Quadro RTX 4000 vs RTX 3070

Dense FP16 performance favors the Quadro RTX 4000 at 57 TFLOPS over the RTX 3070 at 40.6 TFLOPS so training workloads that rely on dense tensor operations complete more operations per second on the Quadro RTX 4000. Inference tasks that accept sparsity benefit from the RTX 3070 when its 81.3 TFLOPS with sparsity exceeds the dense figure of 40.6 TFLOPS on the same card. The FP16 to FP32 ratio reaches 57 divided by 7.1 on the Quadro RTX 4000 while the corresponding ratio on the RTX 3070 equals 40.6 divided by 20.3 indicating different acceleration profiles for mixed precision code. Memory bandwidth of 448 GB/s on the RTX 3070 exceeds 416 GB/s on the Quadro RTX 4000 and therefore supports modestly larger batch sizes in memory bound kernels. The TDP of 125W on the Quadro RTX 4000 remains lower than 220W on the RTX 3070 so sustained dense FP16 workloads draw less power on the earlier card. Direct comparison of dense FP16 figures shows the Quadro RTX 4000 ahead while sparse FP16 figures apply only to the RTX 3070 at 81.3 TFLOPS.

The Quadro RTX 4000 suits environments that prioritize 57 TFLOPS dense FP16 within a 125W TDP envelope. Scientific computing codes that operate at 7.1 TFLOPS FP32 and require lower power draw select this card over alternatives with higher TDP values.

The RTX 3070 fits workloads that need 20.3 TFLOPS FP32 or 81.3 TFLOPS FP16 with sparsity at 448 GB/s bandwidth. Inference pipelines that utilize 162.6 TOPS dense INT8 or 325.2 TOPS with sparsity run on this card when higher throughput at those precisions outweighs the 220W TDP.