B200 vs RTX PRO 6000

The FP16 dense figure of 2250 TFLOPS on the B200 exceeds the 503.8 TFLOPS dense figure on the RTX PRO 6000 by a factor of roughly 4.5 while the FP16 with sparsity figure of 4500 TFLOPS likewise exceeds 1007.6 TFLOPS with sparsity. This gap indicates the B200 can process larger batch sizes during training and inference steps that rely on FP16 dense or FP16 with sparsity operations. Memory bandwidth of 8000 GB/s on the B200 compared with 1792 GB/s on the RTX PRO 6000 further supports larger batch sizes before memory becomes a constraint. In contrast the RTX PRO 6000 records a higher FP32 value of 126 TFLOPS against 75 TFLOPS on the B200 which favors workloads that remain in FP32 precision.

The B200 suits scenarios that require the 180 to 192 GB HBM3e capacity and 8000 GB/s bandwidth for models that exceed 96 GB. Its FP16 dense performance of 2250 TFLOPS and FP8 dense performance of 4500 TFLOPS accelerate large scale training and inference pipelines that benefit from those exact tensor figures.

Choose the RTX PRO 6000 when the whole job fits in 96 GB on one card, which covers single-model inference with FP8 or FP4 weights and adapter style fine tuning. It is a PCIe 5.0 card without NVLink, so tensor parallel serving across cards or multi-node training will run far slower than on a B200 with NVLink and InfiniBand. Its 1792 GB/s of GDDR7 is less than a quarter of the 8000 GB/s on the B200, so expect lower tokens per second on bandwidth bound decode even when the model fits. Where it wins outright is FP32 work at 126 TFLOPS against 75 TFLOPS on the B200.