GTX 1080 Ti vs TITAN Xp

The TITAN Xp outperforms the GTX 1080 Ti in raw compute: 12.1 TFLOPS FP32 versus 11.3 TFLOPS yields about 7 percent faster model training and inference in single-precision tasks. FP16 performance follows suit at the same rates, benefiting half-precision inference without tensor core acceleration native to later architectures. Memory bandwidth defines key differences: 548 GB/s on the TITAN Xp supports 13 percent larger batch sizes than 484 GB/s on the GTX 1080 Ti, crucial for memory-bound operations in convolutional neural networks or large dataset processing. The extra 1 GB VRAM on the TITAN Xp enables handling models up to 12 GB without swapping, while 11 GB suffices for most Pascal-era benchmarks. Both GPUs share identical 250 W TDP, ensuring comparable power efficiency in PCIe deployments. Pascal architecture limits them to general-purpose compute, excelling in FP32-heavy scientific computing over modern AI optimizations.

The GTX 1080 Ti excels in cost-sensitive cloud deployments: availability at $0.60 per hour average pricing makes it viable for extended legacy workloads. Its 11 GB VRAM and 484 GB/s bandwidth support fine-tuning or inference on models under 10 GB, matching the TITAN Xp's 250 W TDP without availability constraints.

The TITAN Xp is preferable for memory-intensive tasks: 12 GB VRAM handles larger datasets than the GTX 1080 Ti's 11 GB, and 548 GB/s bandwidth accelerates data transfers by 13 percent. It delivers 12.1 TFLOPS FP32 for peak Pascal performance in bandwidth-limited training scenarios, despite lacking cloud offers.