Published on September 25, 2024, the report describes running large models by loading one Transformer layer at a time, rather than keeping the entire model in GPU memory.
AirLLM uses layer-wise inference: it loads one Transformer layer at a time from disk, so the entire model does not need to reside in GPU memory. The publication also mentions block-wise quantization and compression support.
To evaluate this approach on memory-limited GPUs, first identify the model and hardware configuration you plan to test. Compare available memory with the model’s requirements, and record the conditions for each test.
Next, consider how layer-wise loading, block-wise quantization, and compression would fit your workflow. The report describes these techniques but provides no performance results or guarantee that they will suit a particular use case.
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