> ## 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
> Dedicated GPU infrastructure for serving trained models via an OpenAI-compatible API.
Deploy gives you a dedicated GPU serving your fine-tuned model. The API is OpenAI-compatible, so switching from an off-the-shelf model to your custom model is a one-line code change. This is the last step in the loop and the beginning of the next one.
## Key concepts
| Concept | Description |
| ------------------------- | ----------------------------------------------------------------------------------------------------------------------------------------------------- |
| **Dedicated GPU** | Your model runs on its own GPU. No shared infrastructure, no noisy neighbors. Compute is determined by the recipe used during training. |
| **OpenAI-compatible API** | Same base URL, same API key, just swap the model parameter. Structured outputs, function calling, and all standard API features work the same way. |
| **Team-scoped** | Deployments belong to a team, not a project. The model path is your team slug followed by the deployment name you choose (e.g. `acme-corp/my-model`). |
| **The improvement loop** | Deploy → observe production performance → run evals to catch regressions → train the next version. The loop continues. |
## What you can deploy
* Models trained on the Inference platform
* Served via an OpenAI-compatible API (chat completions endpoint)
* Same base URL and API key as the rest of the Inference API
## Next steps
Name it, click deploy, start serving.
One line of code to switch over.
Lifecycle operations, scaling, and the improvement loop.