All 143 Dependencies

Every package mesh-llm depends on, ranked by repo health score.

Mesh LLM pools the GPUs and memory of every machine an operator owns and exposes the combined capacity as a single OpenAI-compatible API at `http://localhost:9337/v1`. Running `mesh-llm serve --auto` on a node picks a suitable backend, downloads a model if one isn't already present, joins the best available mesh, and starts both the inference API and a web console — so a laptop, a gaming rig, and a workstation can act as one inference cluster instead of three separate, underused servers.

Two design choices set it apart from single-box local-LLM runners. Skippy stage splits let a dense model too large for any one machine's VRAM load as contiguous transformer-layer stages spread across several peers: the coordinator plans the layer ranges, starts downstream stages first, waits for them to report ready, and only then publishes the stage-0 route, so a request for an oversized model is transparently served by a chain of machines instead of failing outright. An experimental Mixture-of-Agents gateway takes the opposite approach for a single request — sending `"model": "mesh"` fans the prompt out to every model currently running in the mesh, arbitrates the responses deterministically in code, and only calls out to a reducer LLM when the answers genuinely conflict.

Meshes can stay private (joined by invite token) or advertise themselves publicly through Nostr-based discovery, with an explicit owner-control plane kept separate from the public inference/gossip/routing plane so operator actions don't need to trust the same channel as anonymous public traffic. Releases ship as native binaries for macOS, Linux (CPU, ARM64, CUDA, ROCm, Vulkan), and Windows (CPU, CUDA, ROCm, Vulkan), distributed via a companion packaging repository with Homebrew, apt, and pacman channels, checksums, SBOMs, and embedded release attestation so a packaged binary's provenance can be verified after download.

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