RTX 5060 vs RTX 5080

Raw compute power sets the RTX 5080 apart: its 56.3 TFLOPS in FP16 and FP32 exceeds the RTX 5060's 23.1 TFLOPS by 144 percent, accelerating matrix operations central to deep learning. For training large language models, this translates to roughly 2.4 times faster iterations on equivalent datasets. Inference benefits similarly, with higher throughput enabling more queries per second in production servers.

Memory specifications amplify these gains. The RTX 5080's 960 GB/s bandwidth doubles the RTX 5060's 448 GB/s, supporting larger batch sizes without bottlenecks: models exceeding 12 GB VRAM fit comfortably in the 5080's 16 GB pool. The RTX 5060 suits smaller batches where 448 GB/s suffices, avoiding out-of-memory errors for models under 10 billion parameters.

Power draw underscores trade-offs. At 360 W TDP, the RTX 5080 demands robust cooling and higher electricity costs in clouds, compared to the RTX 5060's efficient 180 W. Balanced FP16 and FP32 rates on both indicate versatility for mixed-precision training and inference, but the 5080 excels in bandwidth-intensive scenarios like high-resolution generative AI.

The RTX 5060 excels in cost-sensitive environments. With pricing from $0.07 per hour and an average of $0.15 per hour across six offers, it delivers 23.1 TFLOPS FP16 performance for lightweight inference or fine-tuning models under 12 GB VRAM. Developers prototyping small-scale AI applications or running Stable Diffusion at 448 GB/s bandwidth find its 180 W TDP ideal for dense cloud deployments without excessive power overhead.

Opt for the RTX 5080 in performance-critical workflows. Its 56.3 TFLOPS FP16 and 16 GB VRAM handle large-scale LLM training or inference at 960 GB/s bandwidth, enabling batch sizes twice as large as the RTX 5060's capacity. Despite $0.25 per hour starting pricing, the 144 percent compute uplift justifies selection for production generative tasks or scientific simulations requiring 360 W headroom.