Dedicated Endpoints provide a customizable environment for deploying AI models tailored to specific requirements.

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Create a dedicated inference endpoint once or on a schedule

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Deploy a Dedicated inference model

Select a model from the list. Click on the button labeled “Dedicated”: Dedicated button

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Set compute resources

Select a GPU type, ram, and other system specifications: Dedicated step 1

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Set task details

Set a task name, file path, and other inference details: Dedicated step 2 Task Name: Define task name. File Path: Specify the script file name to be executed in the Docker image(without the file extension). Fox example, if the image includes a script named serve.py, enter serve here. Deployment Name: Specify the deployment name that will be exposed to the Ray cluster by the script. For example, you can use app as the deployment name. Type: One-off or Daily One-off - The task runs once as scheduled time. Daily - The task runs at the first scheduled time and can update replica numbers at subsequent daily scheduled times. This option is designed for recurring, predictable workloads where scaling needs follow a consistent daily pattern. Timezone: Select the timezone for scheduling. Time: Select the time for scheduling. Replicas: Select the Min replicas and Max replicas for schedule.

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Review configuration

Review your configurations to ensure they are correct. After confirmation, click the “Launch” button to launch task. Dedicated step 3

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Active task

In the task list, locate the task and then click button image-20250320145006225 to active the idle task.