RTX 4060 Ti vs RTX 5080

Compute performance differs significantly: the RTX 5080 achieves 56.3 TFLOPS in FP16 and FP32 compared to 15.1 TFLOPS on the RTX 4060 Ti. This yields approximately 3.7 times faster matrix operations critical for machine learning training and inference. In training large language models, the RTX 5080 processes batches quicker due to higher throughput. For inference, it reduces latency on high-volume queries. Memory bandwidth stands at 960 GB/s for the RTX 5080 versus 272 GB/s on the RTX 4060 Ti, enabling larger batch sizes in data-heavy workflows without memory stalls. The 16 GB VRAM on the RTX 5080 handles models exceeding 8 GB, such as complex transformers, while the RTX 4060 Ti limits to smaller ones. Power draw reflects this: 360W TDP for the RTX 5080 versus 115W, impacting density in cloud instances.

The RTX 4060 Ti suits budget-conscious users with light workloads. Its $0.08 per hour starting price and 115W TDP make it ideal for development, small-scale inference, or Stable Diffusion at 15.1 TFLOPS where full power is unnecessary. PCIe form factor fits standard cloud servers without high cooling demands.

The RTX 5080 fits intensive AI tasks requiring scale. With 56.3 TFLOPS and 16 GB VRAM, it excels in LLM training or fine-tuning large models that overwhelm the RTX 4060 Ti's 8 GB limit. Higher 960 GB/s bandwidth supports production inference at volume.