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vLLM: High-Throughput and Memory-Efficient LLM Inference and Serving Engine
An open-source library focused on optimizing the inference and serving of large language models (LLMs) for maximum throughput and minimal memory usage. Essential for production deployments of LLMs.
Python
Added on 2025年7月6日51,535
Stars8,513
ForksPython
LanguageProject Introduction
Summary
This project is a high-performance engine designed specifically for serving large language models. It utilizes cutting-edge algorithms and system optimizations to achieve industry-leading throughput and memory efficiency, making LLM deployment more scalable and cost-effective.
Problem Solved
Traditional LLM serving methods are often memory-inefficient and struggle with high request throughput under production loads, leading to high infrastructure costs and poor performance. This project addresses these limitations.
Core Features
Efficient Memory Management (PagedAttention)
Implements advanced memory management techniques like PagedAttention to efficiently handle key-value caches, significantly reducing memory footprint compared to naive approaches.
Continuous Batching
Processes multiple requests concurrently, even during token generation, maximizing GPU utilization and overall throughput.
Broad Model Support
Supports a wide range of popular LLM architectures out-of-the-box, making it easy to integrate with existing models.
Tech Stack
Python
CUDA
PyTorch
Triton (potentially)
Docker
使用场景
vLLM is suitable for any application requiring performant and scalable serving of large language models, including:
Serving Chatbots and Conversational AI
Details
Powering conversational AI applications like chatbots and virtual assistants with low latency and high user capacity.
User Value
Enables a smooth, responsive user experience for many concurrent users.
Enterprise LLM Deployment
Details
Deploying LLMs for enterprise tasks such as document analysis, content generation, and code assistance within internal systems.
User Value
Reduces infrastructure costs for internal AI initiatives and improves processing speed.
Backend for AI Platforms/APIs
Details
Providing scalable LLM inference APIs for developers building AI-powered products.
User Value
Offers a high-performance backend that can handle large volumes of API requests efficiently.
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