RTX 4090 vs RTX 5080

Dense FP16 performance reaches 165.2 TFLOPS on the RTX 4090 compared with 112.6 TFLOPS on the RTX 5080 so training workloads that rely on dense FP16 complete faster on the RTX 4090. Inference tasks using dense FP16 likewise benefit from the same 165.2 TFLOPS figure on the RTX 4090 versus 112.6 TFLOPS on the RTX 5080. The memory bandwidth difference of 1008 GB/s on the RTX 4090 versus 960 GB/s on the RTX 5080 permits larger batch sizes before memory capacity limits are reached. Sparse FP16 performance stands at 330.4 TFLOPS on the RTX 4090 against 225.1 TFLOPS on the RTX 5080 so sparsity aware workloads maintain the same relative advantage for the RTX 4090.

The RTX 4090 suits workloads that require the 24 GB VRAM capacity and 1008 GB/s bandwidth to accommodate large models or high resolution data. Training and inference jobs that depend on dense FP16 at 165.2 TFLOPS or dense FP8 at 330.3 TFLOPS finish with fewer devices when the RTX 4090 is selected over alternatives with lower figures.

The RTX 5080 suits power constrained environments where the 360W TDP reduces electricity draw compared with the 450W TDP of the RTX 4090. Applications that utilize FP4 dense performance at 900.4 TFLOPS or PCIe 5.0 interconnect gain from the newer architecture even with the lower 16 GB VRAM capacity.