Recent optimizations in Python's Reference Counting

"Reference counting is the primary memory management technique used in CPython. In short, every Python object (the actual value behind a variable) has a reference counter field that tracks how many references point to it. When an object's reference count drops to zero, the memory occupied by that object is immediately deallocated."

"For hot loops, this can lead to significant overhead due to the frequent incrementing and decrementing of reference counts. The counter must be updated whenever an object is referenced or dereferenced, which hurts performance and trashes CPU caches."

"Since Python 3.14, there is a new bytecode instruction called LOAD_FAST_BORROW. This is an optimized instruction that avoids incrementing the reference count when loading local variables."

Reference counting is the primary memory management technique in CPython. Every Python object carries a reference counter that tracks how many references point to it; when the count reaches zero the object is deallocated immediately. Frequent increments and decrements in hot loops impose measurable overhead and pollute CPU caches because reads often trigger writes to counters. Since Python 3.14, a LOAD_FAST_BORROW bytecode enables loading local variables without incrementing reference counts. Replacing LOAD_FAST with LOAD_FAST_BORROW reduces counter traffic for eligible local loads and uses a static lifetime analysis to preserve correct object lifetimes.

Read at Artem Golubin