GTX 1080 Ti vs RTX 2070

Raw compute favors the GTX 1080 Ti: its 11.3 TFLOPS FP32 exceeds RTX 2070's 7.5 TFLOPS, accelerating FP32-centric training and scientific computing. FP16 performance mirrors this at 11.3 TFLOPS versus 7.5 TFLOPS, enabling quicker half-precision inference on larger batches.

Memory subsystem differences impact real-world usage: GTX 1080 Ti's 484 GB/s bandwidth supports bigger batch sizes than RTX 2070's 448 GB/s, minimizing stalls in bandwidth-limited deep learning phases. Coupled with 11 GB VRAM over 8 GB, it handles models demanding more memory without fragmentation issues.

Power draw varies significantly at 250W TDP for GTX 1080 Ti against 175W for RTX 2070, favoring the latter for efficiency-focused deployments. RTX 2070's NVLink interconnect aids multi-GPU scaling, while Turing optimizations enhance mixed-precision workflows beyond listed peaks.

Select GTX 1080 Ti for memory-intensive applications: 11 GB GDDR5X VRAM accommodates large models exceeding 8 GB limits of RTX 2070. High-bandwidth tasks benefit from 484 GB/s, enabling substantial batch sizes in training.

It suits scenarios prioritizing peak 11.3 TFLOPS FP32/FP16 over cost, such as offline scientific computing where $0.60 per hour fits budgets.

RTX 2070 shines in value-driven setups: pricing from $0.02 per hour delivers 7.5 TFLOPS FP32/FP16 at minimal expense. Lower 175W TDP reduces cloud instance power costs compared to 250W.

Choose it for scalable inference or graphics with NVLink and Turing features like tensor cores, ideal when 8 GB VRAM suffices.