B200 vs RTX 6000 Ada

The FP16 dense rating of 2250 TFLOPS on the B200 exceeds the 364 TFLOPS dense rating on the RTX 6000 Ada by a factor of six. This gap means the B200 completes matrix operations in training and inference workloads at substantially higher rates when both GPUs run dense FP16 calculations. The FP16 with sparsity rating of 4500 TFLOPS on the B200 likewise exceeds the 728 TFLOPS with sparsity on the RTX 6000 Ada. Memory bandwidth of 8000 GB/s on the B200 supports larger batch sizes than the 960 GB/s bandwidth on the RTX 6000 Ada during memory intensive phases of model execution. FP32 performance shows the RTX 6000 Ada at 91.1 TFLOPS against 75 TFLOPS on the B200 so certain single precision scientific tasks may favor the RTX 6000 Ada despite its lower tensor throughput.

The B200 suits LLM training runs that require 180 to 192 GB of HBM3e memory to hold large parameter sets. Its 8000 GB/s bandwidth and 2250 TFLOPS FP16 dense rating accelerate distributed training sessions that exceed the capacity of 48 GB cards. Organizations running inference at FP8 dense 4500 TFLOPS also select the B200 when throughput per rack takes priority over the 300W TDP of smaller GPUs.

The RTX 6000 Ada fits fine tuning jobs and Stable Diffusion workloads that operate within 48 GB GDDR6 limits and benefit from its 91.1 TFLOPS FP32 rating. Its 300W TDP allows denser packing in air cooled servers where the 1000W TDP of the B200 would require additional power infrastructure. PCIe 4.0 connectivity further simplifies integration into existing workstation environments.