What hardware is required for Exo Distributed?

While it works over standard TCP/IP networking, it is optimized for Apple Silicon devices (M-series chips) connected via Thunderbolt 4 or 5 cables for low-latency RDMA performance.

Can I use this with existing OpenAI-based applications?

Yes, Exo exposes an OpenAI-compatible REST API endpoint (typically at localhost:8080), making it a drop-in replacement for cloud providers in your existing workflows.

Does it require manual networking configuration?

No, Exo features automatic peer discovery via gossip protocols and mDNS, allowing devices to find each other and form a cluster without manual IP configuration.

Which sharding strategies does this skill support?

It supports Pipeline Parallelism (splitting layers across nodes) and Tensor Parallelism (splitting individual tensors/heads across nodes), depending on the model architecture and hardware count.

Exo Distributed Inference

Name: Exo Distributed Inference
Author: plurigrid

byplurigrid

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数据科学与机器学习

Orchestrates distributed LLM inference across Apple Silicon clusters using RDMA and MLX sharding.

关于

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.

主要功能

Real-time cluster monitoring and performance dashboard
2 GitHub stars
Automatic peer discovery via mDNS/UDP broadcast
Low-latency RDMA over Thunderbolt 5 connectivity
Dynamic MLX sharding with Pipeline and Tensor parallelism
OpenAI-compatible local API endpoint for seamless integration

使用场景

Running massive models like Llama 3.3 70B or DeepSeek R1 across multiple Mac Studios
Optimizing LLM performance on heterogeneous Apple Silicon hardware using MLX acceleration
Building zero-config local AI clusters for high-throughput private inference

关于

主要功能

Real-time cluster monitoring and performance dashboard
2 GitHub stars
Automatic peer discovery via mDNS/UDP broadcast
Low-latency RDMA over Thunderbolt 5 connectivity
Dynamic MLX sharding with Pipeline and Tensor parallelism
OpenAI-compatible local API endpoint for seamless integration

使用场景

Running massive models like Llama 3.3 70B or DeepSeek R1 across multiple Mac Studios
Optimizing LLM performance on heterogeneous Apple Silicon hardware using MLX acceleration
Building zero-config local AI clusters for high-throughput private inference