# vLLM

vLLM is an inference and serving engine for large language models, designed for high-throughput, memory-efficient deployment. It helps developers and infrastructure teams run open-source models through an OpenAI-compatible API across supported compute platforms.

- Findings24: <https://findings24.com/ja/products/vllm>
- ウェブサイト: <https://vllm.ai/>
- 料金: Freemium
- カテゴリー: AI推論プラットフォーム, AIモデルデプロイメント

## vLLMとは？

vLLM is an inference and serving engine for large language models. It is built to run open-source models with high throughput and efficient memory use, giving developers and infrastructure teams a serving layer for deployed LLM applications.

Its central performance approach combines PagedAttention with advanced scheduling and continuous batching. These mechanisms are intended to improve hardware utilization when handling multiple inference requests. vLLM also includes a drop-in OpenAI-compatible API for connecting applications that already use that API pattern.

The site provides installation choices for CUDA, ROCm, XPU, and CPU environments, as well as Python and Docker-oriented options. A documented quick start uses `uv pip install vllm --torch-backend auto`; Python 3.10 or later is required, and Python 3.12 is recommended.

## vLLMでできること

### PagedAttention

Uses PagedAttention as a core serving technique for improving memory efficiency during LLM inference.

### Continuous batching

Combines incoming inference requests into ongoing batches to help maintain throughput while a serving process is active.

### Advanced scheduling

Provides scheduling mechanisms intended to keep available accelerator capacity productively used across inference workloads.

### OpenAI-compatible API

Offers a drop-in API compatible with the OpenAI API style, reducing the changes needed when connecting existing client applications.

### Multiple installation platforms

The quick-start flow presents options for CUDA, ROCm, XPU, and CPU environments, with Python and Docker package paths.

### Stable and nightly builds

Users can choose the currently tested stable release or a nightly build when they need newer development builds.

## 利用シーン

### Serve an open-source model for an application

A development team can install vLLM, launch a supported model, and connect an application through the OpenAI-compatible API instead of implementing a custom inference interface.

### Increase throughput for concurrent requests

An inference operator handling multiple users or application requests can use continuous batching and scheduling to organize work across the serving process.

### Improve memory use during deployment

A team constrained by accelerator memory can evaluate vLLM's PagedAttention-based serving approach when deploying a large language model.

### Adapt deployment to available hardware

Infrastructure teams can select the installation path that matches their environment, including CUDA, ROCm, XPU, or CPU options shown in the product's setup flow.

### Test releases before production adoption

A team can use the stable build for the most currently tested version or select nightly builds to evaluate newer changes before updating its serving workflow.

## よくある質問

### What is vLLM used for?

vLLM is used to run inference and serve large language models. It is aimed at high-throughput, memory-efficient deployment of open-source models.

### Does vLLM provide an API for existing applications?

Yes. The site describes a drop-in OpenAI-compatible API for integrating applications with a vLLM-served model.

### What are the stated Python requirements?

The quick-start information states that Python 3.10 or later is required and recommends Python 3.12 or newer.

### Which compute platforms are shown in the installation flow?

The site lists CUDA, ROCm, XPU, and CPU installation options. It also presents Python and Docker package paths, with the exact compatibility depending on the selected setup.

### Is pricing published on the product site?

The supplied pricing URL returns a 404 page and does not provide plan, usage, or price information. The available evidence therefore does not establish a pricing model.

## クイック情報

- **Product type**: LLM inference and serving engine
- **Primary workflow**: Install vLLM, serve an open-source model, and connect through an OpenAI-compatible API
- **Supported platform options shown**: CUDA, ROCm, XPU, and CPU
- **Python requirement**: Python 3.10+ required; Python 3.12+ recommended
- **Current stable release shown**: v0.30.0
- **Documentation**: docs.vllm.ai