Modern AI Engineering

Lesson 13.16 · 27 min · video lesson

SGLang: Structured LLM Programs for Efficient Inference

When an AI agent calls the same model twenty times with almost the same long prompt, why should the GPU redo all that work twenty times?

In short: SGLang is an open-source LLM serving engine plus a small Python language for writing multi-call LLM programs. Its runtime keeps the KV cache of past requests in a radix tree (RadixAttention) so any later request that shares a prefix reuses that work automatically, and it schedules requests to maximise those cache hits. Together with continuous batching, fast structured (JSON/regex) decoding and many performance features, it delivers high throughput, especially for agents, few-shot prompts and multi-turn chat.

What you will learn

  • What is SGLang
  • A quick recap of how an LLM generates text
  • The problem SGLang solves
  • RadixAttention: the heart of SGLang
  • How RadixAttention reuses past work
  • The frontend language of SGLang
  • How the runtime and the frontend work together
  • Continuous batching in SGLang
  • Structured output and faster decoding
  • A simple end-to-end picture
  • More powerful features of SGLang
  • How SGLang compares to vLLM

In this lesson

  1. What is SGLang
  2. A quick recap of how an LLM generates text
  3. The problem SGLang solves
  4. RadixAttention: the heart of SGLang
  5. How RadixAttention reuses past work
  6. The frontend language of SGLang
  7. How the runtime and frontend work together, and continuous batching
  8. Structured output and faster decoding
  9. A simple end-to-end picture, and more features
  10. How SGLang compares to vLLM
  11. Worked example, step by step
  12. Practice: try it yourself

Key terms in this lesson

SGLang
An open-source LLM serving runtime plus a Python-embedded language for structured, multi-call LLM programs.
Radix tree
A compact prefix tree where shared beginnings of sequences share one path and branches occur where they differ.
RadixAttention
SGLang's technique of storing and reusing KV cache across requests in a radix tree.
Cache-aware scheduling
Ordering waiting requests to favour those with long cached prefixes, so reuse happens before eviction.
Constrained decoding
Masking tokens during generation so output always matches a regex, grammar or JSON schema.
Jump-forward decoding
Appending text the constraint fully determines in one step, without asking the model token by token.

This lesson is part of the Generative AI Engineering and Complete AI Engineer tracks. See plans and pricing to open the full lesson, its video, labs and quiz, or try the free lesson first (free account needed).

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