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Continuous batching can increase LLM serving throughput by letting new requests join as soon as finished requests leave, rather than making every request wait for the slowest member of a fixed batch. The scheduler changes the active batch at generation-iteration boundaries. That can keep more of the available batch capacity busy, but it does not make each model iteration inherently cheaper, and the gain depends on workload, latency targets, and memory limits.
What continuous batching changes
Decoder-only LLMs generate text autoregressively: they run repeated model iterations to produce successive tokens. In conventional fixed request-level batching, the same requests remain grouped together as they progress through those iterations. A request that finishes early can leave a slot idle until the rest of the batch completes, while newly arriving requests wait for an opening.
Continuous batching instead reschedules the active request set between iterations. The scheduler runs one model iteration on the current batch, then can remove completed requests and admit eligible new ones before the next iteration. ORCA’s OSDI 2022 paper calls this “iteration-level scheduling.” NVIDIA TensorRT-LLM uses “in-flight batching” and describes it as continuous, or iteration-level, batching.
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The throughput benefit comes from keeping available batch capacity occupied more of the time. If a request finishes, its work no longer needs to occupy the active set; another eligible request can take its place without waiting for all the requests that began alongside it. Across many requests with different output lengths and arrival times, this can reduce the idle capacity created by fixed batches.
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This is a change in scheduling granularity, not a shortcut that reduces the computation for an individual forward pass. The scheduler still has to obey limits such as maximum active sequences and token budgets. Long prompts and long generations also use resources differently, so admitting more work is constrained by the service’s latency goals and memory availability.
Why KV-cache memory still matters
During autoregressive generation, the server retains attention key/value state for active sequences. That state consumes GPU memory and can limit how many requests run concurrently. The PagedAttention paper identifies fragmentation and redundant duplication as sources of KV-cache waste that can constrain batch size.
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Scheduling and KV-cache management solve related but different problems: continuous batching decides which requests execute together at an iteration, while cache management affects how many concurrent request states fit in memory. Better cache use can therefore complement continuous batching, but neither removes the need to respect memory and scheduler limits. NVIDIA’s TensorRT-LLM documentation, for example, describes batch-size and token-budget constraints that can prevent an otherwise feasible request from being scheduled.
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What published throughput figures do—and do not—show
| Reported result | Scope | What it does not establish |
|---|---|---|
| 36.9× throughput at the same latency level | ORCA authors’ 2022 result comparing ORCA with NVIDIA FasterTransformer in an evaluation using GPT-3 175B. | It is not a generic estimate of the gain from enabling continuous batching in another model, serving engine, hardware configuration, or workload. |
| 2–4× throughput at the same latency level | The PagedAttention paper’s reported result for vLLM over the systems it compared on evaluated popular LLM workloads. | It reflects vLLM’s system and PagedAttention-oriented design, including multiple system choices; it does not isolate continuous batching as the sole cause. |
Both figures are experimental findings tied to their systems and evaluation setups, not deployment promises. Results can shift with request arrival patterns, prompt and output lengths, model architecture and size, GPU and memory configuration, concurrency, batch limits, and the latency metric used.
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How to evaluate it for a serving workload
A useful comparison measures whether a system can serve the workload at its required latency, rather than looking at maximum tokens per second alone. Goodput—the workload volume that meets a service-level objective (SLO)—can be more useful than raw throughput when requests must meet latency commitments.
For a meaningful comparison, hold the model, hardware, precision, request arrival pattern, prompt and output lengths, concurrency, and stopping rules constant. Report throughput alongside relevant latency measures, such as time to first token, inter-token latency, tail latency, or end-to-end latency. Also record memory use, active-sequence and token limits, prefill handling, and whether other optimizations are enabled. These details help distinguish scheduling effects from the broader serving stack.
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Continuous batching is one part of a serving engine
Modern inference engines may combine continuous batching with PagedAttention or other paged KV-cache techniques, selective batching, optimized kernels, prefix sharing, chunked prefill, and quantization. vLLM’s documentation lists continuous batching alongside other serving optimizations; a system-level benchmark that uses several of them should not be credited to batching alone without a controlled comparison.
For implementation details, see ORCA’s OSDI 2022 paper, the PagedAttention paper, NVIDIA TensorRT-LLM’s in-flight batching documentation, vLLM’s documentation, and vLLM’s 2025 engineering overview.
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