B200 vs Quadro RTX 8000

The FP16 dense rating of 2250 TFLOPS on the B200 exceeds the 130.5 TFLOPS dense rating on the Quadro RTX 8000 by a factor derived solely from those two numbers. This gap determines training throughput for models that fit within the respective memory capacities because higher dense FP16 throughput permits more matrix operations per second. Memory bandwidth of 8000 GB per second versus 672 GB per second further influences achievable batch sizes during both training and inference passes. The B200 FP32 figure of 75 TFLOPS likewise surpasses the Quadro RTX 8000 FP32 figure of 16.3 TFLOPS when workloads remain in single precision. Sparsity labeled figures such as 4500 TFLOPS FP16 with sparsity on the B200 cannot be compared against the Quadro RTX 8000 because the latter lists only its dense value.

Workloads that require the 2250 TFLOPS FP16 dense rating or the 8000 GB per second bandwidth favor the B200. Large scale training jobs that exceed 48 GB per GPU also select the B200 because its 180 to 192 GB HBM3e capacity accommodates bigger models without immediate partitioning.

Deployments constrained to 260 W TDP and PCIe form factor select the Quadro RTX 8000. Smaller inference tasks that remain under 48 GB and need only 130.5 TFLOPS FP16 dense complete adequately on this GPU without the 1000 W power draw of the alternative.