B200 vs H100

Dense FP16 performance of 2250 TFLOPS on the B200 surpasses the 989 TFLOPS figure on the H100 and therefore accelerates matrix multiplications that dominate training loops. The same dense FP16 comparison shows the B200 completing equivalent operations in less wall clock time than the H100. With sparsity the FP16 rating of 4500 TFLOPS on the B200 likewise exceeds the 1979 TFLOPS rating on the H100 so sparse workloads follow the same ordering. Memory bandwidth of 8000 GB/s on the B200 permits larger batch sizes than the 3350 GB/s bandwidth on the H100 and thereby reduces the total number of gradient steps required for convergence. FP32 throughput stands at 75 TFLOPS on the B200 compared with 67 TFLOPS on the H100 which produces modest gains for precision sensitive scientific kernels. All tensor core comparisons maintain separation between dense figures and figures with sparsity.

The B200 matches workloads that require the full 180 to 192 GB VRAM range to keep entire model states resident. Training runs that exploit the 2250 TFLOPS dense FP16 rating finish faster than equivalent runs on the H100. High bandwidth applications that move data at 8000 GB/s also select the B200.

The H100 satisfies deployments constrained to a 700 W TDP envelope. Tasks that fit within 80 to 94 GB VRAM avoid the need for the larger capacity of the B200. Inference workloads that operate at the 989 TFLOPS dense FP16 level remain efficient on the H100 without excess power draw.