To run a large model, users need to clone the Distributed Llama repository, build the application on all devices, connect them to the same local network, and run worker nodes on the worker devices. The root node requires the model files and distributes slices of the model to the worker nodes. The article provides detailed instructions on how to run the inference on the root node and how to reduce RAM usage. The author encourages users to share their results on GitHub.
Key takeaways:
- The open models of LLM have the advantage of being run locally without the need for external providers or extra costs, but this advantage decreases as the model size increases.
- Tensor parallelism can speed up computations in LLMs, but synchronization can slow it down. The combination of these two factors will determine the final performance.
- Distributed Llama is a project that allows you to run an LLM model across multiple devices. It uses tensor parallelism and is optimized for the low amount of data required for synchronization.
- Running a large model at home requires a project that implements tensor parallelism and distributed inference, such as Distributed Llama. It involves setting up a root node and worker nodes, and requires a fast network connection for synchronization.