Batching and Scheduling Strategies

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Batching and Scheduling Strategies

Batching is one of the sharpest tools in the inference toolbox. It can turn an expensive, underutilized serving stack into a stable, high-throughput system. It can also turn a product into a latency lottery if used carelessly. Batching is not a free win. It is a negotiation between throughput and responsiveness, and the negotiation only works when you have clear service objectives.

This topic belongs in the Inference and Serving Overview pillar because it is where infrastructure becomes visible. A model that seems “fast enough” in isolation can become slow in production when it is served inefficiently. Conversely, a model that seems too slow can become viable with better scheduling. These are architecture and policy problems as much as they are model problems, which is why batching sits next to Latency Budgeting Across the Full Request Path and cost controls in <Cost Controls: Quotas, Budgets, Policy Routing

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What batching means in modern AI serving

Batching means combining multiple requests so that the underlying compute runs on a larger chunk of work at once. The motivation is simple: modern accelerators are built to do many operations in parallel. If you feed them tiny requests one at a time, you waste capacity.

In text generation systems, batching takes multiple forms:

  • Prefill batching, where multiple prompts are processed together.
  • Decode batching, where token-by-token generation is interleaved across multiple requests.
  • Continuous batching, where new requests can join the batch between steps rather than waiting for a full batch boundary.
  • Microbatching, where you group small chunks to improve utilization without creating long waits.

Batching is also tightly tied to token economics. If tokens are cost, they are also time. Systems that do not track tokens cannot reason about batching outcomes. That is why Token Accounting and Metering is a prerequisite to serious throughput work.

Throughput wins, latency risks

Batching improves throughput because it reduces per-request overhead and improves utilization. The risk is that batching can increase latency by introducing wait time. A request may sit in a queue waiting for a batch to fill.

The practical insight is that most user frustration comes from tail latency, not average latency. A batching strategy that improves average throughput but worsens p95 can still harm the product. That is why batching must be paired with a budget in <Latency Budgeting Across the Full Request Path

The interaction is easiest to understand by splitting latency into two parts:

  • Service time, the time the system spends actually computing your request.
  • Wait time, the time your request spends waiting to be served.

Batching reduces service time per request but can increase wait time. Scheduling is the art of controlling wait time.

Scheduling is policy, not only mechanics

Schedulers decide which requests run, in what order, and with what grouping. In AI products, scheduling decisions become product decisions because they determine who gets fast answers and who waits.

A practical scheduler has to juggle multiple objectives:

This is why scheduling tends to grow into a policy layer rather than staying a simple FIFO queue.

Common scheduling policies and when they fit

Schedulers often start simple and become more sophisticated as traffic and product tiers increase. The core point is not sophistication for its own sake. The aim is stable outcomes.

FIFO with guardrails

FIFO is the simplest policy. It can work when traffic is stable and request sizes are similar. It fails when requests vary widely in cost, because heavy requests create head-of-line blocking where small requests wait behind large ones.

Guardrails that make FIFO viable include:

Priority queues for tiered products

If you have user tiers, you often need priority scheduling. Priority queues can preserve a fast interactive experience for high-priority traffic while still serving batch or background work. The danger is starvation, where low-priority traffic never gets served during load. Mitigation strategies include:

Size-aware scheduling

Size-aware scheduling tries to serve smaller requests earlier to reduce overall waiting. In day-to-day work, “size” correlates with token count and expected decode length. This links directly to Token Accounting and Metering and the calibration mindset in <Calibration and Confidence in Probabilistic Outputs If you can predict which requests are expensive, you can schedule more intelligently.

The challenge is prediction error. If the system mispredicts request size, it can harm fairness and create unexpected tail latency. That is why measurement discipline from Measurement Discipline: Metrics, Baselines, Ablations matters even for “infrastructure” choices.

Continuous batching and the prefill/decode split

Many teams treat text generation as one blob. In practice, it has two phases:

  • Prefill, where the model processes the prompt and context.
  • Decode, where it generates tokens, one step at a time.

Prefill cost grows with context length. Decode cost grows with output length. Many throughput wins come from treating these phases differently. Continuous batching works by interleaving decode steps across many requests, keeping the accelerator busy.

Continuous batching interacts with streaming. If you stream tokens, your scheduler must keep user-visible progress smooth. A system can have good throughput and still feel bad if streaming stutters. The engineering patterns are discussed in <Streaming Responses and Partial-Output Stability

Batching in multi-stage systems: routers and cascades

Batching becomes more valuable and more complex when you have routers or cascades.

In a router-based system, the router can separate traffic into pools that batch well together. For example, short, cheap requests can be batched aggressively, while long, expensive requests are placed in a different lane with stricter budgets. This aligns with the architecture discussion in <Serving Architectures: Single Model, Router, Cascades

In cascades, batching can be applied to intermediate stages:

  • Batch retrieval queries when you can.
  • Batch reranking work, which can be highly parallel.
  • Batch validation or classification tasks that are small but frequent.

Cascades also create opportunities for early exits, reducing compute load and improving throughput. Early exits require confidence estimation and validation discipline, which connects to Calibration and Confidence in Probabilistic Outputs and <Output Validation: Schemas, Sanitizers, Guard Checks

The role of caching in batching outcomes

Caching changes the shape of work. If caching is effective, you may reduce the amount of compute needed for some requests, which changes batch composition. Poor caching can create bursts of cache misses that suddenly overload the model path. That is why batching strategy should be designed together with <Caching: Prompt, Retrieval, and Response Reuse

A practical approach is to treat caching as a throughput stabilizer and to measure cache hit rates alongside batch sizes and queue times. Without those metrics, you cannot tell whether batching is helping or hiding an upstream instability.

Failure modes and anti-patterns

Batching goes wrong in predictable ways. The following anti-patterns appear frequently:

  • Over-batching, where the system waits too long to fill batches and p95 latency gets worse.
  • Mixing incompatible workloads in the same batch, causing tail behavior to be dominated by a few heavy requests.
  • Ignoring backpressure, so bursts turn into queue explosions rather than controlled shedding.
  • Letting retries amplify load, creating a feedback loop where slow responses cause more retries, which causes slower responses.

The fixes are not mysterious. They are the same reliability tools applied deliberately:

How to evaluate batching changes

Batching changes must be evaluated like product changes. The safest workflow looks like this:

  • Define success metrics that include p50, p95, and p99 latency, not only throughput.
  • Track queue wait time separately from compute time so improvements are attributable.
  • Track token metrics so changes can be normalized to request size.
  • Run controlled experiments and ablations, aligning with <Measurement Discipline: Metrics, Baselines, Ablations

Batching is a lever that can move multiple metrics at once. Without disciplined measurement, teams can celebrate a throughput win while harming user experience.

Further reading on AI-RNG

Scheduling policies, fairness, and tail latency

Batching decisions are inseparable from scheduling policy. Once you have a queue, you are making fairness choices, even if you never say the word. A simple first-come first-served queue is fair in one sense, but it can punish interactive users if large jobs arrive first. A strict priority queue can protect premium users, but it can also starve background work until it becomes a backlog crisis.

A practical scheduling system usually balances three goals.

  • Protect the latency budget for interactive requests. The end-to-end view is Latency Budgeting Across the Full Request Path.
  • Keep the GPU busy enough to make batching worthwhile, without creating a “latency lottery” for users.
  • Prevent queue collapse under bursty load, which is where backpressure and admission control become the true safety rails. The relevant companion read is Backpressure and Queue Management.

Tail latency is the enemy because it breaks trust. Users remember the slow request, not the average. This is also why caching and rate limiting sit next to batching in a serious serving stack. If you can avoid redundant work, you reduce queue pressure and make batching less aggressive. See Caching: Prompt, Retrieval, and Response Reuse and Rate Limiting and Burst Control.

A good batching implementation is therefore not only a throughput trick. It is a scheduling system with explicit service guarantees.

Books by Drew Higgins

Explore this field
Serving Architectures
Library Inference and Serving Serving Architectures
Inference and Serving
Batching and Scheduling
Caching and Prompt Reuse
Cost Control and Rate Limits
Inference Stacks
Latency Engineering
Model Compilation
Quantization and Compression
Streaming Responses
Throughput Engineering

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