RTX 2070 vs RTX 3060

FP32 throughput on the RTX 3060 reaches 12.7 TFLOPS while the RTX 2070 reaches 7.5 TFLOPS. This gap means the newer card completes matrix operations in FP32 at a higher rate during both training and inference passes. Memory bandwidth of 448 GB/s on the RTX 2070 exceeds the 360 GB/s on the RTX 3060 and therefore supports larger batch sizes before memory stalls occur. The RTX 2070 also lists dense FP16 at 29.9 TFLOPS and dense INT8 at 119.4 TOPS. Because the RTX 3060 lists no FP16 figure the comparison stays limited to FP32 values. Lower TDP of 170W on the RTX 3060 versus 175W on the RTX 2070 produces modest power savings during sustained FP32 workloads.

Choose the RTX 2070 only when the whole workload fits in 8 GB and is limited by memory bandwidth rather than compute, since its 448 GB/s is about 1.2x the 360 GB/s on the RTX 3060. Its published 29.9 TFLOPS dense FP16 and 119.4 TOPS dense INT8 are real, but the RTX 3060 has tensor cores of its own (112 listed) and 1.7x the FP32, so do not read the missing RTX 3060 FP16 entry as a lack of FP16 acceleration. For fine tuning or image generation, the 8 GB ceiling is the constraint that usually rules the RTX 2070 out.

Choose the RTX 3060 for fine tuning, image generation and quantized LLM inference, and get the 12 GB configuration, which gives 4 GB more than the RTX 2070. It has 12.7 TFLOPS FP32 against 7.5, 112 tensor cores for mixed precision work, PCIe 4.0, and a 170W TDP against 175W. Its 360 GB/s of bandwidth is lower than the 448 GB/s on the RTX 2070, but for this class of card memory capacity decides more workloads than bandwidth does.