> ## Documentation Index
> Fetch the complete documentation index at: https://docs.inference.net/llms.txt
> Use this file to discover all available pages before exploring further.

# Deploy a Trained Model

> Go from a completed training run to a live endpoint in a few clicks.

When training completes, the model is automatically registered and ready to deploy. No manual promotion step.

Deployments are scoped to your team, not a specific project. When you create a deployment, you give it a name — the model path becomes your team slug followed by that name (e.g. `acme-corp/my-model`).

## Starting a deployment

There are a few ways to get started:

* From the **Deployments** page — click **Create**
* From a **completed training run** — click deploy directly from the training detail page
* From the **Models** page — click deploy in the row for your model

All three paths lead to the same flow.

## The flow

<Steps>
  <Step title="Name the deployment">
    Give it a descriptive name. This becomes the second part of your model path (e.g. `acme-corp/my-model`).
  </Step>

  <Step title="Deploy">
    Select your instance configuration and click deploy. If you need more compute than a single GPU, you can reach out to the team directly from this page.
  </Step>

  <Step title="Wait for warm-up">
    The deployment takes a few minutes to 20–30 minutes to come online. This time is spent allocating compute and spinning up the GPU.
  </Step>
</Steps>

## After deployment

Once the endpoint is live, you can [call it](/platform/deploy/call-your-deployment) using the same OpenAI-compatible API you're already using. Just swap in your model path.