SGLang

Field	Details
Category	High-performance LLM and multimodal model serving framework
Primary use	Low-latency, high-throughput serving with structured generation and advanced scheduling
Core techniques	RadixAttention, prefix caching, continuous batching, structured outputs, multi-GPU parallelism
Interfaces	OpenAI-compatible API, SGLang native APIs, offline engine API, Python workflows
Model scope	Language, multimodal, embedding, reward, and selected diffusion models depending on version
Best fit	Production serving, agentic workloads, structured generation, and complex prompt/program execution
Last reviewed	2026-04-29

English

Overview

SGLang is a serving framework for large language and multimodal models. It combines a high-performance runtime with APIs for OpenAI-compatible serving, native Python usage, structured outputs, tool and reasoning parsers, and offline engine workflows.

Its differentiator is strong support for structured and repeated generation patterns. Features such as RadixAttention and prefix caching are designed to reduce redundant computation when prompts share prefixes, branch, loop, or follow structured generation programs.

Why it matters

Agent and structured-generation workloads often reuse long prefixes across branches.
Production inference needs both throughput and predictable latency.
Tool use, reasoning parsing, JSON schemas, and constrained decoding are becoming normal serving requirements.
Multimodal and specialized model support increasingly matter beyond plain chat completion.
Teams need OpenAI compatibility without giving up runtime-specific optimization.

Architecture/Concepts

Concept	Role
Runtime	Executes model requests with batching, scheduling, memory management, and optimized attention.
RadixAttention	Prefix-aware KV cache reuse designed for shared-prefix and structured generation workloads.
Prefix caching	Reuses previously computed prompt segments to reduce prefill cost.
Continuous batching	Keeps accelerators busy by dynamically admitting and progressing requests.
Structured outputs	Constrains model output to JSON schemas, regex-like formats, or parser-driven formats depending on configuration.
Tool and reasoning parsers	Extract tool calls or reasoning-formatted output for application workflows.
Parallelism	Tensor, pipeline, expert, and data parallelism options support larger models and higher traffic.
Quantization	Supports formats such as FP8, INT4, AWQ, GPTQ, and quantized KV cache depending on hardware and model.

Practical usage

Use SGLang when:

The workload has repeated prefixes, branching prompts, agent loops, or structured generation.
You need high-throughput OpenAI-compatible serving.
You want stronger controls around JSON, tool calls, or reasoning parser behavior.
You are serving multimodal or specialized models supported by SGLang.
You need production features such as multi-GPU parallelism, quantization, and advanced scheduling.

Example server shape:

python -m sglang.launch_server \
  --model-path meta-llama/Llama-3.1-8B-Instruct \
  --host 0.0.0.0 \
  --port 30000

Production checks:

Validate exact model support, chat template behavior, and tokenizer compatibility.
Benchmark shared-prefix workloads separately from unrelated one-shot prompts.
Test structured output failure modes, retries, and schema strictness.
Confirm hardware backend support before choosing quantization or attention settings.
Protect OpenAI-compatible endpoints with authentication and network policy.

Learning checklist

Run an SGLang OpenAI-compatible server.
Send basic chat and streaming requests.
Test a structured JSON output request.
Compare prefix-heavy and non-prefix-heavy benchmark results.
Understand RadixAttention and where it helps.
Try a quantized model or quantized KV cache on supported hardware.
Decide when SGLang is a better fit than Ollama or vLLM for a workload.

繁體中文

概覽

SGLang 是大型語言模型與多模態模型的 serving framework。它結合高效 runtime，以及 OpenAI-compatible serving、原生 Python 使用、structured outputs、tool/reasoning parser 與 offline engine workflow。

它的差異化重點是對結構化與重複生成模式的支援。RadixAttention、prefix caching 等功能可在 prompt 共享 prefix、分支、循環或遵循結構化生成程式時，減少重複計算。

為什麼重要

Agent 與 structured-generation 工作負載常會在多個分支中重用長 prefix。
生產推論同時需要吞吐量與可預期延遲。
Tool use、reasoning parsing、JSON schema 與 constrained decoding 已成為常見 serving 需求。
多模態與特殊模型支援的重要性已超出一般聊天補全。
團隊需要 OpenAI compatibility，同時保留 runtime 專屬最佳化。

架構/概念

概念	角色
Runtime	透過 batching、scheduling、memory management 與最佳化 attention 執行請求。
RadixAttention	面向共享 prefix 與結構化生成工作負載的 prefix-aware KV cache 重用技術。
Prefix caching	重用已計算的 prompt 片段以降低 prefill 成本。
Continuous batching	動態納入並推進請求，讓 accelerator 保持忙碌。
Structured outputs	依設定將模型輸出限制在 JSON schema、類 regex 格式或 parser 驅動格式。
Tool and reasoning parsers	為應用工作流抽取 tool call 或 reasoning 格式輸出。
Parallelism	Tensor、pipeline、expert 與 data parallelism 支援更大模型與更高流量。
Quantization	依硬體與模型支援 FP8、INT4、AWQ、GPTQ 與量化 KV cache 等格式。

實務使用

適合使用 SGLang 的情境：

工作負載具有重複 prefix、prompt 分支、Agent loop 或 structured generation。
需要高吞吐量 OpenAI-compatible serving。
想更精準控制 JSON、tool call 或 reasoning parser 行為。
要 serving SGLang 支援的多模態或特殊模型。
需要多 GPU parallelism、量化與進階排程等生產功能。

範例 server：

python -m sglang.launch_server \
  --model-path meta-llama/Llama-3.1-8B-Instruct \
  --host 0.0.0.0 \
  --port 30000

生產檢查：

驗證精確的模型支援、chat template 行為與 tokenizer 相容性。
將共享 prefix 工作負載與無關 one-shot prompt 分開 benchmark。
測試 structured output 的失敗模式、重試與 schema 嚴格度。
選擇量化或 attention 設定前確認硬體 backend 支援。
以認證與網路政策保護 OpenAI-compatible endpoint。

學習檢核表

執行一個 SGLang OpenAI-compatible server。
發送基本 chat 與 streaming request。
測試 structured JSON output request。
比較 prefix-heavy 與非 prefix-heavy benchmark 結果。
理解 RadixAttention 以及它適合的場景。
在支援硬體上嘗試量化模型或量化 KV cache。
判斷何時 SGLang 比 Ollama 或 vLLM 更適合特定工作負載。