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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? 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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":537,"blocks":538,"version":1437},1790407740910,[539,545,553,560,567,573,578,583,588,595,600,626,631,636,641,646,677,682,687,692,697,722,729,738,743,748,753,758,763,768,773,778,783,788,793,798,803,808,814,822,827,832,837,861,866,871,876,881,902,907,912,917,922,950,955,960,965,970,976,981,986,991,996,1001,1028,1033,1038,1043,1048,1053,1058,1085,1090,1095,1100,1105,1110,1115,1139,1144,1149,1154,1159,1164,1169,1174,1179,1184,1192,1197,1202,1207,1212,1217,1222,1246,1251,1256,1261,1266,1271,1276,1281,1286,1291,1296,1301,1306,1311,1316,1351,1356,1395,1400,1410,1419,1428],{"id":540,"data":541,"type":543,"tunes":544},"3cee674645",{"text":542},"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.","paragraph",{},{"id":546,"data":547,"type":551,"tunes":552},"24a1733ca8",{"body":548,"title":549,"variant":550},"\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","info","callout",{},{"id":554,"data":555,"type":551,"tunes":559},"1fcbe512dc",{"body":556,"title":557,"variant":558},"\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","note",{},{"id":561,"data":562,"type":565,"tunes":566},"2a609230a5",{"title":563,"maxLevel":564,"minLevel":46},"Contents",3,"tableOfContents",{},{"id":568,"data":569,"type":571,"tunes":572},"a52f97cb62",{"text":570,"level":46},"First: what is the AI model actually doing?","header",{},{"id":574,"data":575,"type":543,"tunes":577},"1871a0d8e6",{"text":576},"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":579,"data":580,"type":543,"tunes":582},"cfbc6473c4",{"text":581},"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":584,"data":585,"type":543,"tunes":587},"53cadaaeb6",{"text":586},"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":589,"data":590,"type":551,"tunes":594},"5d8e508019",{"body":591,"title":592,"variant":593},"The AI model \u003Cstrong>decides what tool to call\u003C\u002Fstrong>. The tool \u003Cstrong>does the real work\u003C\u002Fstrong>.","The important distinction","success",{},{"id":596,"data":597,"type":571,"tunes":599},"9ce7e91c28",{"text":598,"level":46},"The G-Assist action pipeline",{},{"id":601,"data":602,"type":624,"tunes":625},"992b5d6667",{"steps":603,"title":622,"orientation":623},[604,607,610,613,616,619],{"label":605,"description":606},"1. You give a natural-language request","For example: “Optimize this game for performance.”",{"label":608,"description":609},"2. The local SLM interprets the request","It determines the user's intent and identifies which supported function or plug-in is relevant.",{"label":611,"description":612},"3. The system chooses a tool","That could be a built-in graphics-setting function, driver check, monitoring function or community plug-in.",{"label":614,"description":615},"4. Arguments are extracted","The model converts the request into structured values such as game name, mode, fan profile or DLSS setting.",{"label":617,"description":618},"5. The tool executes","The tool talks to the NVIDIA App, operating system, peripheral software or another service.",{"label":620,"description":621},"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","auto","processFlow",{},{"id":627,"data":628,"type":571,"tunes":630},"aba1e38958",{"text":629,"level":46},"This is tool calling, not magic PC control",{},{"id":632,"data":633,"type":543,"tunes":635},"0a7cb6107d",{"text":634},"A language model cannot safely control an arbitrary computer simply because it understands English.",{},{"id":637,"data":638,"type":543,"tunes":640},"31191a478c",{"text":639},"It needs a defined interface that says which actions exist, which arguments they accept and what the tool returns.",{},{"id":642,"data":643,"type":543,"tunes":645},"af3d1a846f",{"text":644},"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":647,"data":648,"type":675,"tunes":676},"d6fd0eb144",{"rows":649,"title":666,"layout":667,"columns":668},[650,654,658,662],{"id":651,"label":652,"values":653},"understand","Understand “turn on DLSS”",[12,12],{"id":655,"label":656,"values":657},"decide","Choose the correct function",[12,12],{"id":659,"label":660,"values":661},"change","Actually change the setting",[12,12],{"id":663,"label":664,"values":665},"explain","Explain the result",[12,12],"Language model vs tool","table",[669,672],{"id":670,"label":671},"model","Language model",{"id":673,"label":674},"tool","Tool \u002F plug-in","comparison",{},{"id":678,"data":679,"type":571,"tunes":681},"1537be641c",{"text":680,"level":46},"What is the knowledge layer?",{},{"id":683,"data":684,"type":543,"tunes":686},"1db6e2e4b5",{"text":685},"G-Assist also has a knowledge layer for answering questions and making recommendations.",{},{"id":688,"data":689,"type":543,"tunes":691},"616d72b881",{"text":690},"NVIDIA says version 0.2.1 introduced an improved knowledge system that helps G-Assist make more accurate settings recommendations.",{},{"id":693,"data":694,"type":543,"tunes":696},"eb987c41e5",{"text":695},"That knowledge layer is different from the live state of your PC.",{},{"id":698,"data":699,"type":675,"tunes":721},"bf6a4f5502",{"rows":700,"title":713,"layout":667,"columns":714},[701,705,709],{"id":702,"label":703,"values":704},"gpu","GPU",[12,12],{"id":706,"label":707,"values":708},"game","Game settings",[12,12],{"id":710,"label":711,"values":712},"system","Performance",[12,12],"Knowledge vs live PC state",[715,718],{"id":716,"label":717},"knowledge","Knowledge",{"id":719,"label":720},"state","Live state",{},{"id":723,"data":724,"type":551,"tunes":728},"2bac13ef99",{"body":725,"title":726,"variant":727},"\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","warning",{},{"id":730,"data":731,"type":736,"tunes":737},"73708d5e0d",{"url":732,"title":733,"excerpt":734,"ctaLabel":735},"https:\u002F\u002Fstajic.de\u002Fblog\u002Fwhat-is-rag-the-simplest-explanation-of-how-it-works","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","referralArticle",{},{"id":739,"data":740,"type":571,"tunes":742},"acbb9b7940",{"text":741,"level":46},"Does G-Assist use RAG?",{},{"id":744,"data":745,"type":543,"tunes":747},"ce0371e492",{"text":746},"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":749,"data":750,"type":543,"tunes":752},"c2226e5ec5",{"text":751},"The architectural distinction still matters. A retrieval layer can provide relevant knowledge, while system tools provide current PC state and perform actions.",{},{"id":754,"data":755,"type":543,"tunes":757},"e5137fc2c8",{"text":756},"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":759,"data":760,"type":571,"tunes":762},"059b89dedb",{"text":761,"level":46},"Why G-Assist can work offline",{},{"id":764,"data":765,"type":543,"tunes":767},"19447df6a4",{"text":766},"NVIDIA runs the language model locally on the GeForce RTX GPU.",{},{"id":769,"data":770,"type":543,"tunes":772},"55aee6ada7",{"text":771},"That means the core assistant does not require a cloud-hosted language model for every prompt.",{},{"id":774,"data":775,"type":543,"tunes":777},"f265d1fd96",{"text":776},"NVIDIA explicitly says G-Assist can run offline for its local capabilities.",{},{"id":779,"data":780,"type":543,"tunes":782},"1dc8613200",{"text":781},"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":784,"data":785,"type":571,"tunes":787},"ee01c97a66",{"text":786,"level":46},"The local-AI cost: G-Assist uses your GPU and VRAM",{},{"id":789,"data":790,"type":543,"tunes":792},"1784ad3fcf",{"text":791},"Running locally gives privacy and independence from a cloud model, but the work has to execute somewhere.",{},{"id":794,"data":795,"type":543,"tunes":797},"1275ab759f",{"text":796},"For G-Assist, that somewhere is the same GeForce RTX GPU that may already be rendering your game.",{},{"id":799,"data":800,"type":543,"tunes":802},"cec86f847a",{"text":801},"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":804,"data":805,"type":543,"tunes":807},"e6ea700df6",{"text":806},"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":809,"data":810,"type":551,"tunes":813},"2126f74de5",{"body":811,"title":812,"variant":727},"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":815,"data":816,"type":736,"tunes":821},"13c0dcb3f3",{"url":817,"title":818,"excerpt":819,"ctaLabel":820},"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":823,"data":824,"type":571,"tunes":826},"d684b2c1c8",{"text":825,"level":46},"Reasoning Mode vs Flash Mode",{},{"id":828,"data":829,"type":543,"tunes":831},"3c1f2379db",{"text":830},"Current G-Assist versions separate a more capable Reasoning Mode from a faster Flash Mode.",{},{"id":833,"data":834,"type":543,"tunes":836},"86f50ebd33",{"text":835},"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":838,"data":839,"type":675,"tunes":860},"e7be4789db",{"rows":840,"title":825,"layout":667,"columns":853},[841,845,849],{"id":842,"label":843,"values":844},"goal","Primary goal",[12,12],{"id":846,"label":847,"values":848},"vram","Recommended free VRAM",[12,12],{"id":850,"label":851,"values":852},"use","Best fit",[12,12],[854,857],{"id":855,"label":856},"reasoning","Reasoning Mode",{"id":858,"label":859},"flash","Flash Mode",{},{"id":862,"data":863,"type":571,"tunes":865},"cef1bcf553",{"text":864,"level":46},"What plug-ins actually add",{},{"id":867,"data":868,"type":543,"tunes":870},"d380d5e28b",{"text":869},"A plug-in does not replace the language model. It gives the model a new action it is allowed to call.",{},{"id":872,"data":873,"type":543,"tunes":875},"434979ebda",{"text":874},"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":877,"data":878,"type":543,"tunes":880},"21ebe69c79",{"text":879},"That lets the assistant grow beyond NVIDIA's built-in functions.",{},{"id":882,"data":883,"type":624,"tunes":901},"d0ba7ef284",{"steps":884,"title":900,"orientation":623},[885,888,891,894,897],{"label":886,"description":887},"1. Plug-in declares a function","For example: set_keyboard_color(color).",{"label":889,"description":890},"2. Manifest describes the function","The manifest tells G-Assist what the function does and what parameters it needs.",{"label":892,"description":893},"3. User asks naturally","For example: “Make my keyboard green.”",{"label":895,"description":896},"4. SLM selects the function","The model maps the request to the plug-in function and extracts green as the parameter.",{"label":898,"description":899},"5. Plug-in executes","The plug-in talks to the peripheral software or external service.","How a G-Assist plug-in works",{},{"id":903,"data":904,"type":571,"tunes":906},"1dc857ce2f",{"text":905,"level":46},"G-Assist Protocol V2 is JSON-RPC 2.0",{},{"id":908,"data":909,"type":543,"tunes":911},"02bdb0566d",{"text":910},"NVIDIA's current public plug-in repository uses Protocol V2.",{},{"id":913,"data":914,"type":543,"tunes":916},"f82da294cf",{"text":915},"Protocol V2 uses JSON-RPC 2.0 with length-prefixed messages between the G-Assist engine and plug-ins.",{},{"id":918,"data":919,"type":543,"tunes":921},"530c3cf9b9",{"text":920},"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":923,"data":924,"type":675,"tunes":949},"2ce3fa5d75",{"rows":925,"title":941,"layout":667,"columns":942},[926,929,932,935,938],{"id":927,"label":927,"values":928},"initialize",[12,12],{"id":930,"label":930,"values":931},"ping",[12,12],{"id":933,"label":933,"values":934},"execute",[12,12],{"id":936,"label":936,"values":937},"stream",[12,12],{"id":939,"label":939,"values":940},"complete",[12,12],"The important Protocol V2 messages",[943,946],{"id":944,"label":945},"direction","Direction",{"id":947,"label":948},"purpose","Purpose",{},{"id":951,"data":952,"type":571,"tunes":954},"304fbd9944",{"text":953,"level":46},"Where MCP enters the picture",{},{"id":956,"data":957,"type":543,"tunes":959},"fae73881e4",{"text":958},"G-Assist's own plug-in protocol is not MCP. Its current public plug-in system uses JSON-RPC 2.0.",{},{"id":961,"data":962,"type":543,"tunes":964},"551d11710a",{"text":963},"However, a G-Assist plug-in can connect to an MCP server.",{},{"id":966,"data":967,"type":543,"tunes":969},"0537fb222a",{"text":968},"NVIDIA's current version 0.2.2 includes an Elgato integration that can invoke actions exposed through the Elgato Stream Deck MCP server.",{},{"id":971,"data":972,"type":551,"tunes":975},"760a86ad19",{"body":973,"title":974,"variant":593},"\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":977,"data":978,"type":571,"tunes":980},"bd182f72b0",{"text":979,"level":46},"Why this matters beyond RGB lights",{},{"id":982,"data":983,"type":543,"tunes":985},"d072506ce2",{"text":984},"The plug-in architecture turns G-Assist from a fixed assistant into a local tool router.",{},{"id":987,"data":988,"type":543,"tunes":990},"2876e067dc",{"text":989},"The same pattern can connect an SLM to monitoring tools, peripherals, APIs, automation systems, local applications or external services.",{},{"id":992,"data":993,"type":543,"tunes":995},"37689640e9",{"text":994},"The important architectural unit is therefore not the chat window. It is the boundary between natural-language intent and structured tool execution.",{},{"id":997,"data":998,"type":571,"tunes":1000},"550fbb42d4",{"text":999,"level":46},"The Local Agent Stack",{},{"id":1002,"data":1003,"type":624,"tunes":1027},"8518dbf820",{"steps":1004,"title":1026,"orientation":623},[1005,1008,1011,1014,1017,1020,1023],{"label":1006,"description":1007},"1. User intent","Natural language or voice command.",{"label":1009,"description":1010},"2. Local SLM","Understands the request and chooses a capability.",{"label":1012,"description":1013},"3. Knowledge \u002F context","Provides product knowledge, recommendations or information needed for the decision.",{"label":1015,"description":1016},"4. Live system state","Provides current hardware, driver, performance or configuration information when supported.",{"label":1018,"description":1019},"5. Tool selection","Built-in function or plug-in is chosen.",{"label":1021,"description":1022},"6. Structured execution","The function receives arguments and performs the real action.",{"label":1024,"description":1025},"7. Result verification","The tool returns status or data and the model explains what happened.","A useful way to think about G-Assist",{},{"id":1029,"data":1030,"type":571,"tunes":1032},"6c976db763",{"text":1031,"level":46},"Why tool boundaries are a safety feature",{},{"id":1034,"data":1035,"type":543,"tunes":1037},"ba65a3b467",{"text":1036},"An AI assistant that could execute arbitrary operating-system commands would be far more powerful, but also much harder to constrain.",{},{"id":1039,"data":1040,"type":543,"tunes":1042},"a932df9663",{"text":1041},"G-Assist instead exposes defined functions with known parameters.",{},{"id":1044,"data":1045,"type":543,"tunes":1047},"7d1d45c83d",{"text":1046},"That means the model can only perform actions that the built-in function set or installed plug-ins make available.",{},{"id":1049,"data":1050,"type":543,"tunes":1052},"10460fda21",{"text":1051},"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":1054,"data":1055,"type":571,"tunes":1057},"25049448ea",{"text":1056,"level":46},"The Tool-Authority Boundary",{},{"id":1059,"data":1060,"type":675,"tunes":1084},"d3ff568795",{"rows":1061,"title":1077,"layout":667,"columns":1078},[1062,1065,1069,1073],{"id":702,"label":1063,"values":1064},"GPU tuning",[12,12],{"id":1066,"label":1067,"values":1068},"files","Files",[12,12],{"id":1070,"label":1071,"values":1072},"web","Internet",[12,12],{"id":1074,"label":1075,"values":1076},"device","Peripheral control",[12,12],"What the assistant can understand vs what it is allowed to do",[1079,1081],{"id":651,"label":1080},"Model may understand",{"id":1082,"label":1083},"authority","Tool authority",{},{"id":1086,"data":1087,"type":571,"tunes":1089},"8fd8e35c51",{"text":1088,"level":46},"Why G-Assist can temporarily reduce game performance",{},{"id":1091,"data":1092,"type":543,"tunes":1094},"f75234546f",{"text":1093},"The local-model architecture has a straightforward consequence: the AI and the game can compete for the same GPU.",{},{"id":1096,"data":1097,"type":543,"tunes":1099},"28bc054afa",{"text":1098},"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":1101,"data":1102,"type":543,"tunes":1104},"0823573513",{"text":1103},"Once inference finishes, those GPU resources return to the game.",{},{"id":1106,"data":1107,"type":543,"tunes":1109},"905eed843d",{"text":1108},"This is different from a cloud assistant, where most model inference happens on a remote server and does not consume the gaming GPU.",{},{"id":1111,"data":1112,"type":571,"tunes":1114},"839c0e3988",{"text":1113,"level":46},"The Local Assistant Resource Test",{},{"id":1116,"data":1117,"type":624,"tunes":1138},"affa0ee1b3",{"steps":1118,"title":1137,"orientation":623},[1119,1122,1125,1128,1131,1134],{"label":1120,"description":1121},"1. Check installed VRAM","The current G-Assist baseline is an RTX GPU with at least 6 GB VRAM.",{"label":1123,"description":1124},"2. Check free VRAM during the actual game","Installed capacity is not the same as free capacity.",{"label":1126,"description":1127},"3. Choose Reasoning or Flash Mode appropriately","Use the lighter mode when resource pressure matters more than deep reasoning.",{"label":1129,"description":1130},"4. Measure inference-time frame-rate dips","Watch the game while asking G-Assist to perform tasks.",{"label":1132,"description":1133},"5. Inspect tool authority","Know exactly which built-in functions and plug-ins can change your system.",{"label":1135,"description":1136},"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":1140,"data":1141,"type":571,"tunes":1143},"ca52c48d2e",{"text":1142,"level":46},"Why plug-ins deserve the same scrutiny as any other software",{},{"id":1145,"data":1146,"type":543,"tunes":1148},"8e6a760529",{"text":1147},"A plug-in can connect the assistant to APIs, peripheral applications and online services.",{},{"id":1150,"data":1151,"type":543,"tunes":1153},"be793240e1",{"text":1152},"That means a plug-in may handle configuration values, credentials or external requests depending on what it was built to do.",{},{"id":1155,"data":1156,"type":543,"tunes":1158},"3c6c4c9aaf",{"text":1157},"NVIDIA's repository explicitly provides a config.json location for plug-in settings and warns developers not to commit credentials.",{},{"id":1160,"data":1161,"type":543,"tunes":1163},"03413f935a",{"text":1162},"The correct security question is therefore not only “Is G-Assist local?” but also “What does each installed plug-in connect to?”",{},{"id":1165,"data":1166,"type":571,"tunes":1168},"f473162e86",{"text":1167,"level":46},"G-Assist is closer to an agent than a chatbot",{},{"id":1170,"data":1171,"type":543,"tunes":1173},"59dd3df9c2",{"text":1172},"A chatbot mainly produces language.",{},{"id":1175,"data":1176,"type":543,"tunes":1178},"188cb4bb40",{"text":1177},"An agent interprets a goal, observes relevant information, chooses an action, calls a tool and evaluates the result.",{},{"id":1180,"data":1181,"type":543,"tunes":1183},"117d1032f1",{"text":1182},"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":1185,"data":1186,"type":736,"tunes":1191},"ef132fc518",{"url":1187,"title":1188,"excerpt":1189,"ctaLabel":1190},"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":1193,"data":1194,"type":571,"tunes":1196},"9f8fa308a5",{"text":1195,"level":46},"What G-Assist still is not",{},{"id":1198,"data":1199,"type":543,"tunes":1201},"f5f24e8aa3",{"text":1200},"NVIDIA explicitly describes G-Assist as a specialized local assistant, not a general-purpose conversational AI.",{},{"id":1203,"data":1204,"type":543,"tunes":1206},"e4d187593f",{"text":1205},"Its value comes from understanding a focused set of PC and gaming tasks and having tools connected to those tasks.",{},{"id":1208,"data":1209,"type":543,"tunes":1211},"0c1b7f8948",{"text":1210},"That focus is important because a smaller local model can be useful when the surrounding system gives it strong tools and clear boundaries.",{},{"id":1213,"data":1214,"type":571,"tunes":1216},"79a5920e77",{"text":1215,"level":46},"What changed in the current versions?",{},{"id":1218,"data":1219,"type":543,"tunes":1221},"e78d96ef6c",{"text":1220},"The current public release history shows G-Assist moving steadily toward a more capable local agent.",{},{"id":1223,"data":1224,"type":667,"tunes":1245},"87dd1d3d3c",{"content":1225,"stretched":1244,"withHeadings":14},[1226,1229,1232,1235,1238,1241],[1227,1228],"Version","Important change",[1230,1231],"0.1.17","Lighter model, all RTX GPUs with 6 GB+ VRAM, community plug-ins",[1233,1234],"0.1.18","Laptop optimization, BatteryBoost and WhisperMode controls",[1236,1237],"0.2","Reasoning Mode, Flash Mode, multi-action prompts, new device controls",[1239,1240],"0.2.1","Improved knowledge system, better recommendations, RTX feature controls",[1242,1243],"0.2.2","Elgato Stream Deck integration through an MCP server",false,{},{"id":1247,"data":1248,"type":571,"tunes":1250},"09bde21a8d",{"text":1249,"level":46},"What would change this answer?",{},{"id":1252,"data":1253,"type":543,"tunes":1255},"b0e7ca01a4",{"text":1254},"G-Assist is still experimental and its architecture is evolving.",{},{"id":1257,"data":1258,"type":543,"tunes":1260},"537568c224",{"text":1259},"The model size, VRAM requirements, tool set, plug-in protocol and hardware support can all change in future releases.",{},{"id":1262,"data":1263,"type":543,"tunes":1265},"fe24516c11",{"text":1264},"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":1267,"data":1268,"type":571,"tunes":1270},"0e31e2d54d",{"text":1269,"level":46},"Limitations",{},{"id":1272,"data":1273,"type":543,"tunes":1275},"a7881e11e1",{"text":1274},"This article describes NVIDIA's public G-Assist product documentation and public plug-in architecture as of September 2026.",{},{"id":1277,"data":1278,"type":543,"tunes":1280},"71ebc16fda",{"text":1279},"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":1282,"data":1283,"type":543,"tunes":1285},"add780d6ba",{"text":1284},"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":1287,"data":1288,"type":571,"tunes":1290},"f7a38af5be",{"text":1289,"level":46},"Conclusion",{},{"id":1292,"data":1293,"type":543,"tunes":1295},"f1308f3e80",{"text":1294},"The easiest way to understand Project G-Assist is to stop thinking of it as a chatbot.",{},{"id":1297,"data":1298,"type":543,"tunes":1300},"b102a861bb",{"text":1299},"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":1302,"data":1303,"type":543,"tunes":1305},"da805fabf0",{"text":1304},"That is a local agent architecture.",{},{"id":1307,"data":1308,"type":543,"tunes":1310},"675aecee06",{"text":1309},"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":1312,"data":1313,"type":571,"tunes":1315},"0354b8daaf",{"text":1314,"level":46},"FAQ",{},{"id":1317,"data":1318,"type":1349,"tunes":1350},"e75bc04532",{"items":1319,"title":1348},[1320,1324,1328,1332,1336,1340,1344],{"id":1321,"answer":1322,"question":1323},"faq1","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":1325,"answer":1326,"question":1327},"faq2","NVIDIA currently describes it as a local Llama-based instruct model with 8 billion parameters.","What model does G-Assist use?",{"id":1329,"answer":1330,"question":1331},"faq3","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":1333,"answer":1334,"question":1335},"faq4","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":1337,"answer":1338,"question":1339},"faq5","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":1341,"answer":1342,"question":1343},"faq6","Its local assistant can, but individual plug-ins may still require internet access if they call online services.","Can G-Assist work offline?",{"id":1345,"answer":1346,"question":1347},"faq7","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","faq",{},{"id":1352,"data":1353,"type":571,"tunes":1355},"8733f298cd",{"text":1354,"level":46},"Glossary",{},{"id":1357,"data":1358,"type":1393,"tunes":1394},"407257a6d1",{"title":1359,"entries":1360},"Key G-Assist terms",[1361,1365,1369,1373,1377,1381,1385,1389],{"term":1362,"anchor":1363,"definition":1364},"SLM","slm","Small Language Model, a compact language model designed to run locally with lower hardware requirements than large cloud models.",{"term":1366,"anchor":1367,"definition":1368},"Tool calling","tool-calling","The process where a language model chooses a defined function and supplies structured arguments so another component can perform an action.",{"term":1370,"anchor":1371,"definition":1372},"Plug-in","plugin","An extension that exposes additional functions or integrations to G-Assist.",{"term":1374,"anchor":1375,"definition":1376},"JSON-RPC 2.0","json-rpc","The structured message protocol used by the current G-Assist Protocol V2 plug-in system.",{"term":1378,"anchor":1379,"definition":1380},"MCP","mcp","Model Context Protocol, a separate tool and context interoperability protocol that some external integrations can expose.",{"term":1382,"anchor":1383,"definition":1384},"Tool-Authority Boundary","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":1386,"anchor":1387,"definition":1388},"Local Agent Stack","local-agent-stack","A Figure Rocks model combining user intent, local SLM, knowledge, live state, tool selection, execution and result verification.",{"term":1390,"anchor":1391,"definition":1392},"Local Assistant Resource Test","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.","glossary",{},{"id":1396,"data":1397,"type":571,"tunes":1399},"3ab856f2b4",{"text":1398,"level":46},"Primary sources",{},{"id":1401,"data":1402,"type":1408,"tunes":1409},"367d2e3548",{"link":1403,"meta":1404},"https:\u002F\u002Fwww.nvidia.com\u002Fen-us\u002Fsoftware\u002Fnvidia-app\u002Fg-assist\u002F",{"image":1405,"title":1406,"description":1407},{"url":12},"NVIDIA — Project G-Assist","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.","linkTool",{},{"id":1411,"data":1412,"type":1408,"tunes":1418},"5641b514c0",{"link":1413,"meta":1414},"https:\u002F\u002Fgithub.com\u002FNVIDIA\u002FG-Assist",{"image":1415,"title":1416,"description":1417},{"url":12},"NVIDIA — G-Assist GitHub","Official public plug-in repository documenting G-Assist's module architecture, plug-in discovery and Protocol V2.",{},{"id":1420,"data":1421,"type":1408,"tunes":1427},"22a5b77a1b",{"link":1422,"meta":1423},"https:\u002F\u002Fgithub.com\u002FNVIDIA\u002FG-Assist\u002Fblob\u002Fmain\u002FPLUGIN_MIGRATION_GUIDE_V2.md",{"image":1424,"title":1425,"description":1426},{"url":12},"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":1429,"data":1430,"type":1408,"tunes":1436},"cc3301d997",{"link":1431,"meta":1432},"https:\u002F\u002Fblogs.nvidia.com\u002Fblog\u002Frtx-ai-garage-gamescom-g-assist-rtx-remix\u002F",{"image":1433,"title":1434,"description":1435},{"url":12},"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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