# vLLM

> High-throughput and memory-efficient open-source LLM inference engine with PagedAttention, continuous batching, and support for embedding model serving. Widely adopted for production-scale AI inference.

- **URL:** https://vllm.ai
- **Category:** Machine Learning Models
- **Tags:** inference, Gpu Acceleration, Open Source
- **Updated:** 2026-04-04 23:01
- **Canonical page:** https://vectordb.ever.works/pl/items/vllm

## Details

## Overview

vLLM is an open-source inference engine optimized for large language models. It implements PagedAttention to manage KV cache efficiently and continuous batching to maximize GPU throughput. Though designed primarily for LLM inference, vLLM also supports embedding model serving.

## Key Features

- PagedAttention for efficient KV cache management, eliminating memory fragmentation
- Continuous batching to maximize GPU utilization across varying request patterns
- Support for embedding model serving in addition to text generation
- High throughput and low latency compared to standard Hugging Face pipelines
- Support for multiple model architectures and hardware backends (CUDA, ROCm)
- Distributed inference across multiple GPUs and nodes

## Production Use

- Real-time query embedding generation with millisecond-level latencies
- Batch embedding workloads with automated continuous batching
- Containerized deployment with Kubernetes for horizontal scaling

## Pricing

Free and open-source under the Apache 2.0 license.

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