RTX 2080 vs RTX 5070 Ti

Compute performance defines the core disparity between these GPUs: the RTX 5070 Ti's 40.6 TFLOPS in FP16 and FP32 provides approximately four times the throughput of the RTX 2080's 10.1 TFLOPS, accelerating neural network training epochs and inference queries significantly. For training large language models, this enables handling complex optimizations faster on the RTX 5070 Ti; inference benefits similarly through higher tokens per second. The identical FP16 to FP32 ratios on both cards ensure balanced half-precision and single-precision tasks, but the RTX 5070 Ti excels in scale. Memory subsystems reveal trade-offs: the RTX 2080's 616 GB/s bandwidth surpasses the RTX 5070 Ti's 448 GB/s, supporting larger batch sizes in bandwidth-bound scenarios like high-resolution image processing without saturation. However, the RTX 5070 Ti's 12 GB GDDR7 VRAM versus 8 to 11 GB GDDR6 on the RTX 2080 permits bigger models and batches overall, reducing out-of-memory errors in VRAM-constrained training. Power draw reflects this, with the RTX 5070 Ti at 250W TDP exceeding the RTX 2080's 215W, implying higher cloud costs for sustained loads.

The RTX 2080 suits budget-conscious users prioritizing cost over peak performance. At an average of $0.07 per hour versus $0.19 for the RTX 5070 Ti, it delivers value for lightweight inference or legacy applications optimized for Turing architecture. Scenarios with high memory bandwidth demands, such as certain scientific simulations leveraging 616 GB/s, favor the RTX 2080 over the RTX 5070 Ti's 448 GB/s.

The RTX 5070 Ti stands out for demanding AI workloads requiring raw compute power. Its 40.6 TFLOPS FP16 performance handles modern LLM fine-tuning or Stable Diffusion generation far quicker than the RTX 2080's 10.1 TFLOPS. Users benefit from 12 GB GDDR7 VRAM for larger models, making it ideal despite the higher 250W TDP and $0.19 per hour average pricing.