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GPU Server

mesutoezdil/mcp-gpu-server
STDIOregistry active
Summary

Wraps NVIDIA GPU metrics into MCP tools so you can ask Claude about GPU status instead of ssh-ing in to run nvidia-smi. Exposes five tools: gpu_info for driver and CUDA version, gpu_utilization for core and memory bandwidth percentages, gpu_vram for memory stats, gpu_temperature for thermals, and gpu_stats for everything at once. Uses pynvml directly when available, falls back to nvidia-smi subprocess otherwise. Returns clean JSON either way. Useful if you're training models on remote boxes and want to check VRAM or utilization without leaving your conversation. Requires Python 3.10+ and NVIDIA drivers on the host.

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

mcp-gpu-server

PyPI

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.

Install

pip install mcp-gpu-server

Connect to your MCP client

Add this to your MCP client config file:

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

Run tests

python tests/test_gpu.py

Requirements

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

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Registryactive
Packagemcp-gpu-server
TransportSTDIO
UpdatedMay 3, 2026
View on GitHub