B300 vs RTX 6000 Ada

The FP16 dense figure of 2250 TFLOPS on the B300 exceeds the 364 TFLOPS dense figure on the RTX 6000 Ada by a factor of six. This gap determines training throughput for large models and inference speed for reduced precision workloads. The FP32 rating of 75 TFLOPS on the B300 falls below the 91.1 TFLOPS rating on the RTX 6000 Ada so single precision scientific codes may favor the latter. Memory bandwidth of 8000 GB/s on the B300 versus 960 GB/s on the RTX 6000 Ada allows substantially larger batch sizes before memory capacity of 262 to 288 GB versus 48 GB becomes the limit.

The B300 suits workloads that require the FP16 dense rating of 2250 TFLOPS together with 262 to 288 GB HBM3e memory. Large scale training and inference jobs benefit from the 8000 GB/s bandwidth that supports bigger batches without swapping. The 1400W TDP is acceptable when maximum tensor throughput is the priority.

The RTX 6000 Ada suits workloads that need the FP32 rating of 91.1 TFLOPS inside a 300W envelope. Inference or fine tuning on moderate sized models fits within the 48 GB GDDR6 capacity and 960 GB/s bandwidth. Lower power draw favors dense deployments where cooling and electricity budgets are constrained.