RTX 2080 vs RTX 5070

Dense FP16 performance measures 40.3 TFLOPS on the RTX 2080 against 61.7 TFLOPS on the RTX 5070. This gap indicates that the RTX 5070 processes dense FP16 operations at a higher rate for training and inference tasks. FP32 performance of 10 TFLOPS on the RTX 2080 compared with 30.9 TFLOPS on the RTX 5070 shows similar scaling for single precision workloads. Memory bandwidth of 448 GB/s on the RTX 2080 versus 672 GB/s on the RTX 5070 affects the size of batches that fit within memory constraints during large model operations. Dense INT8 performance reaches 161.1 TOPS on the RTX 2080 and 246.9 TOPS on the RTX 5070. These ratios remain consistent when comparing dense figures to dense figures and sparse figures to sparse figures across both GPUs.

The RTX 2080 suits workloads where power draw must stay at 215W. Its NVLink interconnect supports multi GPU configurations that require that specific link type. FP32 performance of 10 TFLOPS remains adequate for smaller scale scientific computing jobs that do not demand the 30.9 TFLOPS available on newer hardware.

The RTX 5070 fits scenarios that require 12-16 GB GDDR7 memory and 672 GB/s bandwidth. Dense FP16 performance of 61.7 TFLOPS supports larger batch sizes in training compared with the 40.3 TFLOPS of the RTX 2080. PCIe 5.0 interconnect provides faster host communication than the PCIe 3.0 on the RTX 2080.