Continuous Batching
The short answer · 15 words
In short
Merging of running model requests, in which finished requests are continuously replaced by new ones.
Flow-alerta.cab (2026): Continuous Batching. https://flow-alerta.cab/en/glossar/continuous-batching. Editorially reviewed on: 09 October 2026.
How much detail would you like?
Practical
Relevant for self-operated LLM services with many incoming requests at the same time.
Example scenario: An internal chat service bundles responses of different lengths on the same GPU.
Test with realistic arrival rates and measure throughput, time to the first token as well as high latency percentiles together.
At a glance
- Core
- An inference server reschedules the batch during generation steps. Finished requests leave it and new requests are added. The entire batch therefore does not have to wait for the longest response. Higher throughput does not automatically mean shorter waiting times for every individual request.
- Use
- Relevant for self-operated LLM services with many incoming requests at the same time.
- Example
- Example scenario: An internal chat service bundles responses of different lengths on the same GPU.
- Limits
- Confuse throughput with single latency; ignore memory limits, queues and long requests; do not test unequal load.
In context
Questions about this term
Is this offline batch processing?
No.
Do you need multiple GPUs?
Not mandatory.
Does each request remain independent?
In substance, yes.
All details and background
An inference server reschedules the batch during generation steps. Finished requests leave it and new requests are added. The entire batch therefore does not have to wait for the longest response. Higher throughput does not automatically mean shorter waiting times for every individual request.
ELI5 – Explained simply
A restaurant occupies vacant seats immediately, instead of waiting until all guests have left.
Why it matters in practice
Relevant for self-operated LLM services with many incoming requests at the same time.
Example
Example scenario: An internal chat service bundles responses of different lengths on the same GPU.
Pitfalls and limits
- Confuse throughput with single latency; ignore memory limits, queues and long requests; do not test unequal load.
Origin & history
Distributed with scalable LLM inference servers as an alternative to rigid generation stacks.
Use in mid-sized companies
- Test with realistic arrival rates and measure throughput, time to the first token as well as high latency percentiles together.
Related terms
- Batching
- Inference
- GPU
- CV cache
- Scheduling
- Throughput
Matching Flow-alerta service
AI assistants & automationAI Agents & Automation – Plan LLM operation under realistic parallel loading.Matching tool / use case
View use casesTool: Hugging Face Transformers ContinuousBatchingManager; Use Case: heavily used internal chat service.Sources
- huggingface.coHugging Face, Continuous batching
Quellenprüfung: 09.10.2026. Definition und dokumentierte Einordnung abgeglichen; Beispiele beschreiben mögliche Anwendungen.
Editorially reviewed on: