RTX 4090 vs RTX 5070

The FP16 dense figure of 165.2 TFLOPS on the RTX 4090 exceeds the 61.7 TFLOPS dense figure on the RTX 5070 by a factor of 2.67. This gap means the RTX 4090 supports larger batch sizes during training and faster token generation during inference when both GPUs run dense FP16 workloads. The FP32 rating of 82.6 TFLOPS on the RTX 4090 compared with 30.9 TFLOPS on the RTX 5070 produces a similar ratio for single precision scientific kernels. Memory bandwidth of 1008 GB/s on the RTX 4090 versus 672 GB/s on the RTX 5070 directly limits the maximum batch size in memory bound models. When sparsity is enabled the RTX 4090 reaches 330.4 TFLOPS FP16 with sparsity while the RTX 5070 reaches 123.5 TFLOPS FP16 with sparsity. The same ratio holds for FP8 dense at 330.3 TFLOPS versus 123.5 TFLOPS.

The RTX 4090 suits workloads that require 24 GB VRAM and 1008 GB/s bandwidth such as training models that exceed 12 GB capacity. Its 165.2 TFLOPS FP16 dense rating and 450 W TDP support sustained high throughput in multi hour jobs where power draw is secondary to completion speed.

The RTX 5070 suits deployments that prioritize 250 W TDP and PCIe 5.0 interconnect over peak throughput. Its FP16 dense rating of 61.7 TFLOPS combined with lower power draw fits dense inference clusters where energy cost and thermal limits constrain total system scale.