RTX 3080 Ti vs RTX 4080

The RTX 4080 demonstrates superior raw compute capability: its 48.7 TFLOPS in FP16 and FP32 exceeds the RTX 3080 Ti's 29.8 TFLOPS by 63 percent. This delta translates to faster neural network training and inference times, as FP16 precision dominates modern deep learning pipelines. Training a large language model benefits from the RTX 4080's higher throughput, potentially reducing epochs by up to 63 percent in compute-bound scenarios. The RTX 3080 Ti's 760 GB/s memory bandwidth surpasses the RTX 4080's 717 GB/s by 6 percent, aiding data-intensive tasks with smaller models. VRAM capacity sets them apart: 16 GB on the RTX 4080 supports larger batch sizes and complex models without swapping, while 10 to 12 GB on the RTX 3080 Ti limits scalability for high-resolution inference or fine-tuning. In practice, bandwidth influences batch size feasibility; the RTX 3080 Ti handles moderate batches efficiently due to higher bandwidth, but the RTX 4080 accommodates bigger ones via extra VRAM. Both share 320 W TDP, ensuring comparable power efficiency in PCIe deployments.

The RTX 3080 Ti suits cost-sensitive deployments where 10 to 12 GB VRAM suffices. Its cloud pricing from $0.08 per hour, averaging $0.14 per hour, undercuts the RTX 4080 by 27 percent at the low end. Higher 760 GB/s bandwidth excels in bandwidth-bound workloads like Stable Diffusion with smaller images, outperforming the RTX 4080's 717 GB/s. Choose it for prototyping or inference on models fitting within 12 GB.

Opt for the RTX 4080 when VRAM demands exceed 12 GB or compute speed is critical. Its 16 GB capacity enables larger batch sizes in LLM training, and 48.7 TFLOPS delivers 63 percent more performance than the RTX 3080 Ti's 29.8 TFLOPS. Despite higher pricing from $0.11 per hour, it accelerates fine-tuning and scientific computing for production-scale tasks.