Forget expensive NVIDIA GPUs, unify your existing devices into one powerful GPU: iPhone, iPad, Android, Mac, Linux, pretty much any device!
Now the default models, run 8B, 70B and 405B parameter models on your own devices
exo is experimental software. Expect bugs early on. Create issues so they can be fixed. The exo labs team will strive to resolve issues quickly.
We also welcome contributions from the community. We have a list of bounties in this sheet.
exo supports LLaMA (MLX and tinygrad) and other popular models.
exo optimally splits up models based on the current network topology and device resources available. This enables you to run larger models than you would be able to on any single device.
exo will automatically discover other devices using the best method available. Zero manual configuration.
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.
Unlike other distributed inference frameworks, exo does not use a master-worker architecture. Instead, exo devices connect p2p. As long as a device is connected somewhere in the network, it can be used to run models.
Exo supports different partitioning strategies to split up a model across devices. The default partitioning strategy is ring memory weighted partitioning. This runs an inference in a ring where each device runs a number of model layers proportional to the memory of the device.
The current recommended way to install exo is from source.
- Python>=3.12.0 is required because of issues with asyncio in previous versions.
git clone https://github.com/exo-explore/exo.git
cd exo
pip install .
# alternatively, with venv
source install.sh- If running on Mac, MLX has an install guide with troubleshooting steps
python3 main.pypython3 main.pyThat's it! No configuration required - exo will automatically discover the other device(s).
The native way to access models running on exo is using the exo library with peer handles. See how in this example for Llama 3.
exo starts a ChatGPT-like WebUI (powered by tinygrad tinychat) on http://localhost:8000
For developers, exo also starts a ChatGPT-compatible API endpoint on http://localhost:8000/v1/chat/completions. Example with curls:
curl http://localhost:8000/v1/chat/completions \
-H "Content-Type: application/json" \
-d '{
"model": "llama-3.1-8b",
"messages": [{"role": "user", "content": "What is the meaning of exo?"}],
"temperature": 0.7
}'curl http://localhost:8000/v1/chat/completions \
-H "Content-Type: application/json" \
-d '{
"model": "llava-1.5-7b-hf",
"messages": [
{
"role": "user",
"content": [
{
"type": "text",
"text": "What are these?"
},
{
"type": "image_url",
"image_url": {
"url": "http://images.cocodataset.org/val2017/000000039769.jpg"
}
}
]
}
],
"temperature": 0.0
}'Enable debug logs with the DEBUG environment variable (0-9).
DEBUG=9 python3 main.py- 🚧 As the library is evolving so quickly, the iOS implementation has fallen behind Python. We have decided for now not to put out the buggy iOS version and receive a bunch of GitHub issues for outdated code. We are working on solving this properly and will make an announcement when it's ready. If you would like access to the iOS implementation now, please email alex@exolabs.net with your GitHub username explaining your use-case and you will be granted access on GitHub.
exo supports the following inference engines: