Python GIL impact on multithreading — when to use multiprocessing

Python multithreading does not speed up CPU-bound tasks. More threads provides no improvement or makes it slower.

GIL allows only one thread to execute Python bytecode at a time. For CPU-bound: use multiprocessing (separate processes). For I/O-bound: threading is fine (GIL released during I/O). Use concurrent.futures with ProcessPoolExecutor for CPU and ThreadPoolExecutor for I/O. Python 3.13+ has experimental free-threaded mode.

CPython's GIL serializes thread execution. Threads share memory but can't execute Python code simultaneously. Multiprocessing bypasses this with separate interpreter processes.