RTX 3080 Ti vs RTX 4060

The RTX 3080 Ti's 29.8 TFLOPS in FP16 and FP32 outperforms the RTX 4060's 15.1 TFLOPS, enabling faster model training and inference by approximately 97 percent in compute-bound workloads. This delta means training a large language model completes in less time on the RTX 3080 Ti, as higher throughput processes more floating-point operations per second. For inference, the superior FP16 performance reduces latency in serving predictions. Memory bandwidth of 760 GB/s on the RTX 3080 Ti versus 272 GB/s on the RTX 4060 supports larger batch sizes: the RTX 3080 Ti handles batches up to 2.8 times larger without memory bottlenecks. The 10 to 12 GB VRAM capacity exceeds the RTX 4060's 8 GB, accommodating bigger models or datasets during fine-tuning. However, the RTX 4060's 115 W TDP delivers better efficiency, yielding about 0.13 TFLOPS per watt compared to the RTX 3080 Ti's 0.09 TFLOPS per watt. In real-world terms, high-bandwidth tasks like Stable Diffusion benefit from the RTX 3080 Ti, while power-constrained environments favor the RTX 4060.

The RTX 3080 Ti suits demanding workloads requiring high compute and memory. For LLM training or Stable Diffusion with large batches, its 29.8 TFLOPS and 760 GB/s bandwidth outperform the RTX 4060. Cloud users benefit from availability at $0.08 per hour from, averaging $0.14 per hour. Its 10 to 12 GB VRAM handles models exceeding 8 GB.

The RTX 4060 excels in efficiency-focused scenarios with its 115 W TDP. Inference on smaller models or fine-tuning lightweight networks leverages Ada Lovelace optimizations despite 15.1 TFLOPS. It suits edge-like cloud deployments where power costs matter, though no live offers exist currently.