Continuous batching is an LLM inference scheduling technique that updates an active batch as requests generate text, adding new work when completed requests free capacity. Unlike a fixed batch, it schedules at token-generation boundaries, which helps serve mixed-length prompts and responses more efficiently. It requires careful queueing and memory management so higher throughput does not create unacceptable response delays.
Continuous Batching
What Is Continuous Batching?
Continuous batching is an LLM inference scheduling technique that updates an active batch as requests generate text, adding new work when completed requests free capacity. Also called in-flight batching or iteration-level scheduling, it helps a model server handle requests with different response lengths.
A batch is a group of requests processed together. A request contains a user prompt. Prefill reads the prompt, while decode generates output one token at a time. A token is a small unit of text, such as part of a word or punctuation.
How Continuous Batching Works in LLM Inference
The server reassesses capacity after each generation iteration.
- It accepts a prompt and places the request in a queue.
- It performs prefill for admitted prompts, within compute and memory limits.
- It generates one token for each active request.
- It removes completed requests or those that meet a stop condition.
- It admits queued requests if token and memory budgets allow.
- It repeats at the next iteration boundary.
Continuous Batching vs. Static and Dynamic Batching
The key difference is when the server can change batch membership.
| Method | Membership changes | Best fit |
|---|---|---|
| Static batching | After the full batch finishes | Predictable, similar-length work |
| Dynamic batching | Before execution begins | Requests arriving close together |
| Continuous batching | At generation iterations | Concurrent, uneven LLM requests |
Why Continuous Batching Improves LLM Serving
It reduces unused capacity when short responses finish before long ones.
- Improves throughput for mixed output lengths.
- Lets waiting requests use released slots.
- Can reduce queue delays during concurrent traffic.
- Uses expensive accelerator capacity more effectively.
Results depend on the model, hardware, traffic pattern, and serving configuration.
Practical Limits and Scheduling Tradeoffs
High accelerator utilization does not guarantee the fastest experience for every user.
- Each active request consumes KV cache memory, which stores generation context.
- Long prompts can compete with decoding for compute time.
- Fairness rules help prevent older or longer requests from being delayed indefinitely.
- Priority, cancellation, and admission controls affect tail latency.
A Simple Continuous Batching Example
Imagine three chat requests generating 20, 200, and 800 tokens. In a fixed batch, capacity assigned to the 20-token response may sit unused until the 800-token response ends. With continuous batching, the server releases that slot and admits a waiting fourth request at the next iteration.
When to Use Continuous Batching
It is most useful when demand and response lengths vary.
- Multi-user chat and AI assistants.
- Code generation and agent workflows.
- Shared LLM APIs with concurrent callers.
- Less suitable for low traffic, predictable responses, or strict workload isolation.
Continuous Batching for AI Applications Built with Emergent
Serving design becomes important when concurrent users reach an LLM endpoint. Teams planning app design and development should consider queueing, model-provider limits, and response latency. For products created with AI agent builders, continuous batching is useful when self-hosted or managed infrastructure must serve many requests efficiently.
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