RTX 4060 vs RTX 5080

Compute performance defines the core disparity: the RTX 5080 achieves 56.3 TFLOPS in FP16 and FP32, surpassing the RTX 4060's 15.1 TFLOPS by a factor of nearly four. This acceleration shortens training durations for neural networks and reduces inference times, critical for iterative development cycles. Equal FP16 and FP32 rates on both GPUs ensure versatility in mixed-precision training and full-precision scientific simulations.

Memory configurations amplify real-world impacts. The RTX 5080's 16 GB GDDR7 doubles the RTX 4060's 8 GB GDDR6, accommodating expansive models like large language models without out-of-memory errors. Bandwidth escalates from 272 GB/s to 960 GB/s, enabling larger batch sizes that optimize GPU utilization, lower per-iteration costs, and faster convergence in training loops.

Power efficiency varies accordingly. The RTX 4060's 115W TDP supports dense deployments with minimal cooling, ideal for edge-like cloud instances. The RTX 5080's 360W draw necessitates higher-capacity hosts but delivers proportional gains in throughput for intensive workloads.

Select the RTX 4060 for budget-conscious projects with moderate demands. Its 8 GB VRAM and 15.1 TFLOPS handle inference on small to medium models or fine-tuning lightweight adapters efficiently. Starting at $0.08 per hour and 115W TDP, it minimizes expenses in prolonged runs or power-limited environments across six cloud offers.

Choose the RTX 5080 for high-throughput applications requiring peak performance. 56.3 TFLOPS and 16 GB VRAM excel in training billion-parameter models or generating high-resolution content, with 960 GB/s bandwidth supporting massive batches. Though averaging $0.38 per hour, its capabilities across four offers justify investment for production-scale AI.