Hey @Alessio Vaccaro​ , Sorry for the really delayed response 😅

I didn't find any documentation or any good resource of this.

I would hope that if only 1 notebook is attached to a cluster, this notebook can use all the RAM - memory allocated for spark driver, when more notebooks are attached then some mechanism to handle it start to work.

Actually i saw a databricks blog that say "Fatal error: The Python kernel is unresponsive." is an error cause because out of RAM

you can see the blog here:

Accelerating Your Deep Learning with PyTorch Lightning on Databricks - The Databricks Blog