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How to run Qwen3-0.6B on SGLang on your own GPU with Zero to Sglang

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

  • Research
  • Intermediate
  • 90 minutes
  • From the README
  • Any agent

Remote script. Install commands and model downloads from the chapter and any inferred install step run code from external sources, so review them before executing on a shared machine.

Who it is forA developer with access to an NVIDIA GPU, local or cloud, who wants a first working SGLang server before studying internals.

The problemSetting up an inference server for the first time has many environment pitfalls and unclear steps.

The outcomeA running SGLang server serving Qwen3-0.6B that you can send a test request to.

What you need first

  • 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)

Steps

  1. git clone https://github.com/datawhalechina/zero-to-sglang.git
  2. cd zero-to-sglang
  3. Open course-material/eng/part0/Part0-Deploy-Your-First-SGLang-Server.md and follow its environment setup section.
  4. Install SGLang in your Python environment as the Part 0.2 chapter describes. (inferred, check the README)
  5. Launch Qwen3-0.6B with SGLang as the chapter shows, then send one test request to the running server.

Prompt for your agent

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.

Caveat. The README does not list the exact install or launch commands, so you must take them from the Part 0.2 chapter file rather than from the README.

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