RTX 3080 vs RTX 4060

The RTX 3080's 29.8 TFLOPS in FP16 and FP32 outperforms the RTX 4060's 15.1 TFLOPS by nearly double, accelerating training and inference for deep learning models. This delta means the RTX 3080 completes FP16 matrix multiplications, common in transformer training, in roughly half the time on equivalent batch sizes. For inference, higher throughput supports more simultaneous queries.

Memory bandwidth of 760 GB/s on the RTX 3080 versus 272 GB/s on the RTX 4060 directly impacts large batch sizes: the RTX 3080 handles bigger batches without stalling, ideal for training datasets exceeding 8 GB VRAM limits on the RTX 4060. The RTX 3080's 10 to 12 GB GDDR6X enables larger models like 7B parameter LLMs, while the RTX 4060's 8 GB GDDR6 suits smaller ones.

Power draw differs significantly at 320W for the RTX 3080 and 115W for the RTX 4060, affecting cloud costs in power-sensitive environments. Ada Lovelace efficiency may yield better performance per watt, but raw specs favor the RTX 3080 for compute-intensive tasks.

Choose the RTX 3080 for workloads requiring high VRAM and bandwidth, such as training models over 8 GB or Stable Diffusion with large resolutions. Its 10 to 12 GB GDDR6X and 760 GB/s bandwidth support batch sizes infeasible on the RTX 4060's 8 GB and 272 GB/s. At $0.06 per hour lowest pricing across 10 offers, it delivers value for heavy compute at 29.8 TFLOPS.

Select the RTX 4060 for power-efficient inference or fine-tuning small models under 8 GB VRAM. Its 115W TDP reduces operational costs compared to the RTX 3080's 320W, and Ada Lovelace architecture provides modern features at $0.08 per hour starting price. Average $0.14 per hour across 8 offers suits light workloads where 15.1 TFLOPS suffices.