vLLM

professionalwidespread

An open-source server program that runs large language models on your own servers, serving many users at once quickly and efficiently.

In plain terms

A language model by itself is just a huge file. To answer questions, a program is needed that loads it into the graphics cards' memory, receives requests, and returns answers. vLLM is such a program, specifically optimized to handle a very large number of requests simultaneously.

Imagine a busy restaurant kitchen. The chef is the model, while vLLM acts as the head chef, allocating orders, stoves, and counter space so that no one waits unnecessarily and no pan sits idle. The same chef can thus serve many more guests.

They are mainly used by those who do not rent artificial intelligence from an external provider's cloud but want to run it on their own machine or rented server, for example due to data protection concerns or costs. What distinguishes it from simpler tools for home experimentation is that it is designed not for one, but for hundreds of simultaneous users.

How it works

It loads the selected open model into the server's graphics card memory and keeps it ready there.
Incoming requests are not processed one by one in sequence, but are continuously batched on the fly: a new request can immediately join those already running, without waiting for them to finish.
It manages the conversation's working memory (the model's running 'notes') by splitting it into small, uniform pages, allocating only what is needed for each conversation. This reduces waste and allows more conversations to run simultaneously on the same hardware.
It returns answers in a widely used format compatible with the OpenAI programming interface (API), so existing applications and agents can typically connect with minimal adjustment.

An everyday example

A hospital, because of its internal rules and data protection risks, does not want to hand patient data over to an external cloud provider, so it runs an open language model in its own server room. When a doctor requests a draft discharge summary in the internal system, a server similar to vLLM ensures in the background that they and a hundred colleagues receive answers at the same time, without significant waiting.

Who is affected

developerIf you want to run an open model on your own server for many users or agents, this is one of the most common choices. → Code

entrepreneur, executiveThis also affects how much self-hosted AI costs and how worthwhile it is compared with a cloud subscription.

lawyer, citizenA model running on your own server allows sensitive data to stay within the organization, which matters from a data protection perspective. → Law

A common misconception

MythvLLM is another language model, similar to GPT or Llama.

In realityIt is not a model, but the program that runs and serves the model. On its own, it cannot answer anything; the knowledge comes from the model loaded into it.

Limits and risks

In live use, it requires powerful, expensive graphics cards; it is not suitable for an average laptop.
Installation and fine-tuning require operational expertise; it is not a click-based end-user program.
It does not improve the quality of responses; it only serves the same model faster and cheaper, along with its errors and mistakes.
Self-operation is only cost-effective at sufficient utilization; with few users, cloud services are often cheaper.

Similar, but not the same

Ollama – Ollama's main strength is simple installation and use on a local machine with few users, while vLLM is designed for server-side serving to many concurrent users.
Large language model – The language model is the trained "brain", while vLLM is the program that runs it and makes it accessible.
llama.cpp – The main goal of llama.cpp is to enable models to run on modest hardware, even without a graphics card, while vLLM aims for maximum throughput on powerful servers.

Related concepts

Broader

Inference, Model Serving, AI infrastructure

Narrower

PagedAttention, Continuous batching

On the edge

Ollama, llama.cpp, Open-source model, Grafikus processzor (GPU), Quantization

A brief history

vLLM originated in the Sky Computing Lab research group at the University of California, Berkeley (UC Berkeley) and was released as open source in 2023. The memory-management idea behind it, the PagedAttention method, was presented in a scientific paper the same year. In 2024, the project moved to the Linux Foundation's artificial intelligence foundation (LF AI & Data), where it has since been developed as an independent community project.

Where things stand

As of late 2026, it is one of the most widespread tools for server-side running of open models, used by companies, research institutions, and cloud providers alike. It is developed as an open, community project with broad industry support.

Status: 2026 November

Also known as

vLLM · VLLM · vllm · vLLM-motor · vLLM server · vLLM inference engine · vLLM serving engine

Sources

This entry was machine-edited and has undergone machine expert review; the “In the news” section updates automatically from Agent Age stories.