> ## Documentation Index
> Fetch the complete documentation index at: https://docs.gmicloud.ai/llms.txt
> Use this file to discover all available pages before exploring further.
# Dedicated Endpoint
Dedicated Endpoints provide a customizable environment for deploying AI models tailored to specific requirements.
## Create Your Dedicated Inference Endpoint
### Deploy a Dedicated Inference Model
Select a model from the list.
Click the "Dedicated" button to start deployment:
Alternatively, you can also click on the model card and then click the "Deploy" button on the top right:
### Review Configurations
Confirm your GPU type, deployment name, auto-scaling policy, and other system configurations:
Then click "Deploy".
### View Deployment Status
To view your deployment status click the "Deployment" tab on the top right.
| **Status** | **Description** |
| ------------- | -------------------------------------------------------------------------------------------------------------------------- |
| **Queued** | The deployment task has been added to the queue. It will start once all higher-priority tasks have been processed. |
| **Deploying** | The system is allocating hardware resources and initializing the model endpoint. |
| **Running** | Deployment is complete, and the endpoint is active and ready for production use. |
| **Stopped** | The deployment has been manually stopped by the user. It can be restarted at any time. |
| **Archived** | The deployment has been terminated permanently. It cannot be restarted, but historical records are retained for reference. |
You will only be billed for the period of time in "Running" status.
### Invoke API Endpoint
Once deployment is in "Running" status, click the "\<>" symol to access endpoint URL:
You can then use this URL to send API requests. An example is provided. Remember to replace "API\_KEY" with your real API key.
