GTX 1080 vs RTX 4090

The FP32 performance shows the RTX 4090 at 82.6 TFLOPS compared to 8.9 TFLOPS on the GTX 1080. In FP16 dense the RTX 4090 reaches 165.2 TFLOPS which supports larger batch sizes in training compared to the GTX 1080 that does not publish FP16 figures. Memory bandwidth of 1008 GB/s on the RTX 4090 versus 320 GB/s on the GTX 1080 allows greater data throughput that can increase batch sizes during inference and training. The RTX 4090 FP16 with sparsity reaches 330.4 TFLOPS while FP8 dense stands at 330.3 TFLOPS. These dense figures on the RTX 4090 exceed the GTX 1080 capabilities by multiples that translate to faster iteration times in compute heavy tasks.

The GTX 1080 suits environments with strict power limits due to its 180W TDP compared to 450W on the RTX 4090. Older systems that rely on PCIe 3.0 interconnect benefit from the GTX 1080 form factor without requiring platform upgrades. Workloads that remain within 8-11 GB VRAM limits avoid the need for higher capacity cards.

The RTX 4090 fits tasks that require 24 GB VRAM and 1008 GB/s bandwidth to handle larger models. FP32 at 82.6 TFLOPS and FP16 dense at 165.2 TFLOPS enable accelerated training and inference relative to the GTX 1080 8.9 TFLOPS FP32. PCIe 4.0 support matches modern server configurations that demand higher throughput.