vLLM — AI Framework

Description

vLLM is an innovative library designed for large language model (LLM) inference and serving, making it accessible for users across various domains. Originally developed in the Sky Computing Lab at UC Berkeley, vLLM has evolved into a prominent open-source project, supported by a diverse community of contributors from numerous academic institutions and companies. With over 2000 contributors, vLLM stands out for its collaborative development approach.

The library is tailored for different types of users. For those interested in running open-source models, the Quickstart Guide provides a straightforward entry point. Developers looking to build applications can refer to the User Guide, while those interested in contributing to the development of vLLM can start with the Developer Guide. The project also maintains a roadmap and release notes to keep users informed about its progress and updates.

vLLM is recognized for its speed and efficiency, boasting state-of-the-art serving throughput. It employs advanced techniques such as PagedAttention for effective memory management and supports continuous batching of incoming requests. The library offers flexible model execution options, including piecewise and full CUDA/HIP graphs, and various quantization methods to optimize performance. Additionally, vLLM integrates seamlessly with popular Hugging Face models, enabling high-throughput serving with multiple decoding algorithms.

The framework supports a wide array of model architectures, including decoder-only LLMs, mixture-of-expert models, hybrid attention models, multi-modal models, and more. This versatility makes vLLM suitable for various applications, from natural language processing to multi-modal tasks. For more detailed information, users can explore the vLLM announcing blog post, the official vLLM paper, and other resources that highlight its capabilities and performance improvements.

In summary, vLLM is a powerful tool for anyone looking to leverage large language models efficiently, whether for research, application development, or contributing to the open-source community.

vLLM's Core Features

• State-of-the-art serving throughput

• Efficient memory management with PagedAttention

• Continuous batching of incoming requests

• Flexible model execution with CUDA/HIP graphs

• Support for various quantization methods

• Integration with Hugging Face models

• OpenAI-compatible API server

• Multi-LoRA support for dense and MoE layers

• Support for NVIDIA and AMD GPUs, x86/ARM/PowerPC CPUs

• Streaming outputs and structured output generation

Getting Started with vLLM

1. 1

   Install via package manager

2. 2

   Configure the library for your environment

3. 3

   Build your desired model architecture

4. 4

   Deploy the model for inference

5. 5

   Optimize performance with quantization options

vLLM's Use Cases

• Model Inference
• Application Development
• Research
• Multi-modal Tasks
• Performance Optimization

FAQ from vLLM

• What is vLLM?

  vLLM is a library for LLM inference and serving, designed for ease of use and efficiency.

• Who developed vLLM?

  vLLM was developed in the Sky Computing Lab at UC Berkeley.

• How can I get started with vLLM?

  Users can start with the Quickstart Guide for running models or the User Guide for building applications.

• What types of models does vLLM support?

  vLLM supports over 200 model architectures, including decoder-only and multi-modal models.

• Is vLLM open-source?

  Yes, vLLM is an open-source project with contributions from a diverse community.

• What are the key performance features of vLLM?

  vLLM offers high throughput, efficient memory management, and flexible model execution.

• Can I use vLLM with GPUs?

  Yes, vLLM supports NVIDIA and AMD GPUs, as well as various CPUs.

• Where can I find more information about vLLM?

  Additional information can be found on the vLLM documentation site and related blog posts.