GTX 1070 Ti vs RTX 3080

Compute performance defines the core disparity: the RTX 3080 achieves 29.8 TFLOPS in both FP16 and FP32, over three times the GTX 1070 Ti's 8.9 TFLOPS. This translates to faster model training and inference, where FP16 accelerates matrix operations in deep learning by enabling larger batch sizes without precision loss. For training large language models, the 3080 processes iterations roughly 3.3 times quicker. Memory bandwidth further amplifies this: 760 GB/s on the 3080 versus 304 GB/s on the 1070 Ti supports bigger batches and higher resolutions in tasks like image generation. Insufficient bandwidth on the older GPU leads to bottlenecks in data-heavy inference. Power draw reflects efficiency gaps, with the 1070 Ti at 180W suiting low-energy scenarios, while the 3080's 320W demands robust cooling for sustained peaks.

The GTX 1070 Ti excels in power-constrained environments, drawing only 180W compared to the RTX 3080's 320W. It fits hobbyist desktops or older servers running lightweight inference on models under 8 GB VRAM. No live cloud offers exist, making it ideal for on-premise setups with existing Pascal hardware where 8.9 TFLOPS suffices for basic tasks.

The RTX 3080 dominates modern workloads with 29.8 TFLOPS FP16/FP32 and 760 GB/s bandwidth, enabling efficient training and large-batch inference. Cloud pricing from $0.06 per hour across six offers provides scalable access without upfront costs. Choose it for VRAM-intensive applications leveraging 10 to 12 GB GDDR6X.