RTX 2060 vs RTX 5080

The RTX 5080's 56.3 TFLOPS in FP16 and FP32 dwarfs the RTX 2060's 6.5 TFLOPS, delivering over 8 times the compute power. This leap accelerates machine learning training and inference: training epochs complete faster on the RTX 5080, while inference latency drops for real-time applications. Equal FP16 and FP32 rates on both GPUs ensure balanced tensor core utilization, but the RTX 5080's scale handles complex models without bottlenecks.

Memory specifications further the advantage. The RTX 5080's 16 GB GDDR7 VRAM and 960 GB/s bandwidth support larger batch sizes compared to the RTX 2060's 6 to 12 GB GDDR6 and 336 GB/s. Higher bandwidth reduces data transfer delays, enabling stable training of models exceeding 12 GB without out-of-memory errors. The RTX 2060 suits smaller datasets, but the RTX 5080 excels in memory-intensive tasks like large language model fine-tuning.

Power draw reflects capability: the RTX 5080's 360W TDP demands robust cooling versus the RTX 2060's efficient 160W, impacting cloud instance selection for sustained loads.

The RTX 2060 fits budget-conscious users with light workloads. Its 6 to 12 GB VRAM and 6.5 TFLOPS handle basic inference or fine-tuning of small models at $0.02 to $0.04 per hour. Low 160W TDP minimizes costs in short bursts or development testing where speed takes second place to affordability.

The RTX 5080 serves demanding AI pipelines. With 56.3 TFLOPS and 16 GB VRAM, it powers full LLM training or high-throughput inference at scales the RTX 2060 cannot match. Despite $0.25 to $0.38 per hour pricing, its 960 GB/s bandwidth justifies selection for production environments needing rapid iteration.