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.
They have around the same amount of cuda cores. Normally the bigger the cuda cores the higher the inference
More tensor and cuda cores mean higher inference and training speed right? Do inference and training get the same benefit from those cores?