B200 vs H200

Dense FP16 throughput on the B200 measures 2250 TFLOPS against 989 TFLOPS on the H200. This ratio indicates the B200 can process larger matrix operations in the same time window during training runs. FP32 figures of 75 TFLOPS versus 67 TFLOPS show a smaller relative gain for general purpose computations. Memory bandwidth of 8000 GB/s on the B200 compared with 4800 GB/s on the H200 supports larger batch sizes before memory stalls occur. Sparsity enabled FP16 reaches 4500 TFLOPS on the B200 and 1979 TFLOPS on the H200. The same pattern holds for FP8 dense at 4500 TFLOPS versus 1979 TFLOPS. Higher bandwidth on the B200 reduces data movement overhead when batch sizes exceed the 141 GB limit of the H200. These deltas translate to shorter iteration times for models that fit within the expanded 180 to 192 GB capacity.

The B200 suits workloads that require FP16 dense performance of 2250 TFLOPS and memory capacity up to 192 GB. Organizations running models that exceed 141 GB VRAM benefit from the 8000 GB/s bandwidth to maintain throughput. Training pipelines that scale with 1000W TDP tolerance gain from the doubled dense FP16 rate over the H200.

The H200 fits deployments where 700W TDP aligns with existing power budgets and models remain under 141 GB. Inference tasks using FP16 dense at 989 TFLOPS achieve adequate speed without the higher consumption of the B200. Clusters already optimized for 4800 GB/s bandwidth avoid unnecessary upgrades when FP32 needs stay near 67 TFLOPS.