A new Hugging Face blog post takes a from-first-principles look at continuous batching, a serving technique that has become central to running large language models efficiently. The post explains the problem with the traditional static batching approach, where requests are grouped into fixed-size batches and processed together. That method can waste GPU cycles whenever a batch is incomplete or when some requests finish earlier than others.
Continuous batching addresses this by treating each request independently and adding new work to the GPU as soon as a slot opens up. Instead of waiting for a full batch, the scheduler keeps the hardware busy with a steady stream of requests. The post walks through the mechanics of this approach, showing how it improves both throughput and latency for real-world inference workloads.
Because the source is a single explainer, there are no conflicting viewpoints to compare. The article focuses on the conceptual foundation rather than benchmarking specific systems, making it a useful starting point for anyone new to LLM serving infrastructure.