# Estimating Processing Time Based on GPU

The following numbers are an approximate guide to the potential runtime performance increment for different card families (baseline = non-TensorCore, standard mode):

Deep Learning operating mode | No Tensor Cores (example GTX) | Volta Tensor Cores (example V100) | Turing Tensor Cores (example T4)
--- | --- | --- | ---
Standard | 100% | 150% | 150%
Low Precision | 125% | 125% | 175%

Note: Compared to other Tesla cards, the T4 is oriented toward runtime operation. It supports Deep Learning training Training is the process that your tool, which is a neural network, is learning about the features (pixels) based on the labels you made. For example, a tool will learn the defect/normal pixels in each image based on the defect/normal labels you drew. The goal of the tool Training is learning enough to give the correct inspection results of whether an unseen image is defective or not. The key to training is to ensure that you include all possible variations within your training set, and that your images are accurately labeled. Training times vary by the application, tool setup and the GPU in the PC being used to train the network. and runtime, but training performance will likely be slower than a V100.