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. By June 2026, it has become a leading choice for complex multi-agent orchestration due to its superior KV cache management.

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. It specifically solves the "First Token Latency" problem in long-context multi-turn conversations.

Where it fits in the stack¶

Infrastructure / Inference Engine. It sits in the serving layer, specifically optimized for complex agentic workflows and vision-language tasks, competing directly with vLLM and Aphrodite Engine.

Typical use cases¶

Multi-turn Chat & Agents: High-performance serving where prompt history (system prompts, context) is reused across multiple turns.
Structured Data Extraction: Applications requiring complex, multi-turn JSON or regex-constrained generation (e.g., Data Copilot Agentic RAG).
Vision-Language Applications: Serving models like LLaVA, Qwen-VL, or Gemini-compatible open weights with high throughput.
Agentic Workflows: Powering frameworks like AG2 or Langflow where state persistence is critical.

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) using compressed finite state machines.
Chunked Prefill: Efficiently handles large prompt processing without blocking small generation tasks, improving overall system throughput.
Comprehensive VLM Support: Native support and high performance for vision-based models with multi-image processing.
Native Interpreter: Includes a high-level Python interface (SGLang runtime) for complex LLM programming and state management.

Limitations¶

Hardware Bound: Primarily targets NVIDIA GPUs (CUDA); support for other accelerators (ROCm, Gaudi) is trailing.
Ecosystem Maturity: While rapidly growing, it has fewer community-contributed adapters compared to vLLM.
Complexity: The native interpreter introduces a learning curve for developers used to simple OpenAI-style API calls.

When to use it¶

When your application relies on multi-turn interactions or shared prompt prefixes.
When you need low-latency, reliable structured generation (e.g., for Answer Synthesis Schema).
When serving VLMs at production scale with high concurrency.

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 (use MLX on Apple Silicon).

Getting started¶

Installation¶

# Install with all dependencies for local serving
pip install "sglang[all]"

Basic Server Launch¶

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

Hardware Verification (RTX 4060 8 GB)¶

Model size	Precision	VRAM Needed	Status	Notes
7-8B	fp16	14-16 GB	❌	Exceeds VRAM
7-8B	AWQ 4-bit	4-5 GB	✅	Use `--quantization awq`
13-14B	AWQ 4-bit	7-8 GB	⚠️	Use `--mem-fraction-static 0.80`

CLI examples¶

Launching with Quantization¶

# Launching an AWQ model for low-memory environments
python -m sglang.launch_server \
    --model-path TheBloke/Mistral-7B-Instruct-v0.2-AWQ \
    --quantization awq \
    --port 30000

Monitoring via CLI¶

# Check server health and stats
curl http://localhost:30000/health
curl http://localhost:30000/stats

API examples¶

Structured Generation (Python SDK)¶

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

import sglang as sgl

@sgl.function
def extract_user_info(s):
    s += sgl.user("Extract name and age from: John is a 30-year-old developer.")
    s += sgl.assistant(sgl.gen("json_output", regex=r'\{"name": ".*", "age": \d+\}'))

# Execute via runtime endpoint
runtime = sgl.RuntimeEndpoint("http://localhost:30000")
state = extract_user_info.run(backend=runtime)
print(state["json_output"])

OpenAI Compatible API¶

from openai import OpenAI

client = OpenAI(base_url="http://localhost:30000/v1", api_key="sglang")

response = client.chat.completions.create(
    model="default",
    messages=[{"role": "user", "content": "What is RadixAttention?"}]
)
print(response.choices[0].message.content)

Sources / References¶

Contribution Metadata¶

Last reviewed: 2026-06-23
Confidence: high