SGLang: Technical Evolution and Industrial Practices of a High-Performance LLM Inference Framework

SGLang is an open-source high-performance large language model (LLM) inference framework developed by LMSYS. It addresses core challenges in large-scale LLM serving through key innovations like RadixAttention, Prefill-Decode (PD) separation, and expert parallelism. Currently deployed on over 400,000 GPUs globally, it handles trillions of tokens daily, becoming a de facto standard in the field.

With LLM parameter scales expanding from billions to trillions, performance optimization of inference services has become a core AI infrastructure issue. Traditional engines struggle with high concurrency, low latency, long context processing, multi-turn dialogue caching, and distributed deployment. SGLang was born in this context as an open-source project by LMSYS, released in early 2024. It quickly gained adoption, deployed on over 400,000 GPUs globally, handling trillions of tokens daily. Its technical route combines compiler optimization, memory management, and distributed systems to ensure production reliability.

SGLang's key innovations include RadixAttention (a prefix tree-based KV cache system that auto-identifies and reuses shared prefixes in multi-turn dialogues, reducing redundant computation) and Zero-Overhead CPU Scheduler (an asynchronous strategy that decouples CPU request orchestration from GPU computation, significantly improving GPU utilization for variable-length sequences and dynamic batching).

Single-card optimizations include PagedAttention (KV cache paging to reduce memory fragmentation), Continuous Batching (dynamic request insertion to boost batch utilization), and Speculative Decoding (draft model prediction to reduce serial dependency). For large models like DeepSeek-V3/R1, expert parallelism distributes MoE model experts across nodes. 2025 tests on 96 H100 GPUs show 3.8x prefill and 4.8x decode throughput gains. PD separation assigns prefill (compute-intensive) and decode (bandwidth-sensitive) to different resources, achieving 2.7x decode throughput on GB200 NVL72 hardware.

Hardware support covers NVIDIA (A100 to GB200/B300), AMD Instinct (MI300/MI355), Google TPU (SGLang-Jax backend since October 2025), Intel Xeon, and Huawei Ascend NPU. Quantization options include FP4, FP8, INT4, AWQ, and GPTQ. Model coverage includes language models (Llama, Qwen, DeepSeek, etc.), embedding models (e5-mistral, gte), reward models (Skywork), diffusion models (WAN, Qwen-Image), and multi-modal models (LLaVA-OneVision). Structured output via Compressed Finite State Machine boosts JSON decoding speed by over 3x.

SGLang is deployed in production by top tech companies (xAI, AMD, NVIDIA, Intel, LinkedIn, Cursor) and cloud vendors (Oracle Cloud, Google Cloud, Microsoft Azure, AWS). Academic institutions like MIT, Stanford, UC Berkeley, and Tsinghua University use it for research. In June 2025, it received funding from a16z's third open-source AI fund. The community is active with Slack channels, weekly developer meetings, and frequent code submissions.

SGLang's success stems from addressing core contradictions in LLM inference (memory/compute mismatch, dynamic/static resource conflict, single-card/cluster balance) via innovations like RadixAttention, zero-overhead scheduling, and PD separation. It provides a production-proven base for teams from startups to enterprises. As multi-modal and Agent applications grow, its continuous evolution will keep leading the field.