H100 vs MI355X

The FP16 dense figure of 2500 TFLOPS on the MI355X exceeds the FP16 dense figure of 989 TFLOPS on the H100 by a factor of approximately 2.5. The same ratio appears when comparing FP16 with sparsity at 5000 TFLOPS against 1979 TFLOPS. This delta indicates that training or inference steps expressed in dense FP16 arithmetic complete in fewer cycles on the MI355X when batch sizes remain constant. Memory bandwidth of 8000 GB/s on the MI355X versus 3350 GB/s on the H100 allows larger batch sizes before memory stalls occur during tensor movement. FP32 performance of 157.3 TFLOPS on the MI355X versus 67 TFLOPS on the H100 further widens the gap for mixed-precision workloads that retain FP32 accumulations. All comparisons maintain dense-to-dense and sparsity-to-sparsity pairings without mixing the two modes. Higher TDP of 1400W on the MI355X compared with 700W on the H100 reflects the increased computational and bandwidth resources. Workloads limited by on-device capacity benefit from the 288 GB VRAM on the MI355X over the 80-94 GB VRAM on the H100.

The H100 suits deployments where power delivery is capped at 700W and existing NVLink fabrics already connect multiple units. Its 3350 GB/s bandwidth and 80-94 GB VRAM remain adequate for inference pipelines that fit within those limits while consuming half the power of the MI355X.

The MI355X suits workloads that require 288 GB VRAM and 8000 GB/s bandwidth to hold larger models or increase batch sizes. Its FP16 dense rating of 2500 TFLOPS and FP32 rating of 157.3 TFLOPS accelerate training steps that scale directly with those dense metrics.