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What you can do with Zero to Sglang

A free, open course from Datawhale and RadixArk that teaches LLM inference by building a mini-sglang from scratch and then reading the real SGLang source.

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datawhalechina/zero-to-sglang on GitHub

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What do you want to do?

5 use cases

  • Learn LLM inference concepts without a GPU

    Read Part I to understand KV Cache, prefill vs. decode, and the benchmark metrics before touching code.

    View 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.

  • Build a mini inference engine step by step

    Follow Part II to add forward pass, KV Cache, HTTP serving, paging, and RadixAttention to a mini-sglang.

    View prompt
    Use the repo https://github.com/datawhalechina/zero-to-sglang and implement Chapter 4 (KV Cache) of Part II in <target-directory>. Expected output: a generation loop that reuses cached keys and values for a short prompt. Verify that the output tokens match a run without the cache for the same prompt.

  • Run Qwen3-0.6B on SGLang on your own GPU

    Follow Part 0.2 to set up the environment and serve a small Qwen3 model with SGLang.

    View prompt
    Clone https://github.com/datawhalechina/zero-to-sglang and follow course-material/eng/part0/Part0-Deploy-Your-First-SGLang-Server.md on the host <gpu-host>. Expected output: an SGLang server running Qwen3-0.6B that returns a completion. Verify the server responds to a single test prompt before reporting success.

  • Map RadixAttention from the course to SGLang source

    Compare the Chapter 8 prefix caching walkthrough with Part III caching chapters and the real SGLang code.

    View prompt
    Using https://github.com/datawhalechina/zero-to-sglang, read the RadixAttention chapter and the Hierarchical Caching chapter, then find the matching functions in a local clone of https://github.com/sgl-project/sglang at <path>. Expected output: a table of course concept to source location. Verify each file path exists in the clone before listing it.

  • Share a mini-sglang build log with the community

    Write down the bugs you hit while following Part II and submit them under community/eng/.

    View prompt
    Clone https://github.com/datawhalechina/zero-to-sglang and read the PR requirements in community/eng/. Draft a build-log file in community/eng/ for <chapter-name> that lists each bug, its cause, and the fix. Verify that the file follows the PR requirements before opening the pull request.

How to use Zero to Sglang

  1. Check you have what it needs: Python and basic software engineering habits; Familiarity with PyTorch and neural network basics; Git installed to clone the repository.
  2. Open datawhalechina/zero-to-sglang on GitHub and read the README and license.
  3. git clone https://github.com/datawhalechina/zero-to-sglang.git
  4. cd zero-to-sglang
  5. Open course-material/eng/part1/Chapter1_Introduction_to_LLM.md and read it.
  6. 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.

These steps come from our top use case, Learn LLM inference concepts without a GPU, which also has a copy-paste agent prompt.

Zero to Sglang alternatives · Best AI repos for Research

About this repo

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)
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Questions about Zero to Sglang

What is Zero to Sglang?

A free, open course from Datawhale and RadixArk that teaches LLM inference by building a mini-sglang from scratch and then reading the real SGLang source. It is an open-source project on GitHub: https://github.com/datawhalechina/zero-to-sglang.

Who is Zero to Sglang for?

Python developers and ML engineers who want to understand how LLM inference engines work and eventually contribute to SGLang.

What can I do with Zero to Sglang?

5 use cases so far, for example: Learn LLM inference concepts without a GPU; Build a mini inference engine step by step; Run Qwen3-0.6B on SGLang on your own GPU. Each has steps and a copy-paste agent prompt.

What do I need to run Zero to Sglang?

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).

What are alternatives to Zero to Sglang?

Other hot AI repos in Research: Scientific Agent Skills, Speaker Mem R1, DSH Council, Evo Bot, AI Engineering Course.

Is Zero to Sglang safe to run?

Deduped does not own, audit or vouch for it. Read the README and license, run it on your own systems first and review every command before you run it.