Does SGLang support multi-modal models?

Yes, SGLang supports a wide variety of vision-language models (VLMs) including LLaVA, Phi-3-Vision, and Qwen2-VL for image-based reasoning tasks.

When should I use vLLM instead of SGLang?

vLLM is often preferred for simple text generation tasks that don't require structured output or prefix caching, as it is a more mature and widely-tested production system.

Can SGLang guarantee valid JSON output?

Yes, SGLang supports constrained decoding using JSON schemas, regex patterns, and EBNF grammars to ensure the model output strictly follows a specific structure.

How does SGLang achieve faster inference than vLLM?

SGLang uses RadixAttention to automatically cache and reuse Key-Value (KV) prefixes across requests, which is significantly faster for workflows with shared system prompts or few-shot examples.

Is SGLang compatible with existing OpenAI-based applications?

SGLang provides an OpenAI-compatible API server, allowing you to swap it into existing workflows that use the OpenAI Python SDK or standard REST requests.

SGLang Inference Serving

Name: SGLang Inference Serving
Author: Orchestra-Research

byOrchestra-Research

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3,983

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Data Science & ML

Optimizes LLM serving and structured data generation with RadixAttention prefix caching for high-performance agentic workflows.

SGLang is a high-performance serving framework designed for Large Language Models (LLMs) and Vision Language Models (VLMs), specializing in fast structured generation and efficient prefix caching. By utilizing RadixAttention, it automatically reuses KV caches for shared prefixes, making it up to 5 times faster for agentic workflows, multi-turn conversations, and few-shot prompting compared to traditional frameworks. It provides robust support for constrained decoding via JSON schemas, regex, and EBNF grammars, making it an ideal choice for developers building complex AI agents and production-scale inference services that require precision and speed.

Key Features

01Fast structured generation with JSON schema, regex, and grammar constraints

02High-performance serving with up to 5x faster inference for agentic workloads

033,983 GitHub stars

04RadixAttention for automatic prefix caching and KV cache reuse

05Native support for multi-turn conversations and function calling

06Compatible with 100+ text and vision models via an OpenAI-compatible API

Use Cases

01Generating guaranteed valid JSON outputs for data extraction and API integrations

02Building AI agents that require repeated system prompts and tool-calling capabilities

03Scaling high-throughput inference for multi-user chat applications with shared context

What are Skills?·How to Install

Install with 🐟 Skill.Fish

npx skillfish add orchestra-research/ai-research-skills sglang

For use in Claude.ai and ChatGPT

Download Skill