Example Concept: Batching in AI inference involves grouping multiple input requests into a single batch, which is then processed by the model in one go. This reduces the overhead of handling each request individually and leverages parallel processing capabilities of modern hardware, such as GPUs. By optimizing the batch size based on the model and hardware specifications, you can achieve a balance between speed and resource utilization.