Continuous Batching

Continuous batching is an inference optimization technique for large language models in which the server dynamically assembles batches of requests — adding new incoming requests to an in-progress batch as slots become available — rather than waiting for a fixed batch to complete before processing the next group. Because LLM inference is autoregressive (tokens are generated one at a time), different requests finish at different times; continuous batching reclaims idle compute capacity by filling those slots with new requests immediately, significantly improving GPU utilization and throughput.

For commerce platforms deploying LLMs in real-time customer-facing applications — such as conversational search, AI-generated product descriptions, or customer service chatbots — continuous batching is a critical infrastructure technique. It allows a given number of GPU resources to serve substantially more concurrent users at acceptable latency, directly affecting the economics of AI inference at scale. Without it, naive batching leaves expensive GPU capacity underutilized and drives up per-query cost. As organizations scale AI-powered experiences, understanding and implementing efficient inference serving patterns like continuous batching is essential for maintaining cost targets as usage grows.