Adaptive request batching
Adaptive request batching is an inference-serving technique that pools arriving requests and executes them together when a configured batch-size or waiting-time threshold is reached.
Machine Learning System Design
Dynamic batching is an inference-serving technique that combines requests arriving within a configured time window into a batch for execution.
- How it is used
- Improves inference throughput while preserving responsive API behavior by accumulating near-simultaneous requests.
- How deep it goes
- Explains accumulation limits, size-based grouping, padding overhead, and latency tradeoffs without detailing scheduler internals.
- Examples
- Uses 20-millisecond or 32-request accumulation, size-grouped language-model inputs, and Triton real-time serving.
- What you can do after
- Operate/configure dynamic batching in Triton or TensorFlow Serving while balancing batch size, padding cost, and response latency.
Read how Machine Learning System Design explains adaptive request batching →
Designing Deep Learning Systems: A guide for software engineers
Dynamic batching is the runtime aggregation of separately arriving inference requests into batches for joint execution.
- How it is used
- Improves inference throughput and hardware utilization by scheduling separately arriving requests for joint execution.
- How deep it goes
- Explains scheduler placement and model-level configuration but stops short of detailing queue timing or batching algorithms.
- Examples
- Uses Triton’s REST or gRPC request path and a model configuration declaring max_batch_size: 8.
- What you can do after
- Operate and configure model-level batching in Triton while selecting compatible backends and serving infrastructure.
Related concepts
- language modeluses · A language model is a computational model that represents patterns in linguistic tokens to estimate sequence probabilities and predict or generate language from context.
- throughputproduces · The amount of work, data, or output a system processes or completes per unit of time.
- batchingkind of · Batching is the grouping of multiple data items, requests, or operations so they can be processed together as a unit.
- batch sizeconfigured by · Batch size is the number of items grouped together for processing or computation as a single batch.
- meancontrasts with · A mean is a measure of central tendency, calculated as the arithmetic average of values or, for a random variable, as the probability-weighted average of its possible values.
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