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Continuous batching and prefill-decode (P/D) disaggregation solve different problems. Continuous batching is a scheduling method: an inference engine admits new sequences into active work and removes completed ones as generation lengths change. P/D disaggregation is a placement architecture: prompt processing (prefill) runs in one instance or pool, while autoregressive token generation (decode) runs in another. They are not competing switches. A disaggregated deployment can use continuous batching inside both its prefill and decode pools.
Choose continuous batching in a shared serving pool when it already meets your time-to-first-token (TTFT), inter-token latency (ITL), throughput and cost targets. Consider disaggregation when measured traffic is prefill-heavy, long prompts disrupt decode latency, or you need independent tuning and scaling for TTFT and ITL—and when your network, KV-cache transfer path and operations team can support the extra complexity.
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The core difference
The shortest accurate comparison is “how requests are scheduled” versus “where each phase runs.” A scheduler can continuously batch requests whether all phases share one worker pool or phases are split across pools.
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| Dimension | Continuous batching | Prefill-decode disaggregation |
|---|---|---|
| What it is | A run-time scheduling technique that keeps an active batch productive as sequences enter and finish. | A serving architecture that places prefill and decode on separate instances or worker pools. |
| Primary objective | Use accelerator time efficiently despite different request and output lengths. | Give prompt processing and token generation separate resource, parallelism and scaling controls. |
| Latency focus | Improves utilization and can reduce queueing compared with waiting for a fixed batch to finish. | Allows independent TTFT and ITL tuning and can protect decode tail latency from long prefills. |
| Data movement | Usually keeps the request and its KV state within the same serving path. | Must transfer or expose the prefill-produced KV state to a decode worker through a connector. |
| Throughput effect | Often increases useful work by avoiding idle batch slots, but the result depends on the engine and workload. | Does not improve throughput by itself; the vLLM documentation states, “Disaggregated prefill DOES NOT improve throughput.” (vLLM documentation) |
| Operational shape | One pool and one scheduling path, with fewer moving parts. | At least two pools, routing and coordination, KV-transfer handling, rate matching and separate scaling decisions. |
Because they operate on different dimensions, “continuous batching versus disaggregation” is not an either-or benchmark unless you explicitly define the systems being compared. A fair test might compare a colocated engine using continuous batching with a disaggregated design in which both the prefill and decode pools also use continuous batching.
What prefill and decode actually do
Prefill: build the prompt state
During prefill, the model processes the tokens already present in the prompt. It computes attention and produces the key/value (KV) state that later generation steps will reuse. A very long prompt therefore creates a substantial burst of computation and KV data before the first output token can be returned.
Decode: generate one token at a time
During decode, the model generates output autoregressively. Each new token depends on the previous tokens, and the worker reuses the KV state created during prefill. Decode tends to be a sequence of smaller, repeated steps, so delays affecting an active decode stream can appear as increased ITL or as long-tail pauses between tokens.
Why the phases interfere in a shared pool
In a colocated engine, requests at both phases compete for the same accelerator memory, compute capacity and scheduler. A large prompt arriving while many users are decoding can consume enough resources to delay token production. The exact interference depends on prompt and output lengths, concurrency, batching limits and the engine’s scheduling policy; it cannot be inferred from the model name alone.
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A conventional static batch waits for all sequences in a batch to complete or reach a synchronization point. That wastes capacity when one short response finishes while another sequence continues generating. Continuous batching treats the active set as changeable.
- The scheduler admits requests that have cleared admission and resource checks.
- It performs the needed prefill work and starts generation for eligible sequences.
- At each scheduling opportunity, it can add new sequences to available batch slots rather than waiting for the original group to drain.
- When a sequence reaches its stop condition, the scheduler removes it and releases its KV memory.
- The cycle repeats while the engine maintains queues for waiting, prefilling and decoding work.
This approach is especially useful when response lengths vary widely. A short answer can leave the active set immediately, while long answers continue without forcing the engine to hold an otherwise idle slot. The resulting throughput and latency still depend on admission policy, maximum batch size, memory pressure, prefill scheduling and the service-level objective (SLO).
What continuous batching does not isolate
Continuous batching does not create a dedicated resource boundary between prompt processing and generation. If a scheduler inserts substantial prefill work into an active decode workload, decode latency can still rise. Some engines mitigate that with token budgets, priority rules or chunked prefill; those are scheduling policies, not P/D disaggregation.
How prefill-decode disaggregation works
In a disaggregated design, a router sends a request to a prefill worker or pool. That worker processes the prompt and makes the resulting KV state available to a decode worker. The decode side then generates output tokens and streams them to the client. vLLM describes a connector as the mechanism through which a KV consumer retrieves cache data from a producer (vLLM documentation).
Separate controls for separate latency goals
TTFT is the elapsed time from request arrival until the first output token. ITL is the time between successive generated tokens; tail ITL refers to high-percentile values rather than the average. P/D separation lets a team tune prefill capacity and parallelism for TTFT while tuning decode capacity and parallelism for ITL. vLLM identifies this independent tuning as a reason to use separate instances and also describes avoiding insertion of prefill jobs into ongoing decode work to control tail ITL (vLLM documentation).
The transfer is a real cost
The KV state has to move between phases or be retrieved through an appropriate shared mechanism. Transfer time depends on KV volume, memory layout, connector implementation, topology and link bandwidth. Coordination also adds queues, routing decisions and failure cases that do not exist in a single colocated worker. A disaggregated design can therefore improve a latency target while reducing effective capacity if the transfer path or pool balance is poor.
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Why throughput is not an automatic win
Separating pools can prevent one phase from blocking the other, but it also reserves resources for each role. If the decode pool is idle while prefill is overloaded—or the reverse—those reserved accelerators may not be usable by the busy phase. The official vLLM guidance explicitly warns that disaggregated prefill does not improve throughput on its own. Any gain must come from better latency isolation, better resource matching or a workload-specific scheduling advantage, not from the existence of two pools.
They can be combined
Think of the designs as two layers:
- Placement layer: colocated workers or separate prefill and decode pools.
- Scheduling layer: static batching, continuous batching, chunked prefill and the engine’s admission policy inside each pool.
A practical architecture may use a continuously batched prefill pool and a continuously batched decode pool. Alternatively, a shared pool can use continuous batching plus chunked prefill to reduce interference. Comparing “continuous batching” with “disaggregation” without stating the batching policy in each phase hides this important interaction.
When a shared continuously batched pool is the better choice
Keep prefill and decode together when measurements show that one serving pool meets the required SLOs and the operational simplicity has value. This is often the sensible starting point because it avoids a KV-transfer protocol and keeps scheduling, autoscaling and observability in one place.
- TTFT and ITL, including their tail percentiles, are within target at expected concurrency.
- Prompt lengths are moderate enough that prefill does not create unacceptable decode stalls.
- Traffic is balanced rather than dominated by long prompts or large bursts of prompt tokens.
- GPU utilization is high without persistent memory pressure or queue buildup.
- The team cannot justify reserving separate accelerators or operating an additional routing and transfer plane.
- Failure recovery and capacity planning benefit from one homogeneous pool.
Continuous batching should still be configured and measured carefully. A larger nominal batch is not automatically better: memory limits, queueing and the mix of prompt and output tokens can make a smaller or more selective batch deliver better SLO compliance.
When disaggregation is worth testing
Disaggregation becomes a credible candidate when the workload, not the architecture’s novelty, points to a phase imbalance.
- Prefill-heavy traffic: requests contain long prompts, document context or large bursts of input tokens. NVIDIA Research’s June 2025 study reports that disaggregation is most effective for prefill-heavy traffic and larger models (NVIDIA Research).
- Decode interference: long or bursty prefills produce unacceptable ITL or tail-ITL spikes for users already receiving tokens.
- Different latency priorities: product requirements demand aggressive TTFT while streaming quality depends on a separate ITL target.
- Unequal scaling patterns: arrival rates and resource needs for prompt processing and generation vary independently enough to justify separate pools.
- Large models: the memory and compute profile makes phase-specific parallelism more valuable, provided the transfer path can keep up.
- Operational readiness: the team can monitor two pools, route requests, size reservations and recover from connector or worker failures.
NVIDIA’s analysis also highlights dynamic rate matching and elastic scaling as important to reaching favorable results. Dedicated prefill workers can improve latency isolation, but they reserve GPUs; NVIDIA Dynamo’s tuning guidance therefore treats capacity matching as a performance requirement rather than an optional refinement (NVIDIA Dynamo documentation).
Chunked prefill: a third option for interference
Disaggregation is not the only way to stop a long prompt from monopolizing a shared worker. Sarathi-Serve splits prefill into smaller chunks and schedules those chunks so new requests can enter without pausing ongoing decode. Its stall-free scheduling approach keeps both phases on a shared serving path rather than creating separate pools (USENIX OSDI 2024 presentation).
Chunking can be attractive when a single pool already has good overall utilization and the main problem is decode disruption. It avoids cross-pool KV transfer and simplifies routing, but it introduces its own scheduling parameters: chunk size, token budgets, admission priorities and the point at which a prefill is allowed to yield. Those settings must be evaluated against the same TTFT and tail-ITL targets used for a disaggregated test.
Costs and failure modes to plan for
KV-transfer bottlenecks
A connector can become the critical path when prompts are long, concurrency is high or the interconnect is shared. Measure bytes moved, transfer duration and queue time separately from model compute. A design that looks faster in isolated GPU timing can lose its advantage once transfer contention appears.
Pool imbalance and stranded capacity
Prefill and decode demand rarely stay in a fixed ratio. Overprovisioning both sides protects latency but leaves idle accelerators; underprovisioning one side creates a queue that the other side cannot compensate for. Rate matching and elastic scaling are therefore central design tasks.
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The router must know which worker owns or can retrieve a request’s KV state. Retries need clear rules so a failed handoff does not duplicate work or strand state. Timeouts should distinguish a slow prefill, a delayed transfer and a decode worker failure, because each requires a different recovery action.
Observability gaps
End-to-end latency alone cannot identify the problem. Record request arrival, queue wait, prefill compute, KV-transfer start and end, decode start, first-token time, per-token intervals and completion. Add pool-level GPU utilization, memory occupancy, connector errors and queue depth. Tail percentiles are essential: a healthy average ITL can hide unacceptable pauses for a minority of users.
Version and compatibility risk
The vLLM disaggregated-prefill page labels the feature experimental and subject to change. Its documentation describes connectors and separate instances but does not establish a complete compatibility matrix for every version, hardware backend or connector. Verify support for the exact software and hardware combination selected for deployment. NVIDIA Dynamo’s documentation is versioned; the cited tuning page is for version 0.7.1 and should not be treated as universal guidance for later releases.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.How to evaluate the two designs fairly
Do not compare a colocated continuous-batching engine with a disaggregated system using different prompts, concurrency or SLO definitions. Use the same model weights, tokenizer, sampling settings, hardware class and traffic trace wherever possible.
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|---|---|---|
| Workload mix | Prompt-length distribution, output-length distribution, arrival pattern, prefill intensity and concurrency. | Disaggregation’s value is highly sensitive to the balance between prompt processing and generation. |
| TTFT | Time from request arrival to the first output token, with median and tail percentiles. | Shows whether prompt capacity and queueing meet the first-token SLO. |
| ITL and tail ITL | Time between generated tokens, including high percentiles during bursts. | Reveals decode interference that an average latency number can hide. |
| Throughput or goodput | Requests or tokens completed while meeting a stated latency SLO. | Raw throughput is misleading if one design violates the service target. |
| KV transfer | Transfer method, data volume, topology, link bandwidth and time added between phases. | Transfer overhead can erase a compute-side advantage. |
| Resource use | GPU allocation for each phase, idle capacity, rate matching and autoscaling behavior. | Separate pools can trade latency isolation for stranded capacity. |
| Operating complexity | Pool count, routing, connector reliability, scaling controls and observability coverage. | A small latency gain may not justify a substantially harder system to operate. |
Use workload traces, not a single synthetic point
Test short and long prompts, short and long outputs, steady traffic and bursts. Include concurrency levels at which the shared baseline first misses its SLO. Report whether a result is a median, a tail percentile, a capacity limit or a goodput point. NVIDIA Research describes evaluating hundreds of thousands of design points in its 2025 study, underscoring how sensitive the answer is to configuration and workload rather than to one universal setting (NVIDIA Research).
A practical rollout sequence
- Define the SLOs. Write separate targets for TTFT, average ITL, tail ITL and goodput. Decide which requests count as successful only when they meet those limits.
- Characterize production traffic. Capture prompt and completion token distributions, arrival bursts, concurrency and model-specific memory behavior. Label workloads that are retrieval-heavy, conversational, batch-oriented or streaming.
- Build a colocated baseline. Measure the current engine with continuous batching under the exact hardware and traffic conditions you intend to support. Record queueing, GPU utilization and tail latency.
- Try scheduling controls first. Tune admission limits and, where supported, evaluate chunked prefill. If this removes decode stalls without unacceptable TTFT or throughput loss, a shared pool may be the lower-risk solution.
- Prototype the split path. Run separate prefill and decode instances with a supported connector. Measure KV-transfer time and failures as first-class metrics, not as an unreported implementation detail.
- Rate-match the pools. Vary the number of prefill and decode workers, test burst handling and observe idle capacity. Add elastic scaling only after the basic queue and transfer behavior is understood.
- Exercise failures. Stop a prefill worker, decode worker and connector path independently. Confirm that requests time out or retry without duplicated generation, leaked KV state or silent SLO violations.
- Compare goodput at equal SLO compliance. Select a design only when its measured benefit survives the same workload, latency definition, hardware budget and operational constraints.
What published results do—and do not—prove
The DistServe paper reports up to 7.4× more requests or 12.6× tighter latency SLOs, with latency constraints met for more than 90% of requests, in its evaluations across selected popular models, applications and latency requirements against the state-of-the-art systems it compared (DistServe paper). These are paper-specific experimental results from its stated baselines, hardware and workloads. They are not a guaranteed gain for every modern deployment and are not a direct head-to-head result against every continuous-batching implementation.
The broader lesson is methodological: attach every speedup to the model, prompt and output distributions, concurrency, hardware, network, batching limits and SLO used to obtain it. A percentage without those conditions cannot tell an inference team which architecture will win in production.
A decision framework
| If your measured situation is… | Start with… | Validate by checking… |
|---|---|---|
| Mixed request lengths, moderate prompts and acceptable tail latency in one pool | Continuous batching in a colocated engine | Goodput, memory pressure and tail ITL as concurrency rises. |
| Long prompts periodically stall active streams | Chunked prefill or stricter prefill scheduling | Whether tail ITL improves without pushing TTFT beyond its SLO. |
| Persistent prefill-heavy bursts and a large model | A disaggregated prototype | KV-transfer time, pool rate matching and elastic-scaling behavior. |
| Separate product targets for first-token speed and streaming smoothness | Disaggregation with independent prefill/decode tuning | TTFT and tail ITL separately, including routing and handoff queues. |
| Low or unpredictable utilization with a tight GPU budget | A shared pool first | Whether reserved phase-specific capacity would sit idle. |
The decision should follow measured phase behavior. Continuous batching is the baseline scheduling capability for variable-length requests; P/D disaggregation is an architectural option for isolating and independently scaling the two phases. Use both when the workload benefits from both, and choose neither as a reflex merely because the service runs a large language model.
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