# GPU MCP Server **Category:** 📊 Monitoring **Repository:** https://github.com/pmady/gpu-mcp-server **Views:** 0 **Installs:** 0 **Upvotes:** 0 **Directory Page:** https://allmcps.com/mcp/gpu-mcp-server ## Description NVIDIA GPU metrics as MCP tools — utilization, memory, temperature, power. Supports MIG. ## Claude Desktop Quick Installation Heuristic fallback — verify the package name and runner against the repository README before running it. Uses `npx` (confidence: low): ```json "mcpServers": { "gpu-mcp-server": { "command": "npx", "args": ["-y","gpu-mcp-server"] } } ``` ## Documentation & README # gpu-mcp-server [![CI](https://github.com/pmady/gpu-mcp-server/actions/workflows/ci.yml/badge.svg)](https://github.com/pmady/gpu-mcp-server/actions/workflows/ci.yml) [![Helm](https://github.com/pmady/gpu-mcp-server/actions/workflows/helm.yaml/badge.svg)](https://github.com/pmady/gpu-mcp-server/actions/workflows/helm.yaml) [![Go Report Card](https://goreportcard.com/badge/github.com/pmady/gpu-mcp-server)](https://goreportcard.com/report/github.com/pmady/gpu-mcp-server) [![Go Reference](https://pkg.go.dev/badge/github.com/pmady/gpu-mcp-server.svg)](https://pkg.go.dev/github.com/pmady/gpu-mcp-server) [![License](https://img.shields.io/badge/License-Apache%202.0-blue.svg)](LICENSE) [![OpenSSF Scorecard](https://api.securityscorecards.dev/projects/github.com/pmady/gpu-mcp-server/badge)](https://securityscorecards.dev/viewer/?uri=github.com/pmady/gpu-mcp-server) [![OpenSSF Best Practices](https://img.shields.io/badge/OpenSSF_Best_Practices-in_progress-yellow)](https://www.bestpractices.dev/en) > **Note:** the OpenSSF Best Practices questionnaire is in progress. Once the > project entry is registered at , swap the > static badge above for the live one: > `[![OpenSSF Best Practices](https://www.bestpractices.dev/projects//badge)](https://www.bestpractices.dev/projects/)` An [MCP](https://modelcontextprotocol.io/) 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](https://github.com/modelcontextprotocol/go-sdk) and [NVIDIA go-nvml](https://github.com/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`: ```json { "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"}`): ```json { "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`: ```json { "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 ```bash # 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`: ```json { "mcpServers": { "gpu": { "command": "/path/to/gpu-mcp-server" } } } ``` ### Goose ```yaml 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: ```json { "mcpServers": { "gpu": { "type": "stdio", "command": "/path/to/gpu-mcp-server" } } } ``` ### Windsurf Add to `~/.codeium/windsurf/mcp_config.json`: ```json { "mcpServers": { "gpu": { "command": "/path/to/gpu-mcp-server" } } } ``` ## Build Requires Go 1.23+, CGO, and NVIDIA drivers on the target machine. ```bash 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. ```bash 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](https://github.com/NVIDIA/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. ```json { "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](https://modelcontextprotocol.io/) - **Related:** [keda-gpu-scaler](https://github.com/pmady/keda-gpu-scaler) (GPU autoscaling for Kubernetes) - **Whitepaper:** [GPU-Aware Autoscaling in Cloud Native AI Infrastructure](https://github.com/pmady/keda-gpu-scaler/blob/main/docs/cncf-tag-infra/gpu-aware-autoscaling-whitepaper.md) — CNCF TAG Infrastructure initiative ([TOC #2188](https://github.com/cncf/toc/issues/2188)) ## Roadmap See [ROADMAP.md](https://github.com/pmady/gpu-mcp-server/blob/HEAD/ROADMAP.md) for the 12-month public roadmap. ## Contributing See [CONTRIBUTING.md](https://github.com/pmady/gpu-mcp-server/blob/HEAD/CONTRIBUTING.md) for how to get involved. ## Contributors Thanks to all our [contributors](https://github.com/pmady/gpu-mcp-server/blob/HEAD/CONTRIBUTORS.md)! Add yourself via PR. ## Governance This project follows [Linux Foundation Minimum Viable Governance](https://github.com/pmady/gpu-mcp-server/blob/HEAD/GOVERNANCE.md). ## Documentation - [Full documentation](https://gpu-mcp-server.readthedocs.io) - hosted on Read the Docs - [ROADMAP.md](https://github.com/pmady/gpu-mcp-server/blob/HEAD/ROADMAP.md) - public roadmap - [GOVERNANCE.md](https://github.com/pmady/gpu-mcp-server/blob/HEAD/GOVERNANCE.md) - decision-making process - [DEPENDENCIES.md](https://github.com/pmady/gpu-mcp-server/blob/HEAD/DEPENDENCIES.md) - external dependencies and licenses - [SECURITY.md](https://github.com/pmady/gpu-mcp-server/blob/HEAD/SECURITY.md) - vulnerability reporting - [AGENTS.md](https://github.com/pmady/gpu-mcp-server/blob/HEAD/AGENTS.md) - instructions for AI agents working on this repo - [CODE_OF_CONDUCT.md](https://github.com/pmady/gpu-mcp-server/blob/HEAD/CODE_OF_CONDUCT.md) - community standards ## Star History Star History Chart