Duality Technologies, a leader in privacy-enhancing technologies and secure data collaboration, announced support for Google Cloud’s Confidential Computing portfolio, including NVIDIA GPU-powered confidential virtual machines on Google Cloud, enabling large-scale secured AI workloads such as LLM training and inference.

With this launch, the Duality Platform now supports GPU-backed LLM inference and encrypted Retrieval-Augmented Generation (RAG) within trusted execution environments (TEEs) – a significant performance leap from previous CPU-only support.

Customers can run a secure generative AI workflow on NVIDIA GPUs with Google Cloud Confidential Computing, now featuring end-to-end protection against data leakage powered by Duality. They can also combine full-stack data confidentiality with NVIDIA H100 GPU performance, unlocking confidential AI use cases that were previously impractical due to latency and throughput constraints.

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“This changes the game,” said Dr. Alon Kaufman, CEO and Co-Founder of Duality Technologies. “Our customers can now run privacy-preserving AI with LLMs at production scale. With GPU acceleration, the performance bottlenecks of secure computing are gone-making secure LLM training and inference practical.”

The new capability is built on Google Cloud’s Confidential Space and Confidential NVIDIA H100-powered confidential VMs, with support for Intel TDX and Cloud KMS integration. Duality has successfully validated the platform running a Mistral-7B model using encrypted vector RAG (via Faiss) in a fully confidential pipeline.

“With Confidential GPUs, organizations can process sensitive AI workloads entirely within trusted execution environments without giving up performance,” said Nelly Porter, Director of Product Management, Google Cloud. “Pairing NVIDIA H100-powered confidential VMs with Duality’s encrypted workflows allows LLM training and inference to happen at scale, with end-to-end protection from data leakage.”

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Key Highlights:

• GPU Support for Confidential AI: Run secure LLMs and encrypted RAG on Confidential NVIDIA H100s
• Scalable Performance: Orders-of-magnitude faster runtimes vs CPU-only workloads
• Enterprise-Ready: Meets the needs of regulated industries, defense, healthcare, and AI-native companies
• Seamless Cloud Integration: Now available via Dynamic Workload Scheduler in Google Cloud’s Confidential Space

Until now, confidential AI was limited to CPU-only environments – suitable for basic testing, but insufficient for the demands of large-scale AI. With the arrival of Confidential GPU as part of the confidential computing portfolio, Duality customers can now run both LLM training and inference securely inside Trusted Execution Environments. This breakthrough enables high-throughput, privacy-preserving AI workloads that were previously impossible to execute – unlocking new possibilities across industries and use cases.

This capability is initially available on the Google Cloud Confidential A3 virtual machine type in preview, with broader rollout expected later this year.