gpu-server

mcp-name: io.github.moezdil/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:

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:

{
  "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. The nvidia-smi query does not include the CUDA version, so cuda is null on that path.

Install

pip install gpu-server

The package moved from mcp-gpu-server to gpu-server on PyPI in 0.2.1. The old name is frozen at 0.1.3, which does not start with mcp 2.x. The mcp-gpu-server command still works, and since 0.2.2 gpu-server does too.

Listed on the official MCP Registry as io.github.moezdil/mcp-gpu-server.

Connect to your MCP client

Add this to your MCP client config file:

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

Or without installing anything, using uv:

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

Run tests

uv run pytest tests/test_server.py   # no GPU needed
uv run python tests/test_gpu.py      # on a GPU host

Requirements

Python 3.10 or higher, MCP Python SDK 2.x. NVIDIA GPU with drivers installed on the host machine.