B200 vs RTX 2080

Dense FP16 performance reaches 2250 TFLOPS on the B200 against 40.3 TFLOPS dense FP16 on the RTX 2080 which produces a ratio of 2250 to 40.3 for equivalent dense operations. This delta indicates that training and inference tasks completed in FP16 dense mode finish substantially faster on the B200 when batch sizes remain identical. Memory bandwidth of 8000 GB/s on the B200 versus 448 GB/s on the RTX 2080 allows larger batch sizes before memory capacity of 192 GB becomes the constraint. FP32 throughput of 75 TFLOPS on the B200 compared with 10 TFLOPS on the RTX 2080 further widens the gap for precision sensitive workloads. With sparsity the B200 FP16 figure of 4500 TFLOPS maintains the same proportional advantage over any dense baseline from the RTX 2080.

The B200 suits workloads that require 180 to 192 GB VRAM and 8000 GB/s bandwidth such as large scale model training that cannot fit within 11 GB. FP16 dense throughput of 2250 TFLOPS and FP32 at 75 TFLOPS support faster iteration on scientific computing jobs that exceed the 10 TFLOPS FP32 limit of older cards. TDP of 1000W is accepted when throughput per watt remains secondary to absolute performance.

The RTX 2080 fits tasks that stay within 8 to 11 GB VRAM and need only 40.3 TFLOPS dense FP16 or 10 TFLOPS FP32. Power draw of 215W makes the card appropriate for sustained desktop inference where 1000W TDP is unnecessary. PCIe form factor and 448 GB/s bandwidth suffice for smaller fine tuning jobs that do not demand NVLink scale.