What is vLLM?

vLLM is an open-source software library that makes it faster and cheaper to run very large language models (LLMs). It does this by using smart tricks to share memory and run many requests at the same time, so you don’t need the biggest, most expensive hardware.

Let's break it down

  • vLLM: the name of the library; “v” stands for “virtual” because it creates a virtual view of the model’s memory.
  • Open-source: the code is free for anyone to see, use, and modify.
  • Software library: a collection of ready-made code you can add to your own programs.
  • Makes it faster: reduces the time it takes for the model to give an answer.
  • Cheaper to run: needs less powerful (and less expensive) computers.
  • Very large language models (LLMs): AI models with billions of parameters that understand and generate text.
  • Smart tricks to share memory: re-uses parts of the model’s data so it doesn’t have to load everything for each request.
  • Run many requests at the same time: processes multiple user queries in parallel, increasing overall speed.

Why does it matter?

Running big AI models is usually expensive and slow, which limits who can use them. vLLM lowers the cost and speeds up responses, making powerful AI accessible to smaller companies, developers, and even hobbyists who don’t have massive GPU farms.

Where is it used?

  • Chatbots and virtual assistants that need instant replies.
  • Code-generation tools (e.g., AI pair programmers) that handle many user prompts simultaneously.
  • Search engines that use LLMs to understand queries and generate summaries on the fly.
  • Content-creation platforms that produce articles, captions, or translations for many users at once.

Good things about it

  • Very high throughput: can handle many requests per second.
  • Low latency: answers come back quickly.
  • Reduces hardware requirements, saving money.
  • Easy to plug into existing Python codebases.
  • Actively maintained and community-driven (open source).

Not-so-good things

  • Still needs at least one GPU with enough memory for the model’s core parts.
  • Setup can be tricky for beginners unfamiliar with GPU environments.
  • Not all model architectures are fully supported yet.
  • Performance gains vary depending on the specific workload and hardware.