# vLLM

> An open-source, high-performance library for serving and running large language models with GPU-optimized inference and efficient memory and batch management.

vLLM is an open-source library designed to deliver high-throughput, low-latency inference for large language models on GPU hardware. It focuses on efficient memory management, batching, and throughput optimizations to make serving transformer-based models faster and more resource-efficient. vLLM exposes a Python API and runtime components that let developers run and integrate models in self-hosted environments.

- **High-performance inference**: Optimized runtimes and batching strategies to maximize GPU utilization for transformer models.
- **Memory-efficient management**: Techniques for KV-cache and attention memory management to reduce GPU memory pressure.
- **Python API and SDK**: Programmatic interfaces for loading models, running inference, and integrating into applications.
- **Support for common model formats**: Designed to run models exported in widely used formats and to interoperate with popular model toolchains.

Getting started typically involves installing or building the library from source, preparing a GPU-enabled environment, loading a compatible model, and invoking the Python API to perform inference. The documentation provides guides on configuration, performance tuning, and deployment patterns for self-hosted inference services.

## Features
- High-throughput GPU inference
- Batching and scheduling for concurrent requests
- Memory-efficient KV-cache and attention management
- Python API for model loading and inference
- Optimizations for transformer-based models

## Integrations
Hugging Face Transformers, Hugging Face Hub, CUDA / NVIDIA GPUs, PyTorch ecosystem

## Platforms
DEVELOPER_SDK

## Pricing
Open Source

## Links
- Website: https://vllm.ai
- Documentation: https://docs.vllm.ai
- Repository: https://github.com/vllm-project/vllm
- EveryDev.ai: https://www.everydev.ai/tools/vllm