# LLM inference batching strategies: static, dynamic, continuous, chunked prefill, and disaggregation

DevFeed: [LLM inference batching strategies: static, dynamic, continuous, chunked prefill, and disaggregation](<https://devfeed.tech/articles/5-llm-inference-batching-techniques-every-ai-engineer-should-know-18279.md>)

Original publisher: [Read original article](<https://www.intoai.pub/p/llm-inference-batching-strategies>)

Author: Dr. Ashish Bamania

Published: 2026-08-22T11:44:27Z

Content type: tutorial

Language: en

Sources: [Into AI](<https://devfeed.tech/sources/into-ai.md>)

Topics: [Large Language Model](<https://devfeed.tech/topics/llm.md>), [AI Inference](<https://devfeed.tech/topics/ai-inference.md>), [Benchmark](<https://devfeed.tech/topics/benchmark.md>), [GPU](<https://devfeed.tech/topics/gpu.md>), [Latency](<https://devfeed.tech/topics/latency.md>)

Tags: [ai-engineer](<https://devfeed.tech/tags/ai-engineer.md>), [batching](<https://devfeed.tech/tags/batching.md>), [gpu](<https://devfeed.tech/tags/gpu.md>), [inference](<https://devfeed.tech/tags/inference.md>), [latency](<https://devfeed.tech/tags/latency.md>), [llm](<https://devfeed.tech/tags/llm.md>)

## AI overview

A developer guide explains how static, dynamic, and continuous batching affect LLM inference throughput, latency, and GPU utilization. It also identifies chunked prefill and prefill-decode disaggregation as additional serving strategies.

## Source excerpt

Static, Dynamic, and Continuous batching, Chunked prefill, and Prefill-Decode disaggregation, simply explained.