gpu-mcp-server
★ 15
vs
rllm
★ 5.9k
gpu-mcp-server vs rllm
gpu-mcp-server: 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.; rllm: rLLM is an open-source framework designed for post-training language agents using reinforcement learning. It allows users to easily build, train, and deploy custom agents and environments for real-world workloads.
01
TL;DR
gpu-mcp-serverLLM Infra · ★ 15
Empowering AI agents with real-time NVIDIA GPU status and performance data.
rllmLLM Infra · ★ 5.9k
Training powerful coding models for tasks like code generation and bug fixing.
02
Side-by-Side Comparison
Field
gpu-mcp-serverrllmCategory
LLM Infra
LLM Infra
Stars
★ 15
★ 5.9k
License
Apache-2.0
Apache-2.0
Install Command
git clone https://github.com/pmady/gpu-mcp-server.git && cd gpu-mcp-server && make build
uv pip install "rllm[verl] @ git+https://github.com/rllm-org/rllm.git"
Updated
6d ago
1w ago
Open Source
Yes
Yes
Tags
GPU Metrics, NVIDIA NVML, MCP Protocol
Reinforcement Learning, Language Agents, LLM
03
Features
gpu-mcp-server01Exposes 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.
rllm01Open-source framework for reinforcement learning-based post-training of language agents.
02Supports building, training, and deploying custom agents and environments.
03Offers multiple training backends including 'verl' and 'tinker'.
04Enables LoRA and VLM training for advanced models.
05Includes AgentWorkflowEngine for training over arbitrary agentic programs.
04
Use Cases
gpu-mcp-server↳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.
rllm↳Training powerful coding models for tasks like code generation and bug fixing.
↳Developing sophisticated software engineering agents for automated tasks.
↳Building and evaluating multi-agent systems using reinforcement learning techniques.
05
Best For
gpu-mcp-server—
rllmTrending
FAQ
FAQ
What is the difference between gpu-mcp-server and rllm?
Both gpu-mcp-server and rllm are in the LLM Infra category. gpu-mcp-server has 15 stars, while rllm has 5.9k stars.
Which is better, gpu-mcp-server or rllm?
The best choice depends on your use case. Choose gpu-mcp-server if Empowering AI agents with real-time NVIDIA GPU status and performance data., and rllm if Training powerful coding models for tasks like code generation and bug fixing..
Is gpu-mcp-server free or open source?
Yes, gpu-mcp-server is open source on GitHub (Apache-2.0).
Is rllm free or open source?
Yes, rllm is open source on GitHub (Apache-2.0).
Cloud Infrastructure
AdDeploy on DigitalOcean
Host your MCP servers, Docker containers, and AI agents with $200 free credit over 60 days.
→