NVIDIA H100 vs NVIDIA H200
Specs, monthly cost in taka and the workloads each GPU suits, side by side.
Tensor figures are NVIDIA's published numbers with sparsity (dense is half). Source: H100, H200.
The short answer
Pick the NVIDIA H100 if: You train or fine-tune transformers seriously, want FP8, or run large-model inference where speed matters. It is the reference GPU most papers and tutorials assume.
Pick the NVIDIA H200 if: You need the H100's speed with 141 GB — 70B models at 8-bit with room for long context, bigger batches, or memory-heavy retrieval workloads.
The NVIDIA H200 costs about 33% more per hour than the NVIDIA H100. If your model fits comfortably on the cheaper GPU and you are not short of time, the cheaper one usually wins. Compare with your own model →
Common questions
Which is faster, the NVIDIA H100 or the NVIDIA H200?
Both have the same tensor compute (1,979 TFLOPS). The difference is memory and memory bandwidth (3.35 TB/s vs 4.8 TB/s), which speeds up memory-bound inference on large models.
Which one costs less?
The NVIDIA H100 is ৳1,047 per hour and the NVIDIA H200 is ৳1,390, about 33% more. For 40 hours that is ৳41,880 vs ৳55,600.
Which has more memory?
The NVIDIA H200 has 141 GB and the NVIDIA H100 has 80 GB. Roughly, the NVIDIA H100 fits 32B in FP16, 70B at 8-bit (tight); the NVIDIA H200 fits 70B at 8-bit with room to spare, 70B in FP16 (tight).
Which should I choose for my project?
Choose the NVIDIA H100 if: You train or fine-tune transformers seriously, want FP8, or run large-model inference where speed matters. It is the reference GPU most papers and tutorials assume. Choose the NVIDIA H200 if: You need the H100's speed with 141 GB — 70B models at 8-bit with room for long context, bigger batches, or memory-heavy retrieval workloads.