text: Title: Reduce memory consumption in broadcast bmm
  Body: ### 🚀 The feature, motivation and pitch
  
  Here is a minimal example, consumes about 66GiB CUDA memory (I guess it may expand `b` to [8192,32,1024,128] before calculation). Is it possible to reduce the memory consumption without expanding?
  `a=torch.rand((8192,32,1,1024),dtype=torch.bfloat16,device='cuda:0')`
  `b=torch.rand((1,32,1024,128),dtype=torch.bfloat16,device='cuda:0')`
  `c=torch.matmul(a,b)`
  
  
  Versions:
  torch: 2.6.0+cu126
  
  ### Alternatives
  
  _No response_
  
  ### Additional context
  
  _No response_
  
  cc @ptrblck @msaroufim @eqy @jianyuh @nikitaved @pearu @mruberry @walterddr @xwang233 @Lezcano