vLLM - AI Model Serving Tool

Overview

vLLM is a high-throughput, memory-efficient library for large language model inference and serving. It supports tensor and pipeline parallelism to scale model inference.

Key Features

• High-throughput inference for large language models
• Memory-efficient runtime to reduce memory footprint
• Supports tensor parallelism
• Supports pipeline parallelism
• Designed for model inference and serving

Ideal Use Cases

• Deploy large language model inference in production
• Serve models with tensor and pipeline parallelism
• Scale inference workloads while reducing memory usage
• Experiment with model parallelism strategies

Getting Started

• Open the vLLM GitHub repository
• Clone the repository to your machine
• Follow repository installation and setup instructions
• Configure model, devices, and parallelism settings
• Run the provided inference or serving examples
• Consult repository documentation for advanced configuration

Pricing

No pricing information disclosed; project repository is available on GitHub.

Limitations

• Requires user-provided compute and infrastructure for hosting models
• Deployment and tuning require knowledge of model parallelism and serving
• No pricing or hosted service information available

Key Information

• Category: Model Serving
• Type: AI Model Serving Tool