Hi @cyril9227, please see my answer to a similar question and links therein:

  
    
    
    Weight Pruning on BERT 
  
  
    The point of PyTorch pruning, at the moment, is not necessarily to guarantee inference time speedups or memory savings. It’s more of an experimental feature to enable pruning research. 
In any case, answers to questions similar to yours were given here and here. 
TL;DR: You can save space by calling .to_sparse() which brings your sparse tensor into coordinate representation. You cannot expect any inference speedups unless you use a custom sparse matrix algebra library to power your computation. t…