How to Run Llama 3.1 405B on Home Devices? Build AI Cluster!

🌈 Abstract

The article discusses the advantages and challenges of running large language models (LLMs) locally on multiple devices, using techniques like tensor parallelism and distributed inference. It introduces the Distributed Llama project, which allows running the Llama 3.1 405B model across multiple devices.

🙋 Q&A

[01] Running Large Language Models Locally

1. What are the main advantages and challenges of running open LLM models locally?

• The main advantage of open LLM models is that you can run them locally without relying on external providers or paying extra beyond electricity and hardware costs.
• However, this advantage starts to wane as the model size increases, as it becomes difficult to run huge models that require large amounts of memory.

2. How can tensor parallelism and distributed inference help with running large models locally?

• Tensor parallelism can speed up the matrix multiplication computations in LLMs by splitting the computation across multiple CPU/GPU cores and devices.
• Distributed inference can help reduce the amount of data required for synchronizing the neural network state across multiple devices, which is a key bottleneck.

3. What are the main factors that determine the final performance when running large models across multiple devices?

• The combination of the speedup from tensor parallelism and the slowdown from synchronization will determine the final performance.
• The speed of the communication links between the devices (e.g., Ethernet, USB4) is a critical factor in minimizing the synchronization overhead.

[02] Distributed Llama Project

1. What are the key components of the Distributed Llama project?

• Distributed Llama distinguishes between two types of nodes: root nodes and worker nodes.
• The root node manages the distribution of the model slices to the worker nodes and coordinates the inference process.
• The worker nodes perform the actual inference computations on their assigned slices of the model.

2. How does Distributed Llama optimize for low synchronization overhead?

• Distributed Llama is designed to minimize the amount of data required for synchronizing the neural network state across devices.
• For example, a quantized Llama 3 8B model in Q40 format (6.3 GB) only requires 1 MB of data to synchronize per token when using 2 devices.

3. What are the steps to run the Llama 3.1 405B model using the Distributed Llama project?

• Clone the Distributed Llama repository and build the dllama application on all the devices.
• Connect the devices to the same local network, preferably using a fast Ethernet switch or USB4 mesh network.
• Run the worker nodes on the worker devices, specifying the number of CPU cores to use.
• Download the Llama 3.1 405B model, convert it to Distributed Llama format, and run the inference on the root node, specifying the worker node addresses.
• Optionally, build and run the dllama-api application on the root node to expose a /v1/chat/completions API endpoint.