The outcome

Serve multiple users without exposing internal protocols, development endpoints, model data, or unbounded GPU capacity.

vLLM is a Open-source high-throughput inference and serving engine with OpenAI-compatible, pooling, speech, custom, distributed, and scale-out APIs. Serving open and custom language, embedding, reranking, transcription, and related models at high throughput through familiar APIs on controlled GPU infrastructure. This guide narrows that broad capability into one repeatable outcome, with checkpoints that keep the source material and your judgment in the loop.

Before you begin

Set the boundary before the tool starts.

Choose one real task, identify who will use the result, and decide what evidence or test will make the result acceptable. Gather only the source material needed for that task. If the work contains confidential, personal, regulated, or client-owned information, confirm that the platform and account are approved before sharing it.

Troiana principle

AI should make the work easier to inspect. If the workflow removes the source, the owner, or the review step, redesign the workflow.

Step by step

A workflow you can repeat.

  1. 01

    Map public and internal ports, nodes, distributed and KV-cache channels, identities, tenants, models, storage, ingress, egress, secrets, quotas, observability, SLOs, and incident owners.

  2. 02

    Run nodes on an isolated trusted network, restrict host firewalls and security groups, disable development and profiling surfaces, mount models read-only, and use non-root pinned images with scanned dependencies.

  3. 03

    Put TLS, strong client identity, tenant authorization, model allowlists, input and output limits, rate and concurrency quotas, request-size controls, and abuse defenses in a hardened gateway.

  4. 04

    Protect and redact logs, metrics and traces, monitor GPU, queue, cache, errors and spend, and test lateral access, rogue nodes, malformed requests, exhaustion, provider crash, and secret rotation.

  5. 05

    Canary images and models, rehearse node and regional loss, drain safely, retain rollback capacity, delete retired weights and caches, and verify network and credential revocation.

Working standard

What good use looks like.

  • Isolate every internode channel.
  • Do not rely on one static API key.
  • Disable development endpoints in production.

OpenAI compatibility is partial and version-specific; unsupported or ignored parameters, chat templates, model-provided generation configuration, tool-call behavior, tokenization, and output can differ. The built-in API key is not a complete identity, tenant, quota, or network-security layer. Isolate all distributed and KV-cache traffic, disable development endpoints, protect metrics and management surfaces, pin model and image revisions, scan custom code, bound context and concurrency, and put production service behind a hardened gateway.

Official references

Check the current product documentation.

Features, plan limits, availability, and data controls change. These official pages are the starting points used for this collection.