GTX 1080 vs RTX 4080 SUPER

The RTX 4080 SUPER outperforms the GTX 1080 by a factor of 5.5 times in FP32 and FP16 compute, with 48.7 TFLOPS versus 8.9 TFLOPS. This delta translates to faster model training and inference in machine learning workloads, where the higher throughput processes larger datasets or more iterations per hour.

Memory bandwidth of 717 GB/s on the RTX 4080 SUPER, double the GTX 1080's 320 GB/s, enables handling bigger batch sizes without bottlenecks. For instance, training deep neural networks benefits from reduced data transfer delays, allowing effective use of the 16 GB VRAM versus 8 to 11 GB.

In real-world terms, the RTX 4080 SUPER supports modern AI tasks like large language models that exceed the GTX 1080's VRAM limits, while the older card suits lighter inference. The 320 W TDP demands more cooling and power in cloud instances compared to the 180 W GTX 1080, but yields proportional gains in sustained workloads.

Choose the GTX 1080 for budget-constrained, low-intensity tasks where 8.9 TFLOPS suffices, such as basic inference on small models fitting within 8 to 11 GB VRAM. Its 180 W TDP fits power-limited cloud instances, and at $0.30 per hour average, it undercuts the RTX 4080 SUPER's $0.32 average for legacy software or Pascal-optimized code.

This GPU excels in scenarios avoiding overkill, like lightweight scientific simulations or older games, preserving compatibility without Ada Lovelace features.

Opt for the RTX 4080 SUPER in demanding AI and graphics workloads requiring 48.7 TFLOPS FP32 performance and 16 GB GDDR6X VRAM. The 717 GB/s bandwidth handles large batch sizes in training, making it ideal for modern deep learning on gpuperhour.com clouds starting at $0.17 per hour.

It outperforms in ray-traced rendering or Stable Diffusion, where the GTX 1080's 320 GB/s and lower compute falter.