Abstract: The technology of this application relates to a neural network model training method, an image processing method, and an apparatus in the artificial intelligence. The training method includes each of at least one first accelerator training a neural network model based on at least one training sample. Before forward computation at an ith layer is performed, different parameters of the ith layer are obtained locally and from another accelerator, to obtain a complete model parameter of the ith layer. According to the method in this application, storage pressure of the first accelerator can be reduced.