RTX 3060 vs RTX 5080

Compute performance defines the core disparity: the RTX 5080 achieves 56.3 TFLOPS in FP16 and FP32, delivering over 4.4 times the 12.7 TFLOPS of the RTX 3060. In training scenarios, this accelerates gradient computations and backpropagation, potentially reducing full training runs by hours or days for models like transformers. Inference benefits similarly, with higher throughput for real-time applications.

Memory bandwidth triples effectively from 360 GB/s to 960 GB/s on the RTX 5080: this sustains larger batch sizes in training without stalling data transfers, crucial for stable convergence. Paired with 16 GB VRAM versus 12 GB, it accommodates bigger models or multi-GPU setups where the RTX 3060 risks out-of-memory errors.

Power efficiency shifts with TDP rising to 360 W: despite higher draw, the RTX 5080 yields 0.156 TFLOPS per watt versus 0.075 for the RTX 3060, a 2.1-fold improvement. Real-world workloads like Stable Diffusion see faster iterations due to these specs, though cooling and instance limits apply in cloud deployments.

The RTX 3060 excels in budget-constrained prototyping: its $0.03 per hour starting price and 12.7 TFLOPS handle lightweight fine-tuning or inference on models under 12 GB VRAM. Low 170 W TDP suits shared cloud instances with power caps.

Entry-level scientific simulations or Stable Diffusion at 512x512 resolution fit perfectly, avoiding overprovisioning costs averaging $0.07 per hour.

The RTX 5080 dominates high-throughput tasks: 56.3 TFLOPS and 960 GB/s bandwidth enable training large LLMs or high-resolution image generation. Users processing batches over 12 GB VRAM capacity justify the $0.25 per hour premium.

Inference servers demanding low latency benefit from 4.4 times faster FP16 performance, scaling to production volumes unavailable on the RTX 3060.