Hi all, I was told to take this to the mailing list. Relevant pull request: https://github.com/numpy/numpy/pull/7686 NumPy's FFT implementation caches some form of execution plan for each encountered input data length. This is currently implemented as a simple dictionary which can grow without bounds. Calculating lots of different FFTs thus cause a memory leak from the users' perspective. We encountered a real world situation where this is an issue. The PR linked above proposes to replace the simple dictionary with an LRU (least recently used) cache. It will remove the least recently used pieces of data if it grows beyond a specified size (currently an arbitrary limit of 100 MB per cache). Thus almost all users will still benefit from the caches but their total memory size is now limited. Things to consider: * This adds quite some additional complexity but I could not find a simple way to achieve the same result. * What is a good limit on cache size? I used 100 MB because it works for my uses cases. Cheers! Lion