High-throughput model serving
How to use
vLLM.
Serving open and custom language, embedding, reranking, transcription, and related models at high throughput through familiar APIs on controlled GPU infrastructure.
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 covers the whole path in one place: official access, a first session that produces something reviewable, the checks that make output trustworthy, and the permissions worth limiting before you connect real work.
AI should make the work easier to inspect. If the workflow removes the source, the owner, or the review step, redesign the workflow.
Access & setup
Find, install, and sign in to vLLM
Get into the official vLLM experience with the right account and a setup you understand.
- 01
Start at https://vllm.ai/ and confirm the domain before entering account or payment information.
- 02
Availability: vLLM is installed as Python software or run from an official container on supported accelerator infrastructure; it provides servers and libraries rather than a consumer desktop app.
- 03
Supported Python, operating system and accelerator hardware, compatible drivers and libraries, a licensed model and tokenizer, enough GPU memory and storage, private networking, authentication and gateway controls, load tests, monitoring, and operations ownership.
- 04
Sign in with the account you intend to keep using, then review plan, data, notification, and permission settings.
- 05
Run one low-risk test task before connecting sensitive files, repositories, or workspace data.
- Use official download pages.
- Review permissions during setup.
- Keep installers and applications updated.
First session
Your first useful vLLM session
Learn the interaction loop using a small task with a clear outcome.
- 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.
- 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.
- 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.
- 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.
- State the outcome before the background.
- Provide the real source material.
- Review the result before expanding the task.
Quality control
Check the quality of vLLM output
Turn a promising generation into an asset you can legally and safely ship.
- 01
Restate the brief, the deliverable format, and where the asset will appear before judging any output.
- 02
Inspect at full resolution: generated media hides its errors at thumbnail size.
- 03
Check the details these systems reliably get wrong — hands, text, logos, reflections, symmetry, and edge artefacts.
- 04
Confirm the licence and commercial-use terms for your plan, and check the output does not reproduce a recognisable style, mark, or likeness you have no right to use.
- 05
Review for accessibility and delivery: alt text, contrast, colour profile, file size, and the exact export format.
- Inspect at full resolution before approving anything.
- Confirm commercial-use rights before an asset reaches a client.
- Keep the prompt and settings with the asset so it can be reproduced.
Privacy & permissions
Use vLLM safely
Use the platform without surrendering rights, likenesses, or client material you do not control.
- 01
Confirm you hold the rights to every reference image, voice, or clip before uploading it.
- 02
Check whether your plan trains on submitted content, and opt out before uploading client work.
- 03
Never upload a real person's likeness or voice without their explicit, documented consent.
- 04
Review the output for marks, styles, or likenesses you have no licence to reproduce.
- 05
Label synthetic media where your client, platform, or jurisdiction requires disclosure.
- 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.
- Follow your organisation's approved-use policy.
- Never treat fluent output as authorization to act.
Core workflows
Step-by-step ways to use vLLM for the work it does best.
Each workflow is a separate guide with its own steps and review checkpoints.
Tune model, template, generation, memory, batching, and scheduling settings against quality and load evidence rather than throughput alone.
↗ 02 WorkflowHarden a scale-out vLLM deploymentPlace public inference behind a production gateway while isolating insecure distributed, KV-cache, metrics, profiling, and management traffic.
↗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 guide.