GTX 1070 Ti vs RTX 5080

The RTX 5080 outperforms the GTX 1070 Ti by a wide margin in raw compute: 56.3 TFLOPS FP16 and FP32 compared to 8.9 TFLOPS on the older card. This translates to roughly 6.3 times faster processing for AI training and inference tasks, where FP16 handles half-precision models efficiently and FP32 supports single-precision computations. Training large language models benefits immensely, as the higher throughput reduces epoch times significantly.

Memory bandwidth presents another bottleneck for the GTX 1070 Ti at 256 GB/s versus 960 GB/s on the RTX 5080, enabling the latter to support much larger batch sizes without stalling data transfers. For inference, this means handling bigger payloads or concurrent requests seamlessly. The 16 GB GDDR7 VRAM on the 5080 accommodates larger models outright, while the 1070 Ti's 8 GB GDDR5 limits it to smaller datasets. Power draw reflects this: 180 W TDP for the 1070 Ti versus 360 W for the 5080, impacting cooling and energy costs in prolonged runs.

The GTX 1070 Ti suits legacy gaming setups or basic compute on existing desktops, where its 180 W TDP fits low-power PSUs and 8.9 TFLOPS FP32 suffices for non-AI workloads. Users with owned hardware avoid cloud costs, as no live rental offers exist. It excels in scenarios not demanding more than 8 GB VRAM or 256 GB/s bandwidth, such as older scientific simulations.

Opt for the RTX 5080 in AI-driven tasks requiring 56.3 TFLOPS FP16/FP32 or 16 GB GDDR7 VRAM, like modern LLM training. Its 960 GB/s bandwidth handles large batches, and cloud availability from $0.25 per hour makes it scalable without upfront hardware investment. High TDP of 360 W is manageable in data centers.