Peaked at #2 Now #2
AirLLM runs big models on low VRAM
AirLLM uses layer-wise inference to run 70B models on 4GB GPUs and 405B on 8GB, supporting Llama, Qwen and Mistral, now open source.
Key points
- AirLLM uses "Layer-wise Inference," loading, computing, and flushing one layer at a time instead of loading the whole model.
- It runs a 70B model on a single 4GB GPU and scales up to Llama 3.1 405B on 8GB of VRAM.
- No quantization is needed by default, and it supports Llama, Qwen, and Mistral on Linux, Windows, and macOS.
- It is 100% open source.
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Reaction on X: Replies were largely mocking, focusing on the extreme slowness — one commenter joked it runs 1 token every 10 minutes, and others quipped about SSD wear, turtle speeds, and running on CPU. One reply conceded that it was previously impossible and could have use cases if energy and time are no concern. The original poster did not respond.
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From @iainsnotes, @rgbman1776, @trailformer and others. Spam removed, with English and Chinese translations.
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