A100 vs B300

The FP16 dense performance reaches 312 TFLOPS on the A100 compared to 2250 TFLOPS on the B300 which indicates the B300 processes dense FP16 operations over seven times faster for training workloads. FP16 with sparsity measures 624 TFLOPS on the A100 against 4500 TFLOPS on the B300 allowing similar scaling benefits when sparsity applies. Memory bandwidth of 2039 GB/s on the A100 versus 8000 GB/s on the B300 supports larger batch sizes on the B300 by sustaining higher data movement rates during both training and inference. FP32 figures of 19.5 TFLOPS versus 75 TFLOPS further favor the B300 for precision sensitive computations while INT8 dense ratings of 624 TOPS on the A100 exceed the 187.5 TOPS on the B300 in integer scenarios.

Choose the A100 when your model and batch fit inside 40 to 80 GB and a slower run is acceptable, since it still has NVLink and InfiniBand for multi-GPU jobs. Choose it outright for double precision HPC: 9.7 TFLOPS FP64 against 1.25 TFLOPS on the B300, a 7.8 times gap. Its 624 TOPS INT8 also exceeds the 187.5 TOPS of the B300, though most modern inference stacks use FP8 or FP4 on Blackwell Ultra rather than INT8, so treat that as a minor point.

The B300 fits applications that demand FP16 dense throughput of 2250 TFLOPS or memory bandwidth of 8000 GB/s to handle expanded batch sizes and larger models. Its 262 to 288 GB VRAM capacity and FP32 rating of 75 TFLOPS support advanced Blackwell Ultra workloads where power availability reaches 1400W.