Continuous Batching
Rebuilding the inference batch after every token so a finished sequence leaves and a waiting request joins immediately, instead of every request waiting for the slowest one. It is what keeps a serving GPU busy.
What is Continuous Batching?
Rebuilding the inference batch after every token so a finished sequence leaves and a waiting request joins immediately, instead of every request waiting for the slowest one. It is what keeps a serving GPU busy.
Continuous Batching is a advanced concept that sits in the LLM and GenAI Ops area of system design. Engineers reach for it whenever they need to reason about real-world trade-offs in that space, not just for textbook correctness, but because real production systems at companies like Netflix, Amazon, and Google make these decisions every day.
If you want to go deeper than this definition, with diagrams, code, and a quiz to lock it in, work through the "LLM Inference Optimization" lesson linked below. It walks through the why, the mechanism, the trade-offs, and how the giants actually use it in production.
Learn Continuous Batching in depth
Full interactive lesson with diagrams, code examples, real-world references, and a quiz.
Open the LLM Inference Optimization lessonSee also
Related glossary terms you might want to look up next.
KV Cache
The stored keys and values of every token processed so far, kept so attention does not recompute the whole history each step. It grows with every token and is what limits how many conversations a GPU can serve.
Inference Optimization
The engineering that serves more tokens per GPU-second: KV caching, continuous batching, quantization, prefix caching, and speculative decoding. Serving cost is dominated by how well these are done.
RAG (Retrieval-Augmented Generation)
A pattern that retrieves relevant text from an external index and puts it in the model's prompt so the answer is grounded in current, verifiable facts rather than the model's memory.
Embedding
A list of numbers that represents the meaning of a piece of text, so that similar meanings sit close together in vector space. The same model must embed both the corpus and the query.
ANN (Approximate Nearest Neighbor)
Search that finds vectors close to a query without scanning every item, trading a small chance of missing a true neighbor for a large gain in speed. HNSW and IVF are the common indexes.
HNSW
Hierarchical Navigable Small World, a graph-based nearest neighbor index that gives high recall at low latency by navigating layers of links, at the cost of more memory than IVF.