How to evaluate the required GPU memory for running neural network models?
It's difficult to calculate total, but you can estimate a minimum to just load a model, which would be roughly the model size. Tensorflow, for example, defaults to reserving 100% of the GPU memory. You can set limits, but the amount of memory to be used is based on many things, such as number of layers, input image size, batch size, etc.
For example, how many Mb will be required for EfficientNetB3? On drive model weights require 187 Mb of memory, does it mean that when the model will be loaded on GPU, it will use 187 Mb of GPU memory?