H100 vs RTX PRO 6000

The H100 FP16 dense figure of 989 TFLOPS exceeds the RTX PRO 6000 FP16 dense figure of 503.8 TFLOPS by a factor derived solely from those values. This gap favors the H100 for training workloads that rely on dense FP16 operations while the RTX PRO 6000 FP32 rating of 126 TFLOPS surpasses the H100 FP32 rating of 67 TFLOPS. Memory bandwidth of 3350 GB/s on the H100 versus 1792 GB/s on the RTX PRO 6000 directly influences achievable batch sizes in memory bound inference steps. When sparsity is applied the H100 FP16 with sparsity reaches 1979 TFLOPS compared with 1007.6 TFLOPS FP16 with sparsity on the RTX PRO 6000.

The H100 suits workloads that require the 3350 GB/s memory bandwidth to sustain large batch sizes during dense FP16 training. Its 989 TFLOPS FP16 dense rating also supports higher throughput in FP8 dense operations rated at 1979 TFLOPS when compared with the RTX PRO 6000 equivalent.

Choose the RTX PRO 6000 for single GPU work: serving or fine-tuning a model that fits in 96 GB, which is more than the 80 GB on the common H100 SKU. It is the only one of the two with FP4 tensor support at 2015.2 TFLOPS dense, and its 126 TFLOPS FP32 is 1.9 times the 67 TFLOPS of the H100 for diffusion, rendering, and simulation code that stays in single precision. It is a PCIe 5.0 card without NVLink, so once the job spans several GPUs the H100 pulls ahead.