RTX 2080 vs RTX 3070 Ti

The RTX 3070 Ti's Ampere architecture yields 20.3 TFLOPS in FP16 and FP32, exactly double the RTX 2080's 10.1 TFLOPS, accelerating machine learning training and inference by enabling faster matrix operations via improved tensor cores. Training large models processes twice the floating-point operations per second, reducing epoch times significantly. Inference benefits similarly, handling more queries per hour in production deployments. The RTX 2080's superior 616 GB/s bandwidth versus 448 GB/s on the RTX 3070 Ti supports larger batch sizes in memory-bound scenarios, minimizing data starvation during transfers. Lower bandwidth on the RTX 3070 Ti may constrain batch sizes to 8 to 16 samples versus 16 to 32 on the RTX 2080 for high-resolution tasks. Both GPUs share 8 GB base VRAM, limiting model sizes to around 7 billion parameters at FP16 precision.

Select the RTX 2080 for bandwidth-sensitive workloads where 616 GB/s exceeds the RTX 3070 Ti's 448 GB/s, such as processing large datasets in scientific computing with batch sizes over 32. Its NVLink interconnect facilitates multi-GPU scaling unavailable on the RTX 3070 Ti. Cloud pricing starts at $0.05 per hour, undercutting the RTX 3070 Ti by up to 17 percent on average.

Choose the RTX 3070 Ti for compute-bound tasks leveraging its 20.3 TFLOPS FP16 and FP32 performance, double the RTX 2080's 10.1 TFLOPS, ideal for LLM training and inference. Ampere architecture optimizes modern frameworks like TensorRT. Pricing at $0.06 per hour average $0.08 remains competitive for doubled throughput.