AirLLM Enables Execution of 70B Models on 4GB GPUs via Layer-by-Layer Loading

Developer lyogavin has released AirLLM, an inference optimization library designed to run massive large language models on low-spec GPUs. Unlike traditional compression techniques such as quantization, distillation, or pruning which often degrade model accuracy, AirLLM preserves the original model weights. By executing models in a layer-by-layer fashion rather than loading the entire architecture into memory, the tool drastically lowers the hardware entry barrier for model evaluation.