H200 vs RTX PRO 6000

Dense FP16 performance of 989 TFLOPS on the H200 exceeds the 503.8 TFLOPS dense FP16 performance on the RTX PRO 6000 by a factor of nearly two. The same ratio appears when comparing the 1979 TFLOPS FP16 with sparsity on the H200 to the 1007.6 TFLOPS FP16 with sparsity on the RTX PRO 6000. Higher memory bandwidth of 4800 GB per s on the H200 permits larger batch sizes during training and inference than the 1792 GB per s bandwidth on the RTX PRO 6000. FP32 performance of 126 TFLOPS on the RTX PRO 6000 exceeds the 67 TFLOPS FP32 performance on the H200 and therefore favors workloads that remain in single precision.

The H200 suits LLM training and scientific computing when 141 GB VRAM and 4800 GB per s bandwidth are required to accommodate large model states. Its 989 TFLOPS dense FP16 and 1979 TFLOPS FP8 dense figures accelerate matrix operations that exceed the capacity of 96 GB VRAM configurations.

Choose the RTX PRO 6000 when your workload lives on one card and fits in 96 GB, which covers most single-model inference and LoRA style fine tuning. It is a PCIe 5.0 card without NVLink, so it is not the right pick for tensor parallel serving or multi-node training. Its 2015.2 TFLOPS of dense FP4 has no published counterpart on the H200, so FP4 quantized serving stacks are where it closes the gap most. Its 126 TFLOPS of FP32 is nearly double the H200 at 67 TFLOPS, which matters for rendering and simulation code that never touches tensor cores.