RTX 3080 vs RTX 5070 Ti

The RTX 5070 Ti achieves 40.6 TFLOPS in FP16 and FP32, surpassing the RTX 3080's 29.8 TFLOPS by 36 percent: this advantage accelerates training and inference for machine learning models reliant on half-precision and single-precision operations. Inference tasks see higher throughput on the RTX 5070 Ti due to Blackwell's architectural optimizations paired with this compute edge. Training large language models benefits similarly from the increased FLOPS, reducing epochs needed for convergence. The RTX 3080's 760 GB/s memory bandwidth, however, doubles the RTX 5070 Ti's 448 GB/s: this enables larger batch sizes in memory-intensive scenarios like fine-tuning or Stable Diffusion where data movement dominates. Lower bandwidth on the RTX 5070 Ti may limit scalability for very large batches despite its 12 GB VRAM matching the upper end of the RTX 3080's capacity. Power efficiency favors the RTX 5070 Ti at 250W TDP over 320W, lowering operational costs in prolonged cloud sessions.

The RTX 3080 excels in bandwidth-heavy workloads such as Stable Diffusion generation or fine-tuning with large datasets: its 760 GB/s bandwidth supports batch sizes twice as large as the RTX 5070 Ti's 448 GB/s limit. Cost drives selection here, with pricing from $0.06 per hour and an average of $0.13 per hour across 4 providers versus the RTX 5070 Ti's higher $0.10 to $0.19 per hour range. Availability across more offers makes the RTX 3080 ideal for budget rentals prioritizing memory throughput over peak compute.

The RTX 5070 Ti outperforms in compute-bound tasks like LLM inference and training: 40.6 TFLOPS in FP16 and FP32 provide 36 percent more power than the RTX 3080's 29.8 TFLOPS. Its 250W TDP versus 320W ensures better efficiency for sustained AI workloads, and Blackwell architecture from 2025 offers future-proof features. Select it when raw performance and lower power draw outweigh bandwidth for smaller batches.