RTX 2080 vs RTX 3070

The FP32 difference of 10 TFLOPS on the RTX 2080 versus 20.3 TFLOPS on the RTX 3070 indicates the RTX 3070 processes twice the single precision operations per second. Dense FP16 figures remain nearly identical at 40.3 TFLOPS and 40.6 TFLOPS so training or inference steps that rely on dense FP16 see little change in throughput. Memory bandwidth stays fixed at 448 GB/s for both GPUs therefore maximum batch sizes stay comparable when VRAM capacity permits. The RTX 3070 adds sparse FP16 at 81.3 TFLOPS and sparse INT8 at 325.2 TOPS while the RTX 2080 reports only dense INT8 at 161.1 TOPS. Workloads that activate sparsity on the RTX 3070 gain higher effective rates than dense-only execution on the RTX 2080.

The RTX 2080 suits cases that require NVLink interconnect or TDP limited to 215W. Its option for up to 11 GB VRAM supports larger resident datasets than the fixed 8 GB on the RTX 3070. Dense FP16 at 40.3 TFLOPS matches the RTX 3070 closely enough for bandwidth-bound tasks that do not benefit from higher FP32.

The RTX 3070 fits scenarios that need 20.3 TFLOPS FP32 or sparse modes at 81.3 TFLOPS FP16. PCIe 4.0 interconnect and 162.6 TOPS dense INT8 provide advantages for inference pipelines that scale with these exact figures. TDP of 220W remains close to the RTX 2080 while doubling FP32 throughput.