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  3. SGLang
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SGLang

SGLang is a fast serving framework for large language models and vision language models.

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Introduction

Introduction to SGLang

SGLang is a fast serving framework designed for large language models (LLMs) and vision language models. It enhances interaction with models by co-designing the backend runtime and frontend language, making it faster and more controllable.

Key Features:
  • Fast Backend Runtime: Efficient serving with advanced techniques like RadixAttention, zero-overhead CPU scheduling, and continuous batching.
  • Flexible Frontend Language: Intuitive interface for programming LLM applications, supporting advanced prompting, control flow, and multi-modal inputs.
  • Extensive Model Support: Compatibility with a wide range of generative and embedding models, allowing easy integration of new models.
  • Active Community: Open-source project backed by a vibrant community and industry adoption, ensuring continuous improvement and support.
Benefits:
  • High Performance: Deployed in large-scale production, generating trillions of tokens daily.
  • Enterprise Ready: Offers technical consulting and partnership opportunities for large-scale deployments.
  • Community Driven: Contributions from a diverse group of developers and institutions, fostering innovation and collaboration.
Highlights:
  • Supported by major institutions like AMD, NVIDIA, and Stanford.
  • Regular updates and improvements, with a clear development roadmap for future enhancements.
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Information

  • Publisher
    AISecKit
  • Websitegithub.com
  • Published date2025/04/28

Categories

  • AI Models
  • AI Application Platforms
  • AI Development Frameworks

Tags

  • Model Robustness
  • Open Source
  • LLM
  • Generative AI

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