RTX 4070 Ti vs RTX 4080 SUPER

Spec differences translate directly to real-world ML outcomes. The RTX 4080 SUPER's 48.7 TFLOPS in FP16 and FP32 exceeds the RTX 4070 Ti's 29.1 TFLOPS by 67 percent, accelerating training epochs and inference queries. In training, higher FP16 throughput speeds matrix multiplications; in inference, it lowers per-token latency for LLMs. The RTX 4070 Ti suffices for models fitting 12 GB VRAM, but the RTX 4080 SUPER's 16 GB capacity handles larger architectures without swapping. Memory bandwidth disparity proves critical: 717 GB/s on the RTX 4080 SUPER supports batch sizes up to 42 percent larger than the RTX 4070 Ti's 504 GB/s, reducing bottlenecks in data-heavy workflows. Higher TDP of 320W on the RTX 4080 SUPER correlates with sustained performance under load, though it elevates power costs versus the RTX 4070 Ti's 200W efficiency.

The RTX 4070 Ti serves cost-optimized scenarios best. At $0.08/hr starting price and 200W TDP, it powers inference on models under 12 GB VRAM or fine-tuning of compact LLMs efficiently. Its 29.1 TFLOPS and 504 GB/s bandwidth deliver solid results for Stable Diffusion generation or lightweight scientific simulations where budget trumps peak speed.

The RTX 4080 SUPER dominates demanding workloads. With 16 GB VRAM and 717 GB/s bandwidth, it manages large-batch LLM training and high-resolution Stable Diffusion without memory constraints. The 48.7 TFLOPS rating ensures 67 percent faster compute over the RTX 4070 Ti, ideal for production inference or complex scientific computing.