Mesh LLM
Pool compute to run powerful open models
65 followers
Pool compute to run powerful open models
65 followers
Turn spare capacity into an auto-configured p2p inference cloud. Serve many models, access your private models from anywhere, or share compute with others, let your agents collaborate p2p.
Free
Launch Team
Render Sandboxes: a computer for every agentGive every agent its own machine. Starts in under a second
Promoted
It's SETI at Home but for inference! 🤩
the auto-configured p2p setup is the clever bit. most self-hosted inference solutions require you to manually manage which node is running what model, handle routing yourself, and accept that you'll be ssh-ing into machines whenever something needs updating. auto-configuring the mesh and exposing a standard openai-compatible endpoint means your existing agent tooling just works without a custom client.
where this gets hard is reliability under real agent workflows. spare capacity is honest framing, but spare capacity is also the most volatile kind. when an agent mid-task makes a follow-up call and the node has dropped or a worker left the mesh, the retry behavior matters a lot. handling partial failures gracefully without surfacing errors to whatever client is consuming the api is a non-trivial coordination problem, especially as the mesh grows beyond a few trusted machines.