RTX 2070 vs RTX 3080

The FP32 figure of 29.8 TFLOPS on the RTX 3080 exceeds the 7.5 TFLOPS on the RTX 2070 by a factor of four. This gap influences training duration because higher dense FP32 throughput processes more operations per second. Dense FP16 performance stands at 59.5 TFLOPS on the RTX 3080 compared with 29.9 TFLOPS dense FP16 on the RTX 2070. The doubled dense FP16 rating shortens inference latency for compatible models. Memory bandwidth of 760 GB/s on the RTX 3080 versus 448 GB/s on the RTX 2070 permits larger batch sizes before memory saturation occurs. The RTX 3080 lists INT8 dense at 238 TOPS against 119.4 TOPS INT8 dense on the RTX 2070. Sparsity on the RTX 3080 yields 119 TFLOPS FP16 with sparsity and 476 TOPS INT8 with sparsity with no matching figures published for the RTX 2070.

The RTX 2070 fits power constrained deployments because its TDP measures 175W. Workloads that fit within 8 GB VRAM and 448 GB/s bandwidth complete adequately on this processor. Basic FP32 tasks at 7.5 TFLOPS meet requirements without excess power draw.

The RTX 3080 serves memory intensive inference because its bandwidth reaches 760 GB/s and VRAM spans 10 to 12 GB. Dense FP16 at 59.5 TFLOPS and FP32 at 29.8 TFLOPS accelerate larger models beyond the RTX 2070 limits. Sparsity support at 119 TFLOPS FP16 with sparsity further extends throughput for eligible operations.