NVIDIA Confidential Computing Powers Apple Private Cloud Compute Expansion

NVIDIA has revealed that its GPUs with Confidential Computing are now powering confidential inference in Apple's Private Cloud Compute (PCC) system. The move comes as Apple expands PCC beyond its own data centers to include Google Cloud infrastructure.

The announcement was made at Apple's annual Worldwide Developers Conference (WWDC), a gathering for developers from around the globe. NVIDIA GPUs will support server-side inference for Apple Foundation Models, which are custom-built by Apple and Google. These models leverage the technologies behind the Gemini family of models.

NVIDIA and Apple Collaborate on Privacy-Focused AI

NVIDIA is working alongside Apple and Google to support several next-generation Apple Intelligence features. The partnership uses NVIDIA Blackwell GPUs with Confidential Computing integrated into Private Cloud Compute's hardware security architecture. These GPUs run on Google Cloud, providing a secure foundation for AI workloads.

Private Cloud Compute, first introduced by Apple at WWDC 2024, is designed to process AI requests that cannot be handled on-device. By extending PCC to Google Cloud, Apple can scale its AI capabilities while maintaining the strong privacy guarantees that the system is known for.

How Confidential Computing Protects User Data

NVIDIA Confidential Computing provides a hardware-based security layer for accelerated AI workloads. The technology protects data while it is being processed by isolating workloads in trusted execution environments. It also enables systems to cryptographically verify that the infrastructure has not been tampered with before any sensitive data is sent to the server.

For end users, this means that no one, not even the system's builders, can view their data, chats, or conversations. The security is enforced through several key capabilities:

• Hardware-rooted trust, which helps establish that systems are running on genuine, untampered NVIDIA GPUs.
• Encrypted communication paths, which protect data as it moves between components.
• Remote attestation, which enables software to verify the security state of the platform before releasing sensitive data.
• Support for accelerated AI inference and training, which helps organizations run privacy-sensitive workloads without giving up GPU performance.

These capabilities are increasingly important for AI services that need to process sensitive information while maintaining strong user privacy controls.

Broader Implications for AI Infrastructure

The adoption of NVIDIA Confidential Computing at this scale reflects a broader shift in AI infrastructure. As AI experiences combine on-device and cloud-based processing for their tasks, there is a need for high-performance server-side inference while preserving strong privacy and security guarantees. The NVIDIA collaboration with Apple and Google underscores the growing demand for secure, accelerated computing in the age of AI.

NVIDIA's confidential computing technology is part of the company's broader commitment to trustworthy AI. By enabling encrypted, verifiable processing environments, it allows organizations to deploy AI workloads in multi-tenant cloud environments without compromising on security.

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