AirLLM Breaks VRAM Barrier: Runs 70B LLMs on a Single 4GB GPU
techNmak · x · 2026-08-03
The open-source Python library AirLLM (currently with 25.5k stars) proposes a breakthrough VRAM solution, enabling 70B parameter LLMs to run on a single 4GB GPU without quantization, distillation, or pruning.
Core Principles:
- Layer-wise Loading: During inference, the model is decomposed and saved layer-wise, avoiding the need to hold the entire model in memory at once.
- Expert Streaming: For MoE (Mixture of Experts) models, it streams one expert at a time rather than a whole layer, since a token typically doesn't need every expert to run.
Additionally, the library supports almost all mainstream models (Llama, Qwen, DeepSeek, etc.) and offers an optional block-wise quantization feature, delivering up to 3x faster inference with negligible accuracy loss.
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