AirLLM runs 70B models on a single 4GB GPU with no quantization or pruning
techNmak · x · 2026-10-26
AirLLM is a Python library that runs 70B-parameter LLMs on a single 4GB GPU without quantization, distillation, or pruning.
It tackles access: open-source models keep growing, but keeping all weights resident in VRAM demands high-end or multiple GPUs.
How it works: at inference time the model is decomposed and saved layer-wise, so the full model never sits in GPU memory at once. For supported MoE models it goes further, streaming only the experts selected by the router, since each token typically activates a subset.
Usage: pass a Hugging Face repo ID through AutoModel; supports Llama, Qwen, and more.
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