As someone who has spent a lot of time optimizing Python code, I can tell you that while CPython 3 doesn’t feature JIT Python support, other implementations, especially PyPy, shine in this area. PyPy uses Just-In-Time compilation to analyze and optimize code during runtime, which minimizes interpreter overhead and speeds up computation-intensive loops or repeated function calls.
Still, relying solely on JIT doesn’t guarantee the best performance. Many Python programs see the greatest improvements when you combine a JIT-enabled interpreter like PyPy with practices like modular design, using efficient algorithms, or delegating heavy lifting to optimized libraries like Cython or TensorFlow. In this way, you get the best of both worlds—JIT and efficient code.