# Estimating GPU Memory Usage

- Display: memory to store images prior to computation.
- VisionPro Deep Learning Reserved: memory reserved for computation.
- Free: memory left free, because the amount of memory used is not known.

An example for 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.:

- Reserved memory at 2.0GB (the minimal value for training)
- Free memory at about 1GB
- Which results in a graphics card with at least 3.5GB of memory

An example for runtime:

- Reserved memory at 512MB
- Free memory at about 256MB
- Which results in a graphics card with no minimal memory required