Hi all, In the NumPy 2.4 cycle, there were some native float16 implementations merged with rather low precision leading to the following issue: https://github.com/numpy/numpy/issues/30821 That is, previously, it used float loops so ~0.5 ULP error, now is is 2+ULP for many algorithms, on _some_ hardware: https://github.com/numpy/numpy/pull/23351 There is always an argument around that users of float16 probably don't care about many ULP, but I guess they also have very few bits of precision to begin with? I don't have a huge opinion on it, but we are more and more in the position where it is unclear if sacrificing a bit of precision is the right thing or not... Similar questions actually arise for float32 math, is it OK to trade- off precision for performance (or to what degree, everything trades a bit)? We have had discussions around this before but it is still a difficult trade-off to make and there is no choice that makes everyone happy. [1] - Sebastian [1] We can work towards something like `np.opts(precision="low")` or so, but that doesn't change the question of defaults much...