--- title: How to Scale LLM Inference for AI Agents Using vLLM description: A tutorial explains how to scale LLM inference for AI agent workloads using vLLM, covering the fundamentals of prefill/decode phases, KV caching, and GPU... canonical: https://daily.dev/posts/how-to-scale-llm-inference-for-ai-agents-using-vllm-fhsi7gofh twitter:card: summary_large_image twitter:site: @dailydotdev og:type: website og:site_name: daily.dev og:title: How to Scale LLM Inference for AI Agents Using vLLM | daily.dev og:description: A tutorial explains how to scale LLM inference for AI agent workloads using vLLM, covering the fundamentals of prefill/decode phases, KV caching, and GPU... og:url: https://daily.dev/posts/how-to-scale-llm-inference-for-ai-agents-using-vllm-fhsi7gofh og:image: https://api.daily.dev/og/posts/Fhsi7gofh.png og:image:alt: How to Scale LLM Inference for AI Agents Using vLLM 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. # How to Scale LLM Inference for AI Agents Using vLLM **[freeCodeCamp](https://daily.dev/sources/freecodecamp)** · 14 min read · 1 upvotes · 1 comments ## Summary A tutorial explains how to scale LLM inference for AI agent workloads using vLLM, covering the fundamentals of prefill/decode phases, KV caching, and GPU memory pressure. It walks through installing vLLM-Metal on Apple Silicon, starting a local OpenAI-compatible vLLM server, and connecting a Python agent to it. The piece also breaks down vLLM's core optimizations—continuous batching, PagedAttention, and automatic prefix caching—with worked numerical examples showing why each matters for concurrent, uneven agent traffic. ## Full article daily.dev links to this article rather than hosting it. Read it at the original source: ## Questions this post answers ### What is PagedAttention in vLLM and why does it help with KV cache memory? PagedAttention is vLLM's memory-management technique that stores the KV cache in smaller fixed-size blocks instead of one large contiguous region per request. Blocks can be allocated and freed independently, which reduces memory fragmentation, makes freed cache blocks easier to reuse, and lets the server handle more concurrent sequences with unpredictable lengths. _Engineers sizing GPU memory for concurrent inference can find vLLM deep dives like this through daily.dev._ ### How does continuous batching in vLLM differ from traditional fixed-round batching for LLM serving? Traditional batching processes a fixed group of requests together until the whole batch finishes, so a completed request's slot sits idle while others keep decoding. Continuous batching refills open slots immediately as requests finish, letting new requests join the very next decoding step, which keeps the GPU busier and improves throughput under concurrent load. _daily.dev surfaces practical explainers like this for teams comparing inference-serving strategies._ ### How much does prefix caching save when many agent requests share the same system prompt in vLLM? With 50 requests sharing an 800-token system prompt, without prefix caching the prefix is processed 800 x 50 = 40,000 times; with automatic prefix caching enabled, vLLM computes the shared prefix once and reuses the cached KV blocks across matching requests, substantially cutting repeated prefill computation, especially valuable for agents reusing long tool instructions or conversation history. _daily.dev helps developers building agent infrastructure keep up with techniques like prefix caching._ ## Community discussion Top comments from developers on daily.dev. **@dmytriis** · 0 upvotes > There's another scaling tax above the GPU. > > vLLM can make an endpoint OpenAI-compatible, but coding agents increasingly depend on behavior outside that schema: tool-call identity, reasoning replay, search, discovery, streaming semantics. > > Inference portability is getting easier while agent portability is getting harder. ## Similar posts on daily.dev - [Why vLLM is the best choice for AI inference today](https://daily.dev/posts/why-vllm-is-the-best-choice-for-ai-inference-today-7zxdzzpx7) · Red Hat Developer · 0 upvotes · 0 comments - [Announcing vllm-metal: Concurrent Serving on Apple Silicon](https://daily.dev/posts/announcing-vllm-metal-concurrent-serving-on-apple-silicon-qubj1vfph) · vLLM · 0 upvotes · 0 comments - [The Infrastructure Behind Making Local LLM Agents Actually Useful](https://daily.dev/posts/the-infrastructure-behind-making-local-llm-agents-actually-useful-ff4qnxir1) · Towards Data Science · 1 upvotes · 0 comments - [Inside vLLM: Anatomy of a High-Throughput LLM Inference System \(2025\)](https://daily.dev/posts/inside-vllm-anatomy-of-a-high-throughput-llm-inference-system-2025--5fkcjpua7) · Hacker News · 2 upvotes · 0 comments --- Tags: [#ai-agents](https://daily.dev/tags/ai-agents), [#gpu](https://daily.dev/tags/gpu), [#ai-inference](https://daily.dev/tags/ai-inference), [#vllm](https://daily.dev/tags/vllm) [View this post on daily.dev](https://daily.dev/posts/how-to-scale-llm-inference-for-ai-agents-using-vllm-fhsi7gofh) ```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":"How to Scale LLM Inference for AI Agents Using vLLM","url":"https://daily.dev/posts/how-to-scale-llm-inference-for-ai-agents-using-vllm-fhsi7gofh","mainEntityOfPage":{"@type":"WebPage","@id":"https://daily.dev/posts/how-to-scale-llm-inference-for-ai-agents-using-vllm-fhsi7gofh"},"datePublished":"2026-08-17T23:04:43.788Z","dateModified":"2026-09-13T19:44:56.249Z","description":"A tutorial explains how to scale LLM inference for AI agent workloads using vLLM, covering the fundamentals of prefill/decode phases, KV caching, and GPU...","image":"https://media.daily.dev/image/upload/f_auto,q_auto/v1/posts/0fd41e75fc38f6e2fdb3669013024a10?_a=AQAEuop","thumbnailUrl":"https://media.daily.dev/image/upload/f_auto,q_auto/v1/posts/0fd41e75fc38f6e2fdb3669013024a10?_a=AQAEuop","isAccessibleForFree":true,"articleSection":"freeCodeCamp","inLanguage":"en","publisher":{"@type":"Organization","name":"daily.dev","url":"https://daily.dev","logo":{"@type":"ImageObject","url":"https://daily.dev/apple-touch-icon.png","width":180,"height":180}},"author":{"@type":"Organization","name":"freeCodeCamp","logo":"https://media.daily.dev/image/upload/t_logo,f_auto/v1628412854/logos/freecodecamp","url":"https://daily.dev/sources/freecodecamp"},"commentCount":1,"discussionUrl":"https://daily.dev/posts/how-to-scale-llm-inference-for-ai-agents-using-vllm-fhsi7gofh","interactionStatistic":[{"@type":"InteractionCounter","interactionType":{"@type":"LikeAction"},"userInteractionCount":1},{"@type":"InteractionCounter","interactionType":{"@type":"CommentAction"},"userInteractionCount":1}],"keywords":"ai-agents,gpu,ai-inference,vllm","timeRequired":"PT14M"} {"@context":"https://schema.org","@type":"BreadcrumbList","itemListElement":[{"@type":"ListItem","position":1,"name":"Home","item":"https://daily.dev"},{"@type":"ListItem","position":2,"name":"freeCodeCamp","item":"https://daily.dev/sources/freecodecamp"},{"@type":"ListItem","position":3,"name":"How to Scale LLM Inference for AI Agents Using vLLM"}]} {"@context":"https://schema.org","@type":"WebPage","@id":"https://daily.dev/posts/how-to-scale-llm-inference-for-ai-agents-using-vllm-fhsi7gofh","comment":[{"@type":"Comment","text":"There’s another scaling tax above the GPU.\nvLLM can make an endpoint OpenAI-compatible, but coding agents increasingly depend on behavior outside that schema: tool-call identity, reasoning replay, search, discovery, streaming semantics.\nInference portability is getting easier while agent portability is getting harder.","datePublished":"2026-08-31T13:24:25.858Z","url":"https://daily.dev/posts/Fhsi7gofh#c-ZTA3pCwCT","author":{"@type":"Person","name":"Dmytrii S.","url":"https://daily.dev/dmytriis","image":"https://avatars.githubusercontent.com/u/946519?v=4"}}]} {"@context":"https://schema.org","@type":"FAQPage","@id":"https://daily.dev/posts/how-to-scale-llm-inference-for-ai-agents-using-vllm-fhsi7gofh#faq","mainEntity":[{"@type":"Question","name":"What is PagedAttention in vLLM and why does it help with KV cache memory?","acceptedAnswer":{"@type":"Answer","text":"PagedAttention is vLLM's memory-management technique that stores the KV cache in smaller fixed-size blocks instead of one large contiguous region per request. 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