TL;DR
AirLLM is an inference tool that optimizes memory usage to run 70B LLMs on a 4GB GPU and 405B models on an 8GB GPU.
Key features
Layer-wise loading and memory management overcoming single GPU memory limits
Inference of large models without quantization, distillation, or pruning
8-bit/4-bit quantization support for additional memory savings
CPU inference support (non-GPU environments)
Automatic detection and support for various model architectures (Llama, ChatGLM, Qwen, Mistral, etc.)
Prefetching overlaps model loading and computation, improving speed by 10%
Model compression feature boosts inference speed by 3x
When to use it
When you need to run large language models (70B, 405B) locally without expensive high-performance GPUs
When optimizing LLM inference in limited VRAM environments (4GB~8GB)
For researchers or developers looking to reduce cloud costs while serving large-scale models