Batch Size

Batch size is the number of training examples a model processes before updating its parameters. A small batch uses less memory and produces noisier gradient estimates, which can sometimes help a model generalize, but it may take longer to use hardware efficiently. A large batch offers steadier updates and better parallel processing while requiring more memory and often careful learning-rate adjustment. The best value depends on the model, dataset, optimizer, hardware, and training objective. Batch size also affects how frequently parameters change during an epoch, so comparisons should account for the total number of examples processed rather than treating the setting as an isolated performance switch.