Orchestrates distributed LLM inference across Apple Silicon clusters using RDMA and MLX sharding.
The exo-distributed skill enables Claude to manage and deploy large language models across heterogeneous Apple Silicon clusters. By leveraging the exo-explore framework, it automates peer discovery, implements pipeline and tensor parallelism, and utilizes low-latency Thunderbolt RDMA for high-performance distributed inference. This skill is specifically designed for developers and researchers who need to run massive models—such as DeepSeek R1 or Llama 405B—that exceed the memory capacity of a single Mac device, transforming multiple Mac Studios or MacBooks into a unified AI powerhouse.
Key Features
01Real-time cluster monitoring and performance dashboard
022 GitHub stars
03Automatic peer discovery via mDNS/UDP broadcast
04Low-latency RDMA over Thunderbolt 5 connectivity
05Dynamic MLX sharding with Pipeline and Tensor parallelism
06OpenAI-compatible local API endpoint for seamless integration
Use Cases
01Running massive models like Llama 3.3 70B or DeepSeek R1 across multiple Mac Studios
02Optimizing LLM performance on heterogeneous Apple Silicon hardware using MLX acceleration
03Building zero-config local AI clusters for high-throughput private inference
