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Continuous batching can increase LLM serving throughput by letting a scheduler add new requests as other requests finish, rather than holding a fixed batch together until its slowest request ends. That keeps more of the available batch capacity doing useful work when prompt and output lengths vary. The gain is not automatic: latency targets, model and hardware, scheduler limits, and memory for active sequences all matter.
What continuous batching changes
Decoder-only language models generate text autoregressively: the model runs repeated iterations to produce successive tokens. In conventional fixed request-level batching, the requests grouped together stay in that batch as generation proceeds. If one request finishes early, its slot may sit idle until the others finish, while newly arriving requests wait for a place.
Continuous batching changes the scheduling boundary. After each model iteration, finished requests can leave and new requests can join the active set. ORCA calls this approach iteration-level scheduling; NVIDIA TensorRT-LLM calls the related mechanism in-flight batching and equates it with continuous or iteration-level batching.
The scheduler still works within constraints such as maximum active sequences and token budgets. The change is when the batch can be re-composed—not a reduction in the computation required for an individual model iteration.
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How that can improve throughput
Throughput is the amount of work completed over time, often measured for LLM serving as generated tokens per second or requests per second. When request lengths differ, a fixed batch can lose useful capacity as short requests finish before long ones. Continuous batching can fill openings sooner, so more requests make progress across successive iterations.
This is a capacity-utilization benefit, not a promise that every request will finish sooner. Admitting more work can increase contention and waiting; an operator has to balance throughput against latency goals. Long prompts and generations also consume resources differently, so the result depends on the workload and on what the service counts as acceptable performance.
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Why KV-cache memory matters
During generation, the server retains attention key/value (KV) state for active sequences. That state consumes GPU memory and can limit how many requests fit in a batch. The PagedAttention paper identifies fragmentation and redundant duplication as sources of KV-cache waste and presents PagedAttention as a way to manage that memory more efficiently.
Scheduling and cache management solve related but different problems: continuous batching decides which requests run together at an iteration, while KV-cache management affects how many active request states fit in memory. NVIDIA’s scheduler documentation describes sequence and token-budget constraints that can also prevent a request from being scheduled. A more flexible scheduler cannot admit unlimited work if memory or configured limits are already binding.
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Continuous batching is one part of a serving system
Serving engines often combine dynamic scheduling with other techniques, including paged KV caches, selective batching, optimized kernels, prefix sharing, chunked prefill, and quantization. For example, vLLM’s feature documentation lists continuous batching alongside several other serving optimizations. A system-level benchmark therefore cannot establish how much improvement came from continuous batching alone unless the comparison isolates that feature.
Two published results illustrate why scope matters:
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| Reported result | What it measures | How to interpret it |
|---|---|---|
| 36.9× throughput at the same latency level | ORCA authors’ 2022 comparison with NVIDIA FasterTransformer on a GPT-3 175B evaluation, as reported in the ORCA paper. | A result for that system, model, baseline, and evaluation—not a general multiplier for enabling continuous batching. |
| 2–4× throughput at the same latency level | The vLLM PagedAttention paper reports this range over compared systems on its evaluated popular LLM workloads. | A result for vLLM’s broader system and PagedAttention-oriented design, not an isolated causal estimate for continuous batching. |
These are experimental results, not deployment guarantees. Request arrival patterns, prompt and output lengths, model architecture and size, GPU and memory configuration, concurrency, batch limits, and the latency measure can all change the outcome.
How to evaluate it for your serving workload
Compare systems or configurations under the same conditions, and measure useful work together with the service quality users receive. vLLM’s engineering overview distinguishes raw throughput from SLO-aware goodput—the workload volume served while meeting service-level objectives. NVIDIA’s scheduler documentation also shows why admission caps can shape actual capacity.
Quick Recap
- Use the same model, hardware, precision, prompt and output length distributions, request arrival pattern, concurrency, and stopping rules.
- Report throughput alongside relevant latency measures: time to first token, inter-token latency, tail latency, or end-to-end latency.
- Record memory use, active-sequence and token limits, how prefill is handled, and which other optimizations are enabled.
- Judge results against the latency objectives that matter for your service, rather than treating maximum tokens per second as the sole measure.
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