B200 vs P100

The FP16 dense performance of the B200 reaches 2250 TFLOPS while the FP16 dense performance of the P100 reaches 18.7 TFLOPS. This gap means the B200 can process matrix operations in training and inference at a scale that exceeds the P100 by the ratio of those two dense figures. The FP32 performance of the B200 stands at 75 TFLOPS compared with 9.3 TFLOPS on the P100 so the newer GPU sustains higher precision workloads without reliance on reduced precision modes. Memory bandwidth of 8000 GB per second on the B200 versus 732 GB per second on the P100 allows larger batch sizes during training because data movement keeps pace with the higher arithmetic throughput. When sparsity is enabled the B200 FP16 figure with sparsity reaches 4500 TFLOPS while the P100 provides no sparsity figure so comparisons remain limited to dense values only.

The B200 suits workloads that require 180 GB or more of memory and 2250 TFLOPS of FP16 dense throughput. Large scale training jobs benefit from the 8000 GB per second bandwidth and the 1000 W TDP when cluster power delivery supports that envelope. Inference pipelines that target FP8 dense performance of 4500 TFLOPS also favor the B200 over older cards.

The P100 fits environments constrained to 250 W TDP and 16 GB of memory where legacy Pascal code already runs. Scientific computing tasks that need only 9.3 TFLOPS of FP32 and 18.7 TFLOPS of FP16 dense performance remain viable on the P100 without migration. Systems that accept only PCIe 3.0 or SXM2 form factors continue to deploy the P100.