Data parallelism enables simultaneous training on multiple data examples by replicating model parameters across devices and processing distinct batches in parallel, creating a strong framework for distributing the computational load of deep learning. Each device computes local gradients based on its assigned batch of data, utilizing a copy of the model that remains identical to the global state at the start of the training step. These local gradients serve as estimates of the