B200 SXM vs H200 NVL

The B200 outperforms the H200 across key compute metrics, delivering 4500 TFLOPS in FP16 versus 1979 TFLOPS and 90 TFLOPS in FP32 against 67 TFLOPS. This translates to 2.27 times higher FP16 throughput, accelerating deep learning training where mixed-precision computations dominate. For inference, the B200's 9000 TFLOPS FP8 capability doubles the H200's 3958 TFLOPS, enabling faster serving of quantized large language models. The B200's 192 GB HBM3e VRAM supports models exceeding 141 GB, preventing out-of-memory errors in fine-tuning or multi-modal tasks. Memory bandwidth stands out: 8000 GB/s in the B200 versus 4800 GB/s permits 1.67 times larger batch sizes, reducing training iterations and wall-clock time for data-intensive workloads like scientific simulations. Higher TDP at 1000W for the B200 demands robust cooling compared to 700W, but yields proportional gains in sustained throughput. These specs favor the B200 in memory-bound scenarios, while the H200 suffices for balanced inference pipelines.

Select the NVIDIA B200 SXM for workloads demanding maximum throughput and capacity. Its 192 GB VRAM handles models too large for the H200's 141 GB, ideal for training billion-parameter LLMs or high-resolution generative tasks. The 8000 GB/s bandwidth supports massive batch sizes, cutting training time by enabling 1.67 times more data per iteration. Cutting-edge Blackwell architecture with 4500 TFLOPS FP16 suits frontier research where FP8 at 9000 TFLOPS accelerates inference at scale.

Opt for the NVIDIA H200 NVL in cost-sensitive or power-constrained environments. At $0.50 per hour starting price versus $1.71 for B200 SXM, it delivers strong 1979 TFLOPS FP16 for mid-scale training without excessive expenditure. Lower 700W TDP eases deployment in clusters with limited power budgets. The 141 GB VRAM and 4800 GB/s bandwidth perform adequately for inference serving or fine-tuning models under 100 billion parameters.