Serialization can be used for the following:

checkpointing: saving the current weights of the model in the middle of training so that if your current training process gets terminated unexpectedly you don’t have to retrain from the start
loading a trained model for inference: if you have trained a model, you’ll probably want to save the weights so that the model can be used in the future. torch.save and torch.load give a way to do that.