vLLM

vLLM is an open-source engine for running and serving large language models. It started in the Sky Computing Lab at UC Berkeley and is now maintained by a community of more than 2,000 contributors from dozens of academic institutions and companies. It tackles the problem of serving a model quickly and cheaply once you have it: PagedAttention manages GPU memory, incoming requests are batched continuously, many quantization formats are supported, and one model can be split across several GPUs. It supports more than 200 model architectures from Hugging Face, can run an OpenAI-compatible API server with Anthropic Messages API support as well, streams output and handles tool calling. On the hardware side it covers NVIDIA, AMD and Intel GPUs and a range of CPUs, with plugins for Google TPUs, Apple Silicon and more. What sets it apart is that it's one of the most widely used open-source engines for serving LLMs, and many teams that host their own models start with it.

Who it's for:developers and teams who want to deploy open-source LLMs on their own servers. Readers who don't write code won't have a use for it.

Before you start: install it with uv or pip. You need hardware that can run the model you choose, and GPU and memory needs depend on that model. The software is free, but compute and hosting costs are yours. If you expose it as a service, you're responsible for access control and logs, so don't put an unprotected endpoint on the public internet. Each model has its own licence, which you'll need to check separately. Apache-2.0 licence.

Based on the project's public documentation. Not tested by AI Inspiration World.