SGLang: High-Performance Serving Framework for LLMs and Vision-Language Models
SGLang is an open-source, high-performance serving framework specifically engineered for large language models (LLMs) and vision-language models (VLMs). Developed under the LMSYS organization, it addresses the critical needs of modern AI deployments by enabling efficient, scalable inference that balances low latency with high throughput. Whether you're running on a single GPU for prototyping or scaling to massive distributed clusters for production workloads, SGLang provides a robust backend runtime and flexible frontend interface to streamline LLM serving and application development.
Core Features and Architecture
At its heart, SGLang's backend runtime is optimized for speed and efficiency. It incorporates RadixAttention, a novel mechanism for efficient prefix caching that significantly reduces recomputation during inference, leading to up to 5x faster performance compared to traditional methods. The framework also features a zero-overhead CPU scheduler that minimizes scheduling bottlenecks, ensuring seamless handling of dynamic workloads. Prefill-decode disaggregation allows for better resource utilization by separating the computationally intensive prefill phase from the iterative decoding phase, which is particularly beneficial in multi-GPU setups.
Additional backend capabilities include:
- Speculative decoding and continuous batching for improved throughput under varying request patterns.
- Paged attention to manage KV cache memory efficiently, preventing out-of-memory issues in long-context scenarios.
- Support for advanced parallelism strategies: tensor parallelism, pipeline parallelism, expert parallelism (EP), and data parallelism, enabling deployments on hundreds of GPUs.
- Structured outputs with compressed finite state machines (FSMs) for faster JSON decoding—up to 3x improvement.
- Chunked prefill for handling ultra-long inputs without performance degradation.
- Quantization options like FP4, FP8, INT4, AWQ, and GPTQ to reduce memory footprint while maintaining accuracy.
- Multi-LoRA batching for efficient fine-tuned model serving.
SGLang's extensibility shines in its model support. It natively handles a broad ecosystem of generative models (e.g., Llama, Qwen, DeepSeek, Kimi, GLM, GPT, Gemma, Mistral), embedding models (e.g., E5-Mistral, GTE, MCDSE), reward models (e.g., Skywork), and even diffusion models (e.g., WAN, Qwen-Image). Compatibility with Hugging Face models and OpenAI-compatible APIs makes integration straightforward, while custom model addition is simplified through modular design.
Hardware Versatility
One of SGLang's standout strengths is its hardware-agnostic approach. It runs seamlessly on:
- NVIDIA GPUs (e.g., GB200, B300, H100, A100, Spark) with CUDA optimizations.
- AMD GPUs (e.g., MI355, MI300X) via ROCm.
- Intel Xeon CPUs for edge deployments.
- Google TPUs with a dedicated SGLang-Jax backend.
- Ascend NPUs and other accelerators.
Recent updates include day-0 support for cutting-edge hardware like NVIDIA GB200 NVL72 racks, achieving 3.8x prefill and 4.8x decode throughput boosts, and native TPU integration for broader cloud compatibility.
Frontend Programming Interface
Beyond raw serving, SGLang offers a powerful frontend language for building sophisticated LLM applications. This intuitive Python-based interface supports:
- Chained generation calls for multi-step reasoning.
- Advanced prompting techniques, including multi-modal inputs (text, images, videos).
- Control flow constructs (loops, conditionals) for complex workflows.
- Parallel execution for concurrent tasks.
- External interactions, such as API calls or database queries.
This makes SGLang ideal for developers creating chatbots, agents, or RAG systems without sacrificing performance.
Performance Benchmarks and Real-World Impact
SGLang consistently outperforms competitors like vLLM and TensorRT-LLM in benchmarks. For instance:
- In Llama3 serving, it delivers higher throughput with lower latency.
- v0.3 release introduced 7x faster DeepSeek MLA and 1.5x speedup via torch.compile.
- v0.4 added zero-overhead batching and cache-aware load balancing for even greater efficiency.
In production, SGLang powers trillions of tokens daily across over 400,000 GPUs worldwide. It's adopted by industry leaders including xAI, AMD, NVIDIA, Intel, LinkedIn, Cursor, Oracle Cloud, Google Cloud, Microsoft Azure, AWS, and academic institutions like MIT, Stanford, and Tsinghua University. Highlights include deployments for DeepSeek-V3 on 96 H100 GPUs and GB200 racks, showcasing massive scale expert parallelism.
Community and Ecosystem
As an open-source project (BSD-3-Clause license), SGLang fosters a vibrant community with resources like documentation, Slack channels, weekly dev meetings, and contribution guides. It builds on inspirations from projects like Guidance, vLLM, and FlashInfer, while contributing back through innovations like SGLang Diffusion for accelerated video/image generation.
Recent news underscores its momentum: awards from a16z's Open Source AI Grant, integration into the PyTorch ecosystem, and meetups with NVIDIA/AMD. For enterprises, dedicated support is available via sglang@lmsys.org.
In summary, SGLang represents a leap forward in LLM serving, combining cutting-edge optimizations with practical usability to democratize high-performance AI inference.
