Hi Calin, unfortunately I was not able to solve the problem. In my case the issue wasn’t non-convergence on one of the machine but rather just different numbers. Though you can see in the post by albanD he mentions that in some settings models won’t be sensible to such noise and will still be able to converge while in other settings this noise can make or brake a model and sometimes it could mean models won’t converge.
I’m not sure if in your case the convergence problem is due to such noise or some other difference caused by different versions.
I would definitely try installing the same version drivers and all other python packages and see if the problem persists, then perhaps change some training hyperparameters that could help with “smoothing” out this noise and helping the model to converge in both cases (probably at the cost of training speed/efficiency).