vLLM: LLM reasoning and service engine for efficient memory utilization

General Introduction

vLLM is a high-throughput and memory-efficient reasoning and service engine designed for Large Language Modeling (LLM). Originally developed by the Sky Computing Lab at UC Berkeley, it is now a community project driven by both academia and industry. vLLM aims to provide fast, easy-to-use and cost-effective LLM reasoning services with support for a wide range of hardware platforms including CUDA, ROCm, TPUs, and more. Its key features include optimized execution loops, zero-overhead prefix caching, and enhanced multimodal support.

Function List

• High Throughput Reasoning: supports massively parallel reasoning, significantly improving reasoning speed.
• Memory Efficient: Reduce memory usage and improve model operation efficiency by optimizing memory management.
• Multi-hardware support: Compatible with CUDA, ROCm, TPU and other hardware platforms for flexible deployment.
• Zero-overhead prefix caching: Reduce duplicate computation and improve inference efficiency.
• Multi-modal support: Supports multiple input types such as text, image, etc. to extend the application scenarios.
• Open source community: maintained by academia and industry, continuously updated and optimized.

Using Help

Installation process

1. Clone the vLLM project repository:

   git clone https://github.com/vllm-project/vllm.git
cd vllm

2. Install the dependencies:

   pip install -r requirements.txt

3. Choose the appropriate Dockerfile for your build based on your hardware platform:

   docker build -f Dockerfile.cuda -t vllm:cuda .

Guidelines for use

1. Start the vLLM service:

   python -m vllm.serve --model <模型路径>

2. Sends a reasoning request:

   import requests
response = requests.post("http://localhost:8000/infer", json={"input": "你好，世界！"})
print(response.json())

Detailed Function Operation

• High Throughput Reasoning: By parallelizing the reasoning task, vLLM is able to process a large number of requests in a short period of time for highly concurrent scenarios.
• Memory Efficient: vLLM uses an optimized memory management strategy to reduce memory footprint and is suitable for running in resource-constrained environments.
• Multi-Hardware Support: Users can choose the right Dockerfile to build according to their hardware configuration and flexibly deploy on different platforms.
• Zero-overhead prefix caching: By caching the results of prefix computation, vLLM reduces repetitive computation and improves inference efficiency.
• multimodal support: vLLM not only supports text input, but also handles a variety of input types such as images, expanding the application scenarios.