Feature Overview (aka. Goal Summary)  

An elevator pitch (value statement) that describes the Feature in a clear, concise way.  Complete during New status.

Introducing Confidential GPU Workloads for Azure: This feature integrates NVIDIA GPUs into Azure cloud instances, enabling the execution of AI workloads in confidential containers. It's designed to protect the confidentiality and integrity of sensitive data, making it feasible for highly regulated customers to use public cloud resources for their AI needs while adhering to strict compliance standards.

Goals (aka. expected user outcomes)

The observable functionality that the user now has as a result of receiving this feature. Complete during New status.

1. Enhanced AI processing: Confidential NVIDIA GPUs boost the power for AI tasks, enabling more complex computations.

2. Secure data handling: Encrypted GPU memory offers heightened security for sensitive data processing.

3. Minimized security risks: Removing the GPU from the trusted computing base reduces potential vulnerabilities.

4. Compliance in sensitive sectors: Additional security features assist in meeting strict industry regulations.

5. Expanded AI possibilities: The integration allows new AI applications in data-sensitive environments.

Requirements (aka. Acceptance Criteria):

A list of specific needs or objectives that a feature must deliver in order to be considered complete.  Be sure to include nonfunctional requirements such as security, reliability, performance, maintainability, scalability, usability, etc.  Initial completion during Refinement status.

1. Seamless GPU Integration: The feature must integrate perfectly with confidential NVIDIA GPUs in Azure instances, ensuring no compatibility issues.

2. Robust Data Encryption: Implement industry-standard encryption protocols for GPU memory to secure sensitive data effectively.

3. Effective TCB Reduction: Achieve a significant reduction in the trusted computing base, especially in relation to GPU integration.

Use Cases (Optional):

Include use case diagrams, main success scenarios, alternative flow scenarios.  Initial completion during Refinement status.

Questions to Answer (Optional):

Include a list of refinement / architectural questions that may need to be answered before coding can begin.  Initial completion during Refinement status.

Installation of Nvidia GPU drivers in the peer pod vm image. Are these drivers part of RHEL or available in a public repository?

Out of Scope

High-level list of items that are out of scope.  Initial completion during Refinement status.

Background

Provide any additional context is needed to frame the feature.  Initial completion during Refinement status.

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Customer Considerations

Provide any additional customer-specific considerations that must be made when designing and delivering the Feature.  Initial completion during Refinement status.

Documentation Considerations

Provide information that needs to be considered and planned so that documentation will meet customer needs.  Initial completion during Refinement status.

Interoperability Considerations

Which other projects and versions in our portfolio does this feature impact?  What interoperability test scenarios should be factored by the layered products?  Initial completion during Refinement status.