We are using NER model. Our model was trained using torch 1.6. It took around 1.9 GB of RAM. We have moved from Tesla V100 to A100. As A100 can be run only on Cuda>=11.0 we have been forced to install the latest PyTorch with 11.3 support. While doing this, the memory needed for our model increased from 1.9 GB to 3.2 GB.
Could anyone shed some idea here on why such a huge difference and what can be done to reduce this?  Will retraining model using torch 1.10 reduce memory usage?
I am attaching stats on different version and their memory usage. Thanks for your help.

Screenshot 2021-12-09 at 5.40.25 PM1380×1568 34.9 KB