--- title: Continuous Batching Mechanics: The Scheduler Behind... description: Serving large language models is fundamentally a scheduling problem. Static batching wastes GPU resources due to head-of-line blocking, idle slots, and no... canonical: https://daily.dev/posts/continuous-batching-mechanics-the-scheduler-behind-vllm-tgi-and-sglang-9x0j2tj3u twitter:card: summary_large_image twitter:site: @dailydotdev og:type: website og:site_name: daily.dev og:title: Continuous Batching Mechanics: The Scheduler Behind vLLM, TGI, and SGLang | daily.dev og:description: Serving large language models is fundamentally a scheduling problem. Static batching wastes GPU resources due to head-of-line blocking, idle slots, and no... og:url: https://daily.dev/posts/continuous-batching-mechanics-the-scheduler-behind-vllm-tgi-and-sglang-9x0j2tj3u og:image: https://api.daily.dev/og/posts/9x0j2tJ3U.png og:image:alt: Continuous Batching Mechanics: The Scheduler Behind vLLM, TGI, and SGLang 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 Mechanics: The Scheduler Behind vLLM, TGI, and SGLang **[DigitalOcean Community](https://daily.dev/sources/do_community)** · 11 min read · 0 upvotes · 0 comments ## Summary Serving large language models is fundamentally a scheduling problem. Static batching wastes GPU resources due to head-of-line blocking, idle slots, and no mid-flight admission. Continuous batching, introduced by the Orca system in 2022, solves this by re-evaluating the batch before every single forward pass, allowing requests to join and leave dynamically. Two supporting mechanisms are essential: PagedAttention (paged KV-cache management) eliminates memory fragmentation and enables efficient preemption, while chunked prefill smooths the compute-heavy prefill bursts into the ongoing decode stream. vLLM, TGI, and SGLang all share this foundation — differing mainly in emphasis: vLLM pioneered PagedAttention, TGI uses a Rust router for batching decisions, and SGLang's RadixAttention organizes prefix caching in a tree structure for workloads with heavy prompt overlap. Future work is moving toward multi-GPU disaggregation and decode-step optimizations like speculative decoding. ## Full article daily.dev links to this article rather than hosting it. Read it at the original source: ## Similar posts on daily.dev - [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 - [Continuous batching achieves high GPU utilization for large language models](https://daily.dev/posts/continuous-batching-achieves-high-gpu-utilization-for-large-language-models-z6b15ng2x) · Arpit Bhayani · 0 upvotes · 0 comments - [Static vs. Dynamic vs. Continuous Batching in LLMs, clearly explained\!](https://daily.dev/posts/static-vs-dynamic-vs-continuous-batching-in-llms-clearly-explained--qbepnmqas) · Daily Dose of Data Science \| Avi Chawla \| Substack · 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 - [vLLM Teardown Part 2: The Virtual Memory for LLMs](https://daily.dev/posts/vllm-teardown-part-2-the-virtual-memory-for-llms-uzjhgrxwa) · Medium · 0 upvotes · 0 comments --- Tags: [#ai-inference](https://daily.dev/tags/ai-inference), [#vllm](https://daily.dev/tags/vllm) [View this post on daily.dev](https://daily.dev/posts/continuous-batching-mechanics-the-scheduler-behind-vllm-tgi-and-sglang-9x0j2tj3u) ```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 Mechanics: The Scheduler Behind vLLM, TGI, and SGLang","url":"https://daily.dev/posts/continuous-batching-mechanics-the-scheduler-behind-vllm-tgi-and-sglang-9x0j2tj3u","mainEntityOfPage":{"@type":"WebPage","@id":"https://daily.dev/posts/continuous-batching-mechanics-the-scheduler-behind-vllm-tgi-and-sglang-9x0j2tj3u"},"datePublished":"2026-07-11T22:13:05.316Z","dateModified":"2026-07-11T22:13:57.411Z","description":"Serving large language models is fundamentally a scheduling problem. 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