在家中使用日常设备运行自己的 AI 集群。由 exo labs 维护。

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topology 36b8456798 collect global topology with local peer visibility, ring memory weighted partitioning strategy 1 year ago
.gitignore 850b72d3ea make StatefulShardedModel callable, add some tests for mlx sharded inference 1 year ago
README.md 32972033dd update readme 1 year ago
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main.py 36b8456798 collect global topology with local peer visibility, ring memory weighted partitioning strategy 1 year ago
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README.md

exo logo exo: Run your own AI cluster at home with everyday devices. Maintained by [exo labs](https://x.com/exolabs_). Turns all your devices into a powerful GPU: iPhone, iPad, Android, Mac, Linux, pretty much any device!

[Homepage](https://github.com/exo-explore/exo) | [Documentation](https://github.com/exo-explore/exo/docs) | [Discord](TODO)


Get Involved

We have a list of bounties in this sheet.

Features

Wide Model Support

exo supports LLaMA and many other popular models.

Dynamic Model Partitioning

exo optimally splits up models based on the current network topology and device resources available.

Automatic Device Discovery

exo will automatically discover other devices using the best method available. Zero manual configuration.

ChatGPT-compatible API

exo provides a ChatGPT-compatible API for running models. It's a one-line change in your application to run models on your own hardware using exo.

Inference Engines

exo supports the following inference engines:

Usage

Documentation

Example Usage on Multiple Devices

Device 1:

./run.sh

Device 2:

./run.sh

That's it! No configuration required - exo will automatically discover the other device(s).

A ChatGPT-like web interface will be available on each device on port 8000 http://localhost:8000.

An API endpoint will be available on port 8001. Example usage:

curl -X POST http://localhost:8001/api/v1/chat -H "Content-Type: application/json" -d '{"messages": [{"role": "user", "content": "What is the meaning of life?"}]}'