What it is
vLLM is an engine for inference and serving large language models. Its job is not training from scratch, but receiving requests, generating outputs, and serving responses through APIs.
The project matters for teams deploying LLMs as services: high throughput, OpenAI-compatible API server, Anthropic Messages API, gRPC, and memory/batching optimizations.
What is inside
The repository contains the runtime stack, server components, installation docs, quickstart, supported models, serving parameters, and contribution materials. It also links to the project paper and documentation.
A practical flow is to choose a supported model, install vLLM, start a server, and send requests through an OpenAI-compatible client.
API server run
This command shows the typical vLLM use: expose a model as a service.
vllm serve meta-llama/Llama-3.1-8B-InstructStrengths and limits
The strength is performance for serving workloads. LLM products care about latency, throughput, batching, GPU use, and API compatibility.
The limitation is infrastructure complexity. GPUs, memory, weights, monitoring, limits, prompt safety, and cost still need engineering. vLLM improves serving; it does not decide model quality for the product.