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Es un sistema de IA local que puede entender una solicitud, inspeccionar el estado compatible del PC, elegir una herramienta o complemento y luego realizar una acción real, como cambiar la configuración de DLSS, verificar un controlador, ajustar un perfil de ventilador o controlar un periférico.\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\">Respuesta directa\u003C\u002Fstrong>\u003Cdiv class=\"text-gray-700 dark:text-gray-200\">\u003Cstrong>G-Assist no es simplemente un chatbot para tu GPU.\u003C\u002Fstrong> Es un modelo de lenguaje pequeño local conectado a un conjunto de herramientas permitidas. El modelo interpreta lo que quieres, selecciona una función compatible, pasa argumentos a esa función y la herramienta del lado del PC realiza la acción.\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\">El modelo mental más sencillo\u003C\u002Fstrong>\u003Cdiv class=\"text-gray-700 dark:text-gray-200\">\u003Cstrong>SLM = entiende la solicitud.\u003C\u002Fstrong>\u003Cbr>\u003Cstrong>Conocimiento = explica conceptos compatibles de NVIDIA\u002FPC.\u003C\u002Fstrong>\u003Cbr>\u003Cstrong>Herramientas = realizan acciones.\u003C\u002Fstrong>\u003Cbr>\u003Cstrong>Complementos = añaden nuevas herramientas.\u003C\u002Fstrong>\u003Cbr>\u003Cstrong>Tu PC = el entorno que se inspecciona o modifica.\u003C\u002Fstrong>\u003C\u002Fdiv>\u003C\u002Faside>\n\u003Cnav class=\"editorjs-toc\" data-editorjs-toc=\"true\" aria-label=\"Contenido\">\u003Cstrong class=\"editorjs-toc__title\">Contenido\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\">Primero: ¿qué hace realmente el modelo de IA?\u003C\u002Fa>\u003C\u002Fli>\u003Cli class=\"editorjs-toc__item\">\u003Ca href=\"#section-10\" class=\"editorjs-toc__link\">El flujo de acción de G-Assist\u003C\u002Fa>\u003C\u002Fli>\u003Cli class=\"editorjs-toc__item\">\u003Ca href=\"#section-12\" class=\"editorjs-toc__link\">Esto es llamada a herramientas, no control mágico del PC\u003C\u002Fa>\u003C\u002Fli>\u003Cli class=\"editorjs-toc__item\">\u003Ca href=\"#section-17\" class=\"editorjs-toc__link\">¿Qué es la capa de conocimiento?\u003C\u002Fa>\u003C\u002Fli>\u003Cli class=\"editorjs-toc__item\">\u003Ca href=\"#section-24\" class=\"editorjs-toc__link\">¿G-Assist usa RAG?\u003C\u002Fa>\u003C\u002Fli>\u003Cli class=\"editorjs-toc__item\">\u003Ca href=\"#section-28\" class=\"editorjs-toc__link\">Por qué G-Assist puede funcionar sin conexión\u003C\u002Fa>\u003C\u002Fli>\u003Cli class=\"editorjs-toc__item\">\u003Ca href=\"#section-33\" class=\"editorjs-toc__link\">El costo de la IA local: G-Assist usa tu GPU y VRAM\u003C\u002Fa>\u003C\u002Fli>\u003Cli class=\"editorjs-toc__item\">\u003Ca href=\"#section-40\" class=\"editorjs-toc__link\">Modo Razonamiento vs Modo Flash\u003C\u002Fa>\u003C\u002Fli>\u003Cli class=\"editorjs-toc__item\">\u003Ca href=\"#section-44\" class=\"editorjs-toc__link\">Qué añaden realmente los complementos\u003C\u002Fa>\u003C\u002Fli>\u003Cli class=\"editorjs-toc__item\">\u003Ca href=\"#section-49\" class=\"editorjs-toc__link\">El Protocolo V2 de G-Assist es JSON-RPC 2.0\u003C\u002Fa>\u003C\u002Fli>\u003Cli class=\"editorjs-toc__item\">\u003Ca href=\"#section-54\" class=\"editorjs-toc__link\">Dónde entra en escena MCP\u003C\u002Fa>\u003C\u002Fli>\u003Cli class=\"editorjs-toc__item\">\u003Ca href=\"#section-59\" class=\"editorjs-toc__link\">Por qué esto importa más allá de las luces RGB\u003C\u002Fa>\u003C\u002Fli>\u003Cli class=\"editorjs-toc__item\">\u003Ca href=\"#section-63\" class=\"editorjs-toc__link\">El stack de agente local\u003C\u002Fa>\u003C\u002Fli>\u003Cli class=\"editorjs-toc__item\">\u003Ca href=\"#section-65\" class=\"editorjs-toc__link\">Por qué los límites de las herramientas son una característica de seguridad\u003C\u002Fa>\u003C\u002Fli>\u003Cli class=\"editorjs-toc__item\">\u003Ca href=\"#section-70\" class=\"editorjs-toc__link\">El límite de autoridad de las herramientas\u003C\u002Fa>\u003C\u002Fli>\u003Cli class=\"editorjs-toc__item\">\u003Ca href=\"#section-72\" class=\"editorjs-toc__link\">Por qué G-Assist puede reducir temporalmente el rendimiento del juego\u003C\u002Fa>\u003C\u002Fli>\u003Cli class=\"editorjs-toc__item\">\u003Ca href=\"#section-77\" class=\"editorjs-toc__link\">La prueba de recursos del asistente local\u003C\u002Fa>\u003C\u002Fli>\u003Cli class=\"editorjs-toc__item\">\u003Ca href=\"#section-79\" class=\"editorjs-toc__link\">Por qué los complementos merecen el mismo escrutinio que cualquier otro software\u003C\u002Fa>\u003C\u002Fli>\u003Cli class=\"editorjs-toc__item\">\u003Ca href=\"#section-84\" class=\"editorjs-toc__link\">G-Assist se parece más a un agente que a un chatbot\u003C\u002Fa>\u003C\u002Fli>\u003Cli class=\"editorjs-toc__item\">\u003Ca href=\"#section-89\" class=\"editorjs-toc__link\">Lo que G-Assist todavía no es\u003C\u002Fa>\u003C\u002Fli>\u003Cli class=\"editorjs-toc__item\">\u003Ca href=\"#section-93\" class=\"editorjs-toc__link\">¿Qué cambió en las versiones actuales?\u003C\u002Fa>\u003C\u002Fli>\u003Cli class=\"editorjs-toc__item\">\u003Ca href=\"#section-96\" class=\"editorjs-toc__link\">¿Qué cambiaría esta respuesta?\u003C\u002Fa>\u003C\u002Fli>\u003Cli class=\"editorjs-toc__item\">\u003Ca href=\"#section-100\" class=\"editorjs-toc__link\">Limitaciones\u003C\u002Fa>\u003C\u002Fli>\u003Cli class=\"editorjs-toc__item\">\u003Ca href=\"#section-104\" class=\"editorjs-toc__link\">Conclusión\u003C\u002Fa>\u003C\u002Fli>\u003Cli class=\"editorjs-toc__item\">\u003Ca href=\"#section-109\" class=\"editorjs-toc__link\">Preguntas frecuentes\u003C\u002Fa>\u003C\u002Fli>\u003Cli class=\"editorjs-toc__item\">\u003Ca href=\"#section-111\" class=\"editorjs-toc__link\">Glosario\u003C\u002Fa>\u003C\u002Fli>\u003Cli class=\"editorjs-toc__item\">\u003Ca href=\"#section-113\" class=\"editorjs-toc__link\">Fuentes primarias\u003C\u002Fa>\u003C\u002Fli>\u003C\u002Fol>\u003C\u002Fnav>\n\u003Ch2 id=\"section-5\">Primero: ¿qué hace realmente el modelo de IA?\u003C\u002Fh2>\n\u003Cp>G-Assist utiliza un modelo de lenguaje pequeño local, o SLM. NVIDIA describe actualmente el sistema como el uso de un modelo instruct basado en Llama de 8 mil millones de parámetros.\u003C\u002Fp>\n\u003Cp>El trabajo principal del modelo no es renderizar gráficos ni cambiar directamente registros de hardware. Su trabajo es entender el lenguaje del usuario, decidir qué capacidad compatible coincide con la solicitud y preparar la llamada a esa capacidad.\u003C\u002Fp>\n\u003Cp>Por ejemplo, si dices “configura DLSS Super Resolution en modo Rendimiento”, el modelo de lenguaje no modifica DLSS por sí mismo. Interpreta el comando y lo dirige a la función compatible que puede cambiar la configuración.\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\">La distinción importante\u003C\u002Fstrong>\u003Cdiv class=\"text-gray-700 dark:text-gray-200\">El modelo de IA \u003Cstrong>decide qué herramienta llamar\u003C\u002Fstrong>. La herramienta \u003Cstrong>hace el trabajo real\u003C\u002Fstrong>.\u003C\u002Fdiv>\u003C\u002Faside>\n\u003Ch2 id=\"section-10\">El flujo de acción de G-Assist\u003C\u002Fh2>\n\u003Csection class=\"editorjs-process my-6\">\u003Ch3 class=\"mb-3 text-lg font-semibold\">Qué sucede después de darle un comando a G-Assist\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. Das una solicitud en lenguaje natural\u003C\u002Fdiv>\u003Cdiv class=\"mt-1 text-sm text-gray-600 dark:text-gray-300\">Por ejemplo: “Optimiza este juego para rendimiento”.\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. El SLM local interpreta la solicitud\u003C\u002Fdiv>\u003Cdiv class=\"mt-1 text-sm text-gray-600 dark:text-gray-300\">Determina la intención del usuario e identifica qué función o complemento compatible es relevante.\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. El sistema elige una herramienta\u003C\u002Fdiv>\u003Cdiv class=\"mt-1 text-sm text-gray-600 dark:text-gray-300\">Podría ser una función integrada de configuración gráfica, verificación de controladores, función de monitoreo o complemento de la comunidad.\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. Se extraen los argumentos\u003C\u002Fdiv>\u003Cdiv class=\"mt-1 text-sm text-gray-600 dark:text-gray-300\">El modelo convierte la solicitud en valores estructurados como nombre del juego, modo, perfil de ventilador o configuración de DLSS.\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. La herramienta se ejecuta\u003C\u002Fdiv>\u003Cdiv class=\"mt-1 text-sm text-gray-600 dark:text-gray-300\">La herramienta se comunica con la NVIDIA App, el sistema operativo, el software del periférico u otro servicio.\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. Se devuelve el resultado\u003C\u002Fdiv>\u003Cdiv class=\"mt-1 text-sm text-gray-600 dark:text-gray-300\">G-Assist informa lo que sucedió o devuelve datos que el modelo puede explicar.\u003C\u002Fdiv>\u003C\u002Fdiv>\u003C\u002Fdiv>\u003C\u002Fsection>\n\u003Ch2 id=\"section-12\">Esto es llamada a herramientas, no control mágico del PC\u003C\u002Fh2>\n\u003Cp>Un modelo de lenguaje no puede controlar de forma segura una computadora arbitraria simplemente porque entiende inglés.\u003C\u002Fp>\n\u003Cp>Necesita una interfaz definida que indique qué acciones existen, qué argumentos aceptan y qué devuelve la herramienta.\u003C\u002Fp>\n\u003Cp>Las capacidades integradas actuales de G-Assist incluyen funciones como optimizar la configuración gráfica, cambiar opciones RTX compatibles, verificar o descargar controladores, mostrar información de rendimiento, cambiar algunas funciones de energía del portátil, controlar funciones de monitor compatibles y usar complementos de periféricos compatibles.\u003C\u002Fp>\n\u003Csection class=\"editorjs-comparison my-6\">\u003Ch3 class=\"mb-3 text-lg font-semibold\">Modelo de lenguaje vs herramienta\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\">Modelo de lenguaje\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\">Herramienta \u002F complemento\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\">Entender “activar 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\">Elegir la función correcta\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\">Cambiar realmente la configuración\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\">Explicar el resultado\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\">¿Qué es la capa de conocimiento?\u003C\u002Fh2>\n\u003Cp>G-Assist también tiene una capa de conocimiento para responder preguntas y hacer recomendaciones.\u003C\u002Fp>\n\u003Cp>NVIDIA dice que la versión 0.2.1 introdujo un sistema de conocimiento mejorado que ayuda a G-Assist a hacer recomendaciones de configuración más precisas.\u003C\u002Fp>\n\u003Cp>Esa capa de conocimiento es diferente del estado en vivo de tu PC.\u003C\u002Fp>\n\u003Csection class=\"editorjs-comparison my-6\">\u003Ch3 class=\"mb-3 text-lg font-semibold\">Conocimiento vs estado en vivo de la PC\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\">Conocimiento\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\">Estado en vivo\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\">Configuración del juego\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\">Rendimiento\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\">No mezcles conocimiento con estado\u003C\u002Fstrong>\u003Cdiv class=\"text-gray-700 dark:text-gray-200\">\u003Cstrong>Conocimiento\u003C\u002Fstrong> le dice al asistente qué significa una función o qué suele importar.\u003Cbr>\u003Cstrong>Estado\u003C\u002Fstrong> le dice qué es verdad en tu máquina en este momento.\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\">¿Qué es RAG? La explicación más simple de cómo funciona\u003C\u002Fstrong>\u003Cp class=\"mt-2 text-sm text-gray-600 dark:text-gray-300\">Una explicación en lenguaje sencillo sobre la recuperación de conocimiento, el estado, la memoria, el contexto y el modelo de lenguaje.\u003C\u002Fp>\u003Cspan class=\"mt-3 inline-flex text-sm font-medium text-primary-600 dark:text-primary-400\">Lee la guía simple de RAG →\u003C\u002Fspan>\u003C\u002Fdiv>\u003C\u002Fa>\u003C\u002Faside>\n\u003Ch2 id=\"section-24\">¿G-Assist usa RAG?\u003C\u002Fh2>\n\u003Cp>La respuesta más segura es: G-Assist claramente tiene un sistema de conocimiento, pero la página pública del producto de NVIDIA no documenta cada mecanismo interno de recuperación con suficiente detalle como para afirmar que cada respuesta de conocimiento es producida específicamente por RAG.\u003C\u002Fp>\n\u003Cp>La distinción arquitectónica sigue siendo importante. Una capa de recuperación puede proporcionar conocimiento relevante, mientras que las herramientas del sistema proporcionan el estado actual de la PC y realizan acciones.\u003C\u002Fp>\n\u003Cp>Esa es la misma separación que se usa en muchos agentes modernos: el conocimiento es una fuente de contexto, las observaciones en vivo son otra, y la ejecución de herramientas es una tercera.\u003C\u002Fp>\n\u003Ch2 id=\"section-28\">Por qué G-Assist puede funcionar sin conexión\u003C\u002Fh2>\n\u003Cp>NVIDIA ejecuta el modelo de lenguaje localmente en la GPU GeForce RTX.\u003C\u002Fp>\n\u003Cp>Eso significa que el asistente principal no requiere un modelo de lenguaje alojado en la nube para cada indicación.\u003C\u002Fp>\n\u003Cp>NVIDIA dice explícitamente que G-Assist puede funcionar sin conexión para sus capacidades locales.\u003C\u002Fp>\n\u003Cp>Un complemento aún puede usar internet si ese complemento llama a un servicio en línea. La ejecución local del modelo y el acceso en línea de los complementos son cuestiones separadas.\u003C\u002Fp>\n\u003Ch2 id=\"section-33\">El costo de la IA local: G-Assist usa tu GPU y VRAM\u003C\u002Fh2>\n\u003Cp>Ejecutarse localmente brinda privacidad e independencia de un modelo en la nube, pero el trabajo tiene que ejecutarse en algún lugar.\u003C\u002Fp>\n\u003Cp>Para G-Assist, ese algún lugar es la misma GPU GeForce RTX que ya podría estar renderizando tu juego.\u003C\u002Fp>\n\u003Cp>NVIDIA advierte que la GPU asigna brevemente recursos de cómputo a la inferencia de IA cuando G-Assist responde. Si se está ejecutando un juego exigente al mismo tiempo, el juego puede perder temporalmente algo de rendimiento de renderizado mientras el modelo está activo.\u003C\u002Fp>\n\u003Cp>Los requisitos actuales también especifican objetivos de VRAM libre más allá de la memoria ya utilizada por el juego: aproximadamente 6 GB de VRAM libre para el Modo Razonamiento y 4,5 GB para el Modo Flash.\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 de VRAM no significa que 8 GB estén disponibles para G-Assist\u003C\u002Fstrong>\u003Cdiv class=\"text-gray-700 dark:text-gray-200\">El juego, Windows, el controlador y otras aplicaciones ya consumen VRAM. El requisito propio de G-Assist se refiere a \u003Cstrong>VRAM libre además de la que ya utiliza la carga de trabajo en ejecución\u003C\u002Fstrong>.\u003C\u002Fdiv>\u003C\u002Faside>\n\u003Caside class=\"editorjs-referral my-6\">\u003Ca href=\"https:\u002F\u002Ffigure.rocks\u002Fes\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\">El uso de VRAM no es un requisito de VRAM: por qué un medidor de memoria lleno no cuenta toda la historia\u003C\u002Fstrong>\u003Cp class=\"mt-2 text-sm text-gray-600 dark:text-gray-300\">Por qué la VRAM instalada, el uso actual, los presupuestos de residencia y la presión de memoria real son cosas diferentes.\u003C\u002Fp>\u003Cspan class=\"mt-3 inline-flex text-sm font-medium text-primary-600 dark:text-primary-400\">Leer la guía de VRAM →\u003C\u002Fspan>\u003C\u002Fdiv>\u003C\u002Fa>\u003C\u002Faside>\n\u003Ch2 id=\"section-40\">Modo Razonamiento vs Modo Flash\u003C\u002Fh2>\n\u003Cp>Las versiones actuales de G-Assist separan un Modo Razonamiento más capaz de un Modo Flash más rápido.\u003C\u002Fp>\n\u003Cp>El Modo Razonamiento está diseñado para decisiones de mayor calidad y puede coordinar múltiples acciones desde un solo prompt. El Modo Flash está optimizado para respuestas más rápidas y una menor demanda de recursos.\u003C\u002Fp>\n\u003Csection class=\"editorjs-comparison my-6\">\u003Ch3 class=\"mb-3 text-lg font-semibold\">Modo Razonamiento vs Modo Flash\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\">Modo Razonamiento\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\">Modo Flash\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\">Objetivo principal\u003C\u002Fth>\u003Ctd class=\"border border-gray-300 px-4 py-3 dark:border-gray-700\">\u003C\u002Ftd>\u003Ctd class=\"border border-gray-300 px-4 py-3 dark:border-gray-700\">\u003C\u002Ftd>\u003C\u002Ftr>\u003Ctr>\u003Cth class=\"border border-gray-300 px-4 py-3 text-left font-semibold dark:border-gray-700\">VRAM libre recomendada\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\">Mejor para\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\">Qué añaden realmente los complementos\u003C\u002Fh2>\n\u003Cp>Un complemento no reemplaza al modelo de lenguaje. Le da al modelo una nueva acción que puede invocar.\u003C\u002Fp>\n\u003Cp>El complemento define funciones, descripciones y parámetros. G-Assist lee esas descripciones, asocia una solicitud del usuario con la función adecuada y le envía argumentos estructurados.\u003C\u002Fp>\n\u003Cp>Eso permite que el asistente crezca más allá de las funciones integradas de NVIDIA.\u003C\u002Fp>\n\u003Csection class=\"editorjs-process my-6\">\u003Ch3 class=\"mb-3 text-lg font-semibold\">Cómo funciona un complemento de G-Assist\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. El complemento declara una función\u003C\u002Fdiv>\u003Cdiv class=\"mt-1 text-sm text-gray-600 dark:text-gray-300\">Por ejemplo: 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. El manifiesto describe la función\u003C\u002Fdiv>\u003Cdiv class=\"mt-1 text-sm text-gray-600 dark:text-gray-300\">El manifiesto le indica a G-Assist qué hace la función y qué parámetros necesita.\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. El usuario pregunta de forma natural\u003C\u002Fdiv>\u003Cdiv class=\"mt-1 text-sm text-gray-600 dark:text-gray-300\">Por ejemplo: “Pon mi teclado en verde.”\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. El SLM selecciona la función\u003C\u002Fdiv>\u003Cdiv class=\"mt-1 text-sm text-gray-600 dark:text-gray-300\">El modelo asigna la solicitud a la función del complemento y extrae verde como parámetro.\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. El complemento se ejecuta\u003C\u002Fdiv>\u003Cdiv class=\"mt-1 text-sm text-gray-600 dark:text-gray-300\">El complemento se comunica con el software del periférico o el servicio externo.\u003C\u002Fdiv>\u003C\u002Fdiv>\u003C\u002Fdiv>\u003C\u002Fsection>\n\u003Ch2 id=\"section-49\">El Protocolo V2 de G-Assist es JSON-RPC 2.0\u003C\u002Fh2>\n\u003Cp>El repositorio público actual de complementos de NVIDIA utiliza el Protocolo V2.\u003C\u002Fp>\n\u003Cp>El Protocolo V2 utiliza JSON-RPC 2.0 con mensajes con prefijo de longitud entre el motor de G-Assist y los complementos.\u003C\u002Fp>\n\u003Cp>El motor puede inicializar un complemento, verificar su estado, ejecutar una función, enviar entrada del usuario y apagarlo. Los complementos pueden devolver resultados, transmitir progreso o informar errores.\u003C\u002Fp>\n\u003Csection class=\"editorjs-comparison my-6\">\u003Ch3 class=\"mb-3 text-lg font-semibold\">Los mensajes importantes del Protocolo V2\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\">Dirección\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\">Propósito\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\">Dónde entra en escena MCP\u003C\u002Fh2>\n\u003Cp>El propio protocolo de complementos de G-Assist no es MCP. Su sistema actual de complementos públicos utiliza JSON-RPC 2.0.\u003C\u002Fp>\n\u003Cp>Sin embargo, un complemento de G-Assist puede conectarse a un servidor MCP.\u003C\u002Fp>\n\u003Cp>La versión actual 0.2.2 de NVIDIA incluye una integración con Elgato que puede invocar acciones expuestas a través del servidor MCP de Elgato Stream Deck.\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\">Distinción importante de protocolo\u003C\u002Fstrong>\u003Cdiv class=\"text-gray-700 dark:text-gray-200\">\u003Cstrong>G-Assist ↔ su complemento:\u003C\u002Fstrong> NVIDIA Protocol V2 \u002F JSON-RPC 2.0.\u003Cbr>\u003Cstrong>Complemento ↔ otro ecosistema de herramientas:\u003C\u002Fstrong> puede usar MCP cuando esa integración lo admita.\u003C\u002Fdiv>\u003C\u002Faside>\n\u003Ch2 id=\"section-59\">Por qué esto importa más allá de las luces RGB\u003C\u002Fh2>\n\u003Cp>La arquitectura de complementos convierte a G-Assist de un asistente fijo en un enrutador de herramientas local.\u003C\u002Fp>\n\u003Cp>El mismo patrón puede conectar un SLM a herramientas de monitoreo, periféricos, API, sistemas de automatización, aplicaciones locales o servicios externos.\u003C\u002Fp>\n\u003Cp>La unidad arquitectónica importante, por lo tanto, no es la ventana de chat. Es el límite entre la intención en lenguaje natural y la ejecución estructurada de herramientas.\u003C\u002Fp>\n\u003Ch2 id=\"section-63\">El stack de agente local\u003C\u002Fh2>\n\u003Csection class=\"editorjs-process my-6\">\u003Ch3 class=\"mb-3 text-lg font-semibold\">Una forma útil de pensar en G-Assist\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. Intención del usuario\u003C\u002Fdiv>\u003Cdiv class=\"mt-1 text-sm text-gray-600 dark:text-gray-300\">Comando de lenguaje natural o voz.\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. SLM local\u003C\u002Fdiv>\u003Cdiv class=\"mt-1 text-sm text-gray-600 dark:text-gray-300\">Comprende la solicitud y elige una capacidad.\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. Conocimiento \u002F contexto\u003C\u002Fdiv>\u003Cdiv class=\"mt-1 text-sm text-gray-600 dark:text-gray-300\">Proporciona conocimiento del producto, recomendaciones o información necesaria para la decisión.\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. Estado del sistema en vivo\u003C\u002Fdiv>\u003Cdiv class=\"mt-1 text-sm text-gray-600 dark:text-gray-300\">Proporciona información actual de hardware, controladores, rendimiento o configuración cuando es compatible.\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. Selección de herramienta\u003C\u002Fdiv>\u003Cdiv class=\"mt-1 text-sm text-gray-600 dark:text-gray-300\">Se elige una función integrada o un complemento.\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. Ejecución estructurada\u003C\u002Fdiv>\u003Cdiv class=\"mt-1 text-sm text-gray-600 dark:text-gray-300\">La función recibe argumentos y realiza la acción real.\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. Verificación del resultado\u003C\u002Fdiv>\u003Cdiv class=\"mt-1 text-sm text-gray-600 dark:text-gray-300\">La herramienta devuelve estado o datos y el modelo explica lo que sucedió.\u003C\u002Fdiv>\u003C\u002Fdiv>\u003C\u002Fdiv>\u003C\u002Fsection>\n\u003Ch2 id=\"section-65\">Por qué los límites de las herramientas son una característica de seguridad\u003C\u002Fh2>\n\u003Cp>Un asistente de IA que pudiera ejecutar comandos arbitrarios del sistema operativo sería mucho más poderoso, pero también mucho más difícil de restringir.\u003C\u002Fp>\n\u003Cp>G-Assist, en cambio, expone funciones definidas con parámetros conocidos.\u003C\u002Fp>\n\u003Cp>Eso significa que el modelo solo puede realizar acciones que el conjunto de funciones integradas o los complementos instalados pongan a disposición.\u003C\u002Fp>\n\u003Cp>Esto no elimina todos los riesgos, especialmente con complementos de terceros, pero crea un límite de permisos y capacidades más claro que el acceso irrestricto al shell.\u003C\u002Fp>\n\u003Ch2 id=\"section-70\">El límite de autoridad de las herramientas\u003C\u002Fh2>\n\u003Csection class=\"editorjs-comparison my-6\">\u003Ch3 class=\"mb-3 text-lg font-semibold\">Lo que el asistente puede entender vs lo que se le permite hacer\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\">El modelo puede entender\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\">Autoridad de la herramienta\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\">Ajuste de 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\">Archivos\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\">Internet\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\">Control de periféricos\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\">Por qué G-Assist puede reducir temporalmente el rendimiento del juego\u003C\u002Fh2>\n\u003Cp>La arquitectura de modelo local tiene una consecuencia directa: la IA y el juego pueden competir por la misma GPU.\u003C\u002Fp>\n\u003Cp>NVIDIA advierte explícitamente que la tasa de renderizado o la velocidad de inferencia pueden disminuir brevemente mientras G-Assist procesa una solicitud durante una carga de trabajo intensiva de GPU.\u003C\u002Fp>\n\u003Cp>Una vez que finaliza la inferencia, esos recursos de GPU vuelven al juego.\u003C\u002Fp>\n\u003Cp>Esto es diferente de un asistente en la nube, donde la mayor parte de la inferencia del modelo ocurre en un servidor remoto y no consume la GPU de juego.\u003C\u002Fp>\n\u003Ch2 id=\"section-77\">La prueba de recursos del asistente local\u003C\u002Fh2>\n\u003Csection class=\"editorjs-process my-6\">\u003Ch3 class=\"mb-3 text-lg font-semibold\">Cómo juzgar si un asistente de juego local se adapta a tu PC\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. Verifica la VRAM instalada\u003C\u002Fdiv>\u003Cdiv class=\"mt-1 text-sm text-gray-600 dark:text-gray-300\">La línea base actual de G-Assist es una GPU RTX con al menos 6 GB de VRAM.\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. Verifica la VRAM libre durante el juego real\u003C\u002Fdiv>\u003Cdiv class=\"mt-1 text-sm text-gray-600 dark:text-gray-300\">La capacidad instalada no es lo mismo que la capacidad libre.\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. Elige el modo Razonamiento o Flash adecuadamente\u003C\u002Fdiv>\u003Cdiv class=\"mt-1 text-sm text-gray-600 dark:text-gray-300\">Usa el modo más ligero cuando la presión de recursos importa más que el razonamiento profundo.\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. Mide las caídas de velocidad de fotogramas durante la inferencia\u003C\u002Fdiv>\u003Cdiv class=\"mt-1 text-sm text-gray-600 dark:text-gray-300\">Observa el juego mientras le pides a G-Assist que realice tareas.\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. Inspecciona la autoridad de las herramientas\u003C\u002Fdiv>\u003Cdiv class=\"mt-1 text-sm text-gray-600 dark:text-gray-300\">Conoce exactamente qué funciones integradas y complementos pueden cambiar tu sistema.\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. Trata los complementos de terceros como software\u003C\u002Fdiv>\u003Cdiv class=\"mt-1 text-sm text-gray-600 dark:text-gray-300\">Revisa su código fuente, permisos, acceso a la red y credenciales almacenadas.\u003C\u002Fdiv>\u003C\u002Fdiv>\u003C\u002Fdiv>\u003C\u002Fsection>\n\u003Ch2 id=\"section-79\">Por qué los complementos merecen el mismo escrutinio que cualquier otro software\u003C\u002Fh2>\n\u003Cp>Un complemento puede conectar el asistente a API, aplicaciones periféricas y servicios en línea.\u003C\u002Fp>\n\u003Cp>Eso significa que un complemento puede manejar valores de configuración, credenciales o solicitudes externas dependiendo de para qué fue creado.\u003C\u002Fp>\n\u003Cp>El repositorio de NVIDIA proporciona explícitamente una ubicación config.json para la configuración de complementos y advierte a los desarrolladores que no confirmen credenciales.\u003C\u002Fp>\n\u003Cp>Por lo tanto, la pregunta de seguridad correcta no es solo \"¿G-Assist es local?\" sino también \"¿A qué se conecta cada complemento instalado?\"\u003C\u002Fp>\n\u003Ch2 id=\"section-84\">G-Assist se parece más a un agente que a un chatbot\u003C\u002Fh2>\n\u003Cp>Un chatbot principalmente produce lenguaje.\u003C\u002Fp>\n\u003Cp>Un agente interpreta un objetivo, observa información relevante, elige una acción, llama a una herramienta y evalúa el resultado.\u003C\u002Fp>\n\u003Cp>G-Assist ahora se ajusta mucho más a esa segunda descripción porque puede coordinar múltiples acciones, inspeccionar el estado compatible de la PC e invocar herramientas reales.\u003C\u002Fp>\n\u003Caside class=\"editorjs-referral my-6\">\u003Ca href=\"https:\u002F\u002Ffigure.rocks\u002Fes\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 muestra por qué los compañeros de equipo de IA necesitan dos cerebros: reflejos rápidos y razonamiento lento\u003C\u002Fstrong>\u003Cp class=\"mt-2 text-sm text-gray-600 dark:text-gray-300\">Un ejemplo de arquitectura de agente de juego que muestra la separación entre razonamiento, estado en vivo, herramientas y ejecución determinista.\u003C\u002Fp>\u003Cspan class=\"mt-3 inline-flex text-sm font-medium text-primary-600 dark:text-primary-400\">Lee la guía de arquitectura de PUBG Ally →\u003C\u002Fspan>\u003C\u002Fdiv>\u003C\u002Fa>\u003C\u002Faside>\n\u003Ch2 id=\"section-89\">Lo que G-Assist todavía no es\u003C\u002Fh2>\n\u003Cp>NVIDIA describe explícitamente a G-Assist como un asistente local especializado, no como una IA conversacional de propósito general.\u003C\u002Fp>\n\u003Cp>Su valor proviene de comprender un conjunto enfocado de tareas de PC y gaming y de tener herramientas conectadas a esas tareas.\u003C\u002Fp>\n\u003Cp>Ese enfoque es importante porque un modelo local más pequeño puede ser útil cuando el sistema que lo rodea le proporciona herramientas sólidas y límites claros.\u003C\u002Fp>\n\u003Ch2 id=\"section-93\">¿Qué cambió en las versiones actuales?\u003C\u002Fh2>\n\u003Cp>El historial de versiones públicas actual muestra que G-Assist avanza constantemente hacia un agente local más capaz.\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\">Versión\u003C\u002Fth>\u003Cth class=\"border border-gray-300 px-4 py-2 text-left font-semibold\">Cambio importante\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\">Modelo más ligero, todas las GPU RTX con 6 GB+ de VRAM, complementos de la comunidad\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\">Optimización para portátiles, controles de BatteryBoost y WhisperMode\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\">Modo de razonamiento, modo flash, prompts de múltiples acciones, nuevos controles de dispositivo\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\">Sistema de conocimiento mejorado, mejores recomendaciones, controles de funciones RTX\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\">Integración con Elgato Stream Deck a través de un servidor MCP\u003C\u002Ftd>\u003C\u002Ftr>\u003C\u002Ftbody>\u003C\u002Ftable>\u003C\u002Fdiv>\n\u003Ch2 id=\"section-96\">¿Qué cambiaría esta respuesta?\u003C\u002Fh2>\n\u003Cp>G-Assist sigue siendo experimental y su arquitectura está evolucionando.\u003C\u002Fp>\n\u003Cp>El tamaño del modelo, los requisitos de VRAM, el conjunto de herramientas, el protocolo de complementos y la compatibilidad de hardware pueden cambiar en futuras versiones.\u003C\u002Fp>\n\u003Cp>Una versión futura también podría trasladar parte de la inferencia a una NPU o introducir una integración MCP más amplia, pero eso no debe darse por sentado hasta que NVIDIA lo documente.\u003C\u002Fp>\n\u003Ch2 id=\"section-100\">Limitaciones\u003C\u002Fh2>\n\u003Cp>Este artículo describe la documentación pública del producto G-Assist de NVIDIA y la arquitectura pública de complementos a septiembre de 2026.\u003C\u002Fp>\n\u003Cp>NVIDIA no documenta públicamente cada detalle interno de su canal de conocimiento y recomendaciones, por lo que este artículo no afirma que cada respuesta de conocimiento utilice RAG ni ninguna otra implementación de recuperación específica.\u003C\u002Fp>\n\u003Cp>Los complementos de terceros pueden tener un comportamiento y un acceso a la red más allá de las funciones integradas de NVIDIA, por lo que cada complemento debe evaluarse por separado.\u003C\u002Fp>\n\u003Ch2 id=\"section-104\">Conclusión\u003C\u002Fh2>\n\u003Cp>La forma más sencilla de entender Project G-Assist es dejar de pensar en él como un chatbot.\u003C\u002Fp>\n\u003Cp>El SLM local interpreta tu solicitud. El conocimiento le ayuda a comprender el problema. La información del sistema en vivo le indica qué es cierto en tu PC. Una función integrada o un complemento define lo que realmente se le permite hacer. La herramienta realiza la acción y devuelve el resultado.\u003C\u002Fp>\n\u003Cp>Eso es una arquitectura de agente local.\u003C\u002Fp>\n\u003Cp>Su idea más fuerte no es que un modelo de 8 mil millones de parámetros pueda hablar sobre tu GPU. Es que un modelo local relativamente pequeño puede volverse útil cuando está conectado a herramientas bien definidas, al estado actual y a un límite de acción controlado.\u003C\u002Fp>\n\u003Ch2 id=\"section-109\">Preguntas frecuentes\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\">NVIDIA Project G-Assist en lenguaje sencillo\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\">¿Es G-Assist un chatbot en la nube?\u003C\u002Fh4>\u003Cdiv class=\"mt-2 text-gray-600 dark:text-gray-300\">No. Su modelo de lenguaje principal se ejecuta localmente en la GPU GeForce RTX y puede funcionar sin conexión para las funciones locales compatibles.\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\">¿Qué modelo utiliza G-Assist?\u003C\u002Fh4>\u003Cdiv class=\"mt-2 text-gray-600 dark:text-gray-300\">NVIDIA actualmente lo describe como un modelo instruct local basado en Llama con 8 mil millones de parámetros.\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\">¿El modelo de IA cambia directamente la configuración de mi GPU?\u003C\u002Fh4>\u003Cdiv class=\"mt-2 text-gray-600 dark:text-gray-300\">No. El modelo selecciona una función integrada o un complemento compatible, y esa herramienta realiza el cambio real.\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 utiliza VRAM mientras se juega?\u003C\u002Fh4>\u003Cdiv class=\"mt-2 text-gray-600 dark:text-gray-300\">Sí. NVIDIA recomienda VRAM libre adicional para el asistente y advierte que la inferencia puede reducir brevemente el rendimiento de renderizado del juego.\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\">¿Los complementos de G-Assist son complementos MCP?\u003C\u002Fh4>\u003Cdiv class=\"mt-2 text-gray-600 dark:text-gray-300\">No directamente. El protocolo de complementos actual propio de G-Assist utiliza JSON-RPC 2.0, aunque un complemento puede conectarse a un servidor MCP.\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\">¿Puede G-Assist funcionar sin conexión?\u003C\u002Fh4>\u003Cdiv class=\"mt-2 text-gray-600 dark:text-gray-300\">Su asistente local puede, pero los complementos individuales aún pueden requerir acceso a internet si llaman a servicios en línea.\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\">¿Es G-Assist un asistente de IA general?\u003C\u002Fh4>\u003Cdiv class=\"mt-2 text-gray-600 dark:text-gray-300\">NVIDIA lo describe como un asistente especializado para PC y juegos en lugar de un modelo conversacional amplio de propósito general.\u003C\u002Fdiv>\u003C\u002Fdiv>\u003C\u002Fsection>\n\u003Ch2 id=\"section-111\">Glosario\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\">Términos clave de G-Assist\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\">Small Language Model (Modelo de Lenguaje Pequeño), un modelo de lenguaje compacto diseñado para ejecutarse localmente con requisitos de hardware menores que los grandes modelos en la nube.\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\">Llamada a herramientas\u003C\u002Fdt>\u003Cdd class=\"mt-1 text-gray-600 dark:text-gray-300\">El proceso mediante el cual un modelo de lenguaje elige una función definida y proporciona argumentos estructurados para que otro componente realice una acción.\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\">Complemento\u003C\u002Fdt>\u003Cdd class=\"mt-1 text-gray-600 dark:text-gray-300\">Una extensión que expone funciones o integraciones adicionales a G-Assist.\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\">El protocolo de mensajes estructurados utilizado por el sistema de complementos actual G-Assist Protocol V2.\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\">Model Context Protocol (Protocolo de Contexto de Modelo), un protocolo separado de interoperabilidad de herramientas y contexto que algunas integraciones externas pueden exponer.\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\">Límite Herramienta-Autoridad\u003C\u002Fdt>\u003Cdd class=\"mt-1 text-gray-600 dark:text-gray-300\">Un modelo de Figure Rocks que separa lo que un modelo de IA puede entender de lo que sus herramientas conectadas realmente le permiten hacer.\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\">Pila de Agente Local\u003C\u002Fdt>\u003Cdd class=\"mt-1 text-gray-600 dark:text-gray-300\">Un modelo de Figure Rocks que combina la intención del usuario, el SLM local, el conocimiento, el estado en vivo, la selección de herramientas, la ejecución y la verificación de resultados.\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\">Prueba de Recursos del Asistente Local\u003C\u002Fdt>\u003Cdd class=\"mt-1 text-gray-600 dark:text-gray-300\">Un flujo de trabajo de Figure Rocks para evaluar VRAM, costo de inferencia, permisos de herramientas y riesgo de complementos antes de usar un asistente de juegos local.\u003C\u002Fdd>\u003C\u002Fdiv>\u003C\u002Fdl>\u003C\u002Fsection>\n\u003Ch2 id=\"section-113\">Fuentes primarias\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\">Página oficial actual del producto que cubre el modelo local, las funciones compatibles, los modos Reasoning y Flash, los requisitos de VRAM, los controles RTX, los complementos y la integración de Elgato basada en MCP versión 0.2.2.\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\">Repositorio público oficial de complementos que documenta la arquitectura de módulos de G-Assist, el descubrimiento de complementos y el 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 — Guía de migración a G-Assist Protocol V2\u003C\u002Fstrong>\u003Cp class=\"text-sm text-gray-600 dark:text-gray-400\">Documentación oficial del protocolo que cubre JSON-RPC 2.0, inicialización, verificaciones de estado, ejecución, transmisión, finalización y comportamiento del SDK de complementos.\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\">Blog de NVIDIA — Modelo ligero de G-Assist\u003C\u002Fstrong>\u003Cp class=\"text-sm text-gray-600 dark:text-gray-400\">Antecedentes oficiales sobre el modelo G-Assist de menor memoria, compatibilidad con GPU RTX de 6 GB y el centro de complementos de la comunidad.\u003C\u002Fp>\u003C\u002Fa>",{"time":538,"blocks":539,"version":1438},1790407911052,[540,546,554,561,568,574,579,584,589,596,601,627,632,637,642,647,678,683,688,693,698,723,730,739,744,749,754,759,764,769,774,779,784,789,794,799,804,809,815,823,828,833,838,862,867,872,877,882,903,908,913,918,923,951,956,961,966,971,977,982,987,992,997,1002,1029,1034,1039,1044,1049,1054,1059,1086,1091,1096,1101,1106,1111,1116,1140,1145,1150,1155,1160,1165,1170,1175,1180,1185,1193,1198,1203,1208,1213,1218,1223,1247,1252,1257,1262,1267,1272,1277,1282,1287,1292,1297,1302,1307,1312,1317,1352,1357,1396,1401,1411,1420,1429],{"id":541,"data":542,"type":544,"tunes":545},"3cee674645",{"text":543},"NVIDIA Project G-Assist parece un chatbot, pero esa descripción omite la parte importante. Es un sistema de IA local que puede entender una solicitud, inspeccionar el estado compatible del PC, elegir una herramienta o complemento y luego realizar una acción real, como cambiar la configuración de DLSS, verificar un controlador, ajustar un perfil de ventilador o controlar un periférico.","paragraph",{},{"id":547,"data":548,"type":552,"tunes":553},"24a1733ca8",{"body":549,"title":550,"variant":551},"\u003Cstrong>G-Assist no es simplemente un chatbot para tu GPU.\u003C\u002Fstrong> Es un modelo de lenguaje pequeño local conectado a un conjunto de herramientas permitidas. El modelo interpreta lo que quieres, selecciona una función compatible, pasa argumentos a esa función y la herramienta del lado del PC realiza la acción.","Respuesta directa","info","callout",{},{"id":555,"data":556,"type":552,"tunes":560},"1fcbe512dc",{"body":557,"title":558,"variant":559},"\u003Cstrong>SLM = entiende la solicitud.\u003C\u002Fstrong>\u003Cbr>\u003Cstrong>Conocimiento = explica conceptos compatibles de NVIDIA\u002FPC.\u003C\u002Fstrong>\u003Cbr>\u003Cstrong>Herramientas = realizan acciones.\u003C\u002Fstrong>\u003Cbr>\u003Cstrong>Complementos = añaden nuevas herramientas.\u003C\u002Fstrong>\u003Cbr>\u003Cstrong>Tu PC = el entorno que se inspecciona o modifica.\u003C\u002Fstrong>","El modelo mental más sencillo","note",{},{"id":562,"data":563,"type":566,"tunes":567},"2a609230a5",{"title":564,"maxLevel":565,"minLevel":47},"Contenido",3,"tableOfContents",{},{"id":569,"data":570,"type":572,"tunes":573},"a52f97cb62",{"text":571,"level":47},"Primero: ¿qué hace realmente el modelo de IA?","header",{},{"id":575,"data":576,"type":544,"tunes":578},"1871a0d8e6",{"text":577},"G-Assist utiliza un modelo de lenguaje pequeño local, o SLM. NVIDIA describe actualmente el sistema como el uso de un modelo instruct basado en Llama de 8 mil millones de parámetros.",{},{"id":580,"data":581,"type":544,"tunes":583},"cfbc6473c4",{"text":582},"El trabajo principal del modelo no es renderizar gráficos ni cambiar directamente registros de hardware. Su trabajo es entender el lenguaje del usuario, decidir qué capacidad compatible coincide con la solicitud y preparar la llamada a esa capacidad.",{},{"id":585,"data":586,"type":544,"tunes":588},"53cadaaeb6",{"text":587},"Por ejemplo, si dices “configura DLSS Super Resolution en modo Rendimiento”, el modelo de lenguaje no modifica DLSS por sí mismo. Interpreta el comando y lo dirige a la función compatible que puede cambiar la configuración.",{},{"id":590,"data":591,"type":552,"tunes":595},"5d8e508019",{"body":592,"title":593,"variant":594},"El modelo de IA \u003Cstrong>decide qué herramienta llamar\u003C\u002Fstrong>. La herramienta \u003Cstrong>hace el trabajo real\u003C\u002Fstrong>.","La distinción importante","success",{},{"id":597,"data":598,"type":572,"tunes":600},"9ce7e91c28",{"text":599,"level":47},"El flujo de acción de G-Assist",{},{"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. Das una solicitud en lenguaje natural","Por ejemplo: “Optimiza este juego para rendimiento”.",{"label":609,"description":610},"2. El SLM local interpreta la solicitud","Determina la intención del usuario e identifica qué función o complemento compatible es relevante.",{"label":612,"description":613},"3. El sistema elige una herramienta","Podría ser una función integrada de configuración gráfica, verificación de controladores, función de monitoreo o complemento de la comunidad.",{"label":615,"description":616},"4. Se extraen los argumentos","El modelo convierte la solicitud en valores estructurados como nombre del juego, modo, perfil de ventilador o configuración de DLSS.",{"label":618,"description":619},"5. La herramienta se ejecuta","La herramienta se comunica con la NVIDIA App, el sistema operativo, el software del periférico u otro servicio.",{"label":621,"description":622},"6. Se devuelve el resultado","G-Assist informa lo que sucedió o devuelve datos que el modelo puede explicar.","Qué sucede después de darle un comando a G-Assist","auto","processFlow",{},{"id":628,"data":629,"type":572,"tunes":631},"aba1e38958",{"text":630,"level":47},"Esto es llamada a herramientas, no control mágico del PC",{},{"id":633,"data":634,"type":544,"tunes":636},"0a7cb6107d",{"text":635},"Un modelo de lenguaje no puede controlar de forma segura una computadora arbitraria simplemente porque entiende inglés.",{},{"id":638,"data":639,"type":544,"tunes":641},"31191a478c",{"text":640},"Necesita una interfaz definida que indique qué acciones existen, qué argumentos aceptan y qué devuelve la herramienta.",{},{"id":643,"data":644,"type":544,"tunes":646},"af3d1a846f",{"text":645},"Las capacidades integradas actuales de G-Assist incluyen funciones como optimizar la configuración gráfica, cambiar opciones RTX compatibles, verificar o descargar controladores, mostrar información de rendimiento, cambiar algunas funciones de energía del portátil, controlar funciones de monitor compatibles y usar complementos de periféricos compatibles.",{},{"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","Entender “activar DLSS”",[13,13],{"id":656,"label":657,"values":658},"decide","Elegir la función correcta",[13,13],{"id":660,"label":661,"values":662},"change","Cambiar realmente la configuración",[13,13],{"id":664,"label":665,"values":666},"explain","Explicar el resultado",[13,13],"Modelo de lenguaje vs herramienta","table",[670,673],{"id":671,"label":672},"model","Modelo de lenguaje",{"id":674,"label":675},"tool","Herramienta \u002F complemento","comparison",{},{"id":679,"data":680,"type":572,"tunes":682},"1537be641c",{"text":681,"level":47},"¿Qué es la capa de conocimiento?",{},{"id":684,"data":685,"type":544,"tunes":687},"1db6e2e4b5",{"text":686},"G-Assist también tiene una capa de conocimiento para responder preguntas y hacer recomendaciones.",{},{"id":689,"data":690,"type":544,"tunes":692},"616d72b881",{"text":691},"NVIDIA dice que la versión 0.2.1 introdujo un sistema de conocimiento mejorado que ayuda a G-Assist a hacer recomendaciones de configuración más precisas.",{},{"id":694,"data":695,"type":544,"tunes":697},"eb987c41e5",{"text":696},"Esa capa de conocimiento es diferente del estado en vivo de tu PC.",{},{"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","Configuración del juego",[13,13],{"id":711,"label":712,"values":713},"system","Rendimiento",[13,13],"Conocimiento vs estado en vivo de la PC",[716,719],{"id":717,"label":718},"knowledge","Conocimiento",{"id":720,"label":721},"state","Estado en vivo",{},{"id":724,"data":725,"type":552,"tunes":729},"2bac13ef99",{"body":726,"title":727,"variant":728},"\u003Cstrong>Conocimiento\u003C\u002Fstrong> le dice al asistente qué significa una función o qué suele importar.\u003Cbr>\u003Cstrong>Estado\u003C\u002Fstrong> le dice qué es verdad en tu máquina en este momento.","No mezcles conocimiento con estado","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","¿Qué es RAG? La explicación más simple de cómo funciona","Una explicación en lenguaje sencillo sobre la recuperación de conocimiento, el estado, la memoria, el contexto y el modelo de lenguaje.","Lee la guía simple de RAG","referralArticle",{},{"id":740,"data":741,"type":572,"tunes":743},"acbb9b7940",{"text":742,"level":47},"¿G-Assist usa RAG?",{},{"id":745,"data":746,"type":544,"tunes":748},"ce0371e492",{"text":747},"La respuesta más segura es: G-Assist claramente tiene un sistema de conocimiento, pero la página pública del producto de NVIDIA no documenta cada mecanismo interno de recuperación con suficiente detalle como para afirmar que cada respuesta de conocimiento es producida específicamente por RAG.",{},{"id":750,"data":751,"type":544,"tunes":753},"c2226e5ec5",{"text":752},"La distinción arquitectónica sigue siendo importante. Una capa de recuperación puede proporcionar conocimiento relevante, mientras que las herramientas del sistema proporcionan el estado actual de la PC y realizan acciones.",{},{"id":755,"data":756,"type":544,"tunes":758},"e5137fc2c8",{"text":757},"Esa es la misma separación que se usa en muchos agentes modernos: el conocimiento es una fuente de contexto, las observaciones en vivo son otra, y la ejecución de herramientas es una tercera.",{},{"id":760,"data":761,"type":572,"tunes":763},"059b89dedb",{"text":762,"level":47},"Por qué G-Assist puede funcionar sin conexión",{},{"id":765,"data":766,"type":544,"tunes":768},"19447df6a4",{"text":767},"NVIDIA ejecuta el modelo de lenguaje localmente en la GPU GeForce RTX.",{},{"id":770,"data":771,"type":544,"tunes":773},"55aee6ada7",{"text":772},"Eso significa que el asistente principal no requiere un modelo de lenguaje alojado en la nube para cada indicación.",{},{"id":775,"data":776,"type":544,"tunes":778},"f265d1fd96",{"text":777},"NVIDIA dice explícitamente que G-Assist puede funcionar sin conexión para sus capacidades locales.",{},{"id":780,"data":781,"type":544,"tunes":783},"1dc8613200",{"text":782},"Un complemento aún puede usar internet si ese complemento llama a un servicio en línea. La ejecución local del modelo y el acceso en línea de los complementos son cuestiones separadas.",{},{"id":785,"data":786,"type":572,"tunes":788},"ee01c97a66",{"text":787,"level":47},"El costo de la IA local: G-Assist usa tu GPU y VRAM",{},{"id":790,"data":791,"type":544,"tunes":793},"1784ad3fcf",{"text":792},"Ejecutarse localmente brinda privacidad e independencia de un modelo en la nube, pero el trabajo tiene que ejecutarse en algún lugar.",{},{"id":795,"data":796,"type":544,"tunes":798},"1275ab759f",{"text":797},"Para G-Assist, ese algún lugar es la misma GPU GeForce RTX que ya podría estar renderizando tu juego.",{},{"id":800,"data":801,"type":544,"tunes":803},"cec86f847a",{"text":802},"NVIDIA advierte que la GPU asigna brevemente recursos de cómputo a la inferencia de IA cuando G-Assist responde. Si se está ejecutando un juego exigente al mismo tiempo, el juego puede perder temporalmente algo de rendimiento de renderizado mientras el modelo está activo.",{},{"id":805,"data":806,"type":544,"tunes":808},"e6ea700df6",{"text":807},"Los requisitos actuales también especifican objetivos de VRAM libre más allá de la memoria ya utilizada por el juego: aproximadamente 6 GB de VRAM libre para el Modo Razonamiento y 4,5 GB para el Modo Flash.",{},{"id":810,"data":811,"type":552,"tunes":814},"2126f74de5",{"body":812,"title":813,"variant":728},"El juego, Windows, el controlador y otras aplicaciones ya consumen VRAM. El requisito propio de G-Assist se refiere a \u003Cstrong>VRAM libre además de la que ya utiliza la carga de trabajo en ejecución\u003C\u002Fstrong>.","8 GB de VRAM no significa que 8 GB estén disponibles para G-Assist",{},{"id":816,"data":817,"type":737,"tunes":822},"13c0dcb3f3",{"url":818,"title":819,"excerpt":820,"ctaLabel":821},"https:\u002F\u002Ffigure.rocks\u002Fes\u002Fblog\u002Fvram-usage-is-not-vram-requirement-why-a-full-memory-meter-does-not-tell-the-whole-story","El uso de VRAM no es un requisito de VRAM: por qué un medidor de memoria lleno no cuenta toda la historia","Por qué la VRAM instalada, el uso actual, los presupuestos de residencia y la presión de memoria real son cosas diferentes.","Leer la guía de VRAM",{},{"id":824,"data":825,"type":572,"tunes":827},"d684b2c1c8",{"text":826,"level":47},"Modo Razonamiento vs Modo Flash",{},{"id":829,"data":830,"type":544,"tunes":832},"3c1f2379db",{"text":831},"Las versiones actuales de G-Assist separan un Modo Razonamiento más capaz de un Modo Flash más rápido.",{},{"id":834,"data":835,"type":544,"tunes":837},"86f50ebd33",{"text":836},"El Modo Razonamiento está diseñado para decisiones de mayor calidad y puede coordinar múltiples acciones desde un solo prompt. El Modo Flash está optimizado para respuestas más rápidas y una menor demanda de recursos.",{},{"id":839,"data":840,"type":676,"tunes":861},"e7be4789db",{"rows":841,"title":826,"layout":668,"columns":854},[842,846,850],{"id":843,"label":844,"values":845},"goal","Objetivo principal",[13,13],{"id":847,"label":848,"values":849},"vram","VRAM libre recomendada",[13,13],{"id":851,"label":852,"values":853},"use","Mejor para",[13,13],[855,858],{"id":856,"label":857},"reasoning","Modo Razonamiento",{"id":859,"label":860},"flash","Modo Flash",{},{"id":863,"data":864,"type":572,"tunes":866},"cef1bcf553",{"text":865,"level":47},"Qué añaden realmente los complementos",{},{"id":868,"data":869,"type":544,"tunes":871},"d380d5e28b",{"text":870},"Un complemento no reemplaza al modelo de lenguaje. Le da al modelo una nueva acción que puede invocar.",{},{"id":873,"data":874,"type":544,"tunes":876},"434979ebda",{"text":875},"El complemento define funciones, descripciones y parámetros. G-Assist lee esas descripciones, asocia una solicitud del usuario con la función adecuada y le envía argumentos estructurados.",{},{"id":878,"data":879,"type":544,"tunes":881},"21ebe69c79",{"text":880},"Eso permite que el asistente crezca más allá de las funciones integradas de NVIDIA.",{},{"id":883,"data":884,"type":625,"tunes":902},"d0ba7ef284",{"steps":885,"title":901,"orientation":624},[886,889,892,895,898],{"label":887,"description":888},"1. El complemento declara una función","Por ejemplo: set_keyboard_color(color).",{"label":890,"description":891},"2. El manifiesto describe la función","El manifiesto le indica a G-Assist qué hace la función y qué parámetros necesita.",{"label":893,"description":894},"3. El usuario pregunta de forma natural","Por ejemplo: “Pon mi teclado en verde.”",{"label":896,"description":897},"4. El SLM selecciona la función","El modelo asigna la solicitud a la función del complemento y extrae verde como parámetro.",{"label":899,"description":900},"5. El complemento se ejecuta","El complemento se comunica con el software del periférico o el servicio externo.","Cómo funciona un complemento de G-Assist",{},{"id":904,"data":905,"type":572,"tunes":907},"1dc857ce2f",{"text":906,"level":47},"El Protocolo V2 de G-Assist es JSON-RPC 2.0",{},{"id":909,"data":910,"type":544,"tunes":912},"02bdb0566d",{"text":911},"El repositorio público actual de complementos de NVIDIA utiliza el Protocolo V2.",{},{"id":914,"data":915,"type":544,"tunes":917},"f82da294cf",{"text":916},"El Protocolo V2 utiliza JSON-RPC 2.0 con mensajes con prefijo de longitud entre el motor de G-Assist y los complementos.",{},{"id":919,"data":920,"type":544,"tunes":922},"530c3cf9b9",{"text":921},"El motor puede inicializar un complemento, verificar su estado, ejecutar una función, enviar entrada del usuario y apagarlo. Los complementos pueden devolver resultados, transmitir progreso o informar errores.",{},{"id":924,"data":925,"type":676,"tunes":950},"2ce3fa5d75",{"rows":926,"title":942,"layout":668,"columns":943},[927,930,933,936,939],{"id":928,"label":928,"values":929},"initialize",[13,13],{"id":931,"label":931,"values":932},"ping",[13,13],{"id":934,"label":934,"values":935},"execute",[13,13],{"id":937,"label":937,"values":938},"stream",[13,13],{"id":940,"label":940,"values":941},"complete",[13,13],"Los mensajes importantes del Protocolo V2",[944,947],{"id":945,"label":946},"direction","Dirección",{"id":948,"label":949},"purpose","Propósito",{},{"id":952,"data":953,"type":572,"tunes":955},"304fbd9944",{"text":954,"level":47},"Dónde entra en escena MCP",{},{"id":957,"data":958,"type":544,"tunes":960},"fae73881e4",{"text":959},"El propio protocolo de complementos de G-Assist no es MCP. Su sistema actual de complementos públicos utiliza JSON-RPC 2.0.",{},{"id":962,"data":963,"type":544,"tunes":965},"551d11710a",{"text":964},"Sin embargo, un complemento de G-Assist puede conectarse a un servidor MCP.",{},{"id":967,"data":968,"type":544,"tunes":970},"0537fb222a",{"text":969},"La versión actual 0.2.2 de NVIDIA incluye una integración con Elgato que puede invocar acciones expuestas a través del servidor MCP de Elgato Stream Deck.",{},{"id":972,"data":973,"type":552,"tunes":976},"760a86ad19",{"body":974,"title":975,"variant":594},"\u003Cstrong>G-Assist ↔ su complemento:\u003C\u002Fstrong> NVIDIA Protocol V2 \u002F JSON-RPC 2.0.\u003Cbr>\u003Cstrong>Complemento ↔ otro ecosistema de herramientas:\u003C\u002Fstrong> puede usar MCP cuando esa integración lo admita.","Distinción importante de protocolo",{},{"id":978,"data":979,"type":572,"tunes":981},"bd182f72b0",{"text":980,"level":47},"Por qué esto importa más allá de las luces RGB",{},{"id":983,"data":984,"type":544,"tunes":986},"d072506ce2",{"text":985},"La arquitectura de complementos convierte a G-Assist de un asistente fijo en un enrutador de herramientas local.",{},{"id":988,"data":989,"type":544,"tunes":991},"2876e067dc",{"text":990},"El mismo patrón puede conectar un SLM a herramientas de monitoreo, periféricos, API, sistemas de automatización, aplicaciones locales o servicios externos.",{},{"id":993,"data":994,"type":544,"tunes":996},"37689640e9",{"text":995},"La unidad arquitectónica importante, por lo tanto, no es la ventana de chat. Es el límite entre la intención en lenguaje natural y la ejecución estructurada de herramientas.",{},{"id":998,"data":999,"type":572,"tunes":1001},"550fbb42d4",{"text":1000,"level":47},"El stack de agente local",{},{"id":1003,"data":1004,"type":625,"tunes":1028},"8518dbf820",{"steps":1005,"title":1027,"orientation":624},[1006,1009,1012,1015,1018,1021,1024],{"label":1007,"description":1008},"1. Intención del usuario","Comando de lenguaje natural o voz.",{"label":1010,"description":1011},"2. SLM local","Comprende la solicitud y elige una capacidad.",{"label":1013,"description":1014},"3. Conocimiento \u002F contexto","Proporciona conocimiento del producto, recomendaciones o información necesaria para la decisión.",{"label":1016,"description":1017},"4. Estado del sistema en vivo","Proporciona información actual de hardware, controladores, rendimiento o configuración cuando es compatible.",{"label":1019,"description":1020},"5. Selección de herramienta","Se elige una función integrada o un complemento.",{"label":1022,"description":1023},"6. Ejecución estructurada","La función recibe argumentos y realiza la acción real.",{"label":1025,"description":1026},"7. Verificación del resultado","La herramienta devuelve estado o datos y el modelo explica lo que sucedió.","Una forma útil de pensar en G-Assist",{},{"id":1030,"data":1031,"type":572,"tunes":1033},"6c976db763",{"text":1032,"level":47},"Por qué los límites de las herramientas son una característica de seguridad",{},{"id":1035,"data":1036,"type":544,"tunes":1038},"ba65a3b467",{"text":1037},"Un asistente de IA que pudiera ejecutar comandos arbitrarios del sistema operativo sería mucho más poderoso, pero también mucho más difícil de restringir.",{},{"id":1040,"data":1041,"type":544,"tunes":1043},"a932df9663",{"text":1042},"G-Assist, en cambio, expone funciones definidas con parámetros conocidos.",{},{"id":1045,"data":1046,"type":544,"tunes":1048},"7d1d45c83d",{"text":1047},"Eso significa que el modelo solo puede realizar acciones que el conjunto de funciones integradas o los complementos instalados pongan a disposición.",{},{"id":1050,"data":1051,"type":544,"tunes":1053},"10460fda21",{"text":1052},"Esto no elimina todos los riesgos, especialmente con complementos de terceros, pero crea un límite de permisos y capacidades más claro que el acceso irrestricto al shell.",{},{"id":1055,"data":1056,"type":572,"tunes":1058},"25049448ea",{"text":1057,"level":47},"El límite de autoridad de las herramientas",{},{"id":1060,"data":1061,"type":676,"tunes":1085},"d3ff568795",{"rows":1062,"title":1078,"layout":668,"columns":1079},[1063,1066,1070,1074],{"id":703,"label":1064,"values":1065},"Ajuste de GPU",[13,13],{"id":1067,"label":1068,"values":1069},"files","Archivos",[13,13],{"id":1071,"label":1072,"values":1073},"web","Internet",[13,13],{"id":1075,"label":1076,"values":1077},"device","Control de periféricos",[13,13],"Lo que el asistente puede entender vs lo que se le permite hacer",[1080,1082],{"id":652,"label":1081},"El modelo puede entender",{"id":1083,"label":1084},"authority","Autoridad de la herramienta",{},{"id":1087,"data":1088,"type":572,"tunes":1090},"8fd8e35c51",{"text":1089,"level":47},"Por qué G-Assist puede reducir temporalmente el rendimiento del juego",{},{"id":1092,"data":1093,"type":544,"tunes":1095},"f75234546f",{"text":1094},"La arquitectura de modelo local tiene una consecuencia directa: la IA y el juego pueden competir por la misma GPU.",{},{"id":1097,"data":1098,"type":544,"tunes":1100},"28bc054afa",{"text":1099},"NVIDIA advierte explícitamente que la tasa de renderizado o la velocidad de inferencia pueden disminuir brevemente mientras G-Assist procesa una solicitud durante una carga de trabajo intensiva de GPU.",{},{"id":1102,"data":1103,"type":544,"tunes":1105},"0823573513",{"text":1104},"Una vez que finaliza la inferencia, esos recursos de GPU vuelven al juego.",{},{"id":1107,"data":1108,"type":544,"tunes":1110},"905eed843d",{"text":1109},"Esto es diferente de un asistente en la nube, donde la mayor parte de la inferencia del modelo ocurre en un servidor remoto y no consume la GPU de juego.",{},{"id":1112,"data":1113,"type":572,"tunes":1115},"839c0e3988",{"text":1114,"level":47},"La prueba de recursos del asistente local",{},{"id":1117,"data":1118,"type":625,"tunes":1139},"affa0ee1b3",{"steps":1119,"title":1138,"orientation":624},[1120,1123,1126,1129,1132,1135],{"label":1121,"description":1122},"1. Verifica la VRAM instalada","La línea base actual de G-Assist es una GPU RTX con al menos 6 GB de VRAM.",{"label":1124,"description":1125},"2. Verifica la VRAM libre durante el juego real","La capacidad instalada no es lo mismo que la capacidad libre.",{"label":1127,"description":1128},"3. Elige el modo Razonamiento o Flash adecuadamente","Usa el modo más ligero cuando la presión de recursos importa más que el razonamiento profundo.",{"label":1130,"description":1131},"4. Mide las caídas de velocidad de fotogramas durante la inferencia","Observa el juego mientras le pides a G-Assist que realice tareas.",{"label":1133,"description":1134},"5. Inspecciona la autoridad de las herramientas","Conoce exactamente qué funciones integradas y complementos pueden cambiar tu sistema.",{"label":1136,"description":1137},"6. Trata los complementos de terceros como software","Revisa su código fuente, permisos, acceso a la red y credenciales almacenadas.","Cómo juzgar si un asistente de juego local se adapta a tu PC",{},{"id":1141,"data":1142,"type":572,"tunes":1144},"ca52c48d2e",{"text":1143,"level":47},"Por qué los complementos merecen el mismo escrutinio que cualquier otro software",{},{"id":1146,"data":1147,"type":544,"tunes":1149},"8e6a760529",{"text":1148},"Un complemento puede conectar el asistente a API, aplicaciones periféricas y servicios en línea.",{},{"id":1151,"data":1152,"type":544,"tunes":1154},"be793240e1",{"text":1153},"Eso significa que un complemento puede manejar valores de configuración, credenciales o solicitudes externas dependiendo de para qué fue creado.",{},{"id":1156,"data":1157,"type":544,"tunes":1159},"3c6c4c9aaf",{"text":1158},"El repositorio de NVIDIA proporciona explícitamente una ubicación config.json para la configuración de complementos y advierte a los desarrolladores que no confirmen credenciales.",{},{"id":1161,"data":1162,"type":544,"tunes":1164},"03413f935a",{"text":1163},"Por lo tanto, la pregunta de seguridad correcta no es solo \"¿G-Assist es local?\" sino también \"¿A qué se conecta cada complemento instalado?\"",{},{"id":1166,"data":1167,"type":572,"tunes":1169},"f473162e86",{"text":1168,"level":47},"G-Assist se parece más a un agente que a un chatbot",{},{"id":1171,"data":1172,"type":544,"tunes":1174},"59dd3df9c2",{"text":1173},"Un chatbot principalmente produce lenguaje.",{},{"id":1176,"data":1177,"type":544,"tunes":1179},"188cb4bb40",{"text":1178},"Un agente interpreta un objetivo, observa información relevante, elige una acción, llama a una herramienta y evalúa el resultado.",{},{"id":1181,"data":1182,"type":544,"tunes":1184},"117d1032f1",{"text":1183},"G-Assist ahora se ajusta mucho más a esa segunda descripción porque puede coordinar múltiples acciones, inspeccionar el estado compatible de la PC e invocar herramientas reales.",{},{"id":1186,"data":1187,"type":737,"tunes":1192},"ef132fc518",{"url":1188,"title":1189,"excerpt":1190,"ctaLabel":1191},"https:\u002F\u002Ffigure.rocks\u002Fes\u002Fblog\u002Fpubg-ally-shows-why-ai-teammates-need-two-brains-fast-reflexes-and-slow-reasoning","PUBG Ally muestra por qué los compañeros de equipo de IA necesitan dos cerebros: reflejos rápidos y razonamiento lento","Un ejemplo de arquitectura de agente de juego que muestra la separación entre razonamiento, estado en vivo, herramientas y ejecución determinista.","Lee la guía de arquitectura de PUBG Ally",{},{"id":1194,"data":1195,"type":572,"tunes":1197},"9f8fa308a5",{"text":1196,"level":47},"Lo que G-Assist todavía no es",{},{"id":1199,"data":1200,"type":544,"tunes":1202},"f5f24e8aa3",{"text":1201},"NVIDIA describe explícitamente a G-Assist como un asistente local especializado, no como una IA conversacional de propósito general.",{},{"id":1204,"data":1205,"type":544,"tunes":1207},"e4d187593f",{"text":1206},"Su valor proviene de comprender un conjunto enfocado de tareas de PC y gaming y de tener herramientas conectadas a esas tareas.",{},{"id":1209,"data":1210,"type":544,"tunes":1212},"0c1b7f8948",{"text":1211},"Ese enfoque es importante porque un modelo local más pequeño puede ser útil cuando el sistema que lo rodea le proporciona herramientas sólidas y límites claros.",{},{"id":1214,"data":1215,"type":572,"tunes":1217},"79a5920e77",{"text":1216,"level":47},"¿Qué cambió en las versiones actuales?",{},{"id":1219,"data":1220,"type":544,"tunes":1222},"e78d96ef6c",{"text":1221},"El historial de versiones públicas actual muestra que G-Assist avanza constantemente hacia un agente local más capaz.",{},{"id":1224,"data":1225,"type":668,"tunes":1246},"87dd1d3d3c",{"content":1226,"stretched":1245,"withHeadings":15},[1227,1230,1233,1236,1239,1242],[1228,1229],"Versión","Cambio importante",[1231,1232],"0.1.17","Modelo más ligero, todas las GPU RTX con 6 GB+ de VRAM, complementos de la comunidad",[1234,1235],"0.1.18","Optimización para portátiles, controles de BatteryBoost y WhisperMode",[1237,1238],"0.2","Modo de razonamiento, modo flash, prompts de múltiples acciones, nuevos controles de dispositivo",[1240,1241],"0.2.1","Sistema de conocimiento mejorado, mejores recomendaciones, controles de funciones RTX",[1243,1244],"0.2.2","Integración con Elgato Stream Deck a través de un servidor MCP",false,{},{"id":1248,"data":1249,"type":572,"tunes":1251},"09bde21a8d",{"text":1250,"level":47},"¿Qué cambiaría esta respuesta?",{},{"id":1253,"data":1254,"type":544,"tunes":1256},"b0e7ca01a4",{"text":1255},"G-Assist sigue siendo experimental y su arquitectura está evolucionando.",{},{"id":1258,"data":1259,"type":544,"tunes":1261},"537568c224",{"text":1260},"El tamaño del modelo, los requisitos de VRAM, el conjunto de herramientas, el protocolo de complementos y la compatibilidad de hardware pueden cambiar en futuras versiones.",{},{"id":1263,"data":1264,"type":544,"tunes":1266},"fe24516c11",{"text":1265},"Una versión futura también podría trasladar parte de la inferencia a una NPU o introducir una integración MCP más amplia, pero eso no debe darse por sentado hasta que NVIDIA lo documente.",{},{"id":1268,"data":1269,"type":572,"tunes":1271},"0e31e2d54d",{"text":1270,"level":47},"Limitaciones",{},{"id":1273,"data":1274,"type":544,"tunes":1276},"a7881e11e1",{"text":1275},"Este artículo describe la documentación pública del producto G-Assist de NVIDIA y la arquitectura pública de complementos a septiembre de 2026.",{},{"id":1278,"data":1279,"type":544,"tunes":1281},"71ebc16fda",{"text":1280},"NVIDIA no documenta públicamente cada detalle interno de su canal de conocimiento y recomendaciones, por lo que este artículo no afirma que cada respuesta de conocimiento utilice RAG ni ninguna otra implementación de recuperación específica.",{},{"id":1283,"data":1284,"type":544,"tunes":1286},"add780d6ba",{"text":1285},"Los complementos de terceros pueden tener un comportamiento y un acceso a la red más allá de las funciones integradas de NVIDIA, por lo que cada complemento debe evaluarse por separado.",{},{"id":1288,"data":1289,"type":572,"tunes":1291},"f7a38af5be",{"text":1290,"level":47},"Conclusión",{},{"id":1293,"data":1294,"type":544,"tunes":1296},"f1308f3e80",{"text":1295},"La forma más sencilla de entender Project G-Assist es dejar de pensar en él como un chatbot.",{},{"id":1298,"data":1299,"type":544,"tunes":1301},"b102a861bb",{"text":1300},"El SLM local interpreta tu solicitud. El conocimiento le ayuda a comprender el problema. La información del sistema en vivo le indica qué es cierto en tu PC. Una función integrada o un complemento define lo que realmente se le permite hacer. La herramienta realiza la acción y devuelve el resultado.",{},{"id":1303,"data":1304,"type":544,"tunes":1306},"da805fabf0",{"text":1305},"Eso es una arquitectura de agente local.",{},{"id":1308,"data":1309,"type":544,"tunes":1311},"675aecee06",{"text":1310},"Su idea más fuerte no es que un modelo de 8 mil millones de parámetros pueda hablar sobre tu GPU. Es que un modelo local relativamente pequeño puede volverse útil cuando está conectado a herramientas bien definidas, al estado actual y a un límite de acción controlado.",{},{"id":1313,"data":1314,"type":572,"tunes":1316},"0354b8daaf",{"text":1315,"level":47},"Preguntas frecuentes",{},{"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","No. Su modelo de lenguaje principal se ejecuta localmente en la GPU GeForce RTX y puede funcionar sin conexión para las funciones locales compatibles.","¿Es G-Assist un chatbot en la nube?",{"id":1326,"answer":1327,"question":1328},"faq2","NVIDIA actualmente lo describe como un modelo instruct local basado en Llama con 8 mil millones de parámetros.","¿Qué modelo utiliza G-Assist?",{"id":1330,"answer":1331,"question":1332},"faq3","No. El modelo selecciona una función integrada o un complemento compatible, y esa herramienta realiza el cambio real.","¿El modelo de IA cambia directamente la configuración de mi GPU?",{"id":1334,"answer":1335,"question":1336},"faq4","Sí. NVIDIA recomienda VRAM libre adicional para el asistente y advierte que la inferencia puede reducir brevemente el rendimiento de renderizado del juego.","¿G-Assist utiliza VRAM mientras se juega?",{"id":1338,"answer":1339,"question":1340},"faq5","No directamente. El protocolo de complementos actual propio de G-Assist utiliza JSON-RPC 2.0, aunque un complemento puede conectarse a un servidor MCP.","¿Los complementos de G-Assist son complementos MCP?",{"id":1342,"answer":1343,"question":1344},"faq6","Su asistente local puede, pero los complementos individuales aún pueden requerir acceso a internet si llaman a servicios en línea.","¿Puede G-Assist funcionar sin conexión?",{"id":1346,"answer":1347,"question":1348},"faq7","NVIDIA lo describe como un asistente especializado para PC y juegos en lugar de un modelo conversacional amplio de propósito general.","¿Es G-Assist un asistente de IA general?","NVIDIA Project G-Assist en lenguaje sencillo","faq",{},{"id":1353,"data":1354,"type":572,"tunes":1356},"8733f298cd",{"text":1355,"level":47},"Glosario",{},{"id":1358,"data":1359,"type":1394,"tunes":1395},"407257a6d1",{"title":1360,"entries":1361},"Términos clave de G-Assist",[1362,1366,1370,1374,1378,1382,1386,1390],{"term":1363,"anchor":1364,"definition":1365},"SLM","slm","Small Language Model (Modelo de Lenguaje Pequeño), un modelo de lenguaje compacto diseñado para ejecutarse localmente con requisitos de hardware menores que los grandes modelos en la nube.",{"term":1367,"anchor":1368,"definition":1369},"Llamada a herramientas","tool-calling","El proceso mediante el cual un modelo de lenguaje elige una función definida y proporciona argumentos estructurados para que otro componente realice una acción.",{"term":1371,"anchor":1372,"definition":1373},"Complemento","plugin","Una extensión que expone funciones o integraciones adicionales a G-Assist.",{"term":1375,"anchor":1376,"definition":1377},"JSON-RPC 2.0","json-rpc","El protocolo de mensajes estructurados utilizado por el sistema de complementos actual G-Assist Protocol V2.",{"term":1379,"anchor":1380,"definition":1381},"MCP","mcp","Model Context Protocol (Protocolo de Contexto de Modelo), un protocolo separado de interoperabilidad de herramientas y contexto que algunas integraciones externas pueden exponer.",{"term":1383,"anchor":1384,"definition":1385},"Límite Herramienta-Autoridad","tool-authority-boundary","Un modelo de Figure Rocks que separa lo que un modelo de IA puede entender de lo que sus herramientas conectadas realmente le permiten hacer.",{"term":1387,"anchor":1388,"definition":1389},"Pila de Agente Local","local-agent-stack","Un modelo de Figure Rocks que combina la intención del usuario, el SLM local, el conocimiento, el estado en vivo, la selección de herramientas, la ejecución y la verificación de resultados.",{"term":1391,"anchor":1392,"definition":1393},"Prueba de Recursos del Asistente Local","local-assistant-resource-test","Un flujo de trabajo de Figure Rocks para evaluar VRAM, costo de inferencia, permisos de herramientas y riesgo de complementos antes de usar un asistente de juegos local.","glossary",{},{"id":1397,"data":1398,"type":572,"tunes":1400},"3ab856f2b4",{"text":1399,"level":47},"Fuentes primarias",{},{"id":1402,"data":1403,"type":1409,"tunes":1410},"367d2e3548",{"link":1404,"meta":1405},"https:\u002F\u002Fwww.nvidia.com\u002Fen-us\u002Fsoftware\u002Fnvidia-app\u002Fg-assist\u002F",{"image":1406,"title":1407,"description":1408},{"url":13},"NVIDIA — Project G-Assist","Página oficial actual del producto que cubre el modelo local, las funciones compatibles, los modos Reasoning y Flash, los requisitos de VRAM, los controles RTX, los complementos y la integración de Elgato basada en MCP versión 0.2.2.","linkTool",{},{"id":1412,"data":1413,"type":1409,"tunes":1419},"5641b514c0",{"link":1414,"meta":1415},"https:\u002F\u002Fgithub.com\u002FNVIDIA\u002FG-Assist",{"image":1416,"title":1417,"description":1418},{"url":13},"NVIDIA — G-Assist GitHub","Repositorio público oficial de complementos que documenta la arquitectura de módulos de G-Assist, el descubrimiento de complementos y el Protocol V2.",{},{"id":1421,"data":1422,"type":1409,"tunes":1428},"22a5b77a1b",{"link":1423,"meta":1424},"https:\u002F\u002Fgithub.com\u002FNVIDIA\u002FG-Assist\u002Fblob\u002Fmain\u002FPLUGIN_MIGRATION_GUIDE_V2.md",{"image":1425,"title":1426,"description":1427},{"url":13},"NVIDIA — Guía de migración a G-Assist Protocol V2","Documentación oficial del protocolo que cubre JSON-RPC 2.0, inicialización, verificaciones de estado, ejecución, transmisión, finalización y comportamiento del SDK de complementos.",{},{"id":1430,"data":1431,"type":1409,"tunes":1437},"cc3301d997",{"link":1432,"meta":1433},"https:\u002F\u002Fblogs.nvidia.com\u002Fblog\u002Frtx-ai-garage-gamescom-g-assist-rtx-remix\u002F",{"image":1434,"title":1435,"description":1436},{"url":13},"Blog de NVIDIA — Modelo ligero de G-Assist","Antecedentes oficiales sobre el modelo G-Assist de menor memoria, compatibilidad con GPU RTX de 6 GB y el centro de complementos de la comunidad.",{},"2.31","NVIDIA Project G-Assist parece un chatbot, pero su arquitectura real se acerca más a la de un agente de IA local. Un modelo de lenguaje pequeño interpreta tu solicitud, el estado del sistema proporciona información actual del PC, las herramientas realizan acciones reales y los complementos amplían aquello que el asistente tiene permitido controlar.","\u002Fuploads\u002F2026\u002F09\u002Fnvidia-g-assist-is-not-just-a-chatbot-how-its-local-slm-tools-plug-ins-and-mcp-actually-work-1790407739731-smbdnv.webp","nvidia-g-assist-is-not-just-a-chatbot-how-its-local-slm-tools-plug-ins-and-mcp-actually-work-1790407739731-smbdnv","PUBLISHED","2026-09-26T03:27:00.000Z","2026-09-26T07:27:18.273Z","2026-09-26T07:34:28.920Z",{"en":1447,"de":1448,"sr":1449,"es":1450,"fr":1451,"it":1452,"ru":1453,"zh":1454},"\u002Fblog\u002Fnvidia-g-assist-is-not-just-a-chatbot-how-its-local-slm-tools-plug-ins-and-mcp-actually-work","\u002Fde\u002Fblog\u002Fnvidia-g-assist-is-not-just-a-chatbot-how-its-local-slm-tools-plug-ins-and-mcp-actually-work","\u002Fsr\u002Fblog\u002Fnvidia-g-assist-is-not-just-a-chatbot-how-its-local-slm-tools-plug-ins-and-mcp-actually-work","\u002Fes\u002Fblog\u002Fnvidia-g-assist-is-not-just-a-chatbot-how-its-local-slm-tools-plug-ins-and-mcp-actually-work","\u002Ffr\u002Fblog\u002Fnvidia-g-assist-is-not-just-a-chatbot-how-its-local-slm-tools-plug-ins-and-mcp-actually-work","\u002Fit\u002Fblog\u002Fnvidia-g-assist-is-not-just-a-chatbot-how-its-local-slm-tools-plug-ins-and-mcp-actually-work","\u002Fru\u002Fblog\u002Fnvidia-g-assist-is-not-just-a-chatbot-how-its-local-slm-tools-plug-ins-and-mcp-actually-work","\u002Fzh\u002Fblog\u002Fnvidia-g-assist-is-not-just-a-chatbot-how-its-local-slm-tools-plug-ins-and-mcp-actually-work",[1456,1460,1464,1468,1472],{"id":1457,"name":1458,"slug":1459},171,"GPUs y controladores","gpus-and-drivers",{"id":1461,"name":1462,"slug":1463},67,"Windows y controladores","windows-and-drivers",{"id":1465,"name":1466,"slug":1467},68,"Ajustes del juego","in-game-settings",{"id":1469,"name":1470,"slug":1471},69,"Ajustes de pantalla","display-settings",{"id":1473,"name":1474,"slug":1475},197,"Los mejores ajustes primero","best-settings-first",{"id":283,"login":1477,"email":1478,"displayName":1479},"aleksandar","aleksandar@stajic.de","Aleksandar Stajic",[1481,2193],{"lang":8,"title":1482,"content":1483,"contentJson":1484,"excerpt":2192},"NVIDIA G-Assist Is Not Just a Chatbot: How Its Local SLM, Tools, Plug-ins and MCP Actually Work","{\"time\":1790407740910,\"blocks\":[{\"id\":\"3cee674645\",\"type\":\"paragraph\",\"data\":{\"text\":\"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.\"},\"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":1485,"blocks":1486,"version":2191},1790407740910,[1487,1491,1496,1501,1505,1509,1513,1517,1521,1526,1530,1553,1557,1561,1565,1569,1591,1595,1599,1603,1607,1625,1630,1636,1640,1644,1648,1652,1656,1660,1664,1668,1672,1676,1680,1684,1688,1692,1697,1704,1708,1712,1716,1734,1738,1742,1746,1750,1770,1774,1778,1782,1786,1806,1810,1814,1818,1822,1827,1831,1835,1839,1843,1847,1873,1877,1881,1885,1889,1893,1897,1918,1922,1926,1930,1934,1938,1942,1965,1969,1973,1977,1981,1985,1989,1993,1997,2001,2008,2012,2016,2020,2024,2028,2032,2049,2053,2057,2061,2065,2069,2073,2077,2081,2085,2089,2093,2097,2101,2105,2131,2135,2161,2165,2171,2177,2184],{"id":541,"data":1488,"type":544,"tunes":1490},{"text":1489},"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":1492,"type":552,"tunes":1495},{"body":1493,"title":1494,"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":1497,"type":552,"tunes":1500},{"body":1498,"title":1499,"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":1502,"type":566,"tunes":1504},{"title":1503,"maxLevel":565,"minLevel":47},"Contents",{},{"id":569,"data":1506,"type":572,"tunes":1508},{"text":1507,"level":47},"First: what is the AI model actually doing?",{},{"id":575,"data":1510,"type":544,"tunes":1512},{"text":1511},"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":1514,"type":544,"tunes":1516},{"text":1515},"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":1518,"type":544,"tunes":1520},{"text":1519},"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":1522,"type":552,"tunes":1525},{"body":1523,"title":1524,"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":1527,"type":572,"tunes":1529},{"text":1528,"level":47},"The G-Assist action pipeline",{},{"id":602,"data":1531,"type":625,"tunes":1552},{"steps":1532,"title":1551,"orientation":624},[1533,1536,1539,1542,1545,1548],{"label":1534,"description":1535},"1. You give a natural-language request","For example: “Optimize this game for performance.”",{"label":1537,"description":1538},"2. The local SLM interprets the request","It determines the user's intent and identifies which supported function or plug-in is relevant.",{"label":1540,"description":1541},"3. The system chooses a tool","That could be a built-in graphics-setting function, driver check, monitoring function or community plug-in.",{"label":1543,"description":1544},"4. Arguments are extracted","The model converts the request into structured values such as game name, mode, fan profile or DLSS setting.",{"label":1546,"description":1547},"5. The tool executes","The tool talks to the NVIDIA App, operating system, peripheral software or another service.",{"label":1549,"description":1550},"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":1554,"type":572,"tunes":1556},{"text":1555,"level":47},"This is tool calling, not magic PC control",{},{"id":633,"data":1558,"type":544,"tunes":1560},{"text":1559},"A language model cannot safely control an arbitrary computer simply because it understands English.",{},{"id":638,"data":1562,"type":544,"tunes":1564},{"text":1563},"It needs a defined interface that says which actions exist, which arguments they accept and what the tool returns.",{},{"id":643,"data":1566,"type":544,"tunes":1568},{"text":1567},"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":1570,"type":676,"tunes":1590},{"rows":1571,"title":1584,"layout":668,"columns":1585},[1572,1575,1578,1581],{"id":652,"label":1573,"values":1574},"Understand “turn on DLSS”",[13,13],{"id":656,"label":1576,"values":1577},"Choose the correct function",[13,13],{"id":660,"label":1579,"values":1580},"Actually change the setting",[13,13],{"id":664,"label":1582,"values":1583},"Explain the result",[13,13],"Language model vs tool",[1586,1588],{"id":671,"label":1587},"Language model",{"id":674,"label":1589},"Tool \u002F plug-in",{},{"id":679,"data":1592,"type":572,"tunes":1594},{"text":1593,"level":47},"What is the knowledge layer?",{},{"id":684,"data":1596,"type":544,"tunes":1598},{"text":1597},"G-Assist also has a knowledge layer for answering questions and making recommendations.",{},{"id":689,"data":1600,"type":544,"tunes":1602},{"text":1601},"NVIDIA says version 0.2.1 introduced an improved knowledge system that helps G-Assist make more accurate settings recommendations.",{},{"id":694,"data":1604,"type":544,"tunes":1606},{"text":1605},"That knowledge layer is different from the live state of your PC.",{},{"id":699,"data":1608,"type":676,"tunes":1624},{"rows":1609,"title":1618,"layout":668,"columns":1619},[1610,1612,1615],{"id":703,"label":704,"values":1611},[13,13],{"id":707,"label":1613,"values":1614},"Game settings",[13,13],{"id":711,"label":1616,"values":1617},"Performance",[13,13],"Knowledge vs live PC state",[1620,1622],{"id":717,"label":1621},"Knowledge",{"id":720,"label":1623},"Live state",{},{"id":724,"data":1626,"type":552,"tunes":1629},{"body":1627,"title":1628,"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":1631,"type":737,"tunes":1635},{"url":733,"title":1632,"excerpt":1633,"ctaLabel":1634},"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":1637,"type":572,"tunes":1639},{"text":1638,"level":47},"Does G-Assist use RAG?",{},{"id":745,"data":1641,"type":544,"tunes":1643},{"text":1642},"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":1645,"type":544,"tunes":1647},{"text":1646},"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":1649,"type":544,"tunes":1651},{"text":1650},"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":1653,"type":572,"tunes":1655},{"text":1654,"level":47},"Why G-Assist can work offline",{},{"id":765,"data":1657,"type":544,"tunes":1659},{"text":1658},"NVIDIA runs the language model locally on the GeForce RTX GPU.",{},{"id":770,"data":1661,"type":544,"tunes":1663},{"text":1662},"That means the core assistant does not require a cloud-hosted language model for every prompt.",{},{"id":775,"data":1665,"type":544,"tunes":1667},{"text":1666},"NVIDIA explicitly says G-Assist can run offline for its local capabilities.",{},{"id":780,"data":1669,"type":544,"tunes":1671},{"text":1670},"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":1673,"type":572,"tunes":1675},{"text":1674,"level":47},"The local-AI cost: G-Assist uses your GPU and VRAM",{},{"id":790,"data":1677,"type":544,"tunes":1679},{"text":1678},"Running locally gives privacy and independence from a cloud model, but the work has to execute somewhere.",{},{"id":795,"data":1681,"type":544,"tunes":1683},{"text":1682},"For G-Assist, that somewhere is the same GeForce RTX GPU that may already be rendering your game.",{},{"id":800,"data":1685,"type":544,"tunes":1687},{"text":1686},"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":1689,"type":544,"tunes":1691},{"text":1690},"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":1693,"type":552,"tunes":1696},{"body":1694,"title":1695,"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":1698,"type":737,"tunes":1703},{"url":1699,"title":1700,"excerpt":1701,"ctaLabel":1702},"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":1705,"type":572,"tunes":1707},{"text":1706,"level":47},"Reasoning Mode vs Flash Mode",{},{"id":829,"data":1709,"type":544,"tunes":1711},{"text":1710},"Current G-Assist versions separate a more capable Reasoning Mode from a faster Flash Mode.",{},{"id":834,"data":1713,"type":544,"tunes":1715},{"text":1714},"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":1717,"type":676,"tunes":1733},{"rows":1718,"title":1706,"layout":668,"columns":1728},[1719,1722,1725],{"id":843,"label":1720,"values":1721},"Primary goal",[13,13],{"id":847,"label":1723,"values":1724},"Recommended free VRAM",[13,13],{"id":851,"label":1726,"values":1727},"Best fit",[13,13],[1729,1731],{"id":856,"label":1730},"Reasoning Mode",{"id":859,"label":1732},"Flash Mode",{},{"id":863,"data":1735,"type":572,"tunes":1737},{"text":1736,"level":47},"What plug-ins actually add",{},{"id":868,"data":1739,"type":544,"tunes":1741},{"text":1740},"A plug-in does not replace the language model. It gives the model a new action it is allowed to call.",{},{"id":873,"data":1743,"type":544,"tunes":1745},{"text":1744},"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":1747,"type":544,"tunes":1749},{"text":1748},"That lets the assistant grow beyond NVIDIA's built-in functions.",{},{"id":883,"data":1751,"type":625,"tunes":1769},{"steps":1752,"title":1768,"orientation":624},[1753,1756,1759,1762,1765],{"label":1754,"description":1755},"1. Plug-in declares a function","For example: set_keyboard_color(color).",{"label":1757,"description":1758},"2. Manifest describes the function","The manifest tells G-Assist what the function does and what parameters it needs.",{"label":1760,"description":1761},"3. User asks naturally","For example: “Make my keyboard green.”",{"label":1763,"description":1764},"4. SLM selects the function","The model maps the request to the plug-in function and extracts green as the parameter.",{"label":1766,"description":1767},"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":1771,"type":572,"tunes":1773},{"text":1772,"level":47},"G-Assist Protocol V2 is JSON-RPC 2.0",{},{"id":909,"data":1775,"type":544,"tunes":1777},{"text":1776},"NVIDIA's current public plug-in repository uses Protocol V2.",{},{"id":914,"data":1779,"type":544,"tunes":1781},{"text":1780},"Protocol V2 uses JSON-RPC 2.0 with length-prefixed messages between the G-Assist engine and plug-ins.",{},{"id":919,"data":1783,"type":544,"tunes":1785},{"text":1784},"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":1787,"type":676,"tunes":1805},{"rows":1788,"title":1799,"layout":668,"columns":1800},[1789,1791,1793,1795,1797],{"id":928,"label":928,"values":1790},[13,13],{"id":931,"label":931,"values":1792},[13,13],{"id":934,"label":934,"values":1794},[13,13],{"id":937,"label":937,"values":1796},[13,13],{"id":940,"label":940,"values":1798},[13,13],"The important Protocol V2 messages",[1801,1803],{"id":945,"label":1802},"Direction",{"id":948,"label":1804},"Purpose",{},{"id":952,"data":1807,"type":572,"tunes":1809},{"text":1808,"level":47},"Where MCP enters the picture",{},{"id":957,"data":1811,"type":544,"tunes":1813},{"text":1812},"G-Assist's own plug-in protocol is not MCP. Its current public plug-in system uses JSON-RPC 2.0.",{},{"id":962,"data":1815,"type":544,"tunes":1817},{"text":1816},"However, a G-Assist plug-in can connect to an MCP server.",{},{"id":967,"data":1819,"type":544,"tunes":1821},{"text":1820},"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":1823,"type":552,"tunes":1826},{"body":1824,"title":1825,"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":1828,"type":572,"tunes":1830},{"text":1829,"level":47},"Why this matters beyond RGB lights",{},{"id":983,"data":1832,"type":544,"tunes":1834},{"text":1833},"The plug-in architecture turns G-Assist from a fixed assistant into a local tool router.",{},{"id":988,"data":1836,"type":544,"tunes":1838},{"text":1837},"The same pattern can connect an SLM to monitoring tools, peripherals, APIs, automation systems, local applications or external services.",{},{"id":993,"data":1840,"type":544,"tunes":1842},{"text":1841},"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":1844,"type":572,"tunes":1846},{"text":1845,"level":47},"The Local Agent Stack",{},{"id":1003,"data":1848,"type":625,"tunes":1872},{"steps":1849,"title":1871,"orientation":624},[1850,1853,1856,1859,1862,1865,1868],{"label":1851,"description":1852},"1. User intent","Natural language or voice command.",{"label":1854,"description":1855},"2. Local SLM","Understands the request and chooses a capability.",{"label":1857,"description":1858},"3. Knowledge \u002F context","Provides product knowledge, recommendations or information needed for the decision.",{"label":1860,"description":1861},"4. Live system state","Provides current hardware, driver, performance or configuration information when supported.",{"label":1863,"description":1864},"5. Tool selection","Built-in function or plug-in is chosen.",{"label":1866,"description":1867},"6. Structured execution","The function receives arguments and performs the real action.",{"label":1869,"description":1870},"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":1874,"type":572,"tunes":1876},{"text":1875,"level":47},"Why tool boundaries are a safety feature",{},{"id":1035,"data":1878,"type":544,"tunes":1880},{"text":1879},"An AI assistant that could execute arbitrary operating-system commands would be far more powerful, but also much harder to constrain.",{},{"id":1040,"data":1882,"type":544,"tunes":1884},{"text":1883},"G-Assist instead exposes defined functions with known parameters.",{},{"id":1045,"data":1886,"type":544,"tunes":1888},{"text":1887},"That means the model can only perform actions that the built-in function set or installed plug-ins make available.",{},{"id":1050,"data":1890,"type":544,"tunes":1892},{"text":1891},"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":1894,"type":572,"tunes":1896},{"text":1895,"level":47},"The Tool-Authority Boundary",{},{"id":1060,"data":1898,"type":676,"tunes":1917},{"rows":1899,"title":1911,"layout":668,"columns":1912},[1900,1903,1906,1908],{"id":703,"label":1901,"values":1902},"GPU tuning",[13,13],{"id":1067,"label":1904,"values":1905},"Files",[13,13],{"id":1071,"label":1072,"values":1907},[13,13],{"id":1075,"label":1909,"values":1910},"Peripheral control",[13,13],"What the assistant can understand vs what it is allowed to do",[1913,1915],{"id":652,"label":1914},"Model may understand",{"id":1083,"label":1916},"Tool authority",{},{"id":1087,"data":1919,"type":572,"tunes":1921},{"text":1920,"level":47},"Why G-Assist can temporarily reduce game performance",{},{"id":1092,"data":1923,"type":544,"tunes":1925},{"text":1924},"The local-model architecture has a straightforward consequence: the AI and the game can compete for the same GPU.",{},{"id":1097,"data":1927,"type":544,"tunes":1929},{"text":1928},"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":1931,"type":544,"tunes":1933},{"text":1932},"Once inference finishes, those GPU resources return to the game.",{},{"id":1107,"data":1935,"type":544,"tunes":1937},{"text":1936},"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":1939,"type":572,"tunes":1941},{"text":1940,"level":47},"The Local Assistant Resource Test",{},{"id":1117,"data":1943,"type":625,"tunes":1964},{"steps":1944,"title":1963,"orientation":624},[1945,1948,1951,1954,1957,1960],{"label":1946,"description":1947},"1. Check installed VRAM","The current G-Assist baseline is an RTX GPU with at least 6 GB VRAM.",{"label":1949,"description":1950},"2. Check free VRAM during the actual game","Installed capacity is not the same as free capacity.",{"label":1952,"description":1953},"3. Choose Reasoning or Flash Mode appropriately","Use the lighter mode when resource pressure matters more than deep reasoning.",{"label":1955,"description":1956},"4. Measure inference-time frame-rate dips","Watch the game while asking G-Assist to perform tasks.",{"label":1958,"description":1959},"5. Inspect tool authority","Know exactly which built-in functions and plug-ins can change your system.",{"label":1961,"description":1962},"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":1966,"type":572,"tunes":1968},{"text":1967,"level":47},"Why plug-ins deserve the same scrutiny as any other software",{},{"id":1146,"data":1970,"type":544,"tunes":1972},{"text":1971},"A plug-in can connect the assistant to APIs, peripheral applications and online services.",{},{"id":1151,"data":1974,"type":544,"tunes":1976},{"text":1975},"That means a plug-in may handle configuration values, credentials or external requests depending on what it was built to do.",{},{"id":1156,"data":1978,"type":544,"tunes":1980},{"text":1979},"NVIDIA's repository explicitly provides a config.json location for plug-in settings and warns developers not to commit credentials.",{},{"id":1161,"data":1982,"type":544,"tunes":1984},{"text":1983},"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":1986,"type":572,"tunes":1988},{"text":1987,"level":47},"G-Assist is closer to an agent than a chatbot",{},{"id":1171,"data":1990,"type":544,"tunes":1992},{"text":1991},"A chatbot mainly produces language.",{},{"id":1176,"data":1994,"type":544,"tunes":1996},{"text":1995},"An agent interprets a goal, observes relevant information, chooses an action, calls a tool and evaluates the result.",{},{"id":1181,"data":1998,"type":544,"tunes":2000},{"text":1999},"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":2002,"type":737,"tunes":2007},{"url":2003,"title":2004,"excerpt":2005,"ctaLabel":2006},"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":2009,"type":572,"tunes":2011},{"text":2010,"level":47},"What G-Assist still is not",{},{"id":1199,"data":2013,"type":544,"tunes":2015},{"text":2014},"NVIDIA explicitly describes G-Assist as a specialized local assistant, not a general-purpose conversational AI.",{},{"id":1204,"data":2017,"type":544,"tunes":2019},{"text":2018},"Its value comes from understanding a focused set of PC and gaming tasks and having tools connected to those tasks.",{},{"id":1209,"data":2021,"type":544,"tunes":2023},{"text":2022},"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":2025,"type":572,"tunes":2027},{"text":2026,"level":47},"What changed in the current versions?",{},{"id":1219,"data":2029,"type":544,"tunes":2031},{"text":2030},"The current public release history shows G-Assist moving steadily toward a more capable local agent.",{},{"id":1224,"data":2033,"type":668,"tunes":2048},{"content":2034,"stretched":1245,"withHeadings":15},[2035,2038,2040,2042,2044,2046],[2036,2037],"Version","Important change",[1231,2039],"Lighter model, all RTX GPUs with 6 GB+ VRAM, community plug-ins",[1234,2041],"Laptop optimization, BatteryBoost and WhisperMode controls",[1237,2043],"Reasoning Mode, Flash Mode, multi-action prompts, new device controls",[1240,2045],"Improved knowledge system, better recommendations, RTX feature controls",[1243,2047],"Elgato Stream Deck integration through an MCP server",{},{"id":1248,"data":2050,"type":572,"tunes":2052},{"text":2051,"level":47},"What would change this answer?",{},{"id":1253,"data":2054,"type":544,"tunes":2056},{"text":2055},"G-Assist is still experimental and its architecture is evolving.",{},{"id":1258,"data":2058,"type":544,"tunes":2060},{"text":2059},"The model size, VRAM requirements, tool set, plug-in protocol and hardware support can all change in future releases.",{},{"id":1263,"data":2062,"type":544,"tunes":2064},{"text":2063},"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":2066,"type":572,"tunes":2068},{"text":2067,"level":47},"Limitations",{},{"id":1273,"data":2070,"type":544,"tunes":2072},{"text":2071},"This article describes NVIDIA's public G-Assist product documentation and public plug-in architecture as of September 2026.",{},{"id":1278,"data":2074,"type":544,"tunes":2076},{"text":2075},"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":2078,"type":544,"tunes":2080},{"text":2079},"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":2082,"type":572,"tunes":2084},{"text":2083,"level":47},"Conclusion",{},{"id":1293,"data":2086,"type":544,"tunes":2088},{"text":2087},"The easiest way to understand Project G-Assist is to stop thinking of it as a chatbot.",{},{"id":1298,"data":2090,"type":544,"tunes":2092},{"text":2091},"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":2094,"type":544,"tunes":2096},{"text":2095},"That is a local agent architecture.",{},{"id":1308,"data":2098,"type":544,"tunes":2100},{"text":2099},"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":2102,"type":572,"tunes":2104},{"text":2103,"level":47},"FAQ",{},{"id":1318,"data":2106,"type":1350,"tunes":2130},{"items":2107,"title":2129},[2108,2111,2114,2117,2120,2123,2126],{"id":1322,"answer":2109,"question":2110},"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":2112,"question":2113},"NVIDIA currently describes it as a local Llama-based instruct model with 8 billion parameters.","What model does G-Assist use?",{"id":1330,"answer":2115,"question":2116},"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":2118,"question":2119},"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":2121,"question":2122},"Not directly. 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Los mayores beneficios aún provienen de un ritmo de fotogramas estable y del control de la carga en segundo plano. Úsalo, pero no lo adores.","2026-02-20T15:00:00.000Z",{"id":2693,"slug":2694,"title":2695,"excerpt":2696,"featuredImage":14,"publishedAt":2697},"209","windows-audio-mixer-traps-why-pc-audio-feels-inconsistent-in-games","Trampas del mezclador de audio de Windows: Por qué el audio de PC se siente inconsistente en los juegos","El audio de PC se siente aleatorio cuando el enrutamiento cambia silenciosamente. Aprende las trampas del mezclador (cambio de dispositivo predeterminado, mejoras, enrutamiento de aplicaciones) y cómo fijar una ruta estable.","2026-02-20T23:40:00.000Z",{"id":2699,"slug":2700,"title":2701,"excerpt":2702,"featuredImage":14,"publishedAt":2703},"217","nvidia-reflex-basics-when-it-helps-and-when-it-does-nothing","Conceptos básicos de NVIDIA Reflex: Cuándo ayuda (y cuándo no hace nada)","Reflex reduce el retraso de la cola de renderizado cuando el juego está limitado por la GPU y es estable. Conoce las condiciones prácticas en las que ayuda y las trampas que hacen que no sirva para nada.","2026-02-20T21:00:00.000Z",{"id":2705,"slug":2706,"title":2707,"excerpt":2708,"featuredImage":14,"publishedAt":2709},"180","router-checklist-v2-the-12-settings-that-prevent-lag-spikes","Router Checklist v2: Los 12 ajustes que previenen los picos de lag","La mayoría de los picos de lag provienen de la carga y la inestabilidad, no de un ‘ping malo’. Usa esta lista de verificación para routers para estabilizar la latencia bajo carga antes de comprar equipo nuevo.","2026-02-20T16:00:00.000Z",{"id":2711,"slug":2712,"title":2713,"excerpt":2714,"featuredImage":14,"publishedAt":2715},"199","wi-fi-bands-decision-2-4-vs-5-vs-6e-gaming-stability-first","Decisión de bandas Wi-Fi: 2.4 vs 5 vs 6E (Estabilidad para gaming primero)","Elige las bandas Wi-Fi por estabilidad, no por el hype. Usa esta guía de decisión para elegir 2.4, 5 o 6E basándote en la distancia, la congestión y el comportamiento real del jitter.","2026-02-20T18:00:00.000Z",{"id":2717,"slug":2718,"title":2719,"excerpt":2720,"featuredImage":14,"publishedAt":2721},"207","120hz-feels-worse-the-diagnosis-checklist-wrong-mode-vrr-range-caps","¿Los 120Hz se sienten peor? Lista de verificación de diagnóstico (Modo incorrecto, rango de VRR, límites)","Una mayor frecuencia de actualización puede exponer la inestabilidad. Usa esta lista de verificación para diagnosticar por qué los 120 Hz se sienten peor: modo incorrecto, ruta de actualización incorrecta, problemas de rango de VRR o falta de límites.","2026-02-20T20:30:00.000Z",{"id":2723,"slug":2724,"title":2725,"excerpt":2726,"featuredImage":14,"publishedAt":2727},"159","ethernet-vs-wi-fi-for-gaming-the-real-reasons-ethernet-wins","Ethernet vs Wi-Fi para gaming: las verdaderas razones por las que Ethernet gana","Ethernet no se trata de la velocidad. Se trata de la consistencia: menos picos, menos interferencia y tiempos predecibles. Usa esto para decidir cuándo el Wi-Fi es ‘suficientemente bueno’.","2026-02-20T13:00:00.000Z",{"id":2729,"slug":2730,"title":2731,"excerpt":2732,"featuredImage":14,"publishedAt":2733},"96","game-mode-on-tvs-and-monitors-the-one-setting-that-changes-everything","Modo Juego en TVs y Monitores: La Única Configuración Que Cambia Todo","Si un juego se siente pesado, revisa primero el modo de juego. Aprende qué desactiva el modo de juego, por qué reduce el retraso y cómo verificar que realmente está funcionando.","2026-02-19T11:00:00.000Z",{"id":2735,"slug":2736,"title":2737,"excerpt":2738,"featuredImage":14,"publishedAt":2739},"206","display-processing-traps-the-settings-that-secretly-ruin-clarity-and-feel","Trampas del procesamiento de pantalla: Los ajustes que arruinan en secreto la claridad y la sensación","Muchas pantallas vienen de fábrica con un procesamiento que se ve ‘bien’ en las películas, pero que arruina el gaming: latencia añadida, artefactos e inestabilidad. Aquí tienes la lista corta de qué desactivar y por qué.","2026-02-21T00:22:00.000Z","fallback",[],[]]