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[D] Why choose an H100 over an A100 for LLM inference? (alien.top)

submitted 10 months ago by faschu@alien.top to c/machinelearning@academy.garden

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What are the benefits of using an H100 over an A100 (both at 80 GB and both using FP16) for LLM inference?

Seeing the datasheet for both GPUS, the H100 has twice the max flops, but they have almost the same memory bandwidth (2000 GB/sec). As memory latency dominates inference, I wonder what benefits the H100 has. One benefit could, of course, be the ability to use FP8 (which is extremely useful), but I'm interested in the difference in the hardware specs in this question.

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[–] SnooHesitations8849@alien.top 1 points 10 months ago

H100 and A100 are best for training. H100 is optimized for lower precision (8/16 bits) and optimized for transformer. A100 is still very good but not that much. A100 is still very GPU-like. Wwhile H100 is a transformer-accelerator.

Using them for inference is not the best econ-friendly though.