RTX 2080 Ti vs RTX 4080

The RTX 4080's 48.7 TFLOPS FP16 and FP32 ratings surpass the RTX 2080 Ti's 10.1 TFLOPS by a factor of 4.8: this accelerates neural network training and inference tasks, where matrix multiplications dominate. Training large models completes faster on the RTX 4080, as higher throughput processes more samples per second. For inference, the elevated FP16 performance supports higher query volumes in production deployments. Memory bandwidth edges from 616 GB/s to 717 GB/s on the RTX 4080: this sustains larger batch sizes during training, minimizing data starvation and improving GPU utilization. The 16 GB VRAM capacity versus 11 GB handles bigger models or datasets without offloading, avoiding slowdowns from host memory access. Higher TDP of 320 W reflects the RTX 4080's capacity for sustained peak performance under load.

The RTX 2080 Ti excels in budget-limited projects: cloud rates from $0.06 per hour make it half the RTX 4080's entry cost. Its 215 W TDP suits power-capped servers or edge deployments where efficiency trumps speed. Legacy workflows optimized for Turing architecture run natively without adaptation.

The RTX 4080 fits high-throughput AI pipelines: 48.7 TFLOPS FP32 outperforms the RTX 2080 Ti's 10.1 TFLOPS by 4.8 times for training and inference. Expanded 16 GB VRAM and 717 GB/s bandwidth manage complex models with large batches. Modern Ada Lovelace features enhance ray tracing and AI acceleration in creative tasks.