Which endpoint attribute displays the number and name of the GPUs per instance?

In Nutanix AI, when deploying a model and creating an inference endpoint, the Accelerator attribute is used to specify the hardware resources for accelerating model computations. This section allows the administrator to select the specific type (name) and quantity (number) of GPUs that will be allocated to each instance of the deployed model. This directly controls the hardware acceleration capabilities of the endpoint.

B. Description: This is a free-text field for a human-readable description of the endpoint's purpose, not for structured hardware configuration.

C. Status: This attribute reflects the operational state of the endpoint, such as 'Active', 'Creating', or 'Failed', not its hardware specifications.

D. Inference Engine: This specifies the serving software framework (e.g., Triton Inference Server, PyTorch Serve) used to run the model, not the physical hardware.

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1. Nutanix AI User Guide: In the section "Deploying a Model," the configuration steps for an inference endpoint are detailed. Step 6, "Configure the resources for the endpoint," explicitly shows the "Accelerator" field where users select the number and type of GPUs.

Reference: Nutanix AI User Guide, Chapter: "Model and Endpoint Management", Section: "Deploying a Model". (Note: Specific page numbers vary by document version, but the section name is consistent).

2. Nutanix University - Nutanix AI Administration (Courseware): The module on "Deploying and Managing AI Models" demonstrates the UI for creating an inference endpoint. The "Accelerator" dropdown menu is shown as the method for assigning specific GPU models (e.g., NVIDIA A100) and their quantity to the endpoint instances.

Reference: Nutanix University, Course: Nutanix AI Administration, Module: "Deploying and Managing AI Models".