{
 "schema_version": 1,
 "generated": "2026-10-20",
 "id": "datawhalechina-zero-to-sglang--learn-llm-inference-concepts-without-gpu",
 "repo": "datawhalechina/zero-to-sglang",
 "repo_url": "https://github.com/datawhalechina/zero-to-sglang",
 "repo_slug": "datawhalechina-zero-to-sglang",
 "title": "Learn LLM inference concepts without a GPU",
 "page_title": "How to learn LLM inference concepts without a GPU with Zero to Sglang",
 "summary": "Read Part I to understand KV Cache, prefill vs. decode, and the benchmark metrics before touching code.",
 "who": "A developer or analyst new to LLM serving who needs a conceptual foundation before working with inference engines and has no GPU available.",
 "problem": "Most inference material either stays abstract or drops you into engine source code without context.",
 "outcome": "A working mental model of inference bottlenecks, KV Cache, and how to read TTFT, TPOT, ITL, and Goodput.",
 "steps": [
  "`git clone https://github.com/datawhalechina/zero-to-sglang.git`",
  "`cd zero-to-sglang`",
  "Open course-material/eng/part1/Chapter1_Introduction_to_LLM.md and read it.",
  "Read the Introduction to Inference chapter and the KV Cache chapter in Part I, which cover prefill/decode, compute-bound vs. memory-bound, and the Roofline model.",
  "Read course-material/eng/part1/Chapter5_Introduction_to_Benchmark.md to learn TTFT, TPOT, ITL, Goodput, and tail latency."
 ],
 "agent_prompt": "Clone https://github.com/datawhalechina/zero-to-sglang and read the Part I chapters in course-material/eng/part1/. Write a one-page summary of how KV Cache reduces repeated attention work and list the four benchmark metrics with a one-line definition each. Verify that every definition matches the chapter text.",
 "agents": [
  "any"
 ],
 "difficulty": "Beginner",
 "time": "60 minutes",
 "basis": "readme",
 "caveat": "Part I is complete in English, but the Introduction to Inference and KV Cache chapter links in the README syllabus are blank, so find them by filename under course-material/eng/part1/.",
 "category": "Research",
 "tags": [
  "Research"
 ],
 "lang": "python",
 "requirements": [
  "Python and basic software engineering habits",
  "Familiarity with PyTorch and neural network basics",
  "Git installed to clone the repository",
  "A GPU for Part 0 deployment and full mini-sglang performance tests (cloud GPU is fine; Part I needs no GPU)"
 ],
 "risk": "none",
 "seen_in_the_wild": false,
 "mentioned_publicly": false,
 "risks": [],
 "best": true,
 "score": 1.0,
 "evidence": [],
 "url": "https://dedupedai.com/use-cases/datawhalechina-zero-to-sglang/learn-llm-inference-concepts-without-gpu/",
 "md": "https://dedupedai.com/use-cases/datawhalechina-zero-to-sglang/learn-llm-inference-concepts-without-gpu/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/datawhalechina-zero-to-sglang--learn-llm-inference-concepts-without-gpu.json",
 "api_version": "v1"
}