RTX 3070 vs RTX 5080

The RTX 5080's 56.3 TFLOPS in FP16 and FP32 dwarfs the RTX 3070's 20.3 TFLOPS, providing nearly three times the compute power for training and inference tasks. Training deep learning models benefits from this uplift, as FP16 accelerates matrix multiplications common in neural networks. Inference workloads similarly gain from higher throughput, reducing latency for real-time applications. The identical FP16 and FP32 ratings on both GPUs indicate tensor core efficiency, but the RTX 5080's scale enables handling complex models without precision bottlenecks. Memory differences prove critical: the RTX 5080's 16 GB GDDR7 versus 8 GB GDDR6 allows loading larger models entirely on-device. Its 960 GB/s bandwidth compared to 448 GB/s supports bigger batch sizes, minimizing data transfer overheads during training epochs. Smaller batches on the RTX 3070 may suffice for lightweight inference but limit scalability. Power draw rises to 360W on the RTX 5080 from 220W, demanding robust cooling in cloud instances.

The RTX 3070 suits budget-conscious users with its pricing from $0.04 per hour and average of $0.08 per hour across six offers. It excels in lightweight inference or fine-tuning small models under 8 GB VRAM, where 20.3 TFLOPS and 448 GB/s bandwidth provide ample performance without excess cost. Developers prototyping on modest datasets or running Stable Diffusion at lower resolutions find its 220W TDP efficient for short bursts.

Opt for the RTX 5080 when workloads demand 16 GB VRAM and 56.3 TFLOPS for large-scale LLM training or inference. Its 960 GB/s bandwidth handles high batch sizes effectively, ideal for production environments. Despite higher costs from $0.25 per hour and average $0.38 per hour across four offers, the 360W TDP supports sustained heavy loads in cloud GPU rentals.