How to Run Llama 3 405B on Home Devices? Build AI Cluster
The article explains how to run the Llama 3.1 405B model on home devices using the Distributed Llama project, detailing setup, resource requirements, and methods for efficient execution across multiple devices.
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The article discusses how to run the Llama 3.1 405B model on home devices by building an AI cluster using the Distributed Llama project. Open LLM models like Llama can be run locally, which eliminates reliance on external providers, but running large models requires significant memory and resources. Tensor parallelism is highlighted as a method to speed up matrix multiplication across multiple devices, although synchronization can be a bottleneck due to slower home network connections. The Distributed Llama project allows users to run LLMs across multiple devices, utilizing a root node for coordination and worker nodes for execution. To set up, users need to clone the Distributed Llama repository, connect devices to a local network, and run the necessary commands to configure the nodes. The root node requires the Llama 3.1 model, which must be downloaded and converted to a compatible format. The process involves significant disk space and time for conversion. Once set up, users can run inference commands on the root node, specifying worker nodes and other parameters. The article also mentions the option to run an API service and manage RAM usage by utilizing disk storage for the KV cache. Overall, the guide provides a comprehensive overview of the steps needed to successfully run the Llama 3.1 model on a home AI cluster.
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5 commentsIt seems impractical that a home would have 4 machines with 64 gb ram that would be dedicated to a distributed system. Max, core count, 16 cores from Consumer AMD CPUs? From a cost perspective build a system with 256gb ram and AMD Epic CPU?
The only thing I can think of that pushes a need for a distributed system is multi-GPU across multiple systems.
GPU inference is another thing, as high-VRAM GPUs are artificially priced way high so they could only be bought by corporations. However, if you attempt to build a cluster with say 10 4090s to obtain some 240GB VRAM, you won’t have enough electricity to run it at home.
I am currently building a 4x4090 rig, but that’s probably the maximum I could have at home giving my budget / available power restrictions. And that’s only 96G VRAM, slightly more than a single A100.
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You can easily set up and run large language models (LLMs) on your PC using tools like Ollama, LM Suite, and Llama.cpp. Ollama supports AMD GPUs and AVX2-compatible CPUs, with straightforward installation across different systems. It offers commands for managing models and now supports select AMD Radeon cards.
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