Hey OliverC - thanks for the question and welcome to the Palantir Developer Community!
When working in a Jupyter workspace, resources aren’t shared across multiple users - each user gets their own session and own memory, which is allocated with the memory profile selected in their workspace settings. In fact, each users session is completely isolated from other users, so that you have your own view of your workspace that other’s can’t see.
When you set 8gb of memory in your own individualized workspace, it means your entire Jupyter instance (e.g. all processes running across notebook files, terminal processes, etc) has access to the pool of 8gb. You can think of it as having your own computer which is allocated 8gb of memory, where all your processes pull from the same resource pool.
Hope this helps answer your question