{
 "schema_version": 1,
 "generated": "2026-10-20",
 "id": "amitshekhariitbhu-llm-inference-engineering--understand-serving-many-users-at-once",
 "repo": "amitshekhariitbhu/llm-inference-engineering",
 "repo_url": "https://github.com/amitshekhariitbhu/llm-inference-engineering",
 "repo_slug": "amitshekhariitbhu-llm-inference-engineering",
 "title": "Understand how LLM servers handle many users at once",
 "page_title": "How to understand how LLM servers handle many users at once with LLM Inference Engineering",
 "summary": "Read about continuous batching, streaming, and disaggregated serving to plan an LLM API.",
 "who": "A backend engineer designing an API that streams LLM answers to many users at the same time.",
 "problem": "Static batching wastes GPU time, and streaming and separating prefill from decode each add design choices.",
 "outcome": "An understanding of how continuous batching, token streaming, and prefill-decode disaggregation fit together in a serving design.",
 "steps": [
  "Open the Continuous Batching in LLMs post at https://outcomeschool.com/blog/continuous-batching-in-llms and read 'The Problem with Static Batching'.",
  "Open the How does Token Streaming work? post at https://outcomeschool.com/blog/how-does-token-streaming-work and read 'SSE vs WebSockets'.",
  "Open the Prefill-Decode Disaggregation post at https://outcomeschool.com/blog/prefill-decode-disaggregation and read 'Where it works well and where it is overkill'.",
  "Write a one-page design note that says which of the three ideas your API needs and which it can skip."
 ],
 "agent_prompt": "Using the amitshekhariitbhu/llm-inference-engineering repo, draft a one-page design note for an LLM API serving <number> concurrent users. Verify that the note explains continuous batching, streaming, and disaggregation using only the linked posts.",
 "agents": [
  "any"
 ],
 "difficulty": "Advanced",
 "time": "60 minutes",
 "basis": "readme",
 "caveat": "The README mentions vLLM, SGLang, and GPUs only in its description and does not explain how to deploy any of them.",
 "category": "Research",
 "tags": [
  "Research"
 ],
 "lang": "markdown",
 "requirements": [
  "A web browser to open the linked blog posts",
  "Basic familiarity with how text generation works, or willingness to start with the Part 1 basics"
 ],
 "risk": "none",
 "seen_in_the_wild": false,
 "mentioned_publicly": false,
 "risks": [],
 "best": false,
 "score": 0.7,
 "evidence": [],
 "url": "https://dedupedai.com/use-cases/amitshekhariitbhu-llm-inference-engineering/understand-serving-many-users-at-once/",
 "md": "https://dedupedai.com/use-cases/amitshekhariitbhu-llm-inference-engineering/understand-serving-many-users-at-once/index.md",
 "disclaimer": "Deduped lists open-source repositories that belong to other people. We do not own, maintain, audit, or vouch for any of them, and a repo appearing here is not an endorsement or a safety check. The use cases, steps, and prompts are written by AI and may be incomplete, outdated, or just wrong. Where a use case is marked \"Seen in the wild\", we link to public posts where someone described something similar, but we have not verified those posts and the use case itself is still our AI's own write-up. Use all of this at your own risk. Only run code, commands, or agents on systems and accounts you are authorized to use, and read the code and every command before you run it, especially anything that touches credentials, files, or the network. Nothing here is professional, security, legal, or financial advice. All trademarks and project names belong to their respective owners, and their use here does not imply affiliation. Content is provided as is, without warranties of any kind. Found a mistake, a safety problem, or something you want removed? Email hello@dedupedai.com and we will look at it promptly.",
 "api": "https://dedupedai.com/api/v1/usecases/amitshekhariitbhu-llm-inference-engineering--understand-serving-many-users-at-once.json",
 "api_version": "v1"
}