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Continuous batching lets an LLM serving system change which requests it processes at each generation step. When one request finishes, the scheduler can remove it and admit waiting work without making the remaining requests wait for the whole batch to finish. That can keep GPU work better utilized, especially when requests produce outputs of different lengths, but the result depends on workload, memory, and scheduling policy.
What continuous batching means
An autoregressive language model generates a response one token at a time. A serving system groups requests so the model can perform work for multiple sequences together. In a fixed, or static, batch, membership is generally held constant while the batch runs. If one sequence finishes early, its position may sit idle until the longest-running sequence is done.
Continuous batching changes membership at generation-iteration boundaries. After a step, the scheduler can retire completed requests, retain unfinished ones, and admit queued requests if resources allow. The next model iteration then runs on the newly formed group. NVIDIA documentation also calls this approach iteration-level or in-flight batching.
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Think of it as a moving queue of active sequences rather than a set of requests that must all start and finish together. The system repeatedly checks what is still running, what has completed, what is waiting, and what can fit within its resource budgets. It then runs the next model work and updates each active request’s state. This describes the general scheduling idea; individual engines differ in their internal implementation and admission rules.
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Fixed batching versus continuous batching
| Behavior | Fixed or static batching | Continuous batching |
|---|---|---|
| Membership | Typically remains fixed until the batch completes. | Can be reconsidered at each generation iteration. |
| When a request finishes | Its position may remain unproductive while other requests continue. | The completed request can be removed; queued work may be admitted for a later iteration. |
| Effect of different output lengths | Short requests can finish well before the longest request. | Short requests can leave without holding their positions until the longest finishes. |
| Admission | Usually occurs when a batch is formed, subject to the system’s rules. | Can occur during ongoing service, but only when policy and available resources permit. |
Continuous batching does not mean a new request joins in the middle of an already-running GPU operation. Nor does it mean every waiting request is admitted immediately. The scheduler’s next iteration is an opportunity to change membership, not a guarantee that capacity is available.
What happens during a scheduling iteration
A typical conceptual loop has four parts: determine which work is eligible, assemble the next iteration, execute model computation, and update request state. The precise sequence and control mechanisms vary by engine, but the loop explains why completed requests can be replaced without waiting for the entire original group.
- Review active and waiting work. The scheduler accounts for requests that are still generating, requests that have completed, and requests in the queue. It also considers each request’s current prompt or generated-token state.
- Check available budgets. The system evaluates whether candidate work fits its token budget and whether the required KV-cache memory is available. Admission can be delayed or limited if either resource is constrained.
- Form the next set of work. The scheduler retains eligible active sequences, removes completed ones, and may add waiting requests or prompt-processing work according to its policy.
- Run the model and update state. Model computation advances sequences; the serving system records newly generated tokens and updates the corresponding cache state before scheduling again.
This iteration-level view is useful because “batch size” alone does not describe the work being done. A batch may include sequences at different stages, and the scheduler can change the combination from one iteration to the next.
Why prefill and decode have different roles
Serving an LLM request involves two kinds of work. Prefill, also called context processing, processes the prompt tokens so the model can begin generating. Decode produces the response, generally advancing each active sequence by a small amount per generation step. A long prompt can therefore create a different shape of workload from ongoing token-by-token generation.
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Some serving implementations can batch sequences in the context phase together with sequences in the generation phase. NVIDIA TensorRT-LLM documents this kind of mixing for its in-flight batching implementation. Its documented implementation also has packed-input tensor ordering requirements; those details should not be assumed to apply to other serving engines.
Chunked prefill
Without chunking, a long prompt can require substantial processing before it becomes an active generation request. Chunked prefill divides prompt processing into portions, allowing a scheduler to fit some prompt tokens into the current token budget while continuing decode work for requests already in progress. If the prompt is longer than the available budget, the scheduler can process what fits and hold the remainder for a later step. Hugging Face’s Transformers architecture documentation describes this budget-based behavior.
The practical trade-off is between serving prompt work and preserving capacity for ongoing generation. A token budget that allows more prefill work can affect prefill throughput and, in a prefill-bound workload, time to first token. The right setting depends on the request mix and latency target; there is no single budget that is optimal for every model, GPU, or traffic pattern.
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How the KV cache affects scheduling
During attention computation, the model produces key and value data for tokens it has already processed. The KV cache retains that data so the model does not have to recompute it from the beginning at every output step. This cache consumes GPU memory and grows with the tokens and sequences being served, so it constrains how much work can be active at once.
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A scheduler may have to defer new work when cache capacity is pressured. In the documented Hugging Face behavior, a soft reset can return an active request to the queue after incorporating its generated tokens into its prompt and freeing its cache blocks; the request can resume when space becomes available. That is one framework’s documented behavior, not a universal rule for continuous batching. Other systems may handle pressure differently.
What continuous batching can improve—and what it cannot promise
The clearest potential gain is avoiding idle batch positions when requests finish at different times. In a fixed batch, short outputs can leave unused positions while longer outputs continue. With continuous batching, the scheduler can reuse capacity at a later iteration, which may improve throughput and GPU utilization for workloads with variable output lengths. NVIDIA describes improved throughput and latency as goals of continuous slot reuse, and Hugging Face describes dynamic rescheduling as a way to keep utilization and throughput high.
Those are goals, not workload-independent guarantees. A result depends on request arrival rate, concurrent load, prompt and output length distributions, scheduler policy, GPU memory, model and kernel support, and the service’s latency objective. A system can admit work more efficiently and still miss a latency target if requests queue, prefill work dominates, or the hardware is saturated.
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- Arrival rate and concurrency: A system with little queued work has fewer opportunities to replace completed requests. Under heavier load, admission and queueing policy become more consequential.
- Prompt and output lengths: Uneven lengths create different scheduling pressures. Long prompts can compete for token budget with ongoing decode; long generations can occupy cache and execution capacity for longer.
- Memory and model support: Cache capacity, GPU memory, kernels, and backend capabilities limit feasible concurrency. Continuous batching cannot schedule work the model-serving stack cannot support.
- Latency objective: A policy that favors high aggregate throughput may not be the best choice for a service that prioritizes prompt responsiveness or steady token delivery.
Some implementation options also have costs. Hugging Face documents that CUDA graphs can reduce dispatch overhead but require handling shapes and storing graphs. Its documentation also describes asynchronous batching as overlapping CPU and GPU work, with input buffers requiring roughly double the VRAM. These are configuration considerations, not universal fixed costs: actual behavior depends on version, hardware, and setup.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.How to compare serving systems fairly
Do not treat tokens per second as a complete measure of user experience. Throughput measures work served over time, while latency measures how long a request or token takes. For interactive generation, evaluate at least time to first token and inter-token latency alongside throughput and concurrency. Aggregate throughput can rise while individual requests become less responsive, so report the measures that reflect the service’s actual objective.
For a meaningful comparison, hold the workload and serving conditions constant. Use the same model, hardware, request-length distribution, sampling settings, and load conditions, and describe the version and configuration tested. A benchmark number without those details cannot establish how another deployment will perform.
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| Measure or condition | What it helps reveal |
|---|---|
| Time to first token | How quickly a request begins producing output, including effects from queueing and prompt processing. |
| Inter-token latency | How steadily output tokens arrive after generation starts. |
| Throughput at target concurrency | How much work the system serves under the concurrency expected in production. |
| KV-cache and total GPU-memory use | How memory pressure constrains admission and active request capacity. |
| Prompt and output length distribution | Whether the test resembles the service’s real mix, including opportunities for chunked prefill. |
| Queueing and overload behavior | How the system handles demand that exceeds available token, memory, or execution capacity. |
| Model, hardware, and backend compatibility | Whether the compared configuration is supported and representative of the intended deployment. |
There is no single speedup figure that can be applied to all continuous-batching deployments. The reviewed framework documentation does not establish a general performance gain for a representative workload. Treat any reported benchmark as specific to its stated model, hardware, request mix, concurrency, settings, and measurement method.
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How implementations differ
Continuous batching describes a broad scheduling behavior, not one uniform API or internal design. The current online documentation for each project is the authority for its available controls; configuration options and support can change between releases.
- vLLM: Its stable documentation lists continuous batching, chunked prefill, prefix caching, and PagedAttention-based KV-memory management. These features address related serving and memory concerns, but should not be collapsed into a single feature.
- Hugging Face Transformers: Its documentation covers
ContinuousBatchingManager, generation APIs, token and cache budgets, scheduling policies, and configuration options. The current page says that placingcontinuous_batching_configonGenerationConfigis deprecated and documents removal in v5.19. Avoid copying that legacy configuration pattern into new code. - NVIDIA TensorRT-LLM: Its documentation uses the term in-flight batching, also describing it as continuous or iteration-level batching. It explains mixing context and generation sequences and documents packed-input ordering constraints specific to that implementation.
- NVIDIA Triton Inference Server: Triton distinguishes stateless dynamic batching from stateful sequence batching. Its iterative-sequence feature can let a backend yield and reschedule work across iterations, but Triton labels the feature provisional and says it is likely to change.
These systems should not be assumed to be equivalent because they use related terminology. Admission rules, token and cache accounting, API surfaces, model support, and overload handling can differ. In particular, a provisional feature is a less stable basis for production decisions than a versioned, supported capability.
What to check before deploying it
Start with the workload rather than a presumed benefit. Establish the typical and tail prompt and output lengths, expected concurrency, and the latency target. Then confirm the serving engine and model support the required batching behavior, cache strategy, and hardware backend.
- Choose a representative traffic mix. Include variation in prompt and generated-output lengths rather than testing only uniform requests.
- Set the service objective. Decide which time-to-first-token, inter-token latency, throughput, and concurrency measures matter, and record the load conditions for each run.
- Inspect token and cache constraints. Understand the scheduler’s token budget and how it accounts for KV-cache capacity; observe queueing and memory pressure under load.
- Validate configuration against the exact release. Check current versioned framework documentation for APIs, supported models, backend requirements, deprecated settings, and provisional features.
- Test overload and recovery behavior. Verify what happens when requests cannot be admitted immediately or cache resources become scarce, including whether requests queue, pause, or are handled by an engine-specific recovery mechanism.
The right decision is not simply whether an engine supports continuous batching. It is whether its scheduling policy and memory behavior meet the service’s needs for the specific model, hardware, request mix, and latency target.
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