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. It sits in the serving layer, specifically optimized for complex agentic workflows and vision-language tasks.

Typical use cases¶

Multi-turn Chat & Agents: High-performance serving where prompt history is reused.
Structured Data Extraction: Applications requiring complex, multi-turn JSON or regex-constrained generation.
Vision-Language Applications: Serving models like LLaVA or Qwen-VL with high throughput.

Strengths¶

RadixAttention: Automatically caches and reuses KV cache across different requests with shared prefixes, essential for agents.
Fast Structured Generation: Optimized engine for constrained generation (JSON Schema, regex).
Chunked Prefill: Efficiently handles large prompt processing without blocking small generation tasks.
Comprehensive VLM Support: Native support and high performance for vision-based models.
Native Interpreter: Includes a high-level Python interface (SGLang runtime) for complex LLM programming.

Limitations¶

Hardware: Primarily targets NVIDIA GPUs (CUDA).
Ecosystem: Newer than vLLM; integration with some third-party orchestrators may require custom adapters.

Hardware requirements¶

SGLang requires NVIDIA GPU (CUDA). Its RadixAttention cache benefit scales with model size and concurrency — most valuable for 13B+ models with many parallel requests. No Apple Silicon support — use MLX or Ollama on macOS.

Model size	Precision	Min VRAM	RTX 4060 8 GB	Notes
7-8B	fp16	14-16 GB	❌ Not viable
7-8B	AWQ 4-bit	4-5 GB	✅ Comfortable	`--quantization awq`
7-8B	fp8	7-8 GB	⚠️ Tight	Ampere/Ada required
13-14B	AWQ 4-bit	7-8 GB	⚠️ Tight	Use `--mem-fraction-static 0.80`
30B+	any	20 GB+	❌ Not viable	Multi-GPU only

Recommended launch for RTX 4060:

python -m sglang.launch_server \
    --model-path TheBloke/Mistral-7B-Instruct-v0.2-AWQ \
    --quantization awq \
    --port 30000 \
    --mem-fraction-static 0.80

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 at production scale.

When not to use it¶

For basic, single-prompt text generation where vLLM might be more widely documented.
On non-NVIDIA hardware or platforms where CUDA is not available.

Licensing and cost¶

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

Getting started¶

Installation¶

pip install "sglang[all]"

Advanced Server Configuration¶

Launch the server with chunked prefill and RadixAttention enabled (default):

python -m sglang.launch_server \
    --model-path meta-llama/Llama-3.1-8B-Instruct \
    --port 30000 \
    --chunked-prefill-size 512 \
    --mem-fraction-static 0.8

Structured Generation (Python SDK)¶

SGLang allows for highly efficient constrained generation using its native interpreter.

import sglang as sgl

@sgl.function
def multi_turn_question(s, question):
    s += sgl.user(question)
    s += sgl.assistant(sgl.gen("answer", max_tokens=100))
    s += sgl.user("Summarize that in 10 words.")
    s += sgl.assistant(sgl.gen("summary", max_tokens=50))

# Execute
state = multi_turn_question.run(
    question="What is RadixAttention?",
    backend=sgl.RuntimeEndpoint("http://localhost:30000")
)

print(state["answer"])
print(state["summary"])

Core Architecture: RadixAttention¶

Unlike traditional LRU caching, RadixAttention manages the KV cache as a radix tree. When multiple requests share a prefix (e.g., a system prompt or a long document), SGLang identifies the shared node in the tree and reuses the pre-computed KV cache, eliminating redundant computation and significantly reducing time-to-first-token (TTFT).

Sources / References¶

Contribution Metadata¶

Last reviewed: 2026-06-03
Confidence: high