Pool local Macs for MLX workloads.

Research. Phase 1 working (daemon, CLI, peer discovery, CRDT state, job orchestration). Phase 2 (credits marketplace) is on the roadmap. Modeled at $0 in any commercial planning.

• Memory-weighted scheduling — a 128GB Mac and a 48GB Mac pool their capacity automatically. Pipeline parallelism proportional to each node's unified memory.
• Heterogeneous compute — Metal, CUDA, and CPU nodes coexist in the same mesh. Same coordination layer, different backends.
• Zero-config LAN discovery — libp2p with mDNS, QUIC, WebRTC. Plug in a Mac; the mesh finds it.
• CRDT state — nodes converge automatically. Works offline. No bootstrap server required.

mlx-mesh is R&D. The hard foundations work: peer discovery, CRDT state, memory-weighted scheduling, and job orchestration. Significant engineering remains around cross-device latency, heterogeneous compute consistency, and marketplace economics. Production use is at your own risk; commercial planning treats it as upside, not revenue.

mlx-mesh sits alongside mlx-go as the distributed-compute arm of the Apple Silicon work. When a model doesn't fit on one Mac — or when a regulated team wants training workloads to stay inside the building — mlx-mesh turns idle devices on the LAN into coordinated capacity. cove is the isolation companion when those training workloads need sandboxing.