GPU Server

MCP server for GPU monitoring: nvidia-smi, VRAM, utilization, temperature

Quick Install

Automated & IDE Setup

Copy the AI prompt to install this server into Claude Code, Cursor, or another agent — or use 1-click editor setup below.

Add to Cursor Add to VS Code

Manual Client & Custom JSON ConfigExpand JSON ▾

Client Config & Setup

Choose your client or environment

Target File:~/Library/Application Support/Claude/claude_desktop_config.json

claude_desktop_config.json

{
  "mcpServers": {
    "gpu-server": {
      "command": "npx",
      "args": [
        "-y",
        "gpu-server"
      ]
    }
  }
}

💡 Paste the JSON block into your client's configuration file under mcpServers, then restart the application.

Documentation Overview

mcp-name: io.github.mesutoezdil/mcp-gpu-server

mcp-gpu-server

An MCP server that exposes NVIDIA GPU metrics as tools. Once connected, any MCP-compatible client can query your GPU status in real time directly from a conversation.

What it does

Instead of running nvidia-smi manually, you ask your AI assistant and it calls these tools automatically:

Code

gpu_info         GPU name, driver version, CUDA version
gpu_utilization  core utilization % and memory bandwidth %
gpu_vram         total, used, free VRAM in MiB and usage %
gpu_temperature  GPU core temperature in Celsius
gpu_stats        everything above in one call

Example response from gpu_stats:

JSON Config

{
  "count": 1,
  "gpus": [{
    "index": 0,
    "name": "NVIDIA L40S",
    "driver": "580.126.09",
    "cuda": "13.0",
    "temp_c": 29,
    "gpu_pct": 0,
    "mem_pct": 0,
    "vram": {
      "total_mib": 46068,
      "used_mib": 610,
      "free_mib": 45457,
      "pct": 1.3
    }
  }]
}

How it works

Queries NVML (pynvml) directly when available. Falls back to nvidia-smi subprocess if NVML is not accessible. Returns clean JSON in both cases.

Install

Terminal

pip install mcp-gpu-server

Connect to your MCP client

Add this to your MCP client config file:

JSON Config

{
  "mcpServers": {
    "gpu": {
      "command": "mcp-gpu-server"
    }
  }
}

Run tests

Code

python tests/test_gpu.py

Requirements

Python 3.10 or higher. NVIDIA GPU with drivers installed on the host machine.

Read the full README →View source on GitHub →

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Frequently Asked Questions about GPU Server

Add the following block to your claude_desktop_config.json under mcpServers: "mcpServers": { "gpu-server": { "command": "npx", "args": ["-y", "GPU Server"] } }

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Technical Specs & Signals

CategoryDeveloper Tools

27Quality signal: Emerging · 27/100How this signal is calculated ▾

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More in 💻 Developer Tools →Best MCP servers for Developers →Alternatives to GPU Server →Install in Claude Desktop Install in Cursor Install in VS Code

Documentation Overview

mcp-name: io.github.mesutoezdil/mcp-gpu-server

mcp-gpu-server

An MCP server that exposes NVIDIA GPU metrics as tools. Once connected, any MCP-compatible client can query your GPU status in real time directly from a conversation.

What it does

Instead of running nvidia-smi manually, you ask your AI assistant and it calls these tools automatically:

Code

gpu_info         GPU name, driver version, CUDA version
gpu_utilization  core utilization % and memory bandwidth %
gpu_vram         total, used, free VRAM in MiB and usage %
gpu_temperature  GPU core temperature in Celsius
gpu_stats        everything above in one call

Example response from gpu_stats:

JSON Config

{
  "count": 1,
  "gpus": [{
    "index": 0,
    "name": "NVIDIA L40S",
    "driver": "580.126.09",
    "cuda": "13.0",
    "temp_c": 29,
    "gpu_pct": 0,
    "mem_pct": 0,
    "vram": {
      "total_mib": 46068,
      "used_mib": 610,
      "free_mib": 45457,
      "pct": 1.3
    }
  }]
}

How it works

Queries NVML (pynvml) directly when available. Falls back to nvidia-smi subprocess if NVML is not accessible. Returns clean JSON in both cases.

Install

Terminal

pip install mcp-gpu-server

Connect to your MCP client

Add this to your MCP client config file:

JSON Config

{
  "mcpServers": {
    "gpu": {
      "command": "mcp-gpu-server"
    }
  }
}

Run tests

Code

python tests/test_gpu.py

Requirements

Python 3.10 or higher. NVIDIA GPU with drivers installed on the host machine.