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1Clear out junk files and repair common Windows errors2Scan for outdated or missing drivers - takes under a minute3Repair Windows errors before they cause bigger problemsFor interactive LLM serving, continuous batching is usually the better default. It admits and removes sequences at token or iteration boundaries, so a request that finishes does not leave an empty slot while a longer request continues. Static batching is still valuable for predictable offline jobs with similar sequence lengths, where fixed batch boundaries make orchestration simple and repeatable.
The choice affects more than headline throughput. It changes time to first token (TTFT), inter-token latency, tail latency, GPU utilization, KV-cache pressure, admission control, and the complexity of the serving scheduler. The right comparison uses your prompt and output-length distribution, arrival pattern, latency target, and available GPU memory—not a single tokens-per-second number.
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What static batching and continuous batching actually do
Static batching: one batch boundary, one completion point
Static batching collects a fixed group of requests, aligns their tensors—usually by padding to a common length—and runs that batch through the model. In autoregressive generation, every sequence normally advances through decode iterations together. A short response may finish early, but its slot remains part of the batch until the longest response is complete.
This design has an intentionally simple contract: form a batch, execute it, return the results, then form the next batch. It is easy to reason about, easy to reproduce, and often efficient when requests have nearly identical prompt and output lengths. Its weakness appears when lengths vary or arrivals are continuous. Finished sequences cannot be replaced until the whole batch ends, so compute capacity and memory remain tied to the slowest member.
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Continuous batching: an active set that changes every iteration
Continuous batching is also called iteration-level batching or in-flight batching. A scheduler evaluates the active requests at generation iterations or token steps. When a sequence finishes, the scheduler removes it; when capacity is available, it admits a waiting request. The active set therefore changes while serving is in progress instead of only between complete batch jobs.
Prefill (processing a prompt) and decode (generating one token at a time) can be interleaved according to scheduler policy. NVIDIA describes TensorRT-LLM’s equivalent feature this way: “TensorRT LLM supports in-flight batching of requests (also known as continuous batching or iteration-level batching) for higher serving throughput.” Its documentation says context-phase and generation-phase sequences can be processed together “to reduce latency as well as make better use of the GPUs.”
| Aspect | Static batching | Continuous or in-flight batching |
|---|---|---|
| When requests enter | Before a batch starts | At scheduler-controlled iteration or token boundaries |
| When completed requests leave | After the complete batch finishes | As soon as each sequence completes |
| Length variability | Padding and idle slots become costly | Finished slots can be reused by waiting work |
| Batch boundary | Fixed and predictable | Moving; determined by capacity and policy |
| Best fit | Offline or tightly controlled, homogeneous jobs | Online APIs, chat, agents, and variable arrivals |
| Scheduler complexity | Lower | Higher, including admission and KV-cache decisions |
Why autoregressive generation exposes the difference
During decode, a model produces a token, updates its key-value (KV) cache, and repeats the process. Requests rarely have identical prompt lengths, maximum output limits, or stopping points. In a static batch, all rows continue to be scheduled around the longest unfinished sequence. If three requests stop at 40, 70, and 300 generated tokens, the first two can occupy resources or padded positions while the third continues.
Continuous batching treats each decode iteration as an opportunity to rebalance the work. The 40-token request can leave, and another queued request can enter if it fits the available token and memory budget. This does not make any individual matrix multiplication free, nor does it eliminate the cost of long contexts. It reduces the idle capacity caused by keeping completed or nearly completed sequences attached to a fixed batch.
The benefit is largest when arrivals are online and lengths are heterogeneous. With a steady stream of chat requests, the scheduler can keep the GPU supplied with useful decode work instead of waiting for a long request to finish. With a carefully constructed offline set in which every sequence is close in length, static batching may have little idle waste and can be competitive.
Throughput, latency, and utilization trade-offs
“Throughput” can mean requests per second, generated tokens per second, or total tokens per second including prompt processing. These measures can move differently. A scheduler that increases token throughput by admitting more work may raise queueing delay or TTFT. Report each metric separately.
| Metric | Static batching behavior | Continuous batching behavior | What to measure |
|---|---|---|---|
| Generated-token throughput | Falls when short sequences wait behind long ones | Usually improves under variable lengths because slots are reused | Decode tokens per second at a stated latency target |
| Request throughput | Bound to whole-batch completion | Can rise as completed slots are refilled | Completed requests per second and queue depth |
| Time to first token (TTFT) | Can include a wait for the next batch to form | Depends on admission policy and contention between prefill and decode | Median, p95, and p99 TTFT |
| Inter-token latency | Regular within a batch, but tied to the slowest active work | Can improve utilization but vary with arrivals and scheduling | Median and tail time between streamed tokens |
| Tail latency | Long requests can stretch the entire batch | More control is possible, but an overloaded scheduler can still create long tails | p95 and p99 by request length class |
| GPU utilization | Idle slots appear when lengths diverge | Often higher because work is replenished continuously | Utilization together with achieved tokens per second |
| KV-cache pressure | Predictable for a fixed batch, but held slots can waste capacity | More active sequences can be packed; fragmentation and admission decisions matter | Allocated blocks, free headroom, evictions or rejections |
| Admission and backpressure | Usually implemented between batches | Required at iteration boundaries, often using token and memory budgets | Queue wait, rejection rate, and fairness |
| Determinism | Fixed membership and order are easier to reproduce | Arrival timing and scheduling can change membership and completion order | Repeatability under identical seeds and load |
A published 2023 vLLM evaluation reported a 2–4× throughput improvement over the compared systems at the same latency level. That result describes the evaluated vLLM design, which included PagedAttention and other serving optimizations; it is not a batching-only multiplier and should not be generalized to every model, GPU, or workload.
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How continuous batching manages memory
KV cache is the practical capacity limit
Each active sequence keeps key and value tensors for its context and generated tokens. As the sequence grows, its KV cache consumes more GPU memory. A continuous scheduler therefore cannot admit requests merely because a compute slot is empty. It must account for current cache allocation, expected growth, maximum sequence length, and a safety margin for model weights, activations, and runtime workspace.
The sustainable active set is constrained by a combination of:
- Maximum context plus generated-token length for each request.
- Maximum active tokens or sequences configured by the serving engine.
- GPU memory available after weights and execution buffers are reserved.
- Allocation granularity and fragmentation in the KV-cache manager.
- Arrival rate and the rate at which completed sequences free cache blocks.
Paged or block-based cache allocation
Continuous batching commonly pairs with paged or block-based KV-cache management. Instead of requiring one large contiguous cache region per sequence, the runtime allocates fixed-size blocks and maps them to logical token positions. vLLM combines continuous batching with PagedAttention and other inference optimizations. Block allocation can make it easier to admit different sequence lengths, but it does not create extra memory: a workload that exceeds the available blocks still waits, is rejected, or must be limited by policy.
Packed inputs in TensorRT-LLM
TensorRT-LLM documents packed-tensor requirements for in-flight batching. During decode, many sequences contribute only one new token, while their total historical lengths differ. Padding every one-token input to a long maximum sequence wastes work. Packed representations avoid that padding, but they impose stricter input-shape and phase-ordering requirements in the current implementation. Integrations must follow the engine’s documented packing format rather than treating in-flight batching as a switch that works with any existing tensor layout.
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A continuous-batching request lifecycle
The exact knobs differ by serving engine, but the control flow is consistent:
- Arrival: A request enters a queue with its prompt, decoding limits, priority, and any deadline or cancellation state.
- Admission check: The scheduler estimates prompt and decode-token demand against available compute capacity, KV-cache blocks, maximum active tokens, and configured fairness or priority rules.
- Prefill: The prompt is processed and its initial KV cache is created. A large prefill can compete with decode work, so the scheduler may chunk or sequence prefills to protect streaming latency.
- Decode iteration: Active sequences produce their next token. The scheduler may combine context-phase and generation-phase sequences in the same iteration when the engine supports it.
- Completion or cancellation: A stop token, length limit, client cancellation, or error removes the sequence and releases its cache blocks.
- Refill: Waiting requests are considered for the newly available capacity. The next active set is formed without waiting for unrelated sequences to finish.
- Backpressure: If no safe capacity exists, requests remain queued, are rejected, or receive a bounded overload response according to policy.
This loop repeats until the queue and active set are empty. A production implementation must also handle disconnects, timeouts, priority changes, and failed generations without leaking cache allocations.
How a static-batching job runs
- Collect: Gather a predetermined number of requests or wait for a fixed collection window.
- Shape: Pad or otherwise align prompts and generation tensors to the batch’s required dimensions.
- Execute: Run prefill and decode for the batch under a common schedule.
- Hold: Keep the batch structure until all sequences finish, even if some outputs stop early.
- Return: Emit results and form the next batch.
Static execution can be attractive when the input set is known in advance, output limits are uniform, and the objective is aggregate completion rather than a responsive stream. Fixed boundaries also simplify accounting, reproducibility, and failure handling: a job can be retried as a unit. The cost is less flexibility when requests arrive continuously or have widely different lengths.
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Which strategy fits your workload?
Interactive chat and completion APIs
Choose continuous batching when users expect streaming output and arrivals are unpredictable. It allows completed conversations to leave quickly and gives the service a way to absorb bursts without waiting for a long batch to drain. Set explicit maximum output and context limits; otherwise a few very long requests can consume the cache and increase everyone else’s queue time.
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Agents generate multiple short and medium requests with variable pauses, prompt sizes, and cancellation behavior. Iteration-level admission generally handles this pattern better than a fixed batch, especially when requests are cancelled after a tool result or user intervention. Priority and fairness rules matter because one agent may issue a burst while others are waiting.
Offline scoring, evaluation, and bulk generation
Static batching remains a sensible choice for a finite corpus with known shapes. If prompts and output limits are closely matched, padding waste is small and fixed boundaries reduce orchestration complexity. Continuous batching can still raise utilization, but its scheduler and queueing machinery may not justify the operational complexity for a one-time, fully controlled run.
Latency-sensitive mixed traffic
Use continuous batching with separate controls for prefill-heavy and decode-heavy work when short interactive requests share a GPU with long-context jobs. Without isolation or a prefill budget, a large prompt can delay the first token of many smaller requests. Measure TTFT and inter-token latency independently; a single average latency number can hide this contention.
vLLM and TensorRT-LLM: terminology and practical implications
vLLM
vLLM describes itself as a fast library for LLM inference and serving and lists continuous batching of incoming requests as a core capability. Its implementation combines the scheduler with PagedAttention and related memory-management techniques. When evaluating vLLM, attribute results to the complete serving stack—model implementation, attention kernel, cache manager, batching policy, and hardware—not to continuous batching in isolation.
TensorRT-LLM
TensorRT-LLM uses the term in-flight batching for the same iteration-level family of techniques. Its documentation emphasizes combining context and generation phases and specifies packed inputs and phase-ordering constraints for the current implementation. Validate those requirements against the exact TensorRT-LLM release and model integration you deploy; an engine optimized for static, padded shapes may need a different input path for in-flight execution.
How to benchmark the two strategies fairly
A useful benchmark mirrors production rather than maximizing one favorable batch shape.
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- Define the workload distribution: Record prompt lengths, output lengths, stop behavior, cancellation rate, concurrency, and arrival pattern. Include short, median, and long requests.
- Fix the model and hardware: Keep model weights, precision, GPU type, tensor parallelism, sampling settings, and software versions constant.
- Set comparable service targets: Compare at the same quality settings and latency objective. A throughput result without a TTFT or tail-latency constraint is incomplete.
- Warm up the engine: Exclude one-time compilation and allocation effects, then state how many warm-up requests were used.
- Measure both phases: Report TTFT separately from inter-token latency, plus end-to-end completion time.
- Measure tails: Include p50, p95, and p99 for TTFT and inter-token latency, not only averages.
- Track resource state: Capture GPU utilization, allocated KV-cache blocks, peak memory, queue depth, active sequences, and rejected or delayed admissions.
- Vary load: Increase arrival rate until the service target fails. The useful capacity point is the highest sustainable load before queueing or memory pressure becomes unacceptable.
- Repeat tests: Run multiple trials and report variability. Continuous scheduling is sensitive to arrival timing, so one trace can be misleading.
For static batching, report the batch-formation window and padding policy. For continuous batching, report maximum active tokens, maximum sequence length, admission limits, and whether prefills are chunked or prioritized. These settings materially affect the result.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.Capacity planning and operational controls
Plan around the worst credible active-token demand, not just the average request count. A useful capacity sheet includes:
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- Model and precision, including the memory reserved for weights and runtime workspace.
- Maximum prompt length and maximum generated tokens.
- Expected concurrent sequences and arrival rate at peak.
- Maximum active tokens and cache-block capacity.
- Target TTFT, inter-token latency, and p95 or p99 limits.
- Reserved GPU-memory headroom for allocator fragmentation and transient work.
- Queue limits, timeout behavior, cancellation handling, and overload responses.
Use admission limits to prevent a few long contexts from exhausting the cache. Apply per-tenant quotas or priorities when traffic classes have different latency commitments. A queue that is technically finite but unbounded in time is not backpressure; it simply moves overload into user-visible latency.
Common failure modes and fixes
High TTFT despite high GPU utilization
GPU utilization can be high while new requests wait behind large prefills or an overfilled active set. Inspect queue time separately from execution time. Reduce prefill batch size, reserve capacity for new prefills, cap context length, or isolate long-context traffic.
Low throughput with heterogeneous lengths
In a static design, check how much padding and early-finished capacity are being retained. Grouping requests by approximate length can reduce waste. If arrivals are online, move the workload to an iteration-level scheduler that can refill completed slots.
Out-of-memory errors after enabling continuous batching
Continuous batching can expose more sequences to the GPU at once, increasing KV-cache demand even when utilization looks healthy. Lower maximum active tokens or maximum sequence length, limit concurrency, reserve more memory headroom, and verify that cache blocks are released on cancellation and errors.
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Large prefills, aggressive admission, or a queue policy that favors throughput can interrupt decode regularity. Inspect prefill/decode scheduling, chunk long prompts, reduce the active-token ceiling, and evaluate p95 and p99 rather than only the mean.
Starvation or unfairness
A continuously refilled queue can favor a stream of short requests over older long ones, or a high-priority tenant over everyone else. Add aging, weighted fairness, per-tenant limits, or separate queues. Verify fairness under sustained overload, not only at low concurrency.
Unexpectedly different outputs or timing between runs
Changing request admission order changes completion order and can alter random sampling outcomes unless seeds and scheduling are controlled. For reproducible evaluations, fix arrival traces, sampling seeds, batch policy, and engine settings; for production, prioritize service objectives over bit-for-bit schedule reproducibility.
Can a service use both strategies?
Yes. A common architecture uses continuous batching for the online serving path and static batches for offline work, or assigns separate GPU pools to interactive and bulk traffic. Another option is to use continuous batching within a traffic class while grouping requests by model, adapter, priority, or approximate length. The boundary should be operationally explicit so a bulk job cannot consume the memory and queue budget reserved for interactive users.
Decision checklist
- Use continuous or in-flight batching when arrivals are variable, outputs stream to users, sequence lengths differ, and tail latency or GPU utilization matters.
- Use static batching when the request set is finite, lengths are similar and known, and simple, deterministic orchestration is more valuable than rapid admission.
- Use a hybrid when interactive and offline traffic share infrastructure but have different service objectives.
- Regardless of strategy, set explicit token and memory limits, measure TTFT separately from decode latency, and test at realistic arrival rates.
Frequently Asked Questions
Does continuous batching guarantee lower latency for every request?
No. It removes batch-boundary waiting and can improve utilization, but an overloaded queue, a large prefill, or an aggressive throughput policy can still increase TTFT or tail latency. The result depends on admission limits and scheduling policy.
Is in-flight batching a different algorithm from continuous batching?
The terms generally refer to the same iteration-level approach: requests enter and leave the active set during generation rather than only between complete batches. Individual engines may differ in packing, phase ordering, and scheduler controls.
What is the single most important number to configure?
There is no universal number. Maximum active tokens must be chosen with maximum sequence length, KV-cache capacity, arrival rate, and latency targets together; increasing it can raise throughput until memory pressure or queueing harms the service objective.
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