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

SGLang is an open-source high-performance large language model inference framework maintained by the LMSYS organization. It has supported over 400,000 GPU inference tasks worldwide and processes trillions of tokens daily. Core technologies include innovative features such as RadixAttention prefix cache buffer, zero-overhead CPU scheduler, and PD separation. It covers multiple models and hardware platforms, and is applied in scenarios like inference serving and reinforcement learning training, making it a widely recognized standard for high-performance inference engines in the industry.

SGLang was born from insights into the performance bottlenecks of existing inference frameworks: In early 2024, the RadixAttention technology was proposed to achieve up to 5x acceleration; v0.2 optimized Llama3 performance to surpass TensorRT-LLM and vLLM; v0.3 achieved 7x acceleration for DeepSeek MLA and 1.5x speedup with torch.compile; v0.4 introduced a zero-overhead batch scheduler and cache-aware load balancer. In 2025, it received support from the a16z Open Source AI Fund, joined the PyTorch ecosystem, implemented native support for multiple hardware, and provided day-0 support for DeepSeek V3/R1.

1. RadixAttention: Uses prefix caching to avoid redundant computations, stores common KV Cache in a tree structure, significantly reduces first-token latency, and achieves 25x performance improvement on NVIDIA GB300 NVL72; 2. Zero-overhead CPU scheduler: Prefetching + asynchronous execution reduces scheduling latency; PD separation: Separates the Prefill and Decode stages onto different hardware, and when combined with expert parallelism on GB200 NVL72, achieves 3.8x Prefill and 4.8x Decode throughput improvement; 3. Multi-dimensional parallelism: Supports tensor, pipeline, expert, and data parallelism; Quantization supports formats like FP4/FP8/INT4, reduces memory usage and improves speed.

In terms of models, it supports mainstream LLMs such as Llama/Qwen/DeepSeek, as well as embedding, reward, and diffusion models. It is deeply integrated with Hugging Face, compatible with the OpenAI API, and provides day-0 support for new models; In terms of hardware, it natively supports multiple platforms including NVIDIA (GB200/B300, etc.), AMD (MI355/MI300, etc.), Intel Xeon, Google TPU, and Huawei Ascend, avoiding vendor lock-in.

Application scenarios include inference serving and reinforcement learning training (used as a rollout backend by frameworks like AReaL/Miles); Production deployment cases: Used by enterprises such as xAI and AMD, and universities like MIT; DeepSeek implemented PD separation and expert parallelism deployment on 96 H100 GPUs; Performance optimization suggestions: Properly configure prefix cache, enable PD separation, choose appropriate parallelism strategies, utilize quantization techniques, and monitor and tune parameters.

SGLang represents the highest level of open-source LLM inference frameworks, with its technological innovations and engineering practices setting an industry benchmark; Future focuses include diffusion model support, ultra-long context optimization, and edge deployment capability enhancement; Enterprises can obtain commercial service support via sglang@lmsys.org.