Console flow
URL:https://console.gmicloud.ai/user-console/ie/deployments
The Deployments page is where you provision and manage dedicated endpoints.
- Create Deployment (top-right): launches the wizard for selecting a model, region, GPU type, and scaling.
- Once running, the page lists each endpoint with its status, model, region, replica count, and per-deployment actions.
You haven’t deployed any models yet Deploy an open source model from the Model Library.Featured shortcut cards offer one-click starts for popular models like Gemini 3.5 Flash, MiMo-V2.5-Pro, GPT-5.5, and DeepSeek-V4-Pro. The Explore more link jumps to the full model catalog. For per-model API docs, see the Text catalog.
Create your Dedicated Inference Endpoint
Deploy a dedicated inference model
Select a model from the list. Click the “Dedicated” button to start deployment:
Review Configurations
Confirm your GPU type, deployment name, auto-scaling policy, and other system configurations:
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. |
Invoke API Endpoint
Once deployment is in “Running” status, click the ”<>” symol to access endpoint URL:
