Hi! I am facing a very strange issue: I have 2 different environments for training NN: (Ubuntu 20.04)

RTX 2070super mobile, CUDA 11.2, pytorch 1.10.0
RTX 3060ti , CUDA 11.4, pytorch 1.10.1

On the first computer, training my NN consumes around 1400MB of GPU ram while the second one uses 2200MB. Their training configs are the same: Its a RL project using PyTorch and stableBaselines. The code is the same. Nonetheless, it still uses more ram from the GPU on the second computer which prevents me from using more parallel training.
I have been advised about pytorch flags. They are the same on both. I wonder if it is an issue with Cuda, PyTorch or a hidden default config of each GPU.