gpu-mcp-server
Active·★ 14·Apache-2.0·Updated 2026-07-14
gpu-mcp-server is an MCP-compatible server that exposes real-time NVIDIA GPU metrics as tools for AI agents. It allows agents like Claude, Goose, and Cursor to query utilization, memory, temperature, and power data without external monitoring systems.
gpu-mcp-server is currently grouped under LLM Infra, which makes it easier to evaluate through workflow fit instead of isolated features alone. Based on the available data, it leans most heavily toward Exposes real-time NVIDIA GPU metrics as AI agent tools. and Empowering AI agents with real-time NVIDIA GPU status and performance data.. The listed license is Apache-2.0, which is useful when adoption constraints matter. It also shows measurable community traction with 14 GitHub stars.
#GPU Metrics#NVIDIA NVML#MCP Protocol#AI Agent Tools#Go Language#Real-time Monitoring#Containerization
01
Features
01Exposes real-time NVIDIA GPU metrics as AI agent tools.
02Provides detailed GPU metrics including utilization, memory, temperature, and power.
03Supports Multi-Instance GPU (MIG) configurations.
04Offers PID-level GPU process attribution.
05Directly integrates with NVML, removing the need for external monitoring systems like Prometheus or dcgm-exporter.
02
Why choose it
+Exposes real-time NVIDIA GPU metrics as AI agent tools.
+Empowering AI agents with real-time NVIDIA GPU status and performance data.
+Covers 5 supported environments or platforms, which is helpful for broader deployment needs.
+Ships with a public repository and a Apache-2.0 license, which makes adoption and review easier.
03
Trade-offs
!There are at least 8 related tools in the same category, so the best choice is easier to make after side-by-side comparison.
04
Compatibility
Go
Language
Verified via docs
NVIDIA GPUs
Hardware
Verified via docs
Linux
OS
Verified via docs
Docker
Container
Verified via docs
Model Context Protocol (MCP)
Protocol
Verified via docs
05
Quick start
1
$ git clone https://github.com/pmady/gpu-mcp-server.git
2
$ cd gpu-mcp-server
3
$ make build
06
Use cases
↳Empowering AI agents with real-time NVIDIA GPU status and performance data.
↳Enabling AI models to make informed decisions based on GPU resource availability.
↳Integrating GPU monitoring capabilities directly into AI development environments (e.g., Cursor IDE).
↳Facilitating dynamic GPU resource management for AI workloads by providing instant metrics.
07
How it compares
≈gpu-mcp-server sits in the LLM Infra category, so it makes more sense to evaluate it alongside tools like MetaGPT instead of in isolation.
≈If your main need is closer to "Empowering AI agents with real-time NVIDIA GPU status and performance data.", that use case is a better lens for comparison than broad feature checklists alone.
≈gpu-mcp-server uses a Apache-2.0 license, and community traction are both easier to judge in category context.
08
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