I wrote a simple bare bones program to check the usage of ram of gpu using pretrained resnet-34 from model zoo.  I found that the creating and shifting the model to GPU utilizes about 532 MBs of gpu ram. Loading the model seems to have no effect on ram usage, showing pytorch reserves the ram for subsequent loading of weights. The weight file is only 87.3 MBs on disk. So why is the model taking up 532 mb of GPU ram, even if we count class/function overhead why is it using over 5.5x the space it occupies on the disk? Is there anyway to reduce this usage?