Distributed LLama is a distributed computing framework that accelerates large language model inference by connecting multiple home devices into a cluster.
The project addresses the challenge of running large language models on resource-constrained hardware by distributing the computational load across multiple devices. It works by partitioning model layers across connected machines, allowing each device to process its assigned portion of the model in parallel. As more devices join the cluster, inference speed increases proportionally, making it possible to run larger models than any single machine could handle alone.
The tool suits developers and enthusiasts who have multiple devices at home or in a lab environment and want to run large language models without relying on cloud services or expensive hardware. It works with models like Llama 2 and Llama 3. This approach is particularly valuable for those seeking to reduce latency and costs associated with centralized inference services, or for experimenting with large models on a budget by pooling existing hardware resources.
The project shows active development with regular commits addressing core functionality and bug fixes. Work spans multiple areas including network communication between nodes, model layer distribution logic, and performance optimization. The codebase demonstrates ongoing refinement of the distributed inference pipeline rather than exploratory or experimental work.
