B200 vs Quadro RTX 4000

FP16 dense performance reaches 2250 TFLOPS on the B200 against 57 TFLOPS on the Quadro RTX 4000. This gap allows the B200 to complete training iterations and inference passes at a higher rate for models that fit in memory. FP16 with sparsity reaches 4500 TFLOPS on the B200 while the Quadro RTX 4000 figure remains at the dense value of 57 TFLOPS. Memory bandwidth of 8000 GB/s on the B200 versus 416 GB/s on the Quadro RTX 4000 supports larger batch sizes during both training and inference without swapping data to host memory. FP32 performance of 75 TFLOPS on the B200 versus 7.1 TFLOPS on the Quadro RTX 4000 further widens the advantage for precision sensitive scientific workloads.

The B200 suits LLM training and large scale inference where 180 to 192 GB of HBM3e memory and 8000 GB/s bandwidth accommodate models that exceed 8 GB capacity. FP16 dense performance of 2250 TFLOPS enables faster iteration than the 57 TFLOPS available on the Quadro RTX 4000. High TDP of 1000W is acceptable in data center racks that require maximum throughput per node.

The Quadro RTX 4000 fits scientific computing and fine tuning tasks that remain within 8 GB of GDDR6 memory and require only 7.1 TFLOPS of FP32 performance. TDP of 125W allows deployment in workstations with limited power and cooling. PCIe form factor and 416 GB/s bandwidth suffice for smaller batch sizes in Stable Diffusion or inference on compact models.