GPU MCP Server

Name: GPU MCP Server
Author: pmady

by pmadyversion 0.1.0i★ –iRuns on your computer

Registry nameio.github.pmady/gpu-mcp-server

Install

Overview

Description from the maker (original English)

NVIDIA GPU metrics as MCP tools — utilization, memory, temperature, power. Supports MIG.

PopNx MCP’s three pillars

These three are never merged into one score: opinions, measurements and popularity are different things.

User voiceiNo reviews yetReviews open together with accounts in a later step
Our basic checksi1 of 1 passedChecked from public information; code not scanned yet
UsageiNot measured yet★ –

What you can do with it

We have not written a plain-language summary for this one yet. Below are the facts we read from the registry.

Connection: Runs on your computer
Transport: stdio
Category: Search & AI memory

Install

Pick the AI app you use, then copy the settings.

Before you install

Basic checks: 1 of 1 passed
Runs on your computer with your user’s permissions (files, network, apps).
The registry lists no key to fill in.

You need

DockerDocker installed

Which app do you use?

ClaudeClaude CodeCursorVS CodeChatGPT

Pick your app above. We remember it, so next time the button says “Install in …”.

Open Claude Desktop → Settings → Developer → Edit Config, and paste this into claude_desktop_config.json

{
  "mcpServers": {
    "gpu": {
      "command": "docker",
      "args": [
        "run",
        "-i",
        "--rm",
        "ghcr.io/pmady/gpu-mcp-server:v0.1.0"
      ]
    }
  }
}

Run this in your terminal

claude mcp add gpu -- docker run -i --rm ghcr.io/pmady/gpu-mcp-server:v0.1.0

Open in Cursor

Or copy the settings yourself

Paste into .cursor/mcp.json in your project, or ~/.cursor/mcp.json

{
  "mcpServers": {
    "gpu": {
      "command": "docker",
      "args": [
        "run",
        "-i",
        "--rm",
        "ghcr.io/pmady/gpu-mcp-server:v0.1.0"
      ]
    }
  }
}

Your browser asks to open the app, then the app shows the settings and asks you to confirm. Key values are left empty for you to fill in.

Open in VS Code

Or copy the settings yourself

Paste into .vscode/mcp.json in your project

{
  "servers": {
    "gpu": {
      "type": "stdio",
      "command": "docker",
      "args": [
        "run",
        "-i",
        "--rm",
        "ghcr.io/pmady/gpu-mcp-server:v0.1.0"
      ]
    }
  }
}

Your browser asks to open the app, then the app shows the settings and asks you to confirm. Key values are left empty for you to fill in.

This one runs on your computer, and ChatGPT can only connect to servers on the internet. Pick another app.

Your choice stays on this device in a cookie; no account needed.

These settings are generated from the maker’s registry entry. If the README says otherwise, follow the README.

What it can do

Runs on your computer; the tool list appears in your AI app after you install it.

Basic checksi

We check information anyone can see; we have not read the code. Checks that do not apply are left out of the count. · Checked Nov 6, 2026

✓ Pass
Maker matches the repo
The registry name was verified with the GitHub account pmady, which owns the repo
… Not checked yet
Still maintained
We have not read this repo from GitHub yet
… Not checked yet
Has a licence
We have not read this repo from GitHub yet
– Not applicable
Endpoint responds
This one runs on your computer; there is no internet endpoint
– Not applicable
Sign-in with OAuth
Runs on your computer and uses local settings instead
… Not checked yet
No hidden instructions in tool descriptions
Runs on your computer; we can only read tool lists from internet endpoints

Compared with others in Search & AI memoryi

More stars than 0% of the other 7234 servers in this category
Published to the registry more recently than 18% of the other 7234 servers in this category
Basic checks equal to or better than 100% of the other 7234 servers in this category

Compared with every active server in this category in the registry. Stars count only when the publisher owns the repo.

Common questions

Is GPU MCP Server safe?

It passed 1 of 1 applicable basic checks. We have not read the code. Before adding a key or token, see what access the server asks for and use a key with only the permissions you need.

What keys or codes does it need?

The registry does not list a required key, though some servers ask you to sign in on first use.

Does it run on my computer or in the cloud?

It runs on your computer. Your AI app starts it when needed, and data does not pass through the maker’s servers (unless it calls an online service itself).

Is it still maintained?

The GitHub repo last changed –, and the latest version (0.1.0) reached the registry 4 months ago.

Which apps can use it?

Apps that support MCP, such as Claude Desktop, Claude Code, Cursor and VS Code. You also need Docker on your computer.

What can I use instead?

In the same category: Context7, Codebase Memory, Exa

Alternatives

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Reading a card:★ GitHub starsiThree pillars: reviews · checks · usageiUpdated = latest version published to the registryi

Context7

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Computer or cloud★ 64,224MITUpdated 3 days ago

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Codebase Memory

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Exa

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Connect AI agents to Exa for web search, content fetching, and multi-step research.

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Install

README from the maker

Open the README

Copied from the maker’s repo as written. PopNx MCP has not reviewed or endorsed it. HTML, images and scripts are removed. Links lead off this site.

gpu-mcp-server

Note: the OpenSSF Best Practices questionnaire is in progress. Once the

project entry is registered at https://www.bestpractices.dev/en, swap the

static badge above for the live one:

An MCP server that exposes NVIDIA GPU metrics as tools. Any MCP-compatible AI agent (Claude, Goose, Cursor, etc.) can query real-time GPU utilization, memory, temperature, power, PCIe and NVLink throughput no Prometheus or dcgm-exporter required.

Built on the official Go MCP SDK and NVIDIA go-nvml.

Tools

Tool	Description
`list_gpus`	List all GPUs with utilization and memory info
`get_gpu_metrics`	Detailed metrics for a GPU by index or UUID
`get_gpu_processes`	PID-level GPU process attribution
`gpu_summary`	Aggregate stats across all devices

All tools support MIG (Multi-Instance GPU) - MIG instances appear as separate devices with their parent GPU's shared metrics (temperature, power, PCIe).

Sample output

Each tool returns structured JSON. The examples below show the shape of the data an agent receives from a node with two NVIDIA A100 GPUs.

list_gpus:

"count": 2, "devices": [ { "index": 0, "uuid": "GPU-aaaa-1111", "name": "NVIDIA A100-SXM4-80GB", "gpu_utilization_percent": 85, "memory_used_mib": 57344, "memory_total_mib": 81920 }, { "index": 1, "uuid": "GPU-bbbb-2222", "name": "NVIDIA A100-SXM4-80GB", "gpu_utilization_percent": 20, "memory_used_mib": 12288, "memory_total_mib": 81920 } ] }

get_gpu_metrics (with {"index": 0} or {"uuid": "GPU-aaaa-1111"}):

"index": 0, "uuid": "GPU-aaaa-1111", "name": "NVIDIA A100-SXM4-80GB", "gpu_utilization_percent": 85, "memory_utilization_percent": 70, "memory_used_mib": 57344, "memory_total_mib": 81920, "temperature_celsius": 72, "power_draw_watts": 300, "power_limit_watts": 400, "pcie_tx_kbps": 0, "pcie_rx_kbps": 0, "nvlink_tx_mbps": 0, "nvlink_rx_mbps": 0 }

gpu_summary:

"device_count": 2, "avg_gpu_utilization": 52.5, "avg_memory_utilization": 42.5, "total_memory_used_mib": 69632, "total_memory_total_mib": 163840, "max_temperature_celsius": 72, "total_power_draw_watts": 375 }

MIG instances add is_mig, parent_gpu, and mig_profile fields to the get_gpu_metrics and list_gpus payloads.

Quick start

# build (requires CGO + NVML headers on Linux)

make build

run the server communicates over stdio

./gpu-mcp-server

Claude Desktop

Add to claude_desktop_config.json:

"mcpServers": { "gpu": { "command": "/path/to/gpu-mcp-server" } } }

Goose

extensions:

gpu-metrics: type: stdio cmd: /path/to/gpu-mcp-server

Cursor

Add to .cursor/mcp.json for a project, or ~/.cursor/mcp.json for all projects:

"mcpServers": { "gpu": { "type": "stdio", "command": "/path/to/gpu-mcp-server" } } }

Windsurf

Add to ~/.codeium/windsurf/mcp_config.json:

"mcpServers": { "gpu": { "command": "/path/to/gpu-mcp-server" } } }

Build

Requires Go 1.23+, CGO, and NVIDIA drivers on the target machine.

make build       # compile binary

make test # run tests (no GPU needed uses mock) make lint # golangci-lint make docker # container image

Tests use a mock collector, so they run anywhere no GPU hardware required.

Docker

Prebuilt multi-arch images (linux/amd64, linux/arm64) are published to GHCR on every release.

docker pull ghcr.io/pmady/gpu-mcp-server:latest

docker run --rm -i --gpus all ghcr.io/pmady/gpu-mcp-server:latest

The host needs the NVIDIA Container Toolkit installed for --gpus all to work. The server speaks MCP over stdio, so the -i flag is required — don't drop it.

"mcpServers": { "gpu": { "command": "docker", "args": ["run", "--rm", "-i", "--gpus", "all", "ghcr.io/pmady/gpu-mcp-server:latest"] } } }

Pin a specific version via tag instead of :latest, e.g. ghcr.io/pmady/gpu-mcp-server:v0.1.0.

Architecture

Agent (Claude/Goose) ─── MCP (stdio) ──→ gpu-mcp-server ──→ NVML ──→ GPU

│ Tools: • list_gpus • get_gpu_metrics • gpu_summary

The server runs as a local process alongside the agent. It calls NVML directly through cgo — no sidecar, no network hops, no metric pipeline to configure.

Project info

License: Apache 2.0
Language: Go
AAIF project alignment: MCP
Related: keda-gpu-scaler (GPU autoscaling for Kubernetes)
Whitepaper: GPU-Aware Autoscaling in Cloud Native AI Infrastructure — CNCF TAG Infrastructure initiative (TOC #2188)

Citing

If you use gpu-mcp-server in your research or writing, please cite it via its DOI. Full citation metadata is in CITATION.cff.

@software{madduri_gpu_mcp_server,

author = {Madduri, Pavan}, title = {gpu-mcp-server: NVIDIA GPU metrics for AI agents over the Model Context Protocol}, year = {2026}, publisher = {Zenodo}, doi = {10.5281/zenodo.22866670}, url = {https://doi.org/10.5281/zenodo.22866670} }

Roadmap

See ROADMAP.md for the 12-month public roadmap.

Contributing

See CONTRIBUTING.md for how to get involved.

Contributors

Thanks to all our contributors! Add yourself via PR.

Governance

This project follows Linux Foundation Minimum Viable Governance.

Documentation

Full documentation - hosted on Read the Docs
ROADMAP.md - public roadmap
GOVERNANCE.md - decision-making process
DEPENDENCIES.md - external dependencies and licenses
SECURITY.md - vulnerability reporting
AGENTS.md - instructions for AI agents working on this repo
CODE_OF_CONDUCT.md - community standards

GPU MCP Server

Overview

PopNx MCP’s three pillars

What you can do with it

Install

Before you install

What it can do

Basic checksi

Maker matches the repo

Still maintained

Has a licence

Endpoint responds

Sign-in with OAuth

No hidden instructions in tool descriptions

Compared with others in Search & AI memoryi

Common questions

Alternatives

Context7

Codebase Memory

Exa

README from the maker

gpu-mcp-server

Tools

Sample output

Quick start

run the server communicates over stdio

Claude Desktop

Goose

Cursor

Windsurf

Build

Docker

Architecture

Project info

Citing

Roadmap

Contributing

Contributors

Governance

Documentation

Star History