RTX 4070 Ti SUPER vs RTX 4080

The RTX 4080 holds a compute advantage with 48.7 TFLOPS in FP16 and FP32 versus the RTX 4070 Ti SUPER's 44.1 TFLOPS, translating to roughly 10 percent faster matrix multiplications essential for neural network training and inference. This delta accelerates LLM training epochs and reduces inference latency in FP16-heavy workloads like transformer models. Higher FP32 performance also benefits scientific simulations requiring single-precision arithmetic.

Memory bandwidth differs notably at 717 GB/s for the RTX 4080 compared to 672 GB/s on the RTX 4070 Ti SUPER, enabling larger batch sizes in memory-bound tasks such as fine-tuning large language models or Stable Diffusion generation. The RTX 4070 Ti SUPER's lower 285 W TDP versus 320 W allows better efficiency in power-limited cloud instances, potentially lowering operational costs despite slightly reduced throughput. Both GPUs share 16 GB VRAM, sufficient for most current AI models but limiting extreme-scale deployments without multi-GPU setups.

The RTX 4070 Ti SUPER suits cost-conscious users and power-sensitive environments. Its pricing from $0.09 per hour (average $0.17 per hour) undercuts the RTX 4080's $0.11 per hour start, while the 285 W TDP fits constrained cloud instances. Ideal for inference, fine-tuning, or Stable Diffusion where 44.1 TFLOPS and 672 GB/s bandwidth deliver strong value without excess capacity.

Opt for the RTX 4080 in performance-critical scenarios demanding maximum throughput. The 48.7 TFLOPS FP16/FP32 rating and 717 GB/s bandwidth excel in LLM training or large-batch inference, handling demanding workloads 10 percent faster than the RTX 4070 Ti SUPER. Despite higher 320 W TDP and $0.26 per hour average pricing, it justifies the premium for time-sensitive projects.