AirLLM: Breaking Memory Limits, Running 70B Models on a 4GB GPU

Run large language models like Qwen 70B on just 4GB GPU with AirLLM. Optimize memory and speed with dynamic loading, quantization, and more.

Large language models (LLMs) continue to grow in parameter size, but this comes with a significant demand for computational resources. Running a 70B parameter model typically requires hundreds of GB of GPU memory.

This undoubtedly raises the usage barrier. Today, we introduce an inference acceleration library—AirLLM—that allows running a 70B-level Qwen model on just 4GB of GPU memory and even running a 405B Llama 3.1 model on 8GB of GPU memory.

How is this achieved?

Let's find out.

Core Principles of AirLLM

The core concept of AirLLM is based on the "divide and conquer" strategy, optimizing memory usage through layered inference.