RTX 2070 vs RTX 6000 Ada

The dense FP16 figure of 364 TFLOPS on the RTX 6000 Ada exceeds the 29.9 TFLOPS dense FP16 figure on the RTX 2070 by a factor of 12. The same ratio appears in FP32 where 91.1 TFLOPS exceeds 7.5 TFLOPS by a factor of 12. These ratios indicate that training and inference workloads complete in substantially less time on the RTX 6000 Ada when dense FP16 operations dominate. Memory bandwidth of 960 GB/s versus 448 GB/s permits batch sizes approximately twice as large on the RTX 6000 Ada before memory capacity or throughput limits are reached. The 48 GB VRAM on the RTX 6000 Ada versus 8 GB on the RTX 2070 further extends feasible model sizes during both training and inference.

The RTX 2070 suits workloads that fit within 8 GB VRAM and that do not require high throughput. Its 175W TDP allows operation in systems with limited power delivery or cooling capacity where the 300W TDP of the RTX 6000 Ada would exceed limits. Older PCIe 3.0 interconnects remain compatible with the RTX 2070 in legacy servers.

Its 960 GB/s bandwidth supports larger batch sizes than the 448 GB/s bandwidth of the RTX 2070 during training or inference. The PCIe 4.0 interconnect provides higher transfer rates than PCIe 3.0 when data movement between host and device occurs frequently.