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https://github.com/exo-explore/exo

Run your own AI cluster at home with everyday devices 📱💻 🖥️⌚
https://github.com/exo-explore/exo

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Run your own AI cluster at home with everyday devices 📱💻 🖥️⌚

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README 

        



  

  exo logo



exo: Run your own AI cluster at home with everyday devices. Maintained by [exo labs](https://x.com/exolabs).






[Discord](https://discord.gg/EUnjGpsmWw) | [Telegram](https://t.me/+Kh-KqHTzFYg3MGNk) | [X](https://x.com/exolabs)






[![GitHub Repo stars](https://img.shields.io/github/stars/exo-explore/exo)](https://github.com/exo-explore/exo/stargazers)

[![Tests](https://dl.circleci.com/status-badge/img/circleci/TrkofJDoGzdQAeL6yVHKsg/4i5hJuafuwZYZQxbRAWS71/tree/main.svg?style=svg)](https://dl.circleci.com/status-badge/redirect/circleci/TrkofJDoGzdQAeL6yVHKsg/4i5hJuafuwZYZQxbRAWS71/tree/main)

[![License: GPL v3](https://img.shields.io/badge/License-GPLv3-blue.svg)](https://www.gnu.org/licenses/gpl-3.0)






---



Forget expensive NVIDIA GPUs, unify your existing devices into one powerful GPU: iPhone, iPad, Android, Mac, Linux, pretty much any device!





  
Update: exo is hiring. See here for more details.





## Get Involved



exo is **experimental** software. Expect bugs early on. Create issues so they can be fixed. The [exo labs](https://x.com/exolabs) team will strive to resolve issues quickly.



We also welcome contributions from the community. We have a list of bounties in [this sheet](https://docs.google.com/spreadsheets/d/1cTCpTIp48UnnIvHeLEUNg1iMy_Q6lRybgECSFCoVJpE/edit?usp=sharing).



## Features



### Wide Model Support



exo supports different models including LLaMA ([MLX](exo/inference/mlx/models/llama.py) and [tinygrad](exo/inference/tinygrad/models/llama.py)), Mistral, LlaVA, Qwen and Deepseek.



### Dynamic Model Partitioning



exo [optimally splits up models](exo/topology/ring_memory_weighted_partitioning_strategy.py) 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.



### Automatic Device Discovery



exo will [automatically discover](https://github.com/exo-explore/exo/blob/945f90f676182a751d2ad7bcf20987ab7fe0181e/exo/orchestration/standard_node.py#L154) other devices using the best method available. Zero manual configuration.



### ChatGPT-compatible API



exo provides a [ChatGPT-compatible API](exo/api/chatgpt_api.py) for running models. It's a [one-line change](examples/chatgpt_api.sh) in your application to run models on your own hardware using exo.



### Device Equality



Unlike other distributed inference frameworks, exo does not use a master-worker architecture. Instead, exo devices [connect p2p](https://github.com/exo-explore/exo/blob/945f90f676182a751d2ad7bcf20987ab7fe0181e/exo/orchestration/standard_node.py#L161). As long as a device is connected somewhere in the network, it can be used to run models.



Exo supports different [partitioning strategies](exo/topology/partitioning_strategy.py) to split up a model across devices. The default partitioning strategy is [ring memory weighted partitioning](exo/topology/ring_memory_weighted_partitioning_strategy.py). This runs an inference in a ring where each device runs a number of model layers proportional to the memory of the device.



!["A screenshot of exo running 5 nodes](docs/exo-screenshot.png)



## Installation



The current recommended way to install exo is from source.



### Prerequisites



- Python>=3.12.0 is required because of [issues with asyncio](https://github.com/exo-explore/exo/issues/5) in previous versions.

- For Linux with NVIDIA GPU support (Linux-only, skip if not using Linux or NVIDIA):

  - NVIDIA driver - verify with `nvidia-smi`

  - CUDA toolkit - install from [NVIDIA CUDA guide](https://docs.nvidia.com/cuda/cuda-installation-guide-linux/index.html#cuda-cross-platform-installation), verify with `nvcc --version`

  - cuDNN library - download from [NVIDIA cuDNN page](https://developer.nvidia.com/cudnn-downloads), verify installation by following [these steps](https://docs.nvidia.com/deeplearning/cudnn/latest/installation/linux.html#verifying-the-install-on-linux:~:text=at%20a%20time.-,Verifying%20the%20Install%20on%20Linux,Test%20passed!,-Upgrading%20From%20Older)



### Hardware Requirements



- The only requirement to run exo is to have enough memory across all your devices to fit the entire model into memory. For example, if you are running llama 3.1 8B (fp16), you need 16GB of memory across all devices. Any of the following configurations would work since they each have more than 16GB of memory in total:

  - 2 x 8GB M3 MacBook Airs

  - 1 x 16GB NVIDIA RTX 4070 Ti Laptop

  - 2 x Raspberry Pi 400 with 4GB of RAM each (running on CPU) + 1 x 8GB Mac Mini

- exo is designed to run on devices with heterogeneous capabilities. For example, you can have some devices with powerful GPUs and others with integrated GPUs or even CPUs. Adding less capable devices will slow down individual inference latency but will increase the overall throughput of the cluster.



### From source



```sh

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

cd exo

pip install -e .

# alternatively, with venv

source install.sh

```



### Troubleshooting



- If running on Mac, MLX has an [install guide](https://ml-explore.github.io/mlx/build/html/install.html) with troubleshooting steps.



### Performance



- There are a number of things users have empirically found to improve performance on Apple Silicon Macs:



1. Upgrade to the latest version of MacOS 15.

2. Run `./configure_mlx.sh`. This runs commands to optimize GPU memory allocation on Apple Silicon Macs.



## Documentation



### Example Usage on Multiple MacOS Devices



#### Device 1:



```sh

exo

```



#### Device 2:

```sh

exo

```



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



exo starts a ChatGPT-like WebUI (powered by [tinygrad tinychat](https://github.com/tinygrad/tinygrad/tree/master/examples/tinychat)) on http://localhost:52415



For developers, exo also starts a ChatGPT-compatible API endpoint on http://localhost:52415/v1/chat/completions. Examples with curl:



#### Llama 3.2 3B:



```sh

curl http://localhost:52415/v1/chat/completions \

  -H "Content-Type: application/json" \

  -d '{

     "model": "llama-3.2-3b",

     "messages": [{"role": "user", "content": "What is the meaning of exo?"}],

     "temperature": 0.7

   }'

```



#### Llama 3.1 405B:



```sh

curl http://localhost:52415/v1/chat/completions \

  -H "Content-Type: application/json" \

  -d '{

     "model": "llama-3.1-405b",

     "messages": [{"role": "user", "content": "What is the meaning of exo?"}],

     "temperature": 0.7

   }'

```



#### Llava 1.5 7B (Vision Language Model):



```sh

curl http://localhost:52415/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

   }'

```



### Example Usage on Multiple Heterogenous Devices (MacOS + Linux)



#### Device 1 (MacOS):



```sh

exo

```



Note: We don't need to explicitly tell exo to use the **tinygrad** inference engine. **MLX** and **tinygrad** are interoperable!



#### Device 2 (Linux):

```sh

exo

```



Linux devices will automatically default to using the **tinygrad** inference engine.



You can read about tinygrad-specific env vars [here](https://docs.tinygrad.org/env_vars/). For example, you can configure tinygrad to use the cpu by specifying `CLANG=1`.



### Example Usage on a single device with "exo run" command



```sh

exo run llama-3.2-3b

```



With a custom prompt:



```sh

exo run llama-3.2-3b --prompt "What is the meaning of exo?"

```



### Model Storage



Models by default are stored in `~/.cache/huggingface/hub`.



You can set a different model storage location by setting the `HF_HOME` env var.



## Debugging



Enable debug logs with the DEBUG environment variable (0-9).



```sh

DEBUG=9 exo

```



For the **tinygrad** inference engine specifically, there is a separate DEBUG flag `TINYGRAD_DEBUG` that can be used to enable debug logs (1-6).



```sh

TINYGRAD_DEBUG=2 exo

```



## Formatting



We use [yapf](https://github.com/google/yapf) to format the code. To format the code, first install the formatting requirements:



```sh

pip3 install -e '.[formatting]'

```



Then run the formatting script:



```sh

python3 format.py ./exo

```



## Known Issues



- On some versions of MacOS/Python, certificates are not installed properly which can lead to SSL errors (e.g. SSL error with huggingface.co). To fix this, run the Install Certificates command, usually:



```sh

/Applications/Python 3.x/Install Certificates.command

```



- 🚧 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 [email protected] with your GitHub username explaining your use-case and you will be granted access on GitHub.



## Inference Engines



exo supports the following inference engines:



- ✅ [MLX](exo/inference/mlx/sharded_inference_engine.py)

- ✅ [tinygrad](exo/inference/tinygrad/inference.py)

- 🚧 [PyTorch](https://github.com/exo-explore/exo/pull/139)

- 🚧 [llama.cpp](https://github.com/exo-explore/exo/issues/167)



## Networking Modules



- ✅ [GRPC](exo/networking/grpc)

- 🚧 [Radio](TODO)

- 🚧 [Bluetooth](TODO)