GTX 1080 vs RTX 2070 SUPER

Performance differences stem from architectural shifts and spec upgrades. The RTX 2070 SUPER's 9.1 TFLOPS FP32 exceeds the GTX 1080's 8.9 TFLOPS by 2 percent, offering marginal gains in general compute tasks. Both GPUs deliver equivalent FP16 shader performance at their respective peaks, meaning no inherent half-precision advantage without tensor core utilization: the RTX 2070 SUPER leverages Turing tensor cores for up to 91 TFLOPS in FP16 tensor operations, vastly superior for deep learning training and inference compared to Pascal's lack thereof.

Memory bandwidth profoundly impacts real-world usage: the RTX 2070 SUPER's 496 GB/s versus 320 GB/s supports 55 percent larger batch sizes in training, reducing overhead in memory-bound scenarios like LLM fine-tuning. GDDR6 on the newer card provides lower latency than GDDR5X. For inference, higher bandwidth accelerates token generation in larger models fitting within 8 GB VRAM. The 215 W TDP on RTX 2070 SUPER demands better cooling than the GTX 1080's 180 W, potentially affecting dense cloud deployments.

Choose the GTX 1080 when cloud availability and cost dominate priorities. At $0.30 per hour across live offers, it undercuts potential RTX 2070 SUPER pricing while delivering 8.9 TFLOPS FP32 and up to 11 GB VRAM for workloads not requiring tensor cores. Legacy applications optimized for Pascal, such as certain scientific simulations or older Stable Diffusion models, perform adequately without Turing-specific features.

Opt for the RTX 2070 SUPER in AI-centric tasks benefiting from tensor cores and higher bandwidth. Its 496 GB/s memory throughput handles larger batches in LLM training or inference better than the GTX 1080's 320 GB/s. Ray tracing workloads or modern DL frameworks gain from Turing architecture despite no current cloud offers.