AirLLM runs frontier-size models layer-by-layer: 671B on 12GB VRAM

Aiden_Tech_Ai · x · 2026-10-16

• Open-source AirLLM processes LLMs one layer at a time instead of loading the whole model, so VRAM needs depend only on single-layer size — no quantization, distillation, or pruning.
• Claimed results: DeepSeek V3 (671B) on 12GB, Llama 3.1 405B on 8GB, and a reported 2.8T-parameter model under 4GB (unverified).
• The trick with huge sparse MoE models: only experts activated per token get loaded, so a multi-trillion-param model can use less memory than a 70B dense one.

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