yes it is possible refer to the topic below

  
    
    
    Model parallelism in pytorch for large(r than 1 GPU) models? 
  
  
    Hi! I have a model that is too large to fit inside a single TITAN X (even with 1 batch size). I want to split it over several GPUs such that the memory cost is shared between GPUs. That is, place different parts of the same model on different GPUs and train it end-to-end. 
Questions: 

Is this possible in pyTorch? If not, is this possible in Torch?
Would inter-GPU communication (say, for transferring activations to later layers) involve GPU->host->GPU type transfers?