RTX 3070 vs RTX 4090

Dense FP16 performance measures 40.6 TFLOPS on the RTX 3070 against 165.2 TFLOPS on the RTX 4090. The same comparison with sparsity shows 81.3 TFLOPS versus 330.4 TFLOPS. FP32 figures remain half the dense FP16 values on each card. Higher memory bandwidth on the RTX 4090 supports larger batch sizes during training or inference passes that move substantial activation data. INT8 dense performance lists 162.6 TOPS on the RTX 3070 compared with 660.6 TOPS on the RTX 4090. These ratios indicate the RTX 4090 processes dense FP16 workloads at roughly four times the rate of the RTX 3070 while maintaining identical dense to sparse scaling on both GPUs.

The RTX 3070 suits workloads constrained to 8 GB VRAM where power draw must stay near 220W. Fine tuning of smaller models or inference on compact networks fits within these limits without exceeding the listed TDP. Scientific computing tasks that rely on 20.3 TFLOPS FP32 also align with the RTX 3070 when higher bandwidth proves unnecessary.

The RTX 4090 fits training or inference jobs that require 24 GB VRAM and 1008 GB/s bandwidth. Dense FP16 throughput at 165.2 TFLOPS accelerates large batch processing beyond the 40.6 TFLOPS available on the RTX 3070. Stable diffusion and LLM inference benefit directly from the 82.6 TFLOPS FP32 rating when memory capacity becomes the limiting factor.