You’re correct that I made a broad statement, without comparing to other debuggers. I appreciate you flagging that, @googlebot. I edited the first post to reflect that.
While there is definitely a similarity I don’t think we can compare this to jupyter/ipython. In ipython you can control what variables remain in scope, whereas debuggers do their own tracking that you can’t control.
Unfortunately, I haven’t saved that particular code that was showing drastically different gpu memory usage  w/ and w/o debugger. I tried a few approaches now and the only correlation I found is the number of breakpoints in frames that had huge vars on cuda - more extra memory used when more breakpoints.
I may get a chance to investigate it more and if I do I will report back. But I’m surely going to be wary of watching memory usage under debugger and will have to check the usage patterns outside of debugger.