Continuous batching is an LLM serving technique that dynamically groups incoming requests into batches over time—often at token-generation boundaries—so a GPU stays highly utilized even when requests arrive asynchronously and have different prompt/output lengths.
What is Continuous Batching?
Traditional batching assumes you gather a fixed set of requests, pad them to a common shape, run inference, then return results. That works well for offline workloads, but it breaks down for real-time chat and agent systems because requests arrive continuously, sequences have variable lengths, and users need token streaming.
Continuous batching treats inference as an ongoing scheduling problem. A serving runtime keeps a queue of active sequences and, at each decoding step, forms a micro-batch of sequences that are ready for the next token. New sequences can be admitted between steps without stopping the whole batch. Many systems schedule prompt processing (prefill) and decoding separately and enforce limits on batch size, total tokens, and KV-cache memory.
Where it’s used and why it matters
Continuous batching is widely used in production LLM inference stacks (chatbots, copilots, agent runtimes) to improve throughput and lower cost per token. It increases GPU utilization under bursty traffic, reduces tail latency by avoiding large batch waits, and supports streaming while still batching decode steps. It pairs well with paged attention, speculative decoding, and prompt/prefix caching.
Examples of Continuous Batching in Practice
- Chat assistant: batch next-token work across many concurrent streamed sessions.
- Agent platform: amortize GPU overhead across many short tool-using calls.
- Mixed workloads: schedule prefill and decode queues separately.
FAQs
How is continuous batching different from normal batching? It forms batches repeatedly during decoding and admits new requests over time, rather than forming one fixed batch upfront.
Does continuous batching reduce latency? It often reduces tail latency, but settings that maximize utilization can still increase latency if they delay admission.
What are common challenges? KV-cache memory management, avoiding starvation, and balancing prefill vs. decode work.
How do I get hands-on experience with this? Implement separate prefill/decode queues, stream tokens, and measure utilization and queueing under synthetic load.
