BatchGröße

BatchGröße, German for “batch size”, denotes the number of units processed together in a single operation or iteration. In manufacturing, it refers to the quantity of items produced in one production run, influencing setup costs, inventory levels, and throughput. Smaller batch sizes can reduce work‑in‑process inventory and increase flexibility, while larger batches often lower per‑unit setup expenses through economies of scale.

In the context of computer science, particularly in data processing and machine‑learning training, batch size specifies how many data samples are fed to an algorithm before the model parameters are updated. A small batch size can provide more frequent updates, potentially leading to faster convergence and better generalisation, but may increase computational overhead due to less efficient hardware utilisation. Conversely, larger batches exploit parallelism on GPUs or TPUs, yielding higher throughput, yet they may require careful tuning of learning‑rate schedules to avoid poorer convergence or overfitting.

Statistical batch processing, such as in survey sampling or quality control, also uses batch size to define the number of observations examined at once, affecting the precision of estimates and the speed of decision‑making. In all domains, selecting an appropriate BatchGröße involves trade‑offs among resource constraints, desired responsiveness, variance reduction, and cost considerations. Standard practice recommends empirical testing or analytical modelling to identify the batch size that balances efficiency with performance for the specific application.