⭐⭐⭐ Kalavai platform is open source, and free to use in both commercial and non-commercial purposes. If you find it useful, consider supporting us by giving a star to our GitHub project, joining our discord channel and follow our Substack.

AI in the cloud is not aligned with you, it's aligned with the company that owns it. Make sure you own your AI

Kalavai is an open source tool that turns any devices into a self-hosted AI platform. It aggregates resources from multiple machines, including cloud, on prem and personal computers, and is compatible with most model engines to make model deployment and orchestration simple and reliable.

Kalavai's goal is to make using self-hosted AI (GenAI models and agents) in real applications accessible and affordable to all.

• Manage multiple devices resources as one, wherever they come from (hybrid cloud, on prem, personal devices)
• Deploy open source models seamlessly across devices, with zero-cost migration
• Beyond LLMs: not just for large language models, but text-to-speech, speech-to-text, image generation, video understanding, coding generation and embedding models.
• Production-ready: models are automatically exposed through a single OpenAI-like API and a ChatGPT-like UI playground, with off-the-shelf monitoring and evaluation framework.
• Compatible with most popular model engines
• Easy to expand to custom workloads

• 11 June 2025: Native support for Mac and Raspberry pi devices (ARM).
• 20 February 2025: New shiny GUI interface to control LLM pools and deploy models
• 31 January 2025: kalavai-client is now a PyPI package, easier to install than ever!

We currently support out of the box the following AI engines:

• vLLM: most popular GPU-based model inference.
• llama.cpp: CPU-based GGUF model inference.
• SGLang: Super fast GPU-based model inference.
• n8n: no-code workload automation framework.
• Flowise: no-code agentic AI workload framework.
• Speaches: audio (speech-to-text and text-to-speech) model inference.
• Langfuse: open source evaluation and monitoring GenAI framework.
• OpenWebUI: ChatGPT-like UI playground to interface with any models.

Coming soon:

• diffusers
• Aphrodite Engine
• Petals
• exo
• GPUstack
• RayServe

Not what you were looking for? Tell us what engines you'd like to see.

Kalavai is at an early stage of its development. We encourage people to use it and give us feedback! Although we are trying to minimise breaking changes, these may occur until we have a stable version (v1.0).

• Get a free Kalavai account and access unlimited AI.
• Full documentation for the project.
• Join our Substack for updates and be part of our community
• Join our discord community

The kalavai-client is the main tool to interact with the Kalavai platform, to create and manage both local and public pools and also to interact with them (e.g. deploy models). Let's go over its installation.

The client is a python package and can be installed with one command:

pip install kalavai-client

You can create and manage your pools with the new kalavai GUI, which can be started with:

kalavai gui start

This will expose the GUI and the backend services in localhost. By default, the GUI is accessible via https://localhost:49153. In the UI users can create and join AI pools, monitor devices, deploy LLMs and more.

Check out our getting started guide for next steps on how to add more workers to your pool, or use our managed seeds service for over-the-internet AI pools.

For an end to end tour on building your own OpenAI-like service, check our self-hosted guide.

Check our examples to put your new AI pool to good use!

If your system is not currently supported, open an issue and request it. We are expanding this list constantly.

• Kalavai client on Linux
• [TEMPLATE] Distributed LLM deployment
• Kalavai client on Windows (worker only)
• Kalavai client on Windows WSL2 (seed and worker)
• Self-hosted LLM pools
• Collaborative LLM deployment
• Ray cluster support
• Kalavai client on Mac (worker only)
• Kalavai pools UI
• Support for AMD GPUs
• Support for Mac M GPUs
• Docker install path

Anything missing here? Give us a shout in the discussion board

• PR welcome!
• Join the community and share ideas!
• Report bugs, issues and new features.
• Help improve our compatibility matrix by testing on different operative systems.
• Follow our Substack channel for news, guides and more.
• Community integrations are template jobs built by Kalavai and the community that makes deploying distributed workflows easy for users. Anyone can extend them and contribute to the repo.

sudo add-apt-repository ppa:deadsnakes/ppa
sudo apt update
sudo apt install python3.10 python3.10-dev python3-virtualenv python3-venv
virtualenv -p python3.10 env
source env/bin/activate
sudo apt install  python3.10-venv python3.10-dev -y
pip install -U setuptools
pip install -e .[dev]

bash publish.sh build

To run the unit tests, use:

python -m unittest