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SGLang

What it is

SGLang is a fast serving framework for large language models and vision-language models. It makes your interaction with models faster and more controllable by optimizing the runtime with features like RadixAttention.

What problem it solves

LLM applications often involve repetitive prompting, structured output requirements, and complex chaining. SGLang addresses these by providing a high-performance runtime that significantly reduces latency through aggressive caching (RadixAttention) and optimized kernels for constrained generation.

Where it fits in the stack

Infra

Typical use cases

  • High-performance serving of LLMs and VLMs (Vision-Language Models).
  • Applications requiring complex, multi-turn structured generation.
  • Serving scenarios where prefix caching can significantly improve throughput (e.g., multi-turn chat, few-shot prompting).

Strengths

  • RadixAttention: Automatically caches and reuses KV cache across different requests with shared prefixes.
  • Fast Structured Generation: Optimized engine for constrained generation.
  • Comprehensive VLM Support: Excellent performance for vision-based models.
  • Native Interpreter: Includes a high-level Python interface for complex LLM programming.

Limitations

  • Hardware: Primarily targets NVIDIA GPUs.
  • Ecosystem: Newer than vLLM, so community integrations and documentation are still maturing.

When to use it

  • When your application relies on multi-turn interactions or shared prompt prefixes.
  • When you need low-latency, reliable structured generation.
  • When serving VLMs.

When not to use it

  • For basic, single-prompt text generation where vLLM might be more widely documented.
  • On non-NVIDIA hardware.

Licensing and cost

  • Open Source: Yes (Apache 2.0)
  • Cost: Free
  • Self-hostable: Yes

Sources / References

Getting started

Installation

pip install "sglang[all]"

Minimal CLI Example (Launch Server)

python -m sglang.launch_server --model-path meta-llama/Llama-3.1-8B-Instruct --port 30000

Minimal Python Example (OpenAI Compatible)

from openai import OpenAI
client = OpenAI(base_url="http://127.0.0.1:30000/v1", api_key="EMPTY")

response = client.chat.completions.create(
    model="default",
    messages=[{"role": "user", "content": "Explain prefix caching in one sentence."}],
)
print(response.choices[0].message.content)

Contribution Metadata

  • Last reviewed: 2026-03-02
  • Confidence: high