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MCP server for GPU monitoring: nvidia-smi, VRAM, utilization, temperature
MCP server for GPU monitoring: nvidia-smi, VRAM, utilization, temperature
Valid MCP server (2 strong, 4 medium validity signals). 3 known CVEs in dependencies (0 critical, 3 high severity) Package registry verified. Imported from the Official MCP Registry.
5 files analyzed · 4 issues found
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Add this to your MCP configuration file:
{
"mcpServers": {
"io-github-mesutoezdil-mcp-gpu-server": {
"args": [
"mcp-gpu-server"
],
"command": "uvx"
}
}
}From the project's GitHub README.
mcp-name: io.github.mesutoezdil/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.
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
}
}]
}
Queries NVML (pynvml) directly when available. Falls back to nvidia-smi subprocess if NVML is not accessible. Returns clean JSON in both cases.
pip install mcp-gpu-server
Add this to your MCP client config file:
{
"mcpServers": {
"gpu": {
"command": "mcp-gpu-server"
}
}
}
python tests/test_gpu.py
Python 3.10 or higher. NVIDIA GPU with drivers installed on the host machine.
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