer\" class=\"editorjs-link-tool block border border-gray-200 dark:border-gray-700 rounded-lg p-4 transition text-gray-900 dark:text-gray-100 hover:border-primary-500 hover:bg-primary-50 dark:hover:bg-gray-900 hover:text-gray-900 dark:hover:text-gray-100\">\u003Cstrong class=\"block font-semibold\">NVIDIA 博客 — 轻量级 G-Assist 模型\u003C\u002Fstrong>\u003Cp class=\"text-sm text-gray-600 dark:text-gray-400\">关于低内存 G-Assist 模型、支持 6 GB RTX GPU 以及社区插件中心的官方背景信息。\u003C\u002Fp>\u003C\u002Fa>",{"time":538,"blocks":539,"version":1436},1790408062519,[540,546,554,561,568,574,579,584,589,596,601,627,632,637,642,647,678,683,688,693,698,723,730,739,744,749,754,759,764,769,774,779,784,789,794,799,804,809,815,823,828,833,838,862,867,872,877,882,903,908,913,918,923,951,956,961,966,971,977,982,987,992,997,1002,1029,1034,1039,1044,1049,1054,1059,1086,1091,1096,1101,1106,1111,1116,1140,1145,1150,1155,1160,1165,1170,1175,1180,1185,1193,1198,1203,1208,1213,1218,1223,1247,1252,1257,1262,1267,1272,1277,1282,1287,1292,1297,1302,1307,1312,1317,1352,1357,1394,1399,1409,1418,1427],{"id":541,"data":542,"type":544,"tunes":545},"3cee674645",{"text":543},"NVIDIA Project G-Assist 看起来像一个聊天机器人，但这种描述忽略了重要的部分。它是一个本地 AI 系统，能够理解请求、检查受支持的 PC 状态、选择工具或插件，然后执行实际操作，例如更改 DLSS 设置、检查驱动程序、调整风扇配置文件或控制外设。","paragraph",{},{"id":547,"data":548,"type":552,"tunes":553},"24a1733ca8",{"body":549,"title":550,"variant":551},"\u003Cstrong>G-Assist 不仅仅是你的 GPU 的聊天机器人。\u003C\u002Fstrong>它是一个本地小型语言模型，连接到一组允许的工具。该模型解释你的需求，选择受支持的函数，将参数传递给该函数，然后 PC 端工具执行操作。","直接回答","info","callout",{},{"id":555,"data":556,"type":552,"tunes":560},"1fcbe512dc",{"body":557,"title":558,"variant":559},"\u003Cstrong>SLM = 理解请求。\u003C\u002Fstrong>\u003Cbr>\u003Cstrong>知识 = 解释受支持的 NVIDIA\u002FPC 概念。\u003C\u002Fstrong>\u003Cbr>\u003Cstrong>工具 = 执行操作。\u003C\u002Fstrong>\u003Cbr>\u003Cstrong>插件 = 添加新工具。\u003C\u002Fstrong>\u003Cbr>\u003Cstrong>你的 PC = 被检查或更改的环境。\u003C\u002Fstrong>","最简单的思维模型","note",{},{"id":562,"data":563,"type":566,"tunes":567},"2a609230a5",{"title":564,"maxLevel":565,"minLevel":47},"目录",3,"tableOfContents",{},{"id":569,"data":570,"type":572,"tunes":573},"a52f97cb62",{"text":571,"level":47},"首先：AI 模型实际上在做什么？","header",{},{"id":575,"data":576,"type":544,"tunes":578},"1871a0d8e6",{"text":577},"G-Assist 使用本地小型语言模型，即 SLM。NVIDIA 目前描述该系统使用基于 Llama 的 80 亿参数指令模型。",{},{"id":580,"data":581,"type":544,"tunes":583},"cfbc6473c4",{"text":582},"该模型的主要工作不是渲染图形或直接更改硬件寄存器。它的工作是理解用户的语言，决定哪个受支持的能力与请求匹配，并准备对该能力的调用。",{},{"id":585,"data":586,"type":544,"tunes":588},"53cadaaeb6",{"text":587},"例如，如果你说“将 DLSS 超分辨率设置为性能模式”，语言模型本身不会修改 DLSS。它解释命令并将其路由到可以更改该设置的受支持函数。",{},{"id":590,"data":591,"type":552,"tunes":595},"5d8e508019",{"body":592,"title":593,"variant":594},"AI 模型\u003Cstrong>决定调用哪个工具\u003C\u002Fstrong>。工具\u003Cstrong>完成实际工作\u003C\u002Fstrong>。","重要的区别","success",{},{"id":597,"data":598,"type":572,"tunes":600},"9ce7e91c28",{"text":599,"level":47},"G-Assist 操作流程",{},{"id":602,"data":603,"type":625,"tunes":626},"992b5d6667",{"steps":604,"title":623,"orientation":624},[605,608,611,614,617,620],{"label":606,"description":607},"1. 你给出自然语言请求","例如：“优化此游戏的性能。”",{"label":609,"description":610},"2. 本地 SLM 解释请求","它确定用户意图并识别哪个受支持的函数或插件相关。",{"label":612,"description":613},"3. 系统选择一个工具","这可以是内置的图形设置函数、驱动程序检查、监控函数或社区插件。",{"label":615,"description":616},"4. 提取参数","模型将请求转换为结构化值，如游戏名称、模式、风扇配置文件或 DLSS 设置。",{"label":618,"description":619},"5. 工具执行","工具与 NVIDIA App、操作系统、外设软件或其他服务通信。",{"label":621,"description":622},"6. 返回结果","G-Assist 报告发生了什么，或返回模型可以解释的数据。","给 G-Assist 命令后会发生什么","auto","processFlow",{},{"id":628,"data":629,"type":572,"tunes":631},"aba1e38958",{"text":630,"level":47},"这是工具调用，不是魔法 PC 控制",{},{"id":633,"data":634,"type":544,"tunes":636},"0a7cb6107d",{"text":635},"语言模型不能仅仅因为理解英语就安全地控制任意计算机。",{},{"id":638,"data":639,"type":544,"tunes":641},"31191a478c",{"text":640},"它需要一个定义的接口，说明存在哪些操作、它们接受哪些参数以及工具返回什么。",{},{"id":643,"data":644,"type":544,"tunes":646},"af3d1a846f",{"text":645},"G-Assist 当前的内置能力包括优化图形设置、更改受支持的 RTX 选项、检查或下载驱动程序、显示性能信息、更改某些笔记本电脑电源功能、控制受支持的显示器功能以及使用受支持的外设插件等功能。",{},{"id":648,"data":649,"type":676,"tunes":677},"d6fd0eb144",{"rows":650,"title":667,"layout":668,"columns":669},[651,655,659,663],{"id":652,"label":653,"values":654},"understand","理解“打开 DLSS”",[13,13],{"id":656,"label":657,"values":658},"decide","选择正确的函数",[13,13],{"id":660,"label":661,"values":662},"change","实际更改设置",[13,13],{"id":664,"label":665,"values":666},"explain","解释结果",[13,13],"语言模型 vs 工具","table",[670,673],{"id":671,"label":672},"model","语言模型",{"id":674,"label":675},"tool","工具 \u002F 插件","comparison",{},{"id":679,"data":680,"type":572,"tunes":682},"1537be641c",{"text":681,"level":47},"什么是知识层？",{},{"id":684,"data":685,"type":544,"tunes":687},"1db6e2e4b5",{"text":686},"G-Assist 还有一个知识层，用于回答问题并提供建议。",{},{"id":689,"data":690,"type":544,"tunes":692},"616d72b881",{"text":691},"NVIDIA 表示，0.2.1 版本引入了改进的知识系统，可帮助 G-Assist 提供更准确的设置建议。",{},{"id":694,"data":695,"type":544,"tunes":697},"eb987c41e5",{"text":696},"该知识层与您 PC 的实时状态不同。",{},{"id":699,"data":700,"type":676,"tunes":722},"bf6a4f5502",{"rows":701,"title":714,"layout":668,"columns":715},[702,706,710],{"id":703,"label":704,"values":705},"gpu","GPU",[13,13],{"id":707,"label":708,"values":709},"game","游戏设置",[13,13],{"id":711,"label":712,"values":713},"system","性能",[13,13],"知识与实时 PC 状态对比",[716,719],{"id":717,"label":718},"knowledge","知识",{"id":720,"label":721},"state","实时状态",{},{"id":724,"data":725,"type":552,"tunes":729},"2bac13ef99",{"body":726,"title":727,"variant":728},"\u003Cstrong>知识\u003C\u002Fstrong>告诉助手某个功能的含义或通常重要的内容。\u003Cbr>\u003Cstrong>状态\u003C\u002Fstrong>告诉助手您机器当前的实际情况。","不要将知识与状态混淆","warning",{},{"id":731,"data":732,"type":737,"tunes":738},"73708d5e0d",{"url":733,"title":734,"excerpt":735,"ctaLabel":736},"https:\u002F\u002Fstajic.de\u002Fblog\u002Fwhat-is-rag-the-simplest-explanation-of-how-it-works","什么是 RAG？其工作原理的最简单解释","用通俗易懂的语言解释知识检索、状态、记忆、上下文和语言模型。","阅读简单的 RAG 指南","referralArticle",{},{"id":740,"data":741,"type":572,"tunes":743},"acbb9b7940",{"text":742,"level":47},"G-Assist 使用 RAG 吗？",{},{"id":745,"data":746,"type":544,"tunes":748},"ce0371e492",{"text":747},"最稳妥的回答是：G-Assist 显然拥有知识系统，但 NVIDIA 的公开产品页面并未详细记录所有内部检索机制，因此无法断定每个知识回答都是由 RAG 专门生成的。",{},{"id":750,"data":751,"type":544,"tunes":753},"c2226e5ec5",{"text":752},"架构上的区别仍然很重要。检索层可以提供相关知识，而系统工具则提供当前 PC 状态并执行操作。",{},{"id":755,"data":756,"type":544,"tunes":758},"e5137fc2c8",{"text":757},"这与许多现代代理中使用的分离方式相同：知识是一种上下文来源，实时观察是另一种，工具执行则是第三种。",{},{"id":760,"data":761,"type":572,"tunes":763},"059b89dedb",{"text":762,"level":47},"为什么 G-Assist 可以离线工作",{},{"id":765,"data":766,"type":544,"tunes":768},"19447df6a4",{"text":767},"NVIDIA 在 GeForce RTX GPU 上本地运行语言模型。",{},{"id":770,"data":771,"type":544,"tunes":773},"55aee6ada7",{"text":772},"这意味着核心助手不需要为每个提示使用云端托管的语言模型。",{},{"id":775,"data":776,"type":544,"tunes":778},"f265d1fd96",{"text":777},"NVIDIA 明确表示，G-Assist 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Deck",false,{},{"id":1248,"data":1249,"type":572,"tunes":1251},"09bde21a8d",{"text":1250,"level":47},"什么会改变这个答案？",{},{"id":1253,"data":1254,"type":544,"tunes":1256},"b0e7ca01a4",{"text":1255},"G-Assist仍处于实验阶段，其架构正在不断演进。",{},{"id":1258,"data":1259,"type":544,"tunes":1261},"537568c224",{"text":1260},"模型大小、显存需求、工具集、插件协议和硬件支持都可能在未来的版本中发生变化。",{},{"id":1263,"data":1264,"type":544,"tunes":1266},"fe24516c11",{"text":1265},"未来的版本还可能将部分推理转移到NPU或引入更广泛的MCP集成，但在NVIDIA正式文档说明之前，不应假设这一点。",{},{"id":1268,"data":1269,"type":572,"tunes":1271},"0e31e2d54d",{"text":1270,"level":47},"局限性",{},{"id":1273,"data":1274,"type":544,"tunes":1276},"a7881e11e1",{"text":1275},"本文描述了截至2026年9月NVIDIA公开的G-Assist产品文档和公开插件架构。",{},{"id":1278,"data":1279,"type":544,"tunes":1281},"71ebc16fda",{"text":1280},"NVIDIA并未公开其知识和推荐管道的所有内部细节，因此本文并不声称每个知识回答都使用RAG或任何其他特定的检索实现。",{},{"id":1283,"data":1284,"type":544,"tunes":1286},"add780d6ba",{"text":1285},"第三方插件可能具有超出NVIDIA内置功能的行为和网络访问权限，因此每个插件都应单独评估。",{},{"id":1288,"data":1289,"type":572,"tunes":1291},"f7a38af5be",{"text":1290,"level":47},"结论",{},{"id":1293,"data":1294,"type":544,"tunes":1296},"f1308f3e80",{"text":1295},"理解Project G-Assist的最简单方法是不要再把它当作聊天机器人。",{},{"id":1298,"data":1299,"type":544,"tunes":1301},"b102a861bb",{"text":1300},"本地SLM解释你的请求。知识帮助它理解问题。实时系统信息告诉它你的PC上的真实情况。内置功能或插件定义了它实际被允许做什么。工具执行操作并返回结果。",{},{"id":1303,"data":1304,"type":544,"tunes":1306},"da805fabf0",{"text":1305},"这就是本地代理架构。",{},{"id":1308,"data":1309,"type":544,"tunes":1311},"675aecee06",{"text":1310},"它最强大的理念不是80亿参数的模型可以谈论你的GPU，而是当相对较小的本地模型连接到定义明确的工具、当前状态和受控的操作边界时，它就能变得有用。",{},{"id":1313,"data":1314,"type":572,"tunes":1316},"0354b8daaf",{"text":1315,"level":47},"常见问题",{},{"id":1318,"data":1319,"type":1350,"tunes":1351},"e75bc04532",{"items":1320,"title":1349},[1321,1325,1329,1333,1337,1341,1345],{"id":1322,"answer":1323,"question":1324},"faq1","不是。其核心语言模型在 GeForce RTX GPU 上本地运行，对于支持的本地功能可以离线工作。","G-Assist 是云端聊天机器人吗？",{"id":1326,"answer":1327,"question":1328},"faq2","NVIDIA 目前将其描述为一个基于 Llama 的本地指令模型，拥有 80 亿参数。","G-Assist 使用什么模型？",{"id":1330,"answer":1331,"question":1332},"faq3","不会。模型会选择受支持的内置功能或插件，由该工具执行实际更改。","AI 模型会直接更改我的 GPU 设置吗？",{"id":1334,"answer":1335,"question":1336},"faq4","会。NVIDIA 建议为该助手预留额外的空闲显存，并警告推理过程可能会短暂降低游戏渲染性能。","G-Assist 在游戏时会占用显存吗？",{"id":1338,"answer":1339,"question":1340},"faq5","不直接是。G-Assist 当前自身的插件协议使用 JSON-RPC 2.0，不过插件可以连接到 MCP 服务器。","G-Assist 插件是 MCP 插件吗？",{"id":1342,"answer":1343,"question":1344},"faq6","其本地助手可以，但如果个别插件调用在线服务，则可能仍需要互联网访问。","G-Assist 可以离线工作吗？",{"id":1346,"answer":1347,"question":1348},"faq7","NVIDIA 将其描述为专用的 PC 和游戏助手，而非广泛的通用对话模型。","G-Assist 是通用 AI 助手吗？","通俗解读 NVIDIA Project G-Assist","faq",{},{"id":1353,"data":1354,"type":572,"tunes":1356},"8733f298cd",{"text":1355,"level":47},"术语表",{},{"id":1358,"data":1359,"type":1392,"tunes":1393},"407257a6d1",{"title":1360,"entries":1361},"G-Assist 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工作流程，用于在使用本地游戏助手前评估显存、推理成本、工具权限和插件风险。","glossary",{},{"id":1395,"data":1396,"type":572,"tunes":1398},"3ab856f2b4",{"text":1397,"level":47},"主要来源",{},{"id":1400,"data":1401,"type":1407,"tunes":1408},"367d2e3548",{"link":1402,"meta":1403},"https:\u002F\u002Fwww.nvidia.com\u002Fen-us\u002Fsoftware\u002Fnvidia-app\u002Fg-assist\u002F",{"image":1404,"title":1405,"description":1406},{"url":13},"NVIDIA — Project G-Assist","官方当前产品页面，涵盖本地模型、支持的功能、推理和闪速模式、显存要求、RTX 控制、插件以及基于 MCP 的 0.2.2 版 Elgato 集成。","linkTool",{},{"id":1410,"data":1411,"type":1407,"tunes":1417},"5641b514c0",{"link":1412,"meta":1413},"https:\u002F\u002Fgithub.com\u002FNVIDIA\u002FG-Assist",{"image":1414,"title":1415,"description":1416},{"url":13},"NVIDIA — G-Assist GitHub","官方公共插件仓库，记录 G-Assist 的模块架构、插件发现和 Protocol V2。",{},{"id":1419,"data":1420,"type":1407,"tunes":1426},"22a5b77a1b",{"link":1421,"meta":1422},"https:\u002F\u002Fgithub.com\u002FNVIDIA\u002FG-Assist\u002Fblob\u002Fmain\u002FPLUGIN_MIGRATION_GUIDE_V2.md",{"image":1423,"title":1424,"description":1425},{"url":13},"NVIDIA — G-Assist Protocol V2 迁移指南","官方协议文档，涵盖 JSON-RPC 2.0、初始化、健康检查、执行、流式传输、完成和插件 SDK 行为。",{},{"id":1428,"data":1429,"type":1407,"tunes":1435},"cc3301d997",{"link":1430,"meta":1431},"https:\u002F\u002Fblogs.nvidia.com\u002Fblog\u002Frtx-ai-garage-gamescom-g-assist-rtx-remix\u002F",{"image":1432,"title":1433,"description":1434},{"url":13},"NVIDIA 博客 — 轻量级 G-Assist 模型","关于低内存 G-Assist 模型、支持 6 GB RTX GPU 以及社区插件中心的官方背景信息。",{},"2.31","NVIDIA Project G-Assist 看起来像一个聊天机器人，但其真正的架构更接近于一个本地 AI 代理。一个小型语言模型解释你的请求，系统状态提供当前 PC 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It is a local AI system that can understand a request, inspect supported PC state, choose a tool or plug-in, and then perform a real action such as changing DLSS settings, checking a driver, adjusting a fan profile or controlling a peripheral.\"},\"tunes\":{}},{\"id\":\"24a1733ca8\",\"type\":\"callout\",\"data\":{\"variant\":\"info\",\"title\":\"Direct answer\",\"body\":\"\u003Cstrong>G-Assist is not simply a chatbot for your GPU.\u003C\u002Fstrong> It is a local small language model connected to a set of allowed tools. The model interprets what you want, selects a supported function, passes arguments to that function, and the PC-side tool performs the action.\"},\"tunes\":{}},{\"id\":\"1fcbe512dc\",\"type\":\"callout\",\"data\":{\"variant\":\"note\",\"title\":\"The easiest mental model\",\"body\":\"\u003Cstrong>SLM = understands the request.\u003C\u002Fstrong>\u003Cbr>\u003Cstrong>Knowledge = explains supported NVIDIA\u002FPC concepts.\u003C\u002Fstrong>\u003Cbr>\u003Cstrong>Tools = perform actions.\u003C\u002Fstrong>\u003Cbr>\u003Cstrong>Plug-ins = add new tools.\u003C\u002Fstrong>\u003Cbr>\u003Cstrong>Your PC = the environment being inspected or changed.\u003C\u002Fstrong>\"},\"tunes\":{}},{\"id\":\"2a609230a5\",\"type\":\"tableOfContents\",\"data\":{\"title\":\"Contents\",\"minLevel\":2,\"maxLevel\":3},\"tunes\":{}},{\"id\":\"a52f97cb62\",\"type\":\"header\",\"data\":{\"text\":\"First: what is the AI model actually doing?\",\"level\":2},\"tunes\":{}},{\"id\":\"1871a0d8e6\",\"type\":\"paragraph\",\"data\":{\"text\":\"G-Assist uses a local small language model, or SLM. NVIDIA currently describes the system as using a Llama-based 8-billion-parameter instruct model.\"},\"tunes\":{}},{\"id\":\"cfbc6473c4\",\"type\":\"paragraph\",\"data\":{\"text\":\"The model's main job is not to render graphics or directly change hardware registers. Its job is to understand the user's language, decide which supported capability matches the request, and prepare the call to that capability.\"},\"tunes\":{}},{\"id\":\"53cadaaeb6\",\"type\":\"paragraph\",\"data\":{\"text\":\"For example, if you say “set DLSS Super Resolution to Performance Mode,” the language model does not itself modify DLSS. It interprets the command and routes it to the supported function that can change the setting.\"},\"tunes\":{}},{\"id\":\"5d8e508019\",\"type\":\"callout\",\"data\":{\"variant\":\"success\",\"title\":\"The important distinction\",\"body\":\"The AI model \u003Cstrong>decides what tool to call\u003C\u002Fstrong>. The tool \u003Cstrong>does the real work\u003C\u002Fstrong>.\"},\"tunes\":{}},{\"id\":\"9ce7e91c28\",\"type\":\"header\",\"data\":{\"text\":\"The G-Assist action pipeline\",\"level\":2},\"tunes\":{}},{\"id\":\"992b5d6667\",\"type\":\"processFlow\",\"data\":{\"title\":\"What happens after you give G-Assist a command\",\"orientation\":\"auto\",\"steps\":[{\"label\":\"1. You give a natural-language request\",\"description\":\"For example: “Optimize this game for performance.”\"},{\"label\":\"2. The local SLM interprets the request\",\"description\":\"It determines the user's intent and identifies which supported function or plug-in is relevant.\"},{\"label\":\"3. The system chooses a tool\",\"description\":\"That could be a built-in graphics-setting function, driver check, monitoring function or community plug-in.\"},{\"label\":\"4. Arguments are extracted\",\"description\":\"The model converts the request into structured values such as game name, mode, fan profile or DLSS setting.\"},{\"label\":\"5. The tool executes\",\"description\":\"The tool talks to the NVIDIA App, operating system, peripheral software or another service.\"},{\"label\":\"6. The result is returned\",\"description\":\"G-Assist reports what happened, or returns data that the model can explain.\"}]},\"tunes\":{}},{\"id\":\"aba1e38958\",\"type\":\"header\",\"data\":{\"text\":\"This is tool calling, not magic PC control\",\"level\":2},\"tunes\":{}},{\"id\":\"0a7cb6107d\",\"type\":\"paragraph\",\"data\":{\"text\":\"A language model cannot safely control an arbitrary computer simply because it understands English.\"},\"tunes\":{}},{\"id\":\"31191a478c\",\"type\":\"paragraph\",\"data\":{\"text\":\"It needs a defined interface that says which actions exist, which arguments they accept and what the tool returns.\"},\"tunes\":{}},{\"id\":\"af3d1a846f\",\"type\":\"paragraph\",\"data\":{\"text\":\"G-Assist's current built-in capabilities include functions such as optimizing graphics settings, changing supported RTX options, checking or downloading drivers, showing performance information, changing some laptop power features, controlling supported monitor functions and using supported peripheral plug-ins.\"},\"tunes\":{}},{\"id\":\"d6fd0eb144\",\"type\":\"comparison\",\"data\":{\"title\":\"Language model vs tool\",\"layout\":\"table\",\"columns\":[{\"id\":\"model\",\"label\":\"Language model\"},{\"id\":\"tool\",\"label\":\"Tool \u002F plug-in\"}],\"rows\":[{\"id\":\"understand\",\"label\":\"Understand “turn on DLSS”\",\"values\":[\"\",\"\"]},{\"id\":\"decide\",\"label\":\"Choose the correct function\",\"values\":[\"\",\"\"]},{\"id\":\"change\",\"label\":\"Actually change the setting\",\"values\":[\"\",\"\"]},{\"id\":\"explain\",\"label\":\"Explain the result\",\"values\":[\"\",\"\"]}]},\"tunes\":{}},{\"id\":\"1537be641c\",\"type\":\"header\",\"data\":{\"text\":\"What is the knowledge layer?\",\"level\":2},\"tunes\":{}},{\"id\":\"1db6e2e4b5\",\"type\":\"paragraph\",\"data\":{\"text\":\"G-Assist also has a knowledge layer for answering questions and making recommendations.\"},\"tunes\":{}},{\"id\":\"616d72b881\",\"type\":\"paragraph\",\"data\":{\"text\":\"NVIDIA says version 0.2.1 introduced an improved knowledge system that helps G-Assist make more accurate settings recommendations.\"},\"tunes\":{}},{\"id\":\"eb987c41e5\",\"type\":\"paragraph\",\"data\":{\"text\":\"That knowledge layer is different from the live state of your PC.\"},\"tunes\":{}},{\"id\":\"bf6a4f5502\",\"type\":\"comparison\",\"data\":{\"title\":\"Knowledge vs live PC state\",\"layout\":\"table\",\"columns\":[{\"id\":\"knowledge\",\"label\":\"Knowledge\"},{\"id\":\"state\",\"label\":\"Live state\"}],\"rows\":[{\"id\":\"gpu\",\"label\":\"GPU\",\"values\":[\"\",\"\"]},{\"id\":\"game\",\"label\":\"Game settings\",\"values\":[\"\",\"\"]},{\"id\":\"system\",\"label\":\"Performance\",\"values\":[\"\",\"\"]}]},\"tunes\":{}},{\"id\":\"2bac13ef99\",\"type\":\"callout\",\"data\":{\"variant\":\"warning\",\"title\":\"Do not mix knowledge with state\",\"body\":\"\u003Cstrong>Knowledge\u003C\u002Fstrong> tells the assistant what a feature means or what usually matters.\u003Cbr>\u003Cstrong>State\u003C\u002Fstrong> tells it what is true on your machine right now.\"},\"tunes\":{}},{\"id\":\"73708d5e0d\",\"type\":\"referralArticle\",\"data\":{\"url\":\"https:\u002F\u002Fstajic.de\u002Fblog\u002Fwhat-is-rag-the-simplest-explanation-of-how-it-works\",\"title\":\"What Is RAG? The Simplest Explanation of How It Works\",\"excerpt\":\"A plain-English explanation of knowledge retrieval, state, memory, context and the language model.\",\"ctaLabel\":\"Read the simple RAG guide\"},\"tunes\":{}},{\"id\":\"acbb9b7940\",\"type\":\"header\",\"data\":{\"text\":\"Does G-Assist use RAG?\",\"level\":2},\"tunes\":{}},{\"id\":\"ce0371e492\",\"type\":\"paragraph\",\"data\":{\"text\":\"The safest answer is: G-Assist clearly has a knowledge system, but NVIDIA's public product page does not document every internal retrieval mechanism in enough detail to say that every knowledge answer is specifically produced by RAG.\"},\"tunes\":{}},{\"id\":\"c2226e5ec5\",\"type\":\"paragraph\",\"data\":{\"text\":\"The architectural distinction still matters. A retrieval layer can provide relevant knowledge, while system tools provide current PC state and perform actions.\"},\"tunes\":{}},{\"id\":\"e5137fc2c8\",\"type\":\"paragraph\",\"data\":{\"text\":\"That is the same separation used in many modern agents: knowledge is one source of context, live observations are another, and tool execution is a third.\"},\"tunes\":{}},{\"id\":\"059b89dedb\",\"type\":\"header\",\"data\":{\"text\":\"Why G-Assist can work offline\",\"level\":2},\"tunes\":{}},{\"id\":\"19447df6a4\",\"type\":\"paragraph\",\"data\":{\"text\":\"NVIDIA runs the language model locally on the GeForce RTX GPU.\"},\"tunes\":{}},{\"id\":\"55aee6ada7\",\"type\":\"paragraph\",\"data\":{\"text\":\"That means the core assistant does not require a cloud-hosted language model for every prompt.\"},\"tunes\":{}},{\"id\":\"f265d1fd96\",\"type\":\"paragraph\",\"data\":{\"text\":\"NVIDIA explicitly says G-Assist can run offline for its local capabilities.\"},\"tunes\":{}},{\"id\":\"1dc8613200\",\"type\":\"paragraph\",\"data\":{\"text\":\"A plug-in can still use the internet if that plug-in calls an online service. Local model execution and online plug-in access are separate questions.\"},\"tunes\":{}},{\"id\":\"ee01c97a66\",\"type\":\"header\",\"data\":{\"text\":\"The local-AI cost: G-Assist uses your GPU and VRAM\",\"level\":2},\"tunes\":{}},{\"id\":\"1784ad3fcf\",\"type\":\"paragraph\",\"data\":{\"text\":\"Running locally gives privacy and independence from a cloud model, but the work has to execute somewhere.\"},\"tunes\":{}},{\"id\":\"1275ab759f\",\"type\":\"paragraph\",\"data\":{\"text\":\"For G-Assist, that somewhere is the same GeForce RTX GPU that may already be rendering your game.\"},\"tunes\":{}},{\"id\":\"cec86f847a\",\"type\":\"paragraph\",\"data\":{\"text\":\"NVIDIA warns that the GPU briefly allocates compute resources to AI inference when G-Assist responds. If a demanding game is running at the same time, the game can temporarily lose some rendering performance while the model is active.\"},\"tunes\":{}},{\"id\":\"e6ea700df6\",\"type\":\"paragraph\",\"data\":{\"text\":\"The current requirements also specify free-VRAM targets beyond the memory already used by the game: approximately 6 GB of free VRAM for Reasoning Mode and 4.5 GB for Flash Mode.\"},\"tunes\":{}},{\"id\":\"2126f74de5\",\"type\":\"callout\",\"data\":{\"variant\":\"warning\",\"title\":\"8 GB VRAM does not mean 8 GB is available to G-Assist\",\"body\":\"The game, Windows, the driver and other applications already consume VRAM. G-Assist's own requirement is about \u003Cstrong>free VRAM in addition to what the running workload already uses\u003C\u002Fstrong>.\"},\"tunes\":{}},{\"id\":\"13c0dcb3f3\",\"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 installed VRAM, current usage, residency budgets and real memory pressure are different things.\",\"ctaLabel\":\"Read the VRAM guide\"},\"tunes\":{}},{\"id\":\"d684b2c1c8\",\"type\":\"header\",\"data\":{\"text\":\"Reasoning Mode vs Flash Mode\",\"level\":2},\"tunes\":{}},{\"id\":\"3c1f2379db\",\"type\":\"paragraph\",\"data\":{\"text\":\"Current G-Assist versions separate a more capable Reasoning Mode from a faster Flash Mode.\"},\"tunes\":{}},{\"id\":\"86f50ebd33\",\"type\":\"paragraph\",\"data\":{\"text\":\"Reasoning Mode is designed for higher-quality decisions and can coordinate multiple actions from one prompt. Flash Mode is optimized for faster responses and lower resource demand.\"},\"tunes\":{}},{\"id\":\"e7be4789db\",\"type\":\"comparison\",\"data\":{\"title\":\"Reasoning Mode vs Flash Mode\",\"layout\":\"table\",\"columns\":[{\"id\":\"reasoning\",\"label\":\"Reasoning Mode\"},{\"id\":\"flash\",\"label\":\"Flash Mode\"}],\"rows\":[{\"id\":\"goal\",\"label\":\"Primary goal\",\"values\":[\"\",\"\"]},{\"id\":\"vram\",\"label\":\"Recommended free VRAM\",\"values\":[\"\",\"\"]},{\"id\":\"use\",\"label\":\"Best fit\",\"values\":[\"\",\"\"]}]},\"tunes\":{}},{\"id\":\"cef1bcf553\",\"type\":\"header\",\"data\":{\"text\":\"What plug-ins actually add\",\"level\":2},\"tunes\":{}},{\"id\":\"d380d5e28b\",\"type\":\"paragraph\",\"data\":{\"text\":\"A plug-in does not replace the language model. It gives the model a new action it is allowed to call.\"},\"tunes\":{}},{\"id\":\"434979ebda\",\"type\":\"paragraph\",\"data\":{\"text\":\"The plug-in defines functions, descriptions and parameters. G-Assist reads those descriptions, matches a user request to the appropriate function and sends structured arguments to it.\"},\"tunes\":{}},{\"id\":\"21ebe69c79\",\"type\":\"paragraph\",\"data\":{\"text\":\"That lets the assistant grow beyond NVIDIA's built-in functions.\"},\"tunes\":{}},{\"id\":\"d0ba7ef284\",\"type\":\"processFlow\",\"data\":{\"title\":\"How a G-Assist plug-in works\",\"orientation\":\"auto\",\"steps\":[{\"label\":\"1. Plug-in declares a function\",\"description\":\"For example: set_keyboard_color(color).\"},{\"label\":\"2. Manifest describes the function\",\"description\":\"The manifest tells G-Assist what the function does and what parameters it needs.\"},{\"label\":\"3. User asks naturally\",\"description\":\"For example: “Make my keyboard green.”\"},{\"label\":\"4. SLM selects the function\",\"description\":\"The model maps the request to the plug-in function and extracts green as the parameter.\"},{\"label\":\"5. Plug-in executes\",\"description\":\"The plug-in talks to the peripheral software or external service.\"}]},\"tunes\":{}},{\"id\":\"1dc857ce2f\",\"type\":\"header\",\"data\":{\"text\":\"G-Assist Protocol V2 is JSON-RPC 2.0\",\"level\":2},\"tunes\":{}},{\"id\":\"02bdb0566d\",\"type\":\"paragraph\",\"data\":{\"text\":\"NVIDIA's current public plug-in repository uses Protocol V2.\"},\"tunes\":{}},{\"id\":\"f82da294cf\",\"type\":\"paragraph\",\"data\":{\"text\":\"Protocol V2 uses JSON-RPC 2.0 with length-prefixed messages between the G-Assist engine and plug-ins.\"},\"tunes\":{}},{\"id\":\"530c3cf9b9\",\"type\":\"paragraph\",\"data\":{\"text\":\"The engine can initialize a plug-in, check its health, execute a function, send user input and shut it down. Plug-ins can return results, stream progress or report errors.\"},\"tunes\":{}},{\"id\":\"2ce3fa5d75\",\"type\":\"comparison\",\"data\":{\"title\":\"The important Protocol V2 messages\",\"layout\":\"table\",\"columns\":[{\"id\":\"direction\",\"label\":\"Direction\"},{\"id\":\"purpose\",\"label\":\"Purpose\"}],\"rows\":[{\"id\":\"initialize\",\"label\":\"initialize\",\"values\":[\"\",\"\"]},{\"id\":\"ping\",\"label\":\"ping\",\"values\":[\"\",\"\"]},{\"id\":\"execute\",\"label\":\"execute\",\"values\":[\"\",\"\"]},{\"id\":\"stream\",\"label\":\"stream\",\"values\":[\"\",\"\"]},{\"id\":\"complete\",\"label\":\"complete\",\"values\":[\"\",\"\"]}]},\"tunes\":{}},{\"id\":\"304fbd9944\",\"type\":\"header\",\"data\":{\"text\":\"Where MCP enters the picture\",\"level\":2},\"tunes\":{}},{\"id\":\"fae73881e4\",\"type\":\"paragraph\",\"data\":{\"text\":\"G-Assist's own plug-in protocol is not MCP. Its current public plug-in system uses JSON-RPC 2.0.\"},\"tunes\":{}},{\"id\":\"551d11710a\",\"type\":\"paragraph\",\"data\":{\"text\":\"However, a G-Assist plug-in can connect to an MCP server.\"},\"tunes\":{}},{\"id\":\"0537fb222a\",\"type\":\"paragraph\",\"data\":{\"text\":\"NVIDIA's current version 0.2.2 includes an Elgato integration that can invoke actions exposed through the Elgato Stream Deck MCP server.\"},\"tunes\":{}},{\"id\":\"760a86ad19\",\"type\":\"callout\",\"data\":{\"variant\":\"success\",\"title\":\"Important protocol distinction\",\"body\":\"\u003Cstrong>G-Assist ↔ its plug-in:\u003C\u002Fstrong> NVIDIA Protocol V2 \u002F JSON-RPC 2.0.\u003Cbr>\u003Cstrong>Plug-in ↔ another tool ecosystem:\u003C\u002Fstrong> can use MCP when that integration supports it.\"},\"tunes\":{}},{\"id\":\"bd182f72b0\",\"type\":\"header\",\"data\":{\"text\":\"Why this matters beyond RGB lights\",\"level\":2},\"tunes\":{}},{\"id\":\"d072506ce2\",\"type\":\"paragraph\",\"data\":{\"text\":\"The plug-in architecture turns G-Assist from a fixed assistant into a local tool router.\"},\"tunes\":{}},{\"id\":\"2876e067dc\",\"type\":\"paragraph\",\"data\":{\"text\":\"The same pattern can connect an SLM to monitoring tools, peripherals, APIs, automation systems, local applications or external services.\"},\"tunes\":{}},{\"id\":\"37689640e9\",\"type\":\"paragraph\",\"data\":{\"text\":\"The important architectural unit is therefore not the chat window. It is the boundary between natural-language intent and structured tool execution.\"},\"tunes\":{}},{\"id\":\"550fbb42d4\",\"type\":\"header\",\"data\":{\"text\":\"The Local Agent Stack\",\"level\":2},\"tunes\":{}},{\"id\":\"8518dbf820\",\"type\":\"processFlow\",\"data\":{\"title\":\"A useful way to think about G-Assist\",\"orientation\":\"auto\",\"steps\":[{\"label\":\"1. User intent\",\"description\":\"Natural language or voice command.\"},{\"label\":\"2. Local SLM\",\"description\":\"Understands the request and chooses a capability.\"},{\"label\":\"3. Knowledge \u002F context\",\"description\":\"Provides product knowledge, recommendations or information needed for the decision.\"},{\"label\":\"4. Live system state\",\"description\":\"Provides current hardware, driver, performance or configuration information when supported.\"},{\"label\":\"5. Tool selection\",\"description\":\"Built-in function or plug-in is chosen.\"},{\"label\":\"6. Structured execution\",\"description\":\"The function receives arguments and performs the real action.\"},{\"label\":\"7. Result verification\",\"description\":\"The tool returns status or data and the model explains what happened.\"}]},\"tunes\":{}},{\"id\":\"6c976db763\",\"type\":\"header\",\"data\":{\"text\":\"Why tool boundaries are a safety feature\",\"level\":2},\"tunes\":{}},{\"id\":\"ba65a3b467\",\"type\":\"paragraph\",\"data\":{\"text\":\"An AI assistant that could execute arbitrary operating-system commands would be far more powerful, but also much harder to constrain.\"},\"tunes\":{}},{\"id\":\"a932df9663\",\"type\":\"paragraph\",\"data\":{\"text\":\"G-Assist instead exposes defined functions with known parameters.\"},\"tunes\":{}},{\"id\":\"7d1d45c83d\",\"type\":\"paragraph\",\"data\":{\"text\":\"That means the model can only perform actions that the built-in function set or installed plug-ins make available.\"},\"tunes\":{}},{\"id\":\"10460fda21\",\"type\":\"paragraph\",\"data\":{\"text\":\"This does not remove all risk, especially with third-party plug-ins, but it creates a clearer permission and capability boundary than unrestricted shell access.\"},\"tunes\":{}},{\"id\":\"25049448ea\",\"type\":\"header\",\"data\":{\"text\":\"The Tool-Authority Boundary\",\"level\":2},\"tunes\":{}},{\"id\":\"d3ff568795\",\"type\":\"comparison\",\"data\":{\"title\":\"What the assistant can understand vs what it is allowed to do\",\"layout\":\"table\",\"columns\":[{\"id\":\"understand\",\"label\":\"Model may understand\"},{\"id\":\"authority\",\"label\":\"Tool authority\"}],\"rows\":[{\"id\":\"gpu\",\"label\":\"GPU tuning\",\"values\":[\"\",\"\"]},{\"id\":\"files\",\"label\":\"Files\",\"values\":[\"\",\"\"]},{\"id\":\"web\",\"label\":\"Internet\",\"values\":[\"\",\"\"]},{\"id\":\"device\",\"label\":\"Peripheral control\",\"values\":[\"\",\"\"]}]},\"tunes\":{}},{\"id\":\"8fd8e35c51\",\"type\":\"header\",\"data\":{\"text\":\"Why G-Assist can temporarily reduce game performance\",\"level\":2},\"tunes\":{}},{\"id\":\"f75234546f\",\"type\":\"paragraph\",\"data\":{\"text\":\"The local-model architecture has a straightforward consequence: the AI and the game can compete for the same GPU.\"},\"tunes\":{}},{\"id\":\"28bc054afa\",\"type\":\"paragraph\",\"data\":{\"text\":\"NVIDIA explicitly warns that render rate or inference speed can briefly dip while G-Assist is processing a request during a GPU-heavy workload.\"},\"tunes\":{}},{\"id\":\"0823573513\",\"type\":\"paragraph\",\"data\":{\"text\":\"Once inference finishes, those GPU resources return to the game.\"},\"tunes\":{}},{\"id\":\"905eed843d\",\"type\":\"paragraph\",\"data\":{\"text\":\"This is different from a cloud assistant, where most model inference happens on a remote server and does not consume the gaming GPU.\"},\"tunes\":{}},{\"id\":\"839c0e3988\",\"type\":\"header\",\"data\":{\"text\":\"The Local Assistant Resource Test\",\"level\":2},\"tunes\":{}},{\"id\":\"affa0ee1b3\",\"type\":\"processFlow\",\"data\":{\"title\":\"How to judge whether a local gaming assistant fits your PC\",\"orientation\":\"auto\",\"steps\":[{\"label\":\"1. Check installed VRAM\",\"description\":\"The current G-Assist baseline is an RTX GPU with at least 6 GB VRAM.\"},{\"label\":\"2. Check free VRAM during the actual game\",\"description\":\"Installed capacity is not the same as free capacity.\"},{\"label\":\"3. Choose Reasoning or Flash Mode appropriately\",\"description\":\"Use the lighter mode when resource pressure matters more than deep reasoning.\"},{\"label\":\"4. Measure inference-time frame-rate dips\",\"description\":\"Watch the game while asking G-Assist to perform tasks.\"},{\"label\":\"5. Inspect tool authority\",\"description\":\"Know exactly which built-in functions and plug-ins can change your system.\"},{\"label\":\"6. Treat third-party plug-ins as software\",\"description\":\"Review their source, permissions, network access and stored credentials.\"}]},\"tunes\":{}},{\"id\":\"ca52c48d2e\",\"type\":\"header\",\"data\":{\"text\":\"Why plug-ins deserve the same scrutiny as any other software\",\"level\":2},\"tunes\":{}},{\"id\":\"8e6a760529\",\"type\":\"paragraph\",\"data\":{\"text\":\"A plug-in can connect the assistant to APIs, peripheral applications and online services.\"},\"tunes\":{}},{\"id\":\"be793240e1\",\"type\":\"paragraph\",\"data\":{\"text\":\"That means a plug-in may handle configuration values, credentials or external requests depending on what it was built to do.\"},\"tunes\":{}},{\"id\":\"3c6c4c9aaf\",\"type\":\"paragraph\",\"data\":{\"text\":\"NVIDIA's repository explicitly provides a config.json location for plug-in settings and warns developers not to commit credentials.\"},\"tunes\":{}},{\"id\":\"03413f935a\",\"type\":\"paragraph\",\"data\":{\"text\":\"The correct security question is therefore not only “Is G-Assist local?” but also “What does each installed plug-in connect to?”\"},\"tunes\":{}},{\"id\":\"f473162e86\",\"type\":\"header\",\"data\":{\"text\":\"G-Assist is closer to an agent than a chatbot\",\"level\":2},\"tunes\":{}},{\"id\":\"59dd3df9c2\",\"type\":\"paragraph\",\"data\":{\"text\":\"A chatbot mainly produces language.\"},\"tunes\":{}},{\"id\":\"188cb4bb40\",\"type\":\"paragraph\",\"data\":{\"text\":\"An agent interprets a goal, observes relevant information, chooses an action, calls a tool and evaluates the result.\"},\"tunes\":{}},{\"id\":\"117d1032f1\",\"type\":\"paragraph\",\"data\":{\"text\":\"G-Assist now fits that second description much more closely because it can coordinate multiple actions, inspect supported PC state and invoke real tools.\"},\"tunes\":{}},{\"id\":\"ef132fc518\",\"type\":\"referralArticle\",\"data\":{\"url\":\"https:\u002F\u002Ffigure.rocks\u002Fblog\u002Fpubg-ally-shows-why-ai-teammates-need-two-brains-fast-reflexes-and-slow-reasoning\",\"title\":\"PUBG Ally Shows Why AI Teammates Need Two Brains: Fast Reflexes and Slow Reasoning\",\"excerpt\":\"A game-agent architecture example showing the separation between reasoning, live state, tools and deterministic execution.\",\"ctaLabel\":\"Read the PUBG Ally architecture guide\"},\"tunes\":{}},{\"id\":\"9f8fa308a5\",\"type\":\"header\",\"data\":{\"text\":\"What G-Assist still is not\",\"level\":2},\"tunes\":{}},{\"id\":\"f5f24e8aa3\",\"type\":\"paragraph\",\"data\":{\"text\":\"NVIDIA explicitly describes G-Assist as a specialized local assistant, not a general-purpose conversational AI.\"},\"tunes\":{}},{\"id\":\"e4d187593f\",\"type\":\"paragraph\",\"data\":{\"text\":\"Its value comes from understanding a focused set of PC and gaming tasks and having tools connected to those tasks.\"},\"tunes\":{}},{\"id\":\"0c1b7f8948\",\"type\":\"paragraph\",\"data\":{\"text\":\"That focus is important because a smaller local model can be useful when the surrounding system gives it strong tools and clear boundaries.\"},\"tunes\":{}},{\"id\":\"79a5920e77\",\"type\":\"header\",\"data\":{\"text\":\"What changed in the current versions?\",\"level\":2},\"tunes\":{}},{\"id\":\"e78d96ef6c\",\"type\":\"paragraph\",\"data\":{\"text\":\"The current public release history shows G-Assist moving steadily toward a more capable local agent.\"},\"tunes\":{}},{\"id\":\"87dd1d3d3c\",\"type\":\"table\",\"data\":{\"withHeadings\":true,\"stretched\":false,\"content\":[[\"Version\",\"Important change\"],[\"0.1.17\",\"Lighter model, all RTX GPUs with 6 GB+ VRAM, community plug-ins\"],[\"0.1.18\",\"Laptop optimization, BatteryBoost and WhisperMode controls\"],[\"0.2\",\"Reasoning Mode, Flash Mode, multi-action prompts, new device controls\"],[\"0.2.1\",\"Improved knowledge system, better recommendations, RTX feature controls\"],[\"0.2.2\",\"Elgato Stream Deck integration through an MCP server\"]]},\"tunes\":{}},{\"id\":\"09bde21a8d\",\"type\":\"header\",\"data\":{\"text\":\"What would change this answer?\",\"level\":2},\"tunes\":{}},{\"id\":\"b0e7ca01a4\",\"type\":\"paragraph\",\"data\":{\"text\":\"G-Assist is still experimental and its architecture is evolving.\"},\"tunes\":{}},{\"id\":\"537568c224\",\"type\":\"paragraph\",\"data\":{\"text\":\"The model size, VRAM requirements, tool set, plug-in protocol and hardware support can all change in future releases.\"},\"tunes\":{}},{\"id\":\"fe24516c11\",\"type\":\"paragraph\",\"data\":{\"text\":\"A future version could also move some inference to an NPU or introduce broader MCP integration, but that should not be assumed until NVIDIA documents it.\"},\"tunes\":{}},{\"id\":\"0e31e2d54d\",\"type\":\"header\",\"data\":{\"text\":\"Limitations\",\"level\":2},\"tunes\":{}},{\"id\":\"a7881e11e1\",\"type\":\"paragraph\",\"data\":{\"text\":\"This article describes NVIDIA's public G-Assist product documentation and public plug-in architecture as of September 2026.\"},\"tunes\":{}},{\"id\":\"71ebc16fda\",\"type\":\"paragraph\",\"data\":{\"text\":\"NVIDIA does not publicly document every internal detail of its knowledge and recommendation pipeline, so this article does not claim that every knowledge answer uses RAG or any other specific retrieval implementation.\"},\"tunes\":{}},{\"id\":\"add780d6ba\",\"type\":\"paragraph\",\"data\":{\"text\":\"Third-party plug-ins can have behavior and network access beyond NVIDIA's built-in functions, so each plug-in should be evaluated separately.\"},\"tunes\":{}},{\"id\":\"f7a38af5be\",\"type\":\"header\",\"data\":{\"text\":\"Conclusion\",\"level\":2},\"tunes\":{}},{\"id\":\"f1308f3e80\",\"type\":\"paragraph\",\"data\":{\"text\":\"The easiest way to understand Project G-Assist is to stop thinking of it as a chatbot.\"},\"tunes\":{}},{\"id\":\"b102a861bb\",\"type\":\"paragraph\",\"data\":{\"text\":\"The local SLM interprets your request. Knowledge helps it understand the problem. Live system information tells it what is true on your PC. A built-in function or plug-in defines what it is actually allowed to do. The tool performs the action and returns the result.\"},\"tunes\":{}},{\"id\":\"da805fabf0\",\"type\":\"paragraph\",\"data\":{\"text\":\"That is a local agent architecture.\"},\"tunes\":{}},{\"id\":\"675aecee06\",\"type\":\"paragraph\",\"data\":{\"text\":\"Its strongest idea is not that an 8-billion-parameter model can talk about your GPU. It is that a relatively small local model can become useful when it is connected to well-defined tools, current state and a controlled action boundary.\"},\"tunes\":{}},{\"id\":\"0354b8daaf\",\"type\":\"header\",\"data\":{\"text\":\"FAQ\",\"level\":2},\"tunes\":{}},{\"id\":\"e75bc04532\",\"type\":\"faq\",\"data\":{\"title\":\"NVIDIA Project G-Assist in plain English\",\"items\":[{\"id\":\"faq1\",\"question\":\"Is G-Assist a cloud chatbot?\",\"answer\":\"No. Its core language model runs locally on the GeForce RTX GPU and can work offline for supported local functions.\"},{\"id\":\"faq2\",\"question\":\"What model does G-Assist use?\",\"answer\":\"NVIDIA currently describes it as a local Llama-based instruct model with 8 billion parameters.\"},{\"id\":\"faq3\",\"question\":\"Does the AI model directly change my GPU settings?\",\"answer\":\"No. The model selects a supported built-in function or plug-in, and that tool performs the actual change.\"},{\"id\":\"faq4\",\"question\":\"Does G-Assist use VRAM while gaming?\",\"answer\":\"Yes. NVIDIA recommends additional free VRAM for the assistant and warns that inference can briefly reduce game render performance.\"},{\"id\":\"faq5\",\"question\":\"Are G-Assist plug-ins MCP plug-ins?\",\"answer\":\"Not directly. G-Assist's own current plug-in protocol uses JSON-RPC 2.0, although a plug-in can connect to an MCP server.\"},{\"id\":\"faq6\",\"question\":\"Can G-Assist work offline?\",\"answer\":\"Its local assistant can, but individual plug-ins may still require internet access if they call online services.\"},{\"id\":\"faq7\",\"question\":\"Is G-Assist a general AI assistant?\",\"answer\":\"NVIDIA describes it as a specialized PC and gaming assistant rather than a broad general-purpose conversational model.\"}]},\"tunes\":{}},{\"id\":\"8733f298cd\",\"type\":\"header\",\"data\":{\"text\":\"Glossary\",\"level\":2},\"tunes\":{}},{\"id\":\"407257a6d1\",\"type\":\"glossary\",\"data\":{\"title\":\"Key G-Assist terms\",\"entries\":[{\"term\":\"SLM\",\"definition\":\"Small Language Model, a compact language model designed to run locally with lower hardware requirements than large cloud models.\",\"anchor\":\"slm\"},{\"term\":\"Tool calling\",\"definition\":\"The process where a language model chooses a defined function and supplies structured arguments so another component can perform an action.\",\"anchor\":\"tool-calling\"},{\"term\":\"Plug-in\",\"definition\":\"An extension that exposes additional functions or integrations to G-Assist.\",\"anchor\":\"plugin\"},{\"term\":\"JSON-RPC 2.0\",\"definition\":\"The structured message protocol used by the current G-Assist Protocol V2 plug-in system.\",\"anchor\":\"json-rpc\"},{\"term\":\"MCP\",\"definition\":\"Model Context Protocol, a separate tool and context interoperability protocol that some external integrations can expose.\",\"anchor\":\"mcp\"},{\"term\":\"Tool-Authority Boundary\",\"definition\":\"A Figure Rocks model separating what an AI model can understand from what its connected tools actually allow it to do.\",\"anchor\":\"tool-authority-boundary\"},{\"term\":\"Local Agent Stack\",\"definition\":\"A Figure Rocks model combining user intent, local SLM, knowledge, live state, tool selection, execution and result verification.\",\"anchor\":\"local-agent-stack\"},{\"term\":\"Local Assistant Resource Test\",\"definition\":\"A Figure Rocks workflow for evaluating VRAM, inference cost, tool permissions and plug-in risk before using a local gaming assistant.\",\"anchor\":\"local-assistant-resource-test\"}]},\"tunes\":{}},{\"id\":\"3ab856f2b4\",\"type\":\"header\",\"data\":{\"text\":\"Primary sources\",\"level\":2},\"tunes\":{}},{\"id\":\"367d2e3548\",\"type\":\"linkTool\",\"data\":{\"link\":\"https:\u002F\u002Fwww.nvidia.com\u002Fen-us\u002Fsoftware\u002Fnvidia-app\u002Fg-assist\u002F\",\"meta\":{\"image\":{\"url\":\"\"},\"title\":\"NVIDIA — Project G-Assist\",\"description\":\"Official current product page covering the local model, supported functions, Reasoning and Flash modes, VRAM requirements, RTX controls, plug-ins and version 0.2.2 MCP-based Elgato integration.\"}},\"tunes\":{}},{\"id\":\"5641b514c0\",\"type\":\"linkTool\",\"data\":{\"link\":\"https:\u002F\u002Fgithub.com\u002FNVIDIA\u002FG-Assist\",\"meta\":{\"image\":{\"url\":\"\"},\"title\":\"NVIDIA — G-Assist GitHub\",\"description\":\"Official public plug-in repository documenting G-Assist's module architecture, plug-in discovery and Protocol V2.\"}},\"tunes\":{}},{\"id\":\"22a5b77a1b\",\"type\":\"linkTool\",\"data\":{\"link\":\"https:\u002F\u002Fgithub.com\u002FNVIDIA\u002FG-Assist\u002Fblob\u002Fmain\u002FPLUGIN_MIGRATION_GUIDE_V2.md\",\"meta\":{\"image\":{\"url\":\"\"},\"title\":\"NVIDIA — G-Assist Protocol V2 Migration Guide\",\"description\":\"Official protocol documentation covering JSON-RPC 2.0, initialization, health checks, execution, streaming, completion and plug-in SDK behavior.\"}},\"tunes\":{}},{\"id\":\"cc3301d997\",\"type\":\"linkTool\",\"data\":{\"link\":\"https:\u002F\u002Fblogs.nvidia.com\u002Fblog\u002Frtx-ai-garage-gamescom-g-assist-rtx-remix\u002F\",\"meta\":{\"image\":{\"url\":\"\"},\"title\":\"NVIDIA Blog — Lightweight G-Assist Model\",\"description\":\"Official background on the lower-memory G-Assist model, support for 6 GB RTX GPUs and the community plug-in hub.\"}},\"tunes\":{}}],\"version\":\"2.31.6\"}",{"time":1483,"blocks":1484,"version":2190},1790407740910,[1485,1489,1494,1499,1503,1507,1511,1515,1519,1524,1528,1551,1555,1559,1563,1567,1589,1593,1597,1601,1605,1623,1628,1634,1638,1642,1646,1650,1654,1658,1662,1666,1670,1674,1678,1682,1686,1690,1695,1702,1706,1710,1714,1732,1736,1740,1744,1748,1768,1772,1776,1780,1784,1804,1808,1812,1816,1820,1825,1829,1833,1837,1841,1845,1871,1875,1879,1883,1887,1891,1895,1917,1921,1925,1929,1933,1937,1941,1964,1968,1972,1976,1980,1984,1988,1992,1996,2000,2007,2011,2015,2019,2023,2027,2031,2048,2052,2056,2060,2064,2068,2072,2076,2080,2084,2088,2092,2096,2100,2104,2130,2134,2160,2164,2170,2176,2183],{"id":541,"data":1486,"type":544,"tunes":1488},{"text":1487},"NVIDIA Project G-Assist looks like a chatbot, but that description misses the important part. It is a local AI system that can understand a request, inspect supported PC state, choose a tool or plug-in, and then perform a real action such as changing DLSS settings, checking a driver, adjusting a fan profile or controlling a peripheral.",{},{"id":547,"data":1490,"type":552,"tunes":1493},{"body":1491,"title":1492,"variant":551},"\u003Cstrong>G-Assist is not simply a chatbot for your GPU.\u003C\u002Fstrong> It is a local small language model connected to a set of allowed tools. The model interprets what you want, selects a supported function, passes arguments to that function, and the PC-side tool performs the action.","Direct answer",{},{"id":555,"data":1495,"type":552,"tunes":1498},{"body":1496,"title":1497,"variant":559},"\u003Cstrong>SLM = understands the request.\u003C\u002Fstrong>\u003Cbr>\u003Cstrong>Knowledge = explains supported NVIDIA\u002FPC concepts.\u003C\u002Fstrong>\u003Cbr>\u003Cstrong>Tools = perform actions.\u003C\u002Fstrong>\u003Cbr>\u003Cstrong>Plug-ins = add new tools.\u003C\u002Fstrong>\u003Cbr>\u003Cstrong>Your PC = the environment being inspected or changed.\u003C\u002Fstrong>","The easiest mental model",{},{"id":562,"data":1500,"type":566,"tunes":1502},{"title":1501,"maxLevel":565,"minLevel":47},"Contents",{},{"id":569,"data":1504,"type":572,"tunes":1506},{"text":1505,"level":47},"First: what is the AI model actually doing?",{},{"id":575,"data":1508,"type":544,"tunes":1510},{"text":1509},"G-Assist uses a local small language model, or SLM. NVIDIA currently describes the system as using a Llama-based 8-billion-parameter instruct model.",{},{"id":580,"data":1512,"type":544,"tunes":1514},{"text":1513},"The model's main job is not to render graphics or directly change hardware registers. Its job is to understand the user's language, decide which supported capability matches the request, and prepare the call to that capability.",{},{"id":585,"data":1516,"type":544,"tunes":1518},{"text":1517},"For example, if you say “set DLSS Super Resolution to Performance Mode,” the language model does not itself modify DLSS. It interprets the command and routes it to the supported function that can change the setting.",{},{"id":590,"data":1520,"type":552,"tunes":1523},{"body":1521,"title":1522,"variant":594},"The AI model \u003Cstrong>decides what tool to call\u003C\u002Fstrong>. The tool \u003Cstrong>does the real work\u003C\u002Fstrong>.","The important distinction",{},{"id":597,"data":1525,"type":572,"tunes":1527},{"text":1526,"level":47},"The G-Assist action pipeline",{},{"id":602,"data":1529,"type":625,"tunes":1550},{"steps":1530,"title":1549,"orientation":624},[1531,1534,1537,1540,1543,1546],{"label":1532,"description":1533},"1. You give a natural-language request","For example: “Optimize this game for performance.”",{"label":1535,"description":1536},"2. The local SLM interprets the request","It determines the user's intent and identifies which supported function or plug-in is relevant.",{"label":1538,"description":1539},"3. The system chooses a tool","That could be a built-in graphics-setting function, driver check, monitoring function or community plug-in.",{"label":1541,"description":1542},"4. Arguments are extracted","The model converts the request into structured values such as game name, mode, fan profile or DLSS setting.",{"label":1544,"description":1545},"5. The tool executes","The tool talks to the NVIDIA App, operating system, peripheral software or another service.",{"label":1547,"description":1548},"6. The result is returned","G-Assist reports what happened, or returns data that the model can explain.","What happens after you give G-Assist a command",{},{"id":628,"data":1552,"type":572,"tunes":1554},{"text":1553,"level":47},"This is tool calling, not magic PC control",{},{"id":633,"data":1556,"type":544,"tunes":1558},{"text":1557},"A language model cannot safely control an arbitrary computer simply because it understands English.",{},{"id":638,"data":1560,"type":544,"tunes":1562},{"text":1561},"It needs a defined interface that says which actions exist, which arguments they accept and what the tool returns.",{},{"id":643,"data":1564,"type":544,"tunes":1566},{"text":1565},"G-Assist's current built-in capabilities include functions such as optimizing graphics settings, changing supported RTX options, checking or downloading drivers, showing performance information, changing some laptop power features, controlling supported monitor functions and using supported peripheral plug-ins.",{},{"id":648,"data":1568,"type":676,"tunes":1588},{"rows":1569,"title":1582,"layout":668,"columns":1583},[1570,1573,1576,1579],{"id":652,"label":1571,"values":1572},"Understand “turn on DLSS”",[13,13],{"id":656,"label":1574,"values":1575},"Choose the correct function",[13,13],{"id":660,"label":1577,"values":1578},"Actually change the setting",[13,13],{"id":664,"label":1580,"values":1581},"Explain the result",[13,13],"Language model vs tool",[1584,1586],{"id":671,"label":1585},"Language model",{"id":674,"label":1587},"Tool \u002F plug-in",{},{"id":679,"data":1590,"type":572,"tunes":1592},{"text":1591,"level":47},"What is the knowledge layer?",{},{"id":684,"data":1594,"type":544,"tunes":1596},{"text":1595},"G-Assist also has a knowledge layer for answering questions and making recommendations.",{},{"id":689,"data":1598,"type":544,"tunes":1600},{"text":1599},"NVIDIA says version 0.2.1 introduced an improved knowledge system that helps G-Assist make more accurate settings recommendations.",{},{"id":694,"data":1602,"type":544,"tunes":1604},{"text":1603},"That knowledge layer is different from the live state of your PC.",{},{"id":699,"data":1606,"type":676,"tunes":1622},{"rows":1607,"title":1616,"layout":668,"columns":1617},[1608,1610,1613],{"id":703,"label":704,"values":1609},[13,13],{"id":707,"label":1611,"values":1612},"Game settings",[13,13],{"id":711,"label":1614,"values":1615},"Performance",[13,13],"Knowledge vs live PC state",[1618,1620],{"id":717,"label":1619},"Knowledge",{"id":720,"label":1621},"Live state",{},{"id":724,"data":1624,"type":552,"tunes":1627},{"body":1625,"title":1626,"variant":728},"\u003Cstrong>Knowledge\u003C\u002Fstrong> tells the assistant what a feature means or what usually matters.\u003Cbr>\u003Cstrong>State\u003C\u002Fstrong> tells it what is true on your machine right now.","Do not mix knowledge with state",{},{"id":731,"data":1629,"type":737,"tunes":1633},{"url":733,"title":1630,"excerpt":1631,"ctaLabel":1632},"What Is RAG? The Simplest Explanation of How It Works","A plain-English explanation of knowledge retrieval, state, memory, context and the language model.","Read the simple RAG guide",{},{"id":740,"data":1635,"type":572,"tunes":1637},{"text":1636,"level":47},"Does G-Assist use RAG?",{},{"id":745,"data":1639,"type":544,"tunes":1641},{"text":1640},"The safest answer is: G-Assist clearly has a knowledge system, but NVIDIA's public product page does not document every internal retrieval mechanism in enough detail to say that every knowledge answer is specifically produced by RAG.",{},{"id":750,"data":1643,"type":544,"tunes":1645},{"text":1644},"The architectural distinction still matters. A retrieval layer can provide relevant knowledge, while system tools provide current PC state and perform actions.",{},{"id":755,"data":1647,"type":544,"tunes":1649},{"text":1648},"That is the same separation used in many modern agents: knowledge is one source of context, live observations are another, and tool execution is a third.",{},{"id":760,"data":1651,"type":572,"tunes":1653},{"text":1652,"level":47},"Why G-Assist can work offline",{},{"id":765,"data":1655,"type":544,"tunes":1657},{"text":1656},"NVIDIA runs the language model locally on the GeForce RTX GPU.",{},{"id":770,"data":1659,"type":544,"tunes":1661},{"text":1660},"That means the core assistant does not require a cloud-hosted language model for every prompt.",{},{"id":775,"data":1663,"type":544,"tunes":1665},{"text":1664},"NVIDIA explicitly says G-Assist can run offline for its local capabilities.",{},{"id":780,"data":1667,"type":544,"tunes":1669},{"text":1668},"A plug-in can still use the internet if that plug-in calls an online service. Local model execution and online plug-in access are separate questions.",{},{"id":785,"data":1671,"type":572,"tunes":1673},{"text":1672,"level":47},"The local-AI cost: G-Assist uses your GPU and VRAM",{},{"id":790,"data":1675,"type":544,"tunes":1677},{"text":1676},"Running locally gives privacy and independence from a cloud model, but the work has to execute somewhere.",{},{"id":795,"data":1679,"type":544,"tunes":1681},{"text":1680},"For G-Assist, that somewhere is the same GeForce RTX GPU that may already be rendering your game.",{},{"id":800,"data":1683,"type":544,"tunes":1685},{"text":1684},"NVIDIA warns that the GPU briefly allocates compute resources to AI inference when G-Assist responds. If a demanding game is running at the same time, the game can temporarily lose some rendering performance while the model is active.",{},{"id":805,"data":1687,"type":544,"tunes":1689},{"text":1688},"The current requirements also specify free-VRAM targets beyond the memory already used by the game: approximately 6 GB of free VRAM for Reasoning Mode and 4.5 GB for Flash Mode.",{},{"id":810,"data":1691,"type":552,"tunes":1694},{"body":1692,"title":1693,"variant":728},"The game, Windows, the driver and other applications already consume VRAM. G-Assist's own requirement is about \u003Cstrong>free VRAM in addition to what the running workload already uses\u003C\u002Fstrong>.","8 GB VRAM does not mean 8 GB is available to G-Assist",{},{"id":816,"data":1696,"type":737,"tunes":1701},{"url":1697,"title":1698,"excerpt":1699,"ctaLabel":1700},"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 installed VRAM, current usage, residency budgets and real memory pressure are different things.","Read the VRAM guide",{},{"id":824,"data":1703,"type":572,"tunes":1705},{"text":1704,"level":47},"Reasoning Mode vs Flash Mode",{},{"id":829,"data":1707,"type":544,"tunes":1709},{"text":1708},"Current G-Assist versions separate a more capable Reasoning Mode from a faster Flash Mode.",{},{"id":834,"data":1711,"type":544,"tunes":1713},{"text":1712},"Reasoning Mode is designed for higher-quality decisions and can coordinate multiple actions from one prompt. Flash Mode is optimized for faster responses and lower resource demand.",{},{"id":839,"data":1715,"type":676,"tunes":1731},{"rows":1716,"title":1704,"layout":668,"columns":1726},[1717,1720,1723],{"id":843,"label":1718,"values":1719},"Primary goal",[13,13],{"id":847,"label":1721,"values":1722},"Recommended free VRAM",[13,13],{"id":851,"label":1724,"values":1725},"Best fit",[13,13],[1727,1729],{"id":856,"label":1728},"Reasoning Mode",{"id":859,"label":1730},"Flash Mode",{},{"id":863,"data":1733,"type":572,"tunes":1735},{"text":1734,"level":47},"What plug-ins actually add",{},{"id":868,"data":1737,"type":544,"tunes":1739},{"text":1738},"A plug-in does not replace the language model. It gives the model a new action it is allowed to call.",{},{"id":873,"data":1741,"type":544,"tunes":1743},{"text":1742},"The plug-in defines functions, descriptions and parameters. G-Assist reads those descriptions, matches a user request to the appropriate function and sends structured arguments to it.",{},{"id":878,"data":1745,"type":544,"tunes":1747},{"text":1746},"That lets the assistant grow beyond NVIDIA's built-in functions.",{},{"id":883,"data":1749,"type":625,"tunes":1767},{"steps":1750,"title":1766,"orientation":624},[1751,1754,1757,1760,1763],{"label":1752,"description":1753},"1. Plug-in declares a function","For example: set_keyboard_color(color).",{"label":1755,"description":1756},"2. Manifest describes the function","The manifest tells G-Assist what the function does and what parameters it needs.",{"label":1758,"description":1759},"3. User asks naturally","For example: “Make my keyboard green.”",{"label":1761,"description":1762},"4. SLM selects the function","The model maps the request to the plug-in function and extracts green as the parameter.",{"label":1764,"description":1765},"5. Plug-in executes","The plug-in talks to the peripheral software or external service.","How a G-Assist plug-in works",{},{"id":904,"data":1769,"type":572,"tunes":1771},{"text":1770,"level":47},"G-Assist Protocol V2 is JSON-RPC 2.0",{},{"id":909,"data":1773,"type":544,"tunes":1775},{"text":1774},"NVIDIA's current public plug-in repository uses Protocol V2.",{},{"id":914,"data":1777,"type":544,"tunes":1779},{"text":1778},"Protocol V2 uses JSON-RPC 2.0 with length-prefixed messages between the G-Assist engine and plug-ins.",{},{"id":919,"data":1781,"type":544,"tunes":1783},{"text":1782},"The engine can initialize a plug-in, check its health, execute a function, send user input and shut it down. Plug-ins can return results, stream progress or report errors.",{},{"id":924,"data":1785,"type":676,"tunes":1803},{"rows":1786,"title":1797,"layout":668,"columns":1798},[1787,1789,1791,1793,1795],{"id":928,"label":928,"values":1788},[13,13],{"id":931,"label":931,"values":1790},[13,13],{"id":934,"label":934,"values":1792},[13,13],{"id":937,"label":937,"values":1794},[13,13],{"id":940,"label":940,"values":1796},[13,13],"The important Protocol V2 messages",[1799,1801],{"id":945,"label":1800},"Direction",{"id":948,"label":1802},"Purpose",{},{"id":952,"data":1805,"type":572,"tunes":1807},{"text":1806,"level":47},"Where MCP enters the picture",{},{"id":957,"data":1809,"type":544,"tunes":1811},{"text":1810},"G-Assist's own plug-in protocol is not MCP. Its current public plug-in system uses JSON-RPC 2.0.",{},{"id":962,"data":1813,"type":544,"tunes":1815},{"text":1814},"However, a G-Assist plug-in can connect to an MCP server.",{},{"id":967,"data":1817,"type":544,"tunes":1819},{"text":1818},"NVIDIA's current version 0.2.2 includes an Elgato integration that can invoke actions exposed through the Elgato Stream Deck MCP server.",{},{"id":972,"data":1821,"type":552,"tunes":1824},{"body":1822,"title":1823,"variant":594},"\u003Cstrong>G-Assist ↔ its plug-in:\u003C\u002Fstrong> NVIDIA Protocol V2 \u002F JSON-RPC 2.0.\u003Cbr>\u003Cstrong>Plug-in ↔ another tool ecosystem:\u003C\u002Fstrong> can use MCP when that integration supports it.","Important protocol distinction",{},{"id":978,"data":1826,"type":572,"tunes":1828},{"text":1827,"level":47},"Why this matters beyond RGB lights",{},{"id":983,"data":1830,"type":544,"tunes":1832},{"text":1831},"The plug-in architecture turns G-Assist from a fixed assistant into a local tool router.",{},{"id":988,"data":1834,"type":544,"tunes":1836},{"text":1835},"The same pattern can connect an SLM to monitoring tools, peripherals, APIs, automation systems, local applications or external services.",{},{"id":993,"data":1838,"type":544,"tunes":1840},{"text":1839},"The important architectural unit is therefore not the chat window. It is the boundary between natural-language intent and structured tool execution.",{},{"id":998,"data":1842,"type":572,"tunes":1844},{"text":1843,"level":47},"The Local Agent Stack",{},{"id":1003,"data":1846,"type":625,"tunes":1870},{"steps":1847,"title":1869,"orientation":624},[1848,1851,1854,1857,1860,1863,1866],{"label":1849,"description":1850},"1. User intent","Natural language or voice command.",{"label":1852,"description":1853},"2. Local SLM","Understands the request and chooses a capability.",{"label":1855,"description":1856},"3. Knowledge \u002F context","Provides product knowledge, recommendations or information needed for the decision.",{"label":1858,"description":1859},"4. Live system state","Provides current hardware, driver, performance or configuration information when supported.",{"label":1861,"description":1862},"5. Tool selection","Built-in function or plug-in is chosen.",{"label":1864,"description":1865},"6. Structured execution","The function receives arguments and performs the real action.",{"label":1867,"description":1868},"7. Result verification","The tool returns status or data and the model explains what happened.","A useful way to think about G-Assist",{},{"id":1030,"data":1872,"type":572,"tunes":1874},{"text":1873,"level":47},"Why tool boundaries are a safety feature",{},{"id":1035,"data":1876,"type":544,"tunes":1878},{"text":1877},"An AI assistant that could execute arbitrary operating-system commands would be far more powerful, but also much harder to constrain.",{},{"id":1040,"data":1880,"type":544,"tunes":1882},{"text":1881},"G-Assist instead exposes defined functions with known parameters.",{},{"id":1045,"data":1884,"type":544,"tunes":1886},{"text":1885},"That means the model can only perform actions that the built-in function set or installed plug-ins make available.",{},{"id":1050,"data":1888,"type":544,"tunes":1890},{"text":1889},"This does not remove all risk, especially with third-party plug-ins, but it creates a clearer permission and capability boundary than unrestricted shell access.",{},{"id":1055,"data":1892,"type":572,"tunes":1894},{"text":1893,"level":47},"The Tool-Authority Boundary",{},{"id":1060,"data":1896,"type":676,"tunes":1916},{"rows":1897,"title":1910,"layout":668,"columns":1911},[1898,1901,1904,1907],{"id":703,"label":1899,"values":1900},"GPU tuning",[13,13],{"id":1067,"label":1902,"values":1903},"Files",[13,13],{"id":1071,"label":1905,"values":1906},"Internet",[13,13],{"id":1075,"label":1908,"values":1909},"Peripheral control",[13,13],"What the assistant can understand vs what it is allowed to do",[1912,1914],{"id":652,"label":1913},"Model may understand",{"id":1083,"label":1915},"Tool authority",{},{"id":1087,"data":1918,"type":572,"tunes":1920},{"text":1919,"level":47},"Why G-Assist can temporarily reduce game performance",{},{"id":1092,"data":1922,"type":544,"tunes":1924},{"text":1923},"The local-model architecture has a straightforward consequence: the AI and the game can compete for the same GPU.",{},{"id":1097,"data":1926,"type":544,"tunes":1928},{"text":1927},"NVIDIA explicitly warns that render rate or inference speed can briefly dip while G-Assist is processing a request during a GPU-heavy workload.",{},{"id":1102,"data":1930,"type":544,"tunes":1932},{"text":1931},"Once inference finishes, those GPU resources return to the game.",{},{"id":1107,"data":1934,"type":544,"tunes":1936},{"text":1935},"This is different from a cloud assistant, where most model inference happens on a remote server and does not consume the gaming GPU.",{},{"id":1112,"data":1938,"type":572,"tunes":1940},{"text":1939,"level":47},"The Local Assistant Resource Test",{},{"id":1117,"data":1942,"type":625,"tunes":1963},{"steps":1943,"title":1962,"orientation":624},[1944,1947,1950,1953,1956,1959],{"label":1945,"description":1946},"1. Check installed VRAM","The current G-Assist baseline is an RTX GPU with at least 6 GB VRAM.",{"label":1948,"description":1949},"2. Check free VRAM during the actual game","Installed capacity is not the same as free capacity.",{"label":1951,"description":1952},"3. Choose Reasoning or Flash Mode appropriately","Use the lighter mode when resource pressure matters more than deep reasoning.",{"label":1954,"description":1955},"4. Measure inference-time frame-rate dips","Watch the game while asking G-Assist to perform tasks.",{"label":1957,"description":1958},"5. Inspect tool authority","Know exactly which built-in functions and plug-ins can change your system.",{"label":1960,"description":1961},"6. Treat third-party plug-ins as software","Review their source, permissions, network access and stored credentials.","How to judge whether a local gaming assistant fits your PC",{},{"id":1141,"data":1965,"type":572,"tunes":1967},{"text":1966,"level":47},"Why plug-ins deserve the same scrutiny as any other software",{},{"id":1146,"data":1969,"type":544,"tunes":1971},{"text":1970},"A plug-in can connect the assistant to APIs, peripheral applications and online services.",{},{"id":1151,"data":1973,"type":544,"tunes":1975},{"text":1974},"That means a plug-in may handle configuration values, credentials or external requests depending on what it was built to do.",{},{"id":1156,"data":1977,"type":544,"tunes":1979},{"text":1978},"NVIDIA's repository explicitly provides a config.json location for plug-in settings and warns developers not to commit credentials.",{},{"id":1161,"data":1981,"type":544,"tunes":1983},{"text":1982},"The correct security question is therefore not only “Is G-Assist local?” but also “What does each installed plug-in connect to?”",{},{"id":1166,"data":1985,"type":572,"tunes":1987},{"text":1986,"level":47},"G-Assist is closer to an agent than a chatbot",{},{"id":1171,"data":1989,"type":544,"tunes":1991},{"text":1990},"A chatbot mainly produces language.",{},{"id":1176,"data":1993,"type":544,"tunes":1995},{"text":1994},"An agent interprets a goal, observes relevant information, chooses an action, calls a tool and evaluates the result.",{},{"id":1181,"data":1997,"type":544,"tunes":1999},{"text":1998},"G-Assist now fits that second description much more closely because it can coordinate multiple actions, inspect supported PC state and invoke real tools.",{},{"id":1186,"data":2001,"type":737,"tunes":2006},{"url":2002,"title":2003,"excerpt":2004,"ctaLabel":2005},"https:\u002F\u002Ffigure.rocks\u002Fblog\u002Fpubg-ally-shows-why-ai-teammates-need-two-brains-fast-reflexes-and-slow-reasoning","PUBG Ally Shows Why AI Teammates Need Two Brains: Fast Reflexes and Slow Reasoning","A game-agent architecture example showing the separation between reasoning, live state, tools and deterministic execution.","Read the PUBG Ally architecture guide",{},{"id":1194,"data":2008,"type":572,"tunes":2010},{"text":2009,"level":47},"What G-Assist still is not",{},{"id":1199,"data":2012,"type":544,"tunes":2014},{"text":2013},"NVIDIA explicitly describes G-Assist as a specialized local assistant, not a general-purpose conversational AI.",{},{"id":1204,"data":2016,"type":544,"tunes":2018},{"text":2017},"Its value comes from understanding a focused set of PC and gaming tasks and having tools connected to those tasks.",{},{"id":1209,"data":2020,"type":544,"tunes":2022},{"text":2021},"That focus is important because a smaller local model can be useful when the surrounding system gives it strong tools and clear boundaries.",{},{"id":1214,"data":2024,"type":572,"tunes":2026},{"text":2025,"level":47},"What changed in the current versions?",{},{"id":1219,"data":2028,"type":544,"tunes":2030},{"text":2029},"The current public release history shows G-Assist moving steadily toward a more capable local agent.",{},{"id":1224,"data":2032,"type":668,"tunes":2047},{"content":2033,"stretched":1245,"withHeadings":15},[2034,2037,2039,2041,2043,2045],[2035,2036],"Version","Important change",[1231,2038],"Lighter model, all RTX GPUs with 6 GB+ VRAM, community plug-ins",[1234,2040],"Laptop optimization, BatteryBoost and WhisperMode controls",[1237,2042],"Reasoning Mode, Flash Mode, multi-action prompts, new device controls",[1240,2044],"Improved knowledge system, better recommendations, RTX feature controls",[1243,2046],"Elgato Stream Deck integration through an MCP server",{},{"id":1248,"data":2049,"type":572,"tunes":2051},{"text":2050,"level":47},"What would change this answer?",{},{"id":1253,"data":2053,"type":544,"tunes":2055},{"text":2054},"G-Assist is still experimental and its architecture is evolving.",{},{"id":1258,"data":2057,"type":544,"tunes":2059},{"text":2058},"The model size, VRAM requirements, tool set, plug-in protocol and hardware support can all change in future releases.",{},{"id":1263,"data":2061,"type":544,"tunes":2063},{"text":2062},"A future version could also move some inference to an NPU or introduce broader MCP integration, but that should not be assumed until NVIDIA documents it.",{},{"id":1268,"data":2065,"type":572,"tunes":2067},{"text":2066,"level":47},"Limitations",{},{"id":1273,"data":2069,"type":544,"tunes":2071},{"text":2070},"This article describes NVIDIA's public G-Assist product documentation and public plug-in architecture as of September 2026.",{},{"id":1278,"data":2073,"type":544,"tunes":2075},{"text":2074},"NVIDIA does not publicly document every internal detail of its knowledge and recommendation pipeline, so this article does not claim that every knowledge answer uses RAG or any other specific retrieval implementation.",{},{"id":1283,"data":2077,"type":544,"tunes":2079},{"text":2078},"Third-party plug-ins can have behavior and network access beyond NVIDIA's built-in functions, so each plug-in should be evaluated separately.",{},{"id":1288,"data":2081,"type":572,"tunes":2083},{"text":2082,"level":47},"Conclusion",{},{"id":1293,"data":2085,"type":544,"tunes":2087},{"text":2086},"The easiest way to understand Project G-Assist is to stop thinking of it as a chatbot.",{},{"id":1298,"data":2089,"type":544,"tunes":2091},{"text":2090},"The local SLM interprets your request. Knowledge helps it understand the problem. Live system information tells it what is true on your PC. A built-in function or plug-in defines what it is actually allowed to do. The tool performs the action and returns the result.",{},{"id":1303,"data":2093,"type":544,"tunes":2095},{"text":2094},"That is a local agent architecture.",{},{"id":1308,"data":2097,"type":544,"tunes":2099},{"text":2098},"Its strongest idea is not that an 8-billion-parameter model can talk about your GPU. It is that a relatively small local model can become useful when it is connected to well-defined tools, current state and a controlled action boundary.",{},{"id":1313,"data":2101,"type":572,"tunes":2103},{"text":2102,"level":47},"FAQ",{},{"id":1318,"data":2105,"type":1350,"tunes":2129},{"items":2106,"title":2128},[2107,2110,2113,2116,2119,2122,2125],{"id":1322,"answer":2108,"question":2109},"No. Its core language model runs locally on the GeForce RTX GPU and can work offline for supported local functions.","Is G-Assist a cloud chatbot?",{"id":1326,"answer":2111,"question":2112},"NVIDIA currently describes it as a local Llama-based instruct model with 8 billion parameters.","What model does G-Assist use?",{"id":1330,"answer":2114,"question":2115},"No. The model selects a supported built-in function or plug-in, and that tool performs the actual change.","Does the AI model directly change my GPU settings?",{"id":1334,"answer":2117,"question":2118},"Yes. NVIDIA recommends additional free VRAM for the assistant and warns that inference can briefly reduce game render performance.","Does G-Assist use VRAM while gaming?",{"id":1338,"answer":2120,"question":2121},"Not directly. G-Assist's own current plug-in protocol uses JSON-RPC 2.0, although a plug-in can connect to an MCP server.","Are G-Assist plug-ins MCP plug-ins?",{"id":1342,"answer":2123,"question":2124},"Its local assistant can, but individual plug-ins may still require internet access if they call online services.","Can G-Assist work offline?",{"id":1346,"answer":2126,"question":2127},"NVIDIA describes it as a specialized PC and gaming assistant rather than a broad general-purpose conversational model.","Is G-Assist a general AI assistant?","NVIDIA Project G-Assist in plain English",{},{"id":1353,"data":2131,"type":572,"tunes":2133},{"text":2132,"level":47},"Glossary",{},{"id":1358,"data":2135,"type":1392,"tunes":2159},{"title":2136,"entries":2137},"Key G-Assist terms",[2138,2140,2143,2146,2148,2150,2153,2156],{"term":1363,"anchor":1364,"definition":2139},"Small Language Model, a compact language model designed to run locally with lower hardware requirements than large cloud models.",{"term":2141,"anchor":1368,"definition":2142},"Tool calling","The process where a language model chooses a defined function and supplies structured arguments so another component can perform an action.",{"term":2144,"anchor":1372,"definition":2145},"Plug-in","An extension that exposes additional functions or integrations to G-Assist.",{"term":1375,"anchor":1376,"definition":2147},"The structured message protocol used by the current G-Assist Protocol V2 plug-in system.",{"term":1379,"anchor":1380,"definition":2149},"Model Context Protocol, a separate tool and context interoperability protocol that some external integrations can expose.",{"term":2151,"anchor":1384,"definition":2152},"Tool-Authority Boundary","A Figure Rocks model separating what an AI model can understand from what its connected tools actually allow it to do.",{"term":2154,"anchor":1387,"definition":2155},"Local Agent Stack","A Figure Rocks model combining user intent, local SLM, knowledge, live state, tool selection, execution and result verification.",{"term":2157,"anchor":1390,"definition":2158},"Local Assistant Resource Test","A Figure Rocks workflow for evaluating VRAM, inference cost, tool permissions and plug-in risk before using a local gaming assistant.",{},{"id":1395,"data":2161,"type":572,"tunes":2163},{"text":2162,"level":47},"Primary sources",{},{"id":1400,"data":2165,"type":1407,"tunes":2169},{"link":1402,"meta":2166},{"image":2167,"title":1405,"description":2168},{"url":13},"Official current product page covering the local model, supported functions, Reasoning and Flash modes, VRAM requirements, RTX controls, plug-ins and version 0.2.2 MCP-based Elgato integration.",{},{"id":1410,"data":2171,"type":1407,"tunes":2175},{"link":1412,"meta":2172},{"image":2173,"title":1415,"description":2174},{"url":13},"Official public plug-in repository documenting G-Assist's module architecture, plug-in discovery and Protocol V2.",{},{"id":1419,"data":2177,"type":1407,"tunes":2182},{"link":1421,"meta":2178},{"image":2179,"title":2180,"description":2181},{"url":13},"NVIDIA — G-Assist Protocol V2 Migration Guide","Official protocol documentation covering JSON-RPC 2.0, initialization, health checks, execution, streaming, completion and plug-in SDK behavior.",{},{"id":1428,"data":2184,"type":1407,"tunes":2189},{"link":1430,"meta":2185},{"image":2186,"title":2187,"description":2188},{"url":13},"NVIDIA Blog — Lightweight G-Assist Model","Official background on the lower-memory G-Assist model, support for 6 GB RTX GPUs and the community plug-in hub.",{},"2.31.6","NVIDIA Project G-Assist looks like a chatbot, but its real architecture is closer to a local AI agent. A small language model interprets your request, system state provides current PC information, tools perform real actions, and plug-ins extend what the assistant is allowed to 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