title: README
emoji: 🐨
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https://cdn-uploads.huggingface.co/production/uploads/60fa66a3c4c6bd8c56ee541f/FEldjz5JpuoMy8dauKA2M.png
short_description: pool compute for huge model inference
mesh-llm turns spare compute into a peer-to-peer inference cloud for open models.
mesh-llm pools GPUs across macOS and Linux machines so teams, researchers, and agents can run local or open-weight models through one OpenAI-compatible endpoint. It can serve a model on one node, distribute large models across nearby peers, route requests to specialized models, and let agents coordinate through mesh gossip.
What it is for
- Share spare GPU capacity across trusted machines.
- Run open models locally without a centralized inference provider.
- Serve an OpenAI-compatible API at http://localhost:9337/v1.
- Route requests across multiple nodes, models, and capabilities.
- Experiment with distributed inference, MoE expert sharding, and agent collaboration.
see: https://docs.anarchai.org/ and: https://github.com/mesh-LLM/
Mesh uses a pipelined/network aware distributed inference approach built on llama.cpp called "skippy" - https://github.com/Mesh-LLM/hf-mesh-skippy-splitter contains current code which prepares models so layers can be efficiently JIT downloaded for participating nodes.