Hi,
I noticed that training a ResNet-50 with AMP on my new laptop with RTX3070 takes much more GPU memory than without AMP. It is not a code issue because I am able to run the same code on a workstation with an Nvidia Tesla p100 with the opposite result.
Laptop:
Ubuntu 20.04 with Kernel 5.8 (and 5.11)
GPU: RTX3070
Latest Nvidia drivers installed with Ubuntu GUI software and updates (CUDA 11.2)
command numba -s returns no error regarding CUDA install. Same for nvidia-smi.
Training  without AMP: 3.9 GB VRAM
Training with AMP: 7.4 GB VRAM
GPU memory consumption is stable during training
I installed Pytroch with:
conda install pytorch torchvision torchaudio cudatoolkit=11.1 -c pytorch -c nvidia
(Also tried Pytorch preview 1.9)
Workstation:
GPU: Nvidia tesla p100
CUDA 10.2
Training  without AMP: 3.5 GB VRAM
Training with AMP: 2.5 GB VRAM
Do you have any idea why I have this strange behavior on my laptop?
Thanks in advance