RTX 3090 vs RTX 5080

The RTX 5080 demonstrates superior raw compute with 56.3 TFLOPS in FP16 and FP32, a 58 percent increase over the RTX 3090's 35.6 TFLOPS in both metrics: this translates to faster model training and inference times, particularly in half-precision workloads common in deep learning. Training large language models benefits from the higher throughput, reducing epochs by up to that proportional gain.

Memory bandwidth edges slightly higher on the RTX 5080 at 960 GB/s versus 936 GB/s on the RTX 3090, enabling marginally larger batch sizes in memory-bound scenarios without spilling to slower system RAM. However, the RTX 3090's 24 GB VRAM surpasses the RTX 5080's 16 GB, accommodating bigger models or datasets outright: for instance, inference on 70B parameter LLMs fits natively on the RTX 3090 but may require quantization on the RTX 5080.

TDP differences of 350W versus 360W imply similar power envelopes, but the Blackwell architecture's efficiency gains in the RTX 5080 optimize performance per watt for sustained cloud runs.

Opt for the RTX 3090 in memory-intensive applications requiring over 16 GB VRAM, such as loading unquantized large language models or high-resolution Stable Diffusion generations. Its 24 GB GDDR6X capacity handles these without compromises, unlike the RTX 5080's 16 GB limit.

Availability drives selection too: 52 live cloud offers at from $0.08/hr provide flexibility over the RTX 5080's 4 offers from $0.25/hr, ideal for budget-conscious prototyping or long-running jobs.

Choose the RTX 5080 for compute-dominant tasks leveraging its 56.3 TFLOPS FP16 and FP32 rates, 58 percent above the RTX 3090's 35.6 TFLOPS: fine-tuning or inference on models fitting within 16 GB sees acceleration here.

The 960 GB/s bandwidth supports slightly larger batches than the RTX 3090's 936 GB/s, and average pricing at $0.38/hr undercuts the RTX 3090's $0.41/hr for high-throughput needs.