Not Really, in professional clusters (ones managed by an administrator) there are usually several versions of cuda and cudnn. It may happen (it happened to me) that when you are exporting environment variables you do it wrongly or pointing to wrong versions. You have to check which version of pytorch you have (it means, which cuda version the pytorch version you installed is compatible with, I guess there are installations for cuda 8 9 9.1 and 9.2 right now) and check what is there available in you cluster.
When you run a job you typically have to export those variables
Those are
CUDA_HOME
LD_LIBRARY
And depending on your cluster some other related to python, pytorch etcetera… it depens a lot on the cluster. Go to the shell and check if those variables exist.
If error is due to hardware compatibility there is no so much you can do. But I guess in the end pytorch layer rely on cuda, and I don’t think cuda drives does not work with those powerful gpus.