GPU Server
MCP server for GPU monitoring: nvidia-smi, VRAM, utilization, temperature
Open source Open in the app JSON README (API)
About
MCP server for GPU monitoring: nvidia-smi, VRAM, utilization, temperature
Details
- Kind
- MCP servers
- Topic
- Cloud & DevOps
- Publisher
- mesutoezdil
- Origin
- official
- Category
- ferramentas
- Transport
- local
- Version
- 0.1.3
- Forks
- 1
- Last push
- 2026-05-03T13:54:47Z
- Repository state
- ativo
- Language
- Python
- Added
- 2026-08-29 04:00:47
- Updated
- 2026-08-29 04:00:47
- Origin id
io.github.mesutoezdil/mcp-gpu-server
README
mcp-name: io.github.mesutoezdil/mcp-gpu-server
# mcp-gpu-server
[](https://pypi.org/project/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.
## 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.