exo: Run your own AI cluster at home with everyday devices. Maintained by exo labs.

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exo connects all your devices into an AI cluster. Not only does exo enable running models larger than would fit on a single device, but with day-0 support for RDMA over Thunderbolt, makes models run faster as you add more devices.

Features

• Automatic Device Discovery: Devices running exo automatically discover each other - no manual configuration.
• RDMA over Thunderbolt: exo ships with day-0 support for RDMA over Thunderbolt 5, enabling 99% reduction in latency between devices.
• Topology-Aware Auto Parallel: exo figures out the best way to split your model across all available devices based on a realtime view of your device topology. It takes into account device resources and network latency/bandwidth between each link.
• Tensor Parallelism: exo supports sharding models, for up to 1.8x speedup on 2 devices and 3.2x speedup on 4 devices.
• MLX Support: exo uses MLX as an inference backend and MLX distributed for distributed communication.

Benchmarks

Qwen3-235B (8-bit) on 4 × M3 Ultra Mac Studio with Tensor Parallel RDMA

Source: Jeff Geerling: 15 TB VRAM on Mac Studio – RDMA over Thunderbolt 5

DeepSeek v3.1 671B (8-bit) on 4 × M3 Ultra Mac Studio with Tensor Parallel RDMA

Source: Jeff Geerling: 15 TB VRAM on Mac Studio – RDMA over Thunderbolt 5

Kimi K2 Thinking (native 4-bit) on 4 × M3 Ultra Mac Studio with Tensor Parallel RDMA

Source: Jeff Geerling: 15 TB VRAM on Mac Studio – RDMA over Thunderbolt 5

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Quick Start

Devices running exo automatically discover each other, without needing any manual configuration. Each device provides an API and a dashboard for interacting with your cluster (runs at http://localhost:52415).

There are two ways to run exo:

Run from Source (Mac & Linux)

Prerequisites:

• brew (for simple package management on MacOS)

  /bin/bash -c "$(curl -fsSL https://raw.githubusercontent.com/Homebrew/install/HEAD/install.sh)"
  

• uv (for Python dependency management)

• macmon (for hardware monitoring on Apple Silicon)

• node (for building the dashboard)

  brew install uv macmon node
  

• rust (to build Rust bindings, nightly for now)

  curl --proto '=https' --tlsv1.2 -sSf https://sh.rustup.rs | sh
  rustup toolchain install nightly
  

Clone the repo, build the dashboard, and run exo:

# Clone exo
git clone https://github.com/exo-explore/exo

# Build dashboard
cd exo/dashboard && npm install && npm run build && cd ..

# Run exo
uv run exo

This starts the exo dashboard and API at http://localhost:52415/

macOS App

exo ships a macOS app that runs in the background on your Mac.

The macOS app requires macOS Tahoe 26.2 or later.

Download the latest build here: EXO-latest.dmg.

The app will ask for permission to modify system settings and install a new Network profile. Improvements to this are being worked on.

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Using the API

If you prefer to interact with exo via the API, here is an example creating an instance of a small model (mlx-community/Llama-3.2-1B-Instruct-4bit), sending a chat completions request and deleting the instance.

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1. Preview instance placements

The /instance/previews endpoint will preview all valid placements for your model.

curl "http://localhost:52415/instance/previews?model_id=llama-3.2-1b"

Sample response:

{
  "previews": [
    {
      "model_id": "mlx-community/Llama-3.2-1B-Instruct-4bit",
      "sharding": "Pipeline",
      "instance_meta": "MlxRing",
      "instance": {...},
      "memory_delta_by_node": {"local": 729808896},
      "error": null
    }
    // ...possibly more placements...
  ]
}

This will return all valid placements for this model. Pick a placement that you like. To pick the first one, pipe into jq:

curl "http://localhost:52415/instance/previews?model_id=llama-3.2-1b" | jq -c '.previews[] | select(.error == null) | .instance' | head -n1

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2. Create a model instance

Send a POST to /instance with your desired placement in the instance field (the full payload must match types as in CreateInstanceParams), which you can copy from step 1:

curl -X POST http://localhost:52415/instance \
  -H 'Content-Type: application/json' \
  -d '{
    "instance": {...}
  }'

Sample response:

{
  "message": "Command received.",
  "command_id": "e9d1a8ab-...."
}

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3. Send a chat completion

Now, make a POST to /v1/chat/completions (the same format as OpenAI's API):

curl -N -X POST http://localhost:52415/v1/chat/completions \
  -H 'Content-Type: application/json' \
  -d '{
    "model": "mlx-community/Llama-3.2-1B-Instruct-4bit",
    "messages": [
      {"role": "user", "content": "What is Llama 3.2 1B?"}
    ],
    "stream": true
  }'

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4. Delete the instance

When you're done, delete the instance by its ID (find it via /state or /instance endpoints):

curl -X DELETE http://localhost:52415/instance/YOUR_INSTANCE_ID

Other useful API endpoints:*

• List all models: curl http://localhost:52415/models
• Inspect instance IDs and deployment state: curl http://localhost:52415/state

For further details, see API types and endpoints in src/exo/master/api.py.

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Hardware Accelerator Support

On macOS, exo uses the GPU. On Linux, exo currently runs on CPU. We are working on extending hardware accelerator support. 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.

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Contributing

See CONTRIBUTING.md for guidelines on how to contribute to exo.