H200 SXM vs RTX 3090

The H200's FP16 throughput of 1979 TFLOPS dwarfs the RTX 3090's 35.6 TFLOPS, enabling up to 55 times faster half-precision computations essential for training large neural networks. Its FP32 performance of 67 TFLOPS also exceeds the RTX 3090's 35.6 TFLOPS, supporting superior single-precision tasks in scientific simulations. FP8 at 3958 TFLOPS on the H200 further accelerates inference for quantized models. Memory bandwidth tells a similar story: 4800 GB/s on the H200 permits larger batch sizes and handling of models exceeding 100 GB, whereas the RTX 3090's 936 GB/s limits it to smaller datasets around 20 GB. In training scenarios, this delta reduces epochs dramatically on the H200; for inference, it sustains higher throughput without memory swaps. Power draw reflects capability: 700W TDP for H200 versus 350W for RTX 3090, demanding robust cooling in clusters.

The H200 excels in large-scale LLM training and inference where 141 GB VRAM accommodates models like GPT-4 equivalents without sharding. Its 4800 GB/s bandwidth supports massive batch sizes, cutting training time via 1979 TFLOPS FP16. Enterprise users prioritize NVLink and InfiniBand interconnects for multi-GPU scaling at $1.19 per hour starting price.

The RTX 3090 fits budget-conscious setups for Stable Diffusion or fine-tuning small models under 24 GB VRAM. At $0.08 per hour, it delivers 35.6 TFLOPS FP16 for cost-effective prototyping. Single PCIe form factor suits individual workstations without data center infrastructure.