I think these pruning methods do work if you put some effort into engineering them properly. But I came here to agree about most researchers having no interest

I think these pruning methods do work if you put some effort into engineering them properly. But I came here to agree about most researchers having no interest in actual inference time performance. I just tried a library that was meant to be a "drop in replacement" for an embedding table that as meant to use a lot less memory, and after a little bit of fiddling, yes, it was a good drop in replacement for an embedding table if all you wanted to do was write papers and compute compression rations using theoretical bits needed. In practice, only the training/eval version of the code was written and nobody had actually written the the theoretically possible efficient inference path in the 3 implementations I looked at, and all the numbers in the paper were on theoretical memory savings, so my memory usage actually went up...