GTX 1080 Ti vs RTX 4080 SUPER

The compute disparity dominates real-world performance: the RTX 4080 SUPER achieves 48.7 TFLOPS in FP16 and FP32, compared to 8.9 TFLOPS on the GTX 1080 Ti, enabling up to 5.5 times faster training and inference for deep learning models. This delta accelerates matrix multiplications central to neural networks, reducing epoch times significantly on large datasets.

Memory bandwidth profoundly impacts workload efficiency: 717 GB/s on the RTX 4080 SUPER versus 320 GB/s on the GTX 1080 Ti supports larger batch sizes without stalling, ideal for training LLMs where data throughput bottlenecks older cards. The 16 GB VRAM on the RTX 4080 SUPER handles bigger models than the 8 to 11 GB on the GTX 1080 Ti, minimizing out-of-memory errors during fine-tuning.

Power consumption reflects capability gaps: the 320W TDP of the RTX 4080 SUPER demands more cooling than the 180W GTX 1080 Ti, but yields superior throughput per watt in FP16-heavy tasks like Stable Diffusion generation.

The GTX 1080 Ti suits legacy applications or light inference on small models under 8 GB VRAM. Its 180W TDP enables deployment in power-constrained cloud instances where 320 GB/s bandwidth suffices for low-batch workloads. At $0.60 per hour, it fits scenarios prioritizing compatibility with Pascal-era software over peak performance.

The RTX 4080 SUPER excels in demanding AI tasks requiring 48.7 TFLOPS FP16/FP32 or 16 GB VRAM for large models. Its 717 GB/s bandwidth handles high-batch training efficiently, and $0.17 per hour starting price offers better value than the GTX 1080 Ti's $0.60 rate. Choose it for modern workflows like LLM fine-tuning where speed dominates.