Introduction
Continuous Batching for LLM Inference helps AI platforms serve many users efficiently without making every request wait for an entire batch to finish. Unlike traditional static batching, requests can enter and leave the processing batch dynamically as generation progresses. This helps GPUs stay productive, increases throughput, and gives production AI systems better control over response time as traffic grows.
What Is Continuous Batching for LLM Inference?
Continuous Batching for LLM Inference is a serving technique that dynamically groups multiple LLM requests together while they are being processed.
To understand why this matters, consider how an LLM generates responses.
When several users send requests to an AI application, their responses can have very different lengths. One user might ask:
“What is the capital of Japan?”
Another might request:
“Analyse this business report and provide a detailed summary.”
The first response may finish quickly, while the second requires hundreds of generated tokens.
With traditional static batching, requests are grouped together before processing. Depending on the implementation, shorter requests can leave compute capacity underused while the system continues working through longer sequences.
Continuous batching makes this process more dynamic.
When one request finishes, its available processing slot can be used for another waiting request instead of waiting for the entire original group to complete.
Think of it like a restaurant where a table becomes available and the next customer is seated immediately rather than waiting until every table in the restaurant becomes empty.
How Continuous Batching Works
Imagine an AI platform currently processing four requests:
Request A: 50 output tokens
Request B: 300 output tokens
Request C: 120 output tokens
Request D: 500 output tokens
These requests do not finish simultaneously.
When Request A completes, Continuous Batching for LLM Inference allows another waiting request—Request E—to join the active workload while B, C, and D continue generating.
Later, when C finishes, Request F can take its place.
The batch therefore changes continuously.
Instead of thinking about a batch as a fixed group of requests that must begin and finish together, it becomes a changing collection of active sequences.
This is particularly useful for LLM workloads because output lengths are unpredictable. Two users can send prompts of similar size and still receive responses containing very different numbers of tokens.
Why Continuous Batching Improves GPU Utilisation
GPUs are expensive resources, so production AI platforms need to keep them productively occupied.
Serving every request individually can leave substantial opportunities for parallel processing unused. But creating large static batches can introduce another problem: requests may need to wait while the system collects enough work to form a batch.
Continuous Batching for LLM Inference helps balance these competing requirements.
The serving engine can combine active sequences for efficient processing while continuously admitting new work as capacity becomes available.
This can increase throughput, meaning the infrastructure can process more tokens or requests over a given period.
Higher throughput can also improve the economics of AI infrastructure. If the same GPU fleet can serve more requests while meeting latency targets, businesses may avoid scaling hardware as quickly as they otherwise would.
However, continuous batching does not make GPU capacity unlimited. When incoming traffic exceeds available compute, requests can still queue and latency can increase.
A Real-World Business Example
Imagine an e-commerce company operates an AI shopping assistant used by thousands of customers.
Some users ask simple questions such as:
“Do you offer free delivery?”
Others ask more complicated questions such as:
“Compare these three laptops based on performance, battery life, display quality, and price.”
If the platform processes requests inefficiently, short questions may compete with long generations for available resources, and GPUs may not achieve the throughput the company expects.
The engineering team introduces Continuous Batching for LLM Inference.
Now, when short requests finish, new customer queries can be admitted into the active workload without waiting for every longer response to complete.
During busy shopping periods, this allows the serving infrastructure to handle more concurrent workloads efficiently.
Customers still have different response times depending on prompt length, generated output, model size, and server load, but the underlying infrastructure uses available compute more effectively.
Throughput vs Response Time
One important challenge with Continuous Batching for LLM Inference is balancing throughput and latency.
Increasing batch size can improve hardware utilisation because the GPU processes more work together. But aggressively packing requests into batches can also increase latency if individual requests receive compute less frequently or wait longer for admission.
Production platforms therefore monitor metrics such as time to first token (TTFT) and inter-token latency, sometimes called time per output token.
Time to first token measures how long users wait before the response begins.
Inter-token latency measures how quickly subsequent tokens are generated once the response has started.
For a conversational AI application, both matter. A system that handles enormous throughput but makes every user wait several seconds before anything appears may still provide a poor experience.
The goal is not simply the largest possible batch. It is to achieve strong throughput while staying within the application’s latency targets.
Continuous Batching Is Only Part of LLM Optimization
Continuous Batching for LLM Inference becomes even more effective when combined with other serving optimizations.
KV Cache Optimization can reduce redundant computation and improve memory management during generation. Quantization can reduce model memory requirements. Prefix caching can avoid repeatedly processing identical prompt prefixes, while model routing can send simple requests to smaller models and complex requests to more capable ones.
Together, these techniques address different bottlenecks.
Continuous batching focuses primarily on how concurrent requests are scheduled and processed efficiently.
This is important because improving an LLM itself is only one part of building a fast AI application. The serving infrastructure around the model can have a major impact on latency, throughput, and cost.
Conclusion
Continuous Batching for LLM Inference gives production AI platforms a more efficient way to serve users whose requests have different prompt and response lengths.
Instead of treating each batch as a fixed group, the serving system can continuously replace completed requests with new ones. This helps improve GPU utilisation and throughput while allowing engineering teams to manage latency targets.
As AI applications grow from prototypes to systems serving thousands of simultaneous users, efficient request scheduling becomes just as important as choosing the right model.
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