Batching is the practice of grouping multiple inference requests into a single GPU run instead of processing each request one by one. By sharing the same model execution across many inputs, an AI system can raise throughput, improve GPU utilization, and lower the cost per request. The trade-off is usually higher latency for individual requests, since the system waits briefly to assemble a batch.
In cyber security, batching matters because many defenses rely on AI services for classification, extraction, summarization, and alert triage. A detector that batches file scans or event analysis can handle more traffic with the same hardware footprint. Attackers also benefit from batching when they abuse AI APIs at scale, sending many prompts or queries together to reduce overhead and increase volume. Security teams therefore watch both performance and abuse patterns: good batching improves efficiency, but poorly controlled batching can hide bursty load, raise queue times, or expose an inference service to resource exhaustion.