Static vs. Dynamic vs. Continuous Batching in LLM Inference
Continuous Batching in LLM Inference By Bala Priya C on August 4, 2026 in Artificial Intelligence 0 Share Post Share In this article, you will learn how static, dynamic, and continuous batching work in LLM inference, and why the differences between them matter at production scale. A request comes in, the model runs it, and the GPU sits idle waiting for the next one while it could have handled several at once for close to the same cost. This gets worse with large language models specifically, since one request might finish in a few tokens and another might run for a thousand, so whatever handles the traffic has to deal with highly uneven work, not identical jobs arriving one after another.
- ▪Continuous Batching in LLM Inference By Bala Priya C on August 4, 2026 in Artificial Intelligence 0 Share Post Share In this article, you will learn how static, dynamic, and continuous batching work in LLM inference, and why the differences
- ▪A request comes in, the model runs it, and the GPU sits idle waiting for the next one while it could have handled several at once for close to the same cost.
- ▪This gets worse with large language models specifically, since one request might finish in a few tokens and another might run for a thousand, so whatever handles the traffic has to deal with highly uneven work, not identical jobs arriving o
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| Original publisher | MachineLearningMastery.com |
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| Publication time | Tue, 04 Aug 2026 12:20:27 +0000 |
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Static vs. Dynamic vs. Continuous Batching in LLM Inference By Bala Priya C on August 4, 2026 in Artificial Intelligence 0 Share Post Share In this article, you will learn how static, dynamic, and continuous batching work in LLM inference, and why the differences between them matter at production scale. Topics we will cover include: Static batching, and why waiting for a full batch is simple but costly under real traffic Dynamic batching, and how a timeout window fixes the worst of that cost Continuous batching, and why large language models need scheduling at the token level instead of the request level Introduction Most GPUs serving AI models spend most of their time doing nothing.
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Excerpt limited to ~120 words for fair-use compliance. The full article is at MachineLearningMastery.com.
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