RTX 4060 vs RTX 4080 SUPER

FP16 and FP32 metrics highlight capability gaps: the RTX 4060 delivers 15.1 TFLOPS in each precision, adequate for entry-level training of small language models or basic inference. The RTX 4080 SUPER's 48.7 TFLOPS accelerates these tasks threefold, reducing epoch times in LLM training and enabling real-time inference on complex models. Equal FP16 and FP32 rates within each GPU support mixed-precision workflows seamlessly. Memory bandwidth profoundly affects real-world use: 272 GB/s on the RTX 4060 constrains batch sizes during fine-tuning, often halving them compared to the RTX 4080 SUPER's 717 GB/s, which sustains larger batches and minimizes data loading bottlenecks. VRAM limits model scale: 8 GB fits 7B-parameter LLMs in inference on the RTX 4060, whereas 16 GB handles 30B-parameter models on the RTX 4080 SUPER without quantization.

The RTX 4060 fits scenarios demanding low power and efficiency: its 115W TDP suits battery-constrained or dense cloud deployments without current live pricing offers. Prototyping small-scale inference, such as 1B to 3B parameter LLMs or lightweight Stable Diffusion generations, leverages its 15.1 TFLOPS and 8 GB VRAM effectively while keeping thermal demands minimal.

High-performance needs favor the RTX 4080 SUPER, available from $0.17 per hour (average $0.32 per hour) across three providers. Its 48.7 TFLOPS and 717 GB/s bandwidth excel in training mid-sized models or serving inference at scale, where 16 GB VRAM prevents out-of-memory errors common on the RTX 4060.