B300 vs RTX 2070

The FP16 dense figure of 2250 TFLOPS on the B300 exceeds the 29.9 TFLOPS dense FP16 on the RTX 2070 by a factor of 75 while the FP32 figure of 75 TFLOPS on the B300 exceeds the 7.5 TFLOPS on the RTX 2070 by a factor of 10. These ratios indicate that training workloads using dense FP16 arithmetic complete far more operations per second on the B300 and inference tasks using the same dense FP16 arithmetic achieve correspondingly higher throughput. Memory bandwidth of 8000 GB/s on the B300 compared with 448 GB/s on the RTX 2070 permits substantially larger batch sizes before memory capacity or transfer limits are reached during either training or inference.

The B300 suits workloads that require the 2250 TFLOPS dense FP16 rate or the 8000 GB/s memory bandwidth such as large scale model training and inference where the 262 to 288 GB HBM3e capacity accommodates models that exceed the 8 GB limit of the RTX 2070. Organizations running sustained dense FP32 operations at 75 TFLOPS also select the B300 because its 1400W TDP supports continuous high utilization in data center environments equipped with NVLink.

The RTX 2070 fits tasks constrained to 175W TDP and PCIe form factor such as local development or inference on smaller models that fit within 8 GB GDDR6 where the 29.9 TFLOPS dense FP16 rate remains sufficient. Users who need only the 7.5 TFLOPS FP32 performance and 119.4 TOPS dense INT8 rate without requiring NVLink or high bandwidth select the RTX 2070 for its lower power draw and simpler integration.