It's... complicated. Certainly you can write parallel code in Python using the GIL; there are several scenarios. The shortest answer is "the multiprocessing library, when used carefully, can speed up the runtime of your CPU-intensive, multiple process python program by spreading work across multiple processors/cores". The longer answer is: many IO-bound Python programs can be sped up using multithreading within a single Python process (because the application is mostly waiting for IO), and many CPU-intensive Python programs can be sped up using multithreading where work is done in C functions that release the GIL.

Many python programs I write end up using 8+ cores on a single machine using either multiprocessing or C functions with released GIL.