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 models across devices and optimize throughput and memory usage.

Key Features

• High-throughput inference for large language models
• Memory-efficient execution to reduce memory footprint
• Supports tensor parallelism
• Supports pipeline parallelism
• Designed for scalable model serving
• Repository and source available on GitHub

Ideal Use Cases

• Deploy large language models for low-latency inference
• Scale model inference across multiple devices using parallelism
• Serve models in production environments
• Experiment with parallelism strategies for performance tuning

Getting Started

• Clone the vLLM repository from GitHub
• Install vLLM and its Python dependencies
• Configure model checkpoint path and parallelism settings
• Launch the vLLM inference server or runtime
• Test inference with a small sample request

Pricing

Pricing not disclosed. The project repository is hosted on GitHub; deployment and runtime costs depend on your chosen infrastructure.

Key Information

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