Explained: Continuous Batching for LLM Inference Optimization

blaizedsouza · x · 2026-08-08

The thread clearly explains Continuous Batching, a core technique in LLM inference serving.

Compared to traditional Static Batching, Continuous Batching offers significant advantages:

Mechanism: Static batching waits for the entire batch to finish before starting a new one; continuous batching instantly fills finished slots with new requests from the queue.
Granularity: Static batching works at the request level, while continuous batching operates at the token level.
Benefits: Massively increases GPU throughput (more tokens per second) and effectively reduces waiting time.

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