RTX 3080 vs RTX 3090

The RTX 3090 outperforms the RTX 3080 in raw compute by 19 percent: 35.6 TFLOPS versus 29.8 TFLOPS in both FP16 and FP32. This delta translates to faster model training and inference, particularly in mixed-precision workflows common in deep learning. For training large neural networks, the higher FP16 throughput on the RTX 3090 accelerates gradient computations and reduces epoch times.

Memory capacity defines the core distinction: the RTX 3090's 24 GB GDDR6X versus the RTX 3080's 10-12 GB limits the latter to smaller batch sizes or models. The RTX 3090's 936 GB/s bandwidth, compared to 760 GB/s, sustains higher throughput for memory-bound tasks like inference on large language models, enabling larger batches without swapping to system RAM. The 350W TDP on the RTX 3090 demands more power than the 320W of the RTX 3080, potentially increasing operational costs in prolonged runs.

In practice, these specs favor the RTX 3090 for VRAM-intensive applications, while the RTX 3080 suffices for lighter loads where cost savings matter.

The RTX 3080 suits cost-sensitive projects with moderate memory needs. Its pricing from $0.06 per hour, averaging $0.15 per hour across 10 offers, undercuts the RTX 3090's $0.08 per hour start and $0.41 per hour average. Choose it for fine-tuning smaller models or inference on datasets fitting within 10-12 GB VRAM, where 29.8 TFLOPS FP32 performance delivers ample speed at lower power draw of 320W.

Opt for the RTX 3090 in memory-heavy workloads requiring 24 GB VRAM. Its 936 GB/s bandwidth and 35.6 TFLOPS FP16/FP32 excel in training large models or running inference with big batch sizes. NVLink support enables multi-GPU scaling, ideal despite higher 350W TDP and $0.41 per hour average pricing across 52 offers.