GTX 1080 Ti vs RTX 4070 Ti SUPER

The RTX 4070 Ti SUPER demonstrates superior compute capability over the GTX 1080 Ti: 29.1 TFLOPS compared to 8.9 TFLOPS in both FP16 and FP32 marks a 3.26 times increase. This delta translates to faster model training and inference, where FP16 accelerates half-precision computations common in deep learning frameworks. For training large language models, the higher TFLOPS enables quicker iterations on datasets that would bottleneck the GTX 1080 Ti. Inference benefits similarly, supporting higher throughput for real-time applications. Memory bandwidth plays a critical role: 504 GB/s on the RTX 4070 Ti SUPER versus 320 GB/s allows larger batch sizes without spilling to system RAM, reducing latency in memory-bound tasks like Stable Diffusion generation. The 12 GB VRAM edges out the GTX 1080 Ti's 11 GB for handling bigger models. TDP differs minimally at 200W versus 180W, but the RTX 4070 Ti SUPER delivers better performance per watt.

The GTX 1080 Ti suits scenarios with strict power constraints or legacy software optimized for Pascal architecture. Its 180W TDP consumes less energy than the 200W RTX 4070 Ti SUPER, making it preferable in environments limited to 180W slots. Compatibility with older CUDA versions avoids recompilation costs for specific scientific computing pipelines reliant on 8.9 TFLOPS FP32 performance.

The RTX 4070 Ti SUPER excels in modern workloads demanding high throughput and cost efficiency. At $0.09 per hour starting price, it undercuts the GTX 1080 Ti's $0.60 per hour while offering 29.1 TFLOPS and 504 GB/s bandwidth for LLM training or inference. Users prioritizing 12 GB VRAM and Ada Lovelace features like improved tensor cores select it for fine-tuning or Stable Diffusion.