H100 vs H200

The core compute performance remains identical between H100 and H200: both achieve 1979 TFLOPS in FP16 for mixed-precision training and 3958 TFLOPS in FP8 for efficient inference. FP32 performance stands at 67 TFLOPS on each, suiting scientific simulations. This parity means raw FLOPS-driven workloads run at the same speed on either GPU.

Memory specifications set them apart: H200's 141 GB HBM3e VRAM versus H100's 80-94 GB HBM3 allows fitting larger language models without model parallelism. The 4800 GB/s bandwidth on H200, up from 3350 GB/s, accelerates data transfers, enabling larger batch sizes in training and reducing latency in inference. For LLM training, this supports batches that would otherwise require sharding on H100, improving throughput by handling more tokens per step.

In inference scenarios, higher bandwidth minimizes bottlenecks during prompt processing, allowing H200 to serve bigger contexts. Overall, H200 excels in memory-intensive applications, while H100 suffices for balanced workloads.

Choose H100 for workloads fitting within 80-94 GB VRAM, such as fine-tuning mid-sized models or Stable Diffusion generation. Its availability across 22 cloud offers at an average $2.62/hr provides more rental options than H200's 9 offers at $3.77/hr average. PCIe and SXM5 form factors offer greater deployment flexibility in diverse clusters.

Cost-sensitive users benefit from H100's established ecosystem and lower entry barriers for non-memory-bound tasks.

Select H200 for memory-heavy tasks like training or inferring large LLMs exceeding 94 GB, thanks to its 141 GB HBM3e VRAM. The 4800 GB/s bandwidth supports larger batches, boosting efficiency in high-throughput inference. Despite higher average pricing of $3.77/hr, its $0.49/hr minimum appeals for short-term memory-critical jobs.