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

submitted 1 year 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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[–] cyril1991@alien.top 1 points 1 year ago

The H100 is more recent and beefier. It is also more interesting to use it for the multi-instance GPU (MIG) feature where you “split it” for use on different workloads, so you could run multiple LLMs in parallel. The A100 has the same feature, but less memory/compute to split.