EXO lets you run your own AI cluster at home with everyday devices. We take advantage of Apple's M-series hardware and unified memory to run large language models, building a cluster to enable even more memory.
EXO underwent a full rewrite for v1. For legacy exo, see this repo's history or exo-explore/ex-exo for a snapshot.
Features
- Automatic discovery: Devices running EXO automatically find each other on your local network - no manual configuration.
- RDMA over Thunderbolt: Ultra-low latency communication between macOS devices using RDMA over Thunderbolt.
- Super-linear scaling: Get up to 3.2x performance running large models across 4 machines with Tensor parallelism and RDMA.
- MLX Support: Uses the mlx-explore/mlx library for compute, enabling efficient and flexible machine learning on Apple silicon.
Quick Start
You need at least one Mac device running macOS Tahoe 26.2 (released December 12th 2025).
You can download the latest build here: EXO-latest.dmg. It will ask for permission to modify system settings and install a new Network profile. We hope to make this smoother in the future!
To run from source, clone the repo and run uv run exo.
After starting with either of these methods go to http://localhost:8000 in your browser, and you'll have EXO.
Requirements
- Mac devices with Apple Silicon (M-series chips)
- macOS Tahoe 26.2 or later (released December 12th 2025)
- Older macOS versions may work without RDMA, but only 26.2+ is officially supported
- For RDMA over Thunderbolt: a high quality Thunderbolt 5 cable
We intend to add support for other hardware platforms like the DGX Spark in the future, but they are not currently supported. If you'd like support for a new hardware platform, please search for an existing feature request and add a thumbs up so we know what hardware is important to the community.
Contributing
See CONTRIBUTING.md for guidelines on how to contribute to EXO.