B300 vs B200

The identical FP16 dense rating of 2250 TFLOPS on both the B300 and B200 indicates equivalent capability for mixed precision training workloads. The FP32 figure of 75 TFLOPS remains the same across both GPUs which supports consistent performance in single precision scientific calculations. Memory bandwidth at 8000 GB/s for each allows similar scaling of batch sizes in memory intensive operations. Differences appear in FP4 dense where the B300 achieves 13500 TFLOPS against 9000 TFLOPS for the B200. This gap affects inference tasks that utilize lower precision formats. The INT8 dense specification shows 187.5 TOPS for the B300 and 4500 TOPS for the B200 which influences integer based processing efficiency.

Choose the B300 when you serve large models or long contexts, since its 262 to 288 GB of HBM3e holds weights and KV cache that overflow the 180 to 192 GB on the B200. It is also the pick for FP4 serving, with 13500 TFLOPS of dense FP4 against 9000 TFLOPS on the B200, while FP8 at 4500 TFLOPS dense, FP16 at 2250 TFLOPS dense and bandwidth at 8000 GB/s are identical. Training throughput per GPU is the same on both, so the B300 only earns its 1400 W over the 1000 W of the B200 when the extra memory or the FP4 rate is actually used.

Choose the B200 when your model fits in 180 to 192 GB and B300 capacity is scarce, since dense FP16 at 2250 TFLOPS, dense FP8 at 4500 TFLOPS and bandwidth at 8000 GB/s match the B300 exactly, so training and FP8 serving run the same. It is the only sensible choice for FP64 simulation, delivering 37 TFLOPS against 1.25 TFLOPS on the B300. The 4500 TOPS INT8 dense figure against 187.5 TOPS on the B300 helps only if your inference stack still uses INT8 kernels. Both carry NVLink, PCIe 6.0 and InfiniBand, so cluster fabric does not decide it.