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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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[–] Gurrako@alien.top 1 points 10 months ago (2 children)

At first I thought that number was almost unbelievably high. It appears it can be 8x faster when using FlashAttention and a multi-GPU setup. Without multi-gpu and flash attention, it is a bit more than 2x faster.

Source: https://lambdalabs.com/blog/flashattention-2-lambda-cloud-h100-vs-a100

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

Sure but isn’t it the case that the H100 is what can sustain such a high throughput system whereas A100s are generally independent?

[–] 3DHydroPrints@alien.top 1 points 10 months ago

Thanks for clarifying:)