Hypha is a self-managing "Kubernetes for AI" designed for distributed machine learning training and inference. Built on libp2p for decentralized networking, Hypha enables organizations to train and serve massive models across heterogeneous, poorly-connected infrastructure—from HPC GPU farms to commodity hardware—without requiring centralized coordination.
The system implements DiLoCo (Distributed Low-Communication) style training, reducing network communication by approximately 500x compared to traditional data-parallel approaches. With automatic resource discovery, workload distribution, and fault tolerance, Hypha maintains enterprise-grade security and reliability while eliminating single points of failure.
Install Hypha using the standalone installer script:
curl -fsSL https://github.com/hypha-space/hypha/releases/download/v<VERSION>/install.sh | shFor alternative installation methods (GitHub releases, Cargo), see the Installation Guide.
Follow the Quick Start Guide to set up your first end-to-end decentralized training system.
Make cutting-edge machine learning accessible to organizations by efficiently utilizing heterogeneous compute resources.
Develop a self-managing system that automatically handles resource discovery, workload distribution, fault tolerance, and scaling with minimal configuration requirements and administrative overhead.
Provide a reliable, high-performance inference backbone to support real-world ML applications with the scale, latency, and reliability requirements of production systems.
Build a secure, maintainable, and observable system that meets enterprise requirements for encryption, resilience, repeatable deployment, and comprehensive logging.
Want to help improve Hypha and its capabilities for distributed training and inference? We encourage contributions of all kinds, from bug fixes and feature enhancements to documentation improvements. Hypha aims to provide a robust platform for efficient and scalable machine learning workflows, and your contributions can help make it even better. Consult CONTRIBUTING.md for detailed instructions on how to contribute effectively.