--- title: Continuous batching achieves high GPU utilization for... description: Continuous batching is a scheduling technique for serving LLMs that decouples generation steps from individual requests. Instead of processing whole requests... canonical: https://daily.dev/posts/continuous-batching-achieves-high-gpu-utilization-for-large-language-models-z6b15ng2x twitter:card: summary_large_image twitter:site: @dailydotdev og:type: website og:site_name: daily.dev og:title: Continuous batching achieves high GPU utilization for large language models | daily.dev og:description: Continuous batching is a scheduling technique for serving LLMs that decouples generation steps from individual requests. Instead of processing whole requests... og:url: https://daily.dev/posts/continuous-batching-achieves-high-gpu-utilization-for-large-language-models-z6b15ng2x og:image: https://api.daily.dev/og/posts/z6B15Ng2x.png og:image:alt: Continuous batching achieves high GPU utilization for large language models og:image:width: 1200 og:image:height: 630 og:locale: en --- > ## Documentation Index > Fetch the complete documentation index at: https://daily.dev/llms.txt > Use this file to discover all available pages before exploring further. # Continuous batching achieves high GPU utilization for large language models **[Arpit Bhayani](https://daily.dev/sources/arpit-bhayani)** · 2 min read · 0 upvotes · 0 comments ## Summary Continuous batching is a scheduling technique for serving LLMs that decouples generation steps from individual requests. Instead of processing whole requests sequentially, a GPU handles one token-generation step at a time across a shared batch. Finished sequences are evicted immediately and new requests are slotted into the freed spot mid-batch. This works because attention and the KV cache are computed per-sequence, with PagedAttention managing memory so mixing requests at different generation stages doesn't corrupt context. The result is higher GPU utilization and lower average latency since no request is blocked behind a slower one. ## Full article daily.dev links to this article rather than hosting it. Read it at the original source: ## Questions this post answers ### What is continuous batching in LLM serving and why does it improve GPU utilization? Continuous batching decouples the generation step from the request, letting a GPU process one token-generation step at a time across a shared batch rather than finishing one request before starting another. When a sequence hits its EOS token it is evicted immediately, and a waiting request is slotted into that freed spot mid-batch. Because attention and the KV cache are computed per-sequence, mixing requests at different generation stages does not corrupt context, keeping GPU utilization high and average latency low. _daily.dev surfaces engineering deep dives like this for teams tuning LLM serving throughput._ ### How does PagedAttention relate to continuous batching for LLM inference? PagedAttention manages the per-sequence key-value cache memory efficiently, which is what makes continuous batching safe. Since each request's keys and values live in their own memory region, the scheduler can freely mix sequences at different generation stages (for example token 3 of a new request next to token 400 of a long-running one) in the same batch without corrupting anyone's context. _Engineers evaluating LLM serving stacks can track memory-management techniques like this via daily.dev._ ## Similar posts on daily.dev - [Maximizing speed: How continuous batching unlocks unprecedented LLM throughput](https://daily.dev/posts/maximizing-speed-how-continuous-batching-unlocks-unprecedented-llm-throughput-30o1t1obl) · InfoWorld · 0 upvotes · 1 comments - [Continuous batching from first principles](https://daily.dev/posts/continuous-batching-from-first-principles-dnuo5a7tp) · Hugging Face · 1 upvotes · 0 comments - [Continuous Batching Mechanics: The Scheduler Behind vLLM, TGI, and SGLang](https://daily.dev/posts/continuous-batching-mechanics-the-scheduler-behind-vllm-tgi-and-sglang-9x0j2tj3u) · DigitalOcean Community · 0 upvotes · 0 comments - [Continuous Batching vs. Static Batching in LLM Inference](https://daily.dev/posts/continuous-batching-vs-static-batching-in-llm-inference-xygdguiaz) · DigitalOcean Community · 0 upvotes · 0 comments - [Continuous Batching in LLMs](https://daily.dev/posts/continuous-batching-in-llms-yrzppfm2w) · Daily Dose of Data Science \| Avi Chawla \| Substack · 1 upvotes · 0 comments --- Tags: [#machine-learning](https://daily.dev/tags/machine-learning), [#gpu](https://daily.dev/tags/gpu), [#ai-inference](https://daily.dev/tags/ai-inference) [View this post on daily.dev](https://daily.dev/posts/continuous-batching-achieves-high-gpu-utilization-for-large-language-models-z6b15ng2x) ```json {"@context":"https://schema.org","@graph":[{"@type":"Organization","@id":"https://daily.dev/#organization","name":"daily.dev","url":"https://daily.dev","logo":{"@type":"ImageObject","url":"https://daily.dev/apple-touch-icon.png","width":180,"height":180},"sameAs":["https://twitter.com/dailydotdev","https://github.com/dailydotdev","https://www.linkedin.com/company/daily-dev-ltd"]},{"@type":"WebSite","@id":"https://daily.dev/#website","url":"https://daily.dev","name":"daily.dev","publisher":{"@id":"https://daily.dev/#organization"},"potentialAction":{"@type":"SearchAction","target":{"@type":"EntryPoint","urlTemplate":"https://daily.dev/search?q={search_term_string}"},"query-input":"required name=search_term_string"}}]} {"@context":"https://schema.org","@type":"TechArticle","headline":"Continuous batching achieves high GPU utilization for large language models","url":"https://daily.dev/posts/continuous-batching-achieves-high-gpu-utilization-for-large-language-models-z6b15ng2x","mainEntityOfPage":{"@type":"WebPage","@id":"https://daily.dev/posts/continuous-batching-achieves-high-gpu-utilization-for-large-language-models-z6b15ng2x"},"datePublished":"2026-09-02T20:27:18.421Z","dateModified":"2026-09-03T00:35:50.165Z","description":"Continuous batching is a scheduling technique for serving LLMs that decouples generation steps from individual requests. 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