SGLang: High-Performance LLM Inference with RadixAttention and 27.7k+ GitHub Stars

Tosin Akinosho

— 4 min read

SGLang is a high-performance serving framework for large language models and multimodal models, designed to deliver low-latency and high-throughput inference across single GPUs to large distributed clusters. With 27.7k+ GitHub stars and active development, SGLang powers over 400,000 GPUs worldwide, generating trillions of tokens daily in production environments. It represents a critical infrastructure layer for organizations deploying AI agents, multi-turn conversational systems, and reasoning-heavy workloads at scale.

What is SGLang?

SGLang is an open-source inference engine developed by LMSYS (the organization behind Chatbot Arena and Vicuna). It addresses a fundamental challenge in LLM deployment: achieving both low latency and high throughput without sacrificing model quality or requiring manual optimization. Unlike traditional inference approaches that waste 60-80% of GPU memory on KV cache allocation, SGLang introduces RadixAttention—a radix tree-based prefix caching system that automatically discovers and reuses shared prefixes across requests.

The framework is production-ready and trusted by major organizations including xAI (serving Grok 3), Microsoft Azure (DeepSeek R1 on AMD GPUs), and hundreds of enterprises. SGLang's architecture supports everything from single-GPU inference to distributed deployments across thousands of GPUs, making it suitable for both research prototypes and mission-critical production systems.

Created by the LMSYS team, SGLang combines cutting-edge research with practical engineering. The project maintains active development with commits within the last 34 minutes (as of May 13, 2026), indicating a vibrant community and continuous optimization efforts.

Core Features and Architecture

RadixAttention: Automatic Prefix Caching

RadixAttention is SGLang's flagship innovation. It uses a radix tree (trie) data structure to manage the KV cache, automatically discovering shared prefixes across requests without manual configuration. When multiple requests share a common system prompt or conversation history, RadixAttention identifies this overlap and stores it once, providing instant cache hits for subsequent requests. This is particularly powerful for multi-turn conversations, AI agents with iterative reasoning, and batch processing with similar prefixes.

Zero-Overhead CPU Scheduler

SGLang implements a CPU scheduler that eliminates scheduling overhead—a critical bottleneck in high-throughput scenarios. Traditional schedulers introduce latency spikes; SGLang's design maintains consistent performance even under extreme concurrency. Benchmarks show SGLang maintaining 30-31 tokens/second constant throughput while competitors drop from 22 to 16 tokens/second under load.

Prefill-Decode Disaggregation

This feature separates the prefill phase (processing input tokens) from the decode phase (generating output tokens), allowing independent optimization of each stage. Prefill-decode disaggregation enables better GPU utilization and more predictable latency profiles, especially critical for real-time applications.

Speculative Decoding

SGLang supports EAGLE and EAGLE3 speculative decoding, which uses a small draft model to propose multiple tokens, then validates them with the large model in a single pass. This can achieve 2-3x latency improvements in memory-bound scenarios without sacrificing output quality.

Structured Output Generation

Native support for JSON, XML, and other structured formats with compressed finite state machines. This eliminates the need for post-processing and ensures outputs conform to expected schemas—critical for AI agents and tool-calling workflows.

Multi-GPU Parallelism

SGLang supports tensor parallelism, pipeline parallelism, expert parallelism (for MoE models), and data parallelism. This enables seamless scaling from single-GPU inference to distributed deployments across thousands of GPUs. The framework includes specialized optimizations for DeepSeek MoE models with MLA-optimized kernels.

Quantization Support

Comprehensive quantization options including FP4, FP8, INT4, AWQ, and GPTQ. These reduce memory footprint and improve throughput, enabling deployment of larger models on constrained hardware.

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Getting Started

Installation

System Requirements: Python 3.10+, CUDA 12.2+, NVIDIA GPU SM75+ (T4, A100, H100, etc.), 32GB RAM minimum, 50GB disk space.

Method 1: pip with uv (Recommended)

pip install --upgrade pip
pip install uv
uv pip install "sglang[all]>=0.4.6.post2"

Method 2: Docker (Production)

docker run --gpus all \
    --shm-size 32g \
    -p 30000:30000 \
    -v ~/.cache/huggingface:/root/.cache/huggingface \
    lmsysorg/sglang:latest \
    python3 -m sglang.launch_server \
    --model-path meta-llama/Llama-3.1-8B-Instruct \
    --host 0.0.0.0 --port 30000

Quick Start

Launch a server and send requests via OpenAI-compatible API:

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

# Send a request
curl http://localhost:30000/v1/chat/completions \
    -H "Content-Type: application/json" \
    -d '{
        "model": "default",
        "messages": [{"role": "user", "content": "Explain quantum computing"}],
        "temperature": 0.7
    }'

Real-World Use Cases

Multi-Turn Conversational AI

SGLang excels at chatbot and dialogue systems where RadixAttention automatically caches shared conversation history. A customer support system handling 1,000 concurrent conversations benefits from automatic prefix reuse, reducing latency by 10-15% compared to traditional approaches.

AI Agent Orchestration

Agents performing iterative reasoning (planning, tool-calling, reflection) generate many requests with overlapping context. SGLang's prefix caching and structured output support make it ideal for frameworks like CrewAI, AutoGen, and LangGraph deployments.

DeepSeek Model Deployment

SGLang provides day-0 support for DeepSeek models with MLA-optimized kernels. Organizations deploying DeepSeek R1 or V3 achieve 2-3x better throughput on SGLang compared to generic inference engines.

High-Throughput Batch Processing

Content generation pipelines (summarization, translation, code generation) benefit from SGLang's 29% throughput advantage over competitors. A news organization generating 100,000 article summaries daily saves significant compute costs.

How It Compares

SGLang vs vLLM: SGLang delivers 29% higher throughput on H100 GPUs (16,215 vs 12,553 tokens/second) with lower latency (79ms vs 103ms TTFT). RadixAttention excels at multi-turn conversations; vLLM's PagedAttention is more memory-efficient. vLLM has broader hardware support (TPU, AWS Trainium); SGLang focuses on NVIDIA/AMD GPUs.

SGLang vs TensorRT-LLM: TensorRT-LLM offers slightly higher peak throughput but requires NVIDIA-specific optimization and model compilation. SGLang provides broader model support and easier deployment with OpenAI-compatible APIs.

SGLang vs Ollama: Ollama prioritizes simplicity and local deployment; SGLang targets production-scale inference with advanced features like speculative decoding and expert parallelism. Ollama is better for prototyping; SGLang for enterprise deployments.

What's Next

SGLang's roadmap includes native TPU support (SGLang-Jax backend already available), enhanced AMD GPU optimization, and expanded multimodal model support. Recent releases added SGLang Diffusion for video and image generation, extending the framework beyond text-only inference. The community is actively working on large-scale expert parallelism for trillion-parameter models and elastic failure tolerance for distributed deployments.

With 400,000+ GPUs running SGLang and trillions of tokens generated daily, the framework has become the de facto industry standard for high-performance LLM serving. As AI workloads grow more complex and latency-sensitive, SGLang's innovations in prefix caching and scheduling will remain critical infrastructure for the AI economy.

Sources

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