Distributed Llama (github.com)

A new technology called Distributed Llama has been announced, enabling users to connect multiple home devices into a powerful cluster to boost the inference speed of large language models (LLMs). This system utilizes tensor parallelism and high-speed synchronization over Ethernet, effectively leveraging the combined processing power of connected devices. It is compatible with Linux, macOS, and Windows, and is optimized for both ARM and x86_64 architecture with AVX2 support. This development is significant for the AI/ML community as it democratizes access to high-performance computing for LLM tasks, making it feasible for individuals with standard home setups to conduct advanced model inference without needing expensive dedicated servers. Users can deploy a root node to manage model loading and state synchronization while distributing processing to up to 2^n worker nodes, thereby splitting the RAM usage and improving efficiency. With detailed specifications for running various model configurations and an open-source approach, Distributed Llama offers an innovative solution for scaling LLM applications in personal and small-scale environments.