Mesh LLM pools your GPUs into one OpenAI-compatible API across machines
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Mesh LLM enables distributed AI inference by pooling existing GPUs into a mesh, allowing models up to 235B parameters to run across multiple modest machines via layer-splitting ("Skippy" mode). This lets teams deploy large models without upgrading hardware, reduces cloud costs, and maintains control over data and model versions by keeping inference local or within a private mesh. Engineers can now scale LLMs horizontally across existing infrastructure while maintaining compatibility with OpenAI clients via a local API endpoint.
Mesh LLM pools your existing, disparate hardware into a single, decentralized network that exposes a single OpenAI-compatible API at localhost, allowing you to run giant models like 235B MoEs by splitting layer ranges across multiple modest GPUs. By using iroh's peer-to-peer NAT traversal and QUIC streams to handle pipeline parallelism directly between nodes, you can stop paying metered API bills and run local agent workloads on underutilized office hardware with zero central server infrastructure. This completely eliminates dependency on cloud API provider pricing and model deprecation cycles, though your system latency will now be bound by the WAN transit time of activations flowing between your partitioned nodes.