RTX 3070 vs RTX 5080

Performance metrics show clear advantages for the RTX 5080 in dense computations. The FP16 dense performance of 112.6 TFLOPS on the RTX 5080 exceeds the 40.6 TFLOPS on the RTX 3070. This delta implies faster training iterations and inference throughput for models using dense FP16 computations. Memory bandwidth of 960 GB/s versus 448 GB/s allows larger batch sizes during processing without memory bottlenecks. FP32 performance follows a similar pattern with 56.3 TFLOPS against 20.3 TFLOPS. When sparsity is applied the FP16 with sparsity reaches 225.1 TFLOPS on the RTX 5080 and 81.3 TFLOPS on the RTX 3070. These ratios indicate substantial gains in sparse tensor operations for the newer GPU. The INT8 dense rating of 450.2 TOPS on the RTX 5080 compared to 162.6 TOPS supports accelerated quantized inference workloads.

The RTX 3070 suits scenarios with strict power limits where 220W TDP keeps consumption low. Its 8 GB VRAM and 40.6 TFLOPS FP16 dense performance handle smaller models adequately. PCIe 4.0 interconnect matches existing infrastructure without requiring upgrades. These factors favor the RTX 3070 when workloads fit within its 448 GB/s bandwidth and 162.6 TOPS INT8 dense capacity.

The RTX 5080 fits tasks needing doubled 16 GB VRAM and 960 GB/s bandwidth for larger batches. Its 112.6 TFLOPS FP16 dense and 450.2 TOPS INT8 dense figures accelerate demanding inference and training. PCIe 5.0 support pairs with modern systems while 56.3 TFLOPS FP32 handles complex scientific calculations efficiently.