How deeply does LLMs in Production cover batch size?

Explains power-of-two choices, memory effects, dynamic batching, and latency trade-offs without detailing batching algorithms or hardware internals.

Batch size

As explained in LLMs in Production

Batch size is the number of input examples or inference requests processed together in one computation batch.

Batch size is the number of training examples or inference requests processed together in one model computation. Grouping work can improve throughput and GPU efficiency, but it consumes more memory and may increase latency. section 3.4.7 section 6.1.3

Use batch size as a practical speed–memory–latency trade-off:

Choose powers of two when possible, such as 16, 32, or 64, to improve speed and memory efficiency on GPUs. section 5.4.2 section 6.1.3
Expect larger inference batches to require additional memory for the tokens processed concurrently; training requires substantially more memory for gradients and optimizer tensors. section 3.4.7
Use adaptive or dynamic batching for services: pool requests until the maximum batch size or delay is reached, then return results to each client. section 6.1.3
Increase the maximum batch size or batch delay only when the system has latency budget; doing so generally improves throughput while adding waiting time. section 6.1.3

A production constraint

A development environment achieved single-digit millisecond responses, but production imposed a batch size of 20. The fixed value limited optimization because surrounding systems depended on it. section 5.4.2

Table 5.2 Training choices to consider

Method	Improve speed	Improves memory utilization	Difficulty
Batch size choice	Yes	Yes	Easy
Gradient accumulation	No	Yes	Medium
Gradient checkpointing	No	Yes	Medium
Mixed precision	Yes	No	Hard
Optimizer choice	Yes	Yes	Easy
Data preloading	Yes	No	Medium
Compiling	Yes	No	Easy

Compare batch size with gradient accumulation, checkpointing, mixed precision, optimizer choice, data preloading, and compilation across speed, memory utilization, and difficulty. section 5.4.2

How LLMs in Production covers it

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Also explained in

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Deep Learning with PyTorch, Second EditionThe number of training examples processed together in one update step.
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Natural Language Processing in Action, Second EditionBatch size is the number of examples processed together in one training or evaluation step.
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Build a Large Language Model (From Scratch)The number of training examples processed together in one update or evaluation step.
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Deep Learning with Python, Third EditionThe number of sequence examples processed together in one forward or training step.
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Apache Kafka in ActionA batch size is the maximum amount of record data grouped into a producer batch before transmission.
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Machine Learning Platform EngineeringA batch size is the number of training samples processed in one iteration of model training.
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Evolutionary Deep Learning: Genetic algorithms and neural networksBatch size is the number of input examples processed in one training update or forward pass.
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Kafka for ArchitectsBatch size is a configuration value specifying how many records or units are grouped for collective transmission or processing.
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Grokking AI Algorithms, Second EditionBatch size is the number of training examples processed together in one model update.
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Transformers in ActionBatch size is the number of training examples processed in one forward and backward pass before a parameter update.
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Designing Deep Learning Systems: A guide for software engineersThe number of training examples processed together in one model-training step.

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Related concepts

GPUuses · A graphics processing unit (GPU) is a processor designed for highly parallel computation, especially graphics and data-parallel workloads.
batchingconfigures · Batching is the grouping of multiple data items, requests, or operations so they can be processed together as a unit.
vLLMimplemented by · vLLM is an open-source library and serving engine for high-throughput, memory-efficient inference of large language models.
DataLoaderconfigures · PyTorch DataLoader is an iterable utility that retrieves dataset samples, optionally shuffles and transforms them, and groups them into batches.
hyperparameterkind of · A hyperparameter is a setting chosen outside ordinary parameter learning that controls a model, algorithm, or learning procedure.
gradient accumulationproduced by · Gradient accumulation is a training technique that combines gradients from multiple smaller batches before applying an optimizer update to model parameters.

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