GTX 1080 vs RTX 2080 Ti

Memory bandwidth stands out as the largest gap: RTX 2080 Ti doubles GTX 1080 at 616 GB/s versus 320 GB/s. This enables larger batch sizes in machine learning training and inference, reducing overhead and improving throughput for data-intensive tasks. Higher bandwidth minimizes bottlenecks when loading large datasets or models into VRAM. Compute throughput favors RTX 2080 Ti with 10.1 TFLOPS in FP32 and FP16, a 13 percent increase over GTX 1080's 8.9 TFLOPS. For training, this translates to faster iterations; for inference, quicker predictions on FP32 workloads. Turing architecture introduces tensor cores optimized for FP16, enhancing mixed-precision training beyond raw specs. TDP rises from 180 W on GTX 1080 to 215 W on RTX 2080 Ti, reflecting added performance at moderate power cost. NVLink interconnect on RTX 2080 Ti supports multi-GPU scaling, absent on GTX 1080.

The GTX 1080 suits power-constrained environments where 180 W TDP matters over peak performance. It fits scenarios without multi-GPU needs, as it lacks NVLink and relies solely on PCIe form factor. At $0.30 per hour, it serves basic FP32 tasks like legacy scientific computing when 8.9 TFLOPS suffices and 320 GB/s bandwidth handles modest batch sizes.

The RTX 2080 Ti excels in modern workloads demanding high memory bandwidth of 616 GB/s for large-batch training or inference. Its 10.1 TFLOPS FP32 performance accelerates compute-bound tasks, and NVLink enables efficient multi-GPU setups. Superior cloud pricing from $0.06 per hour makes it ideal for cost-effective scaling across six providers.