RTX 3080 Ti vs RTX 4080 SUPER

Compute performance differs markedly between these GPUs: the RTX 4080 SUPER achieves 48.7 TFLOPS in FP16 and FP32, surpassing the RTX 3080 Ti's 29.8 TFLOPS by 63 percent. This advantage accelerates LLM training and inference, reducing iteration times for models requiring high half-precision throughput. Inference workloads benefit similarly, as higher TFLOPS enable faster token generation without precision loss since FP16 matches FP32 on both.

Memory specifications impact batch sizes and model capacity. The RTX 4080 SUPER's 16 GB VRAM supports larger models or bigger batches than the RTX 3080 Ti's 12 GB. However, the RTX 3080 Ti's 760 GB/s bandwidth exceeds the 717 GB/s of the RTX 4080 SUPER by 6 percent, aiding memory-bound tasks like Stable Diffusion where data transfer rates limit performance. Both share 320W TDP, ensuring comparable power costs in cloud deployments.

Ada Lovelace architecture in the RTX 4080 SUPER introduces efficiency gains over Ampere, amplifying real-world gains beyond raw specs in optimized frameworks.

The RTX 3080 Ti suits budget-conscious users targeting memory bandwidth-sensitive workloads. At $0.08 per hour starting price and $0.14 average, it costs 53 percent less than the RTX 4080 SUPER's $0.17 to $0.32 range. Its 760 GB/s bandwidth excels in tasks like high-batch scientific computing where 6 percent faster data movement outweighs compute deficits.

Opt for the RTX 4080 SUPER in performance-critical scenarios demanding more VRAM or throughput. Its 16 GB capacity handles larger LLMs than the RTX 3080 Ti's 12 GB, while 48.7 TFLOPS speeds training by 63 percent over 29.8 TFLOPS. The 2022 Ada Lovelace architecture ensures future-proofing despite higher $0.32 average hourly cost.