Coordinate distributed LLM inference across Apple Silicon devices with auto peer discovery.
npx skills add https://github.com/plurigrid/asi --skill exo-distributed
Please help me install this Agent Skill. Skill: exo-distributed Source: https://github.com/plurigrid/asi/tree/main/skills/exo-distributed Command: npx skills add https://github.com/plurigrid/asi --skill exo-distributed
This Skill enables distributed LLM inference across Apple Silicon devices with auto peer discovery, low-latency RDMA, and native MLX acceleration, removing the complexity of manual cluster setup.
Install exo, start on the first device to elect master, then start on additional devices; the cluster forms and serves at http://localhost:8080 by default.
High-intent search queries and answers about installing and using this skill.
Distributed LLM inference across Apple Silicon uses auto peer discovery and RDMA over Thunderbolt 5 for low-latency communication. Install exo, start it on the first device to elect master, then start on additional devices; the cluster forms automatically and serves via OpenAI-compatible API at http://localhost:8080.
Yes. RDMA over Thunderbolt 5 enables low-latency cross-device communication for distributed inference. The Skill coordinates model sharding and pipeline and tensor parallelism across Mac Studios, with MLX backend optimization for native Apple Silicon acceleration.
Distributed clustering requires auto peer discovery, MLX initialization, RDMA over Thunderbolt 5 networking, and model shard distribution for parallelism. The Skill enforces these requirements and exposes an OpenAI-compatible API across a configured multi-device cluster with no manual setup complexity.
Model sharding distributes large language models across devices using pipeline and tensor parallelism. The Skill coordinates shard distribution across your Apple Silicon cluster, allowing larger models to run by splitting computation across multiple Mac Studios while maintaining low-latency RDMA communication.
No. Auto peer discovery handles cluster formation automatically as devices come online. You install exo, elect a master on the first device, start additional devices, and the cluster joins and serves inference without manual network or configuration work.
Distributed inference across Apple Silicon exposes an OpenAI-compatible API, allowing existing applications to serve inference from a multi-device cluster at http://localhost:8080 without rewriting client code.
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