# Choosing an LLM for your server: start with its model and terms

How to prepare a language-model choice for external inference: version, model card, license, compatibility, quality checks and change tracking.

Author: Retrivium team
Updated: 2026-11-01
Canonical URL: https://retrivium.ai/journal/model-licenses/
Language: en

It is easy to choose a model by size and popularity. An external service needs more. Establish exactly which version runs, which terms apply and how it behaves on expected requests.

This guide describes information preparation. It does not establish that a particular model may be used commercially: that decision follows its documents and applicable requirements.

## Record the exact artifact

Record repository name, revision, weight format and file source. A modified or quantized build may include additional information from its creator. A short name in the server dashboard is insufficient for reproducibility.

Keep the launch description and engine versions alongside it. Test results then refer to a defined component set, rather than the constantly changing word “latest.”

## Read the model card and license

[Hugging Face model cards](https://huggingface.co/docs/hub/model-cards) describe models, intended uses, limitations and other information. A card helps locate information but does not replace the complete applicable license and additional terms.

Check documents for the exact version. If commercial serving, derivative-build distribution or naming conditions are unclear, clarify them before launch. A freely downloadable file is not sufficient evidence.

## Check technical compatibility

Confirm the selected engine supports the architecture, weight format and required functions. Receiving a text response does not prove tool use or structured output works.

Check the quantization method for the specific GPU too. The [vLLM tables](https://docs.vllm.ai/en/latest/features/quantization/) show that compatibility depends on method and hardware. Retain the documentation version with the configuration decision.

## Assess quality on your tasks

Create a small test set covering language, instruction following, response format and long-context behavior. Compare models under equivalent conditions. Do not automatically apply another ranking’s result to your request profile.

Repeat checks before updates. Changed weights, chat templates or numerical formats can produce different user results under the same API name.

## What GPU owners should know

Owners should understand who selects the model and checks its usage terms. Record that responsibility with the operator rather than relying on the label “open model.”

For the next step, review the [VRAM assessment](/journal/vram-and-models/) and [pilot plan](/journal/pilot-checklist/).