TotzkePaul:

When I run get_cuda_version() in Ubuntu 18.04, I get (‘11.0’, 8005) so slightly different cudnn.
I did the same install steps using conda in Ubuntu 18.04 as I do in Windows.
I’m getting the same results as Windows for same conda and pytorch as I do in Ubuntu for same GPU.


That’s good to hear. So the different memory behavior was observed between your local Windows machine and Colab?



 TotzkePaul:

Is Cuda 10 less memory intensive?


It might be the case, but would also depend on the used device.
Could you create a single CUDATensor and check the memory used by the CUDA context via nvidia-smi? Make sure that the GPU memory is empty before running this test or subtract the already used memory.