GTX 1080 Ti vs RTX 3090 Ti

Compute capabilities differ markedly between these GPUs: the RTX 3090 Ti achieves 35.6 TFLOPS in FP32, while the GTX 1080 Ti delivers 8.9 TFLOPS. This gap translates to approximately four times faster matrix multiplications in training workloads. FP16 performance matches FP32 at 35.6 TFLOPS versus 8.9 TFLOPS, indicating no specialized half-precision boost beyond base shaders on either card. Training large neural networks benefits from the RTX 3090 Ti's superior throughput, reducing epoch times significantly. The memory bandwidth contrast is stark: 936 GB/s on RTX 3090 Ti versus 320 GB/s on GTX 1080 Ti. Higher bandwidth sustains larger batch sizes during inference, preventing stalls in data-heavy operations. With 24 GB VRAM against 11 GB, the RTX 3090 Ti accommodates bigger models or datasets without swapping to host memory. Inference latency drops due to ample capacity for concurrent requests. Overall, these specs position the Ampere GPU for demanding AI pipelines, while Pascal suits lighter loads.

The GTX 1080 Ti suits low-intensity inference on small models fitting within 11 GB VRAM. Its 180 W TDP appeals in power-constrained cloud instances where efficiency trumps peak performance. At $0.60 per hour with stable single-provider availability, it serves prototyping or hobbyist tasks without overprovisioning.

The RTX 3090 Ti excels in training and fine-tuning where 35.6 TFLOPS FP32 outperforms 8.9 TFLOPS by four times. Larger 24 GB VRAM handles extensive datasets, and 936 GB/s bandwidth supports big batches. Cloud pricing from $0.10 per hour averaging $0.25 across 5 providers offers better value for production workloads.