sglang
A high-performance serving framework for large language models and multimodal models, delivering low-latency and high-throughput inference.
What is sglang?
SGLang is a high-performance serving framework specifically designed for deploying and running large language models (LLMs) and multimodal models in production. It solves the problem of achieving low-latency and high-throughput inference across various hardware setups, from single GPUs to large distributed clusters. The framework includes optimizations like RadixAttention, speculative decoding, and continuous batching to maximize efficiency.
AI engineers, ML researchers, and DevOps teams who need to deploy and serve LLMs and multimodal models at scale in production environments. It is also suitable for organizations requiring efficient inference on diverse hardware, including NVIDIA, AMD, and TPU systems.
Developers choose SGLang for its exceptional performance, broad model and hardware support, and proven scalability in production. Its unique selling point is being a comprehensive, open-source framework that combines cutting-edge optimizations with extensive compatibility, making it a reliable choice for large-scale AI serving.
Overview
SGLang is a high-performance serving framework for large language models and multimodal models.
Use Cases
Best For
- Deploying large language models like Llama or DeepSeek in production with high throughput
- Serving multimodal models (e.g., image/video generation models) efficiently
- Running inference on distributed GPU clusters across multiple nodes
- Optimizing LLM serving latency and cost with advanced techniques like speculative decoding
- Integrating with existing Hugging Face model pipelines or OpenAI-compatible APIs
- Scaling AI inference to handle trillions of tokens daily in enterprise environments
Not Ideal For
- Small-scale prototyping or hobby projects where the overhead of distributed systems isn't justified
- Teams that only require basic, single-GPU inference without advanced features like speculative decoding or expert parallelism
- Projects heavily dependent on a specific cloud provider's managed LLM service who prefer to avoid infrastructure management
Pros & Cons
Pros
Incorporates RadixAttention for prefix caching and speculative decoding, leading to up to 5x faster inference as highlighted in blog posts, with features like zero-overhead CPU scheduler for efficient serving.
Supports a wide range of models from Hugging Face and runs on diverse hardware including NVIDIA, AMD GPUs, TPUs, and CPUs, ensuring compatibility across production environments as noted in the extensive support list.
Enables tensor, pipeline, expert, and data parallelism for efficient inference on large clusters, powering over 400,000 GPUs worldwide and handling trillions of tokens daily.
Adopted by major enterprises like xAI and AMD, with native integrations for RL and post-training frameworks such as AReaL and Tunix, making it a trusted backbone for large-scale deployments.
Cons
Setting up and tuning distributed clusters with features like prefill-decode disaggregation requires deep expertise in ML serving and systems engineering, which can be a barrier for smaller teams.
As a fast-moving project with frequent updates and day-0 support for new models, users may face breaking changes or need to constantly adapt configurations, as implied by the active news stream and roadmap.
The advanced optimizations and scalability features add unnecessary complexity and resource overhead for deployments that only need basic, low-volume inference on single GPUs.
Frequently Asked Questions
Related Projects
HuggingFace Transformers🤗 Transformers: the model-definition framework for state-of-the-art machine learning models in text, vision, audio, and multimodal models, for both inference and training.
langchainThe agent engineering platform
vllmA high-throughput and memory-efficient inference and serving engine for LLMs
unslothWeb UI for training and running open models like Gemma 4, Qwen3.5, DeepSeek, gpt-oss locally.
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