RTX 3060 vs RTX 3070

The FP32 rating of 20.3 TFLOPS on the RTX 3070 exceeds the 12.7 TFLOPS rating on the RTX 3060. This difference scales arithmetic throughput for operations that rely on single precision floating point. The RTX 3070 also lists dense FP16 performance at 40.6 TFLOPS and FP16 performance with sparsity at 81.3 TFLOPS while the RTX 3060 provides no published FP16 figure. Dense FP16 throughput therefore applies only to the RTX 3070 for training and inference steps that utilize half precision. Memory bandwidth of 448 GB/s on the RTX 3070 versus 360 GB/s on the RTX 3060 influences the feasible batch sizes during data movement between memory and compute units. Higher bandwidth supports larger batches when the workload remains memory bound. The 220W TDP on the RTX 3070 compared with the 170W TDP on the RTX 3060 reflects the additional power required to sustain the elevated FP32 and dense FP16 figures.

Choose the RTX 3060 for anything that needs more than 8 GB, and make sure you get the 12 GB configuration rather than the 8 GB one, since the listed range is 8 to 12 GB. That covers quantized language models with longer context, higher resolution or batched image generation, and small fine tuning runs that would not fit on the RTX 3070. It draws 170W against 220W, but power is a side benefit; memory is the reason to pick it.

Choose the RTX 3070 when the whole job fits in 8 GB and you want it done faster: 20.3 TFLOPS FP32 against 12.7, 448 GB/s against 360, and 40.6 TFLOPS of dense FP16 with 162.6 TOPS of dense INT8 for quantized inference. Typical fits are small classifiers, embedding models, and image generation at modest resolution. The moment your workload needs the RTX 3060's 12 GB, the speed advantage no longer matters.