ai-agent

vllm: Fast LLM Inference

Name: vllm: Fast LLM Inference
Author: vllm-project

Get high-throughput LLM serving with vllm, for founders. 85k+ GitHub stars

intermediate⏱ 1-2 hours💵 Free (self-hosted)

87,269 stars19,904 forksPythonQuality 8/10Updated 7/27/2026100% free · open source

What it is

Run large language models (LLMs) quickly and efficiently

What you can make with it

Automations like high-performing language translation workflows

How it helps

vllm optimizes LLM inference for high-throughput applications, outperforming other engines, and reducing costs.

Real use case example

"A founder wants to build a language translation feature for their travel agency's website. They use vllm to host their LLM model and set up an API endpoint to translate user input in real-time, allowing them to deliver instant results."

If you're new

Beginners should pick this up when they need a simple way to run and deploy high-quality LLMs.

If you're senior

Senior engineers will reach for this when requiring high-throughput, high-performance LLM inference for critical applications

Common confusion cleared up

Don't confuse vllm with other open-source LLMs; vllm is specifically designed as a high-throughput inference server

Best inside these AI tools

Claude DesktopCursor

Pairs with

Claude API

Why we list it on WorkflowStacks: This skill is included in the AI tool marketplace as the open-source, high-throughput inference server that saves cost and improves performance

What it does

vllm is a high-throughput and memory-efficient inference and serving engine for Large Language Models (LLMs) that enables fast and scalable deployment of language models

Install / run

git clone https://github.com/vllm-project/vllm.git && cd vllm && pip install -r requirements.txt

When to use it

•When you need to serve LLMs in a production environment with high traffic
•When you want to reduce the memory footprint of your LLM deployment
•When you need to integrate LLMs with other AI services or microservices

Quick start

1Clone the vllm repository and navigate to the project directory
2Install the required dependencies using `pip install -r requirements.txt`
3Configure the `vllm_config.json` file to specify the LLM model and serving settings
4Start the vllm server using `python -m vllm.serve`
5Test the vllm API using a tool like `curl` or a Python client library

Ready-to-paste prompt

python -m vllm.serve --model-name bert-base-uncased --port 8000

Heads up: vllm requires a compatible version of the transformers library, so ensure you have the correct version installed by running `pip install transformers==4.20.1`

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