Sparse gradients refer to the phenomenon where many elements of the gradient are zero, which can occur in large-scale machine learning models, particularly in scenarios involving sparse data or regularization techniques. This sparsity can be exploited to improve computational efficiency and reduce memory usage during model training and inference.