Announcement
SGLang - 高效服务大型语言模型与视觉语言模型的框架
SGLang is a fast serving framework specifically designed for large language models (LLMs) and vision language models (VLMs), optimizing inference performance and throughput.
Project Introduction
Summary
SGLang is an open-source framework built to enable fast and efficient serving of large language models and vision language models, focusing on optimizing inference speed and throughput.
Problem Solved
Serving large language and vision models efficiently and at scale is computationally expensive and challenging, often leading to high latency and operational costs. SGLang addresses this by providing a high-performance serving framework tailored for these models.
Core Features
High Performance Inference
Achieves significantly lower latency and higher throughput compared to standard serving solutions for large models.
Broad Model Compatibility
Supports a wide range of popular large language and vision language models.
Optimized Resource Utilization
Includes features for efficient batching and request scheduling to maximize hardware utilization.
Tech Stack
使用场景
SGLang is ideal for scenarios requiring high-performance, low-latency inference for large language and vision models.
构建低延迟AI应用API
Details
Deploying LLMs or VLMs as part of a user-facing application API where response time is critical for user experience.
User Value
Provides faster response times for AI features, leading to a better user experience and higher engagement.
处理高吞吐量推理请求
Details
Handling a large volume of simultaneous requests for LLM/VLM inference, such as in a chatbot service or image captioning platform.
User Value
Maximizes the number of requests processed per second on given hardware, reducing infrastructure costs.
部署多模态AI服务
Details
Deploying complex vision-language tasks that require processing both image and text inputs through a single model.
User Value
Efficiently serves integrated VLM models for sophisticated AI applications like visual question answering.
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