RTX 3060 vs RTX 4080 SUPER

The RTX 4080 SUPER demonstrates superior compute with 48.7 TFLOPS in both FP16 and FP32, compared to the RTX 3060's 12.7 TFLOPS: this enables roughly 3.8 times faster matrix operations critical for neural network training and inference. In training scenarios, higher FP16 performance accelerates gradient computations; for inference, it supports greater throughput on batched requests.

Memory bandwidth presents a clear divide: 717 GB/s on the RTX 4080 SUPER versus 360 GB/s on the RTX 3060 permits larger batch sizes in memory-bound tasks like LLM fine-tuning, reducing data transfer bottlenecks. The RTX 4080 SUPER's 16 GB VRAM handles models exceeding 12 GB capacities without swapping, enhancing efficiency in Stable Diffusion or scientific simulations. Power draw at 320W reflects these gains, demanding robust cloud hosts.

The RTX 3060 fits scenarios prioritizing low cost, such as prototyping small ML models or running inference on lightweight networks. Its 12 GB VRAM and 12.7 TFLOPS suffice for Stable Diffusion at 512x512 resolutions or basic fine-tuning, with pricing from $0.03/hr enabling extended experimentation across 10 cloud offers. Users with 170W power constraints or PCIe-only setups benefit most.

Opt for the RTX 4080 SUPER in performance-critical applications like training mid-sized LLMs or high-resolution image generation. The 48.7 TFLOPS FP16/FP32 and 717 GB/s bandwidth support large batch sizes and complex models up to 16 GB VRAM. Despite higher $0.17/hr starting price, it delivers superior speed for production inference across fewer but capable providers.