The outcome

Learn the interaction loop using a small task with a clear outcome.

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

    Define the task, exact model and license, hardware, dtype or quantization, context, chat template, sampling defaults, quality, safety, latency, throughput, availability, and cost targets.

  2. 02

    Install a pinned vLLM release or container in an isolated environment, verify the model revision and files, and start a private test server with an API key and explicit generation configuration.

  3. 03

    Run correctness and safety evals plus concurrent load tests, recording warm-up, throughput, time to first token, tail latency, GPU memory, errors, output parity, and cost.

  4. 04

    Choose bounded engine and scheduling settings, place the service behind authenticated TLS ingress, canary the pinned model and image, and retain rollback and capacity procedures.

Working standard

What good use looks like.

  • State the outcome before the background.
  • Provide the real source material.
  • Review the result before expanding the task.

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.