SGLang: Technical Analysis and Application Practice of a High-Performance Large Language Model Inference Service Framework

This article will conduct an in-depth analysis of the core technical architecture of SGLang, a high-performance large language model inference service framework (including key features such as RadixAttention prefix caching, zero-overhead CPU scheduler, and PD separation), as well as its large-scale deployment practices in production environments. Currently, SGLang runs on over 400,000 GPUs worldwide, generating trillions of tokens daily, and is a strong alternative to frameworks like vLLM.

With the growth of LLM parameter scales, traditional inference frameworks struggle to balance latency and throughput in scenarios such as high concurrency and long contexts. Developed by LMSYS, SGLang is positioned as a high-performance service framework for large models and multimodal models, supporting deployment from single cards to distributed clusters. Compared to vLLM and TensorRT-LLM, its advantages lie in its end-to-end optimized architecture and rapid support for cutting-edge hardware and new models.

SGLang's core competitiveness lies in three key technologies:

1. RadixAttention Prefix Caching: Reuses shared prefix KV Cache through a tree structure, reducing first-token latency by over 50%, improving throughput, and being transparent to users;
2. Zero-overhead CPU Scheduler: Implements continuous batching via asynchronous scheduling, keeping GPU utilization stable at over 95%;
3. PD Separation Architecture: Decouples compute-intensive prefill from memory-intensive decode, achieving a 3.8x increase in prefill throughput and a 4.8x increase in decode throughput on GB200 NVL72 clusters.

SGLang natively supports hardware such as NVIDIA (5090/GB200, etc.), AMD (MI355/MI300), Intel Xeon, Google TPU, and Huawei Ascend NPU. Ecologically, it is compatible with Hugging Face model formats and OpenAI API interfaces, supporting mainstream model families like Llama, Qwen, DeepSeek, as well as types such as embedding, reward, and diffusion models, allowing developers to seamlessly migrate applications.

SGLang has been applied in production environments of enterprises such as xAI, AMD, NVIDIA, LinkedIn, and Oracle Cloud, as well as universities like MIT and Stanford. Over 400,000 GPUs worldwide run this framework, generating trillions of tokens daily. In addition, it is used as a Rollout backend by RL training frameworks like AReaL and Miles, supporting complex sampling strategies and dynamic batching.

The SGLang community is active, providing detailed documentation, tutorials, weekly developer meetings, Slack communication channels, and regular technical meetups. In the future, the team will explore longer context optimization, deep multimodal support, and adaptation to new hardware, continuing to maintain its leading position in the field of high-performance inference.