> Running a GC takes time
Yes, but for a moving collector that's less time than it takes to run malloc and free. The interaction of a moving collector with most object is bump allocation when they're allocated (similar to stack allocation) and... that's it. The GC never sees them again, scans them again, or is even aware of their existence (moving collectors don't have a free operation). Overall, moving collectors (but not other kinds of GC) reduce the work of memory management compared to malloc/free.
In low-level languages we try to avoid doing a lot of malloc/free not because heap memory management is slow in general, but because that approach to memory management is slow. Moving collectors are an optimisation designed to make heap memory management fast, but it requires that (nearly) all pointers be movable, something that low-level languages can't do because they have constraints that are more important to them than speed (you can't interact with the OS or hardware directly, i.e. without an FFI API, if your pointers are movable, and such direct interaction is the point of low-level languages).
That moving collectors (NOT the GC Python has; NOT the GC Go has) can, in principle, make heap memory management cheaper than stack allocation has been well known since the eighties. But until recently they had excellent throughput (somewhat similar to arenas) but potentially long pauses. It was only recently that they were made "pauseless".
> and is often run at an unpredictable time
How much work malloc and free need to do is also unpredictable, and a modern pauseless moving collector like ZGC spreads the work needed for memory management more evenly than malloc and free.
> Sure, the GC is not necessarily the SLOWEST thing about the language (python), but it's not helping, either.
There is very little resemblance between CPython's GC and Java. Python's memory management is closer to C's than to Java's. GCs cover such a wide spectrum of algorithms that it doesn't make sense to talk about them as a single category as far as performance tradeoffs are concerned.