{"slug": "hardware-backed-security-at-global-scale-confidential-g4-with-nvidia-blackwell", "title": "Hardware-backed security at global scale: Confidential G4 with NVIDIA Blackwell is GA", "summary": "Google Cloud announced that Confidential Computing on its G4 instances, enabled by NVIDIA RTX PRO 6000 Blackwell Server edition GPUs, is now generally available across 36 zones on Compute Engine VMs and GKE nodes. The release integrates NVIDIA Confidential Computing into Google Cloud's AI Hypercomputer, combining non-exportable hardware keys, hardware-rooted remote attestation and transparent memory encryption so enterprises can run agentic and inference workloads without code refactoring on standard Ubuntu images and NVIDIA CUDA-X libraries. Support for Confidential Space, which enables secure multi-party data sharing, is rolling out to general availability in the coming weeks.", "body_md": "Bringing autonomous agents into production means processing sensitive data and valuable intellectual property in real time. Protecting proprietary model weights, user prompts, and generated outputs requires security that safeguards data while it is actively being computed.\n\nAs part of our efforts to enable your enterprise securely, today we are announcing that Confidential Computing on Google Cloud’s G4 instances enabled by NVIDIA RTX PRO 6000 Blackwell Server edition GPUs is now generally available. This release integrates NVIDIA Confidential Computing directly into [Google Cloud's AI Hypercomputer](https://cloud.google.com/ai-infrastructure), delivering the dynamic performance needed to scale without compromising privacy.\n\nWhen we announced [Confidential G4 in preview](https://cloud.google.com/blog/products/identity-security/verifiable-trust-in-the-ai-era-whats-new-in-confidential-computing?e=48754805) earlier this year, we talked about its global footprint and scale. Today, these instances are generally available across [36 zones](https://docs.cloud.google.com/confidential-computing/confidential-vm/docs/supported-configurations#g4-machine-type), using [multiple consumption models](https://docs.cloud.google.com/compute/docs/accelerator-optimized-machines#consumption_option_availability_by_machine_type), on [Compute Engine VMs](https://docs.cloud.google.com/confidential-computing/confidential-vm/docs/create-a-confidential-vm-instance-with-gpu) and [GKE nodes](https://docs.cloud.google.com/kubernetes-engine/docs/how-to/gpus-confidential-nodes).\n\nIn addition, support for [Confidential Space](https://docs.cloud.google.com/confidential-computing/confidential-space/docs/confidential-space-overview) enabling secure multi-party data sharing is rolling out to general availability in the coming weeks.\n\nProtecting data in use shouldn't mean sacrificing GPU throughput or operational agility. By combining non-exportable hardware keys, verifiable remote attestation, and transparent memory encryption, Confidential G4 helps organizations maximize their investments in agentic and inference workloads. Enterprises can keep costs low while meeting strict regulatory and compliance requirements.\n\n“As organizations deploy AI across increasingly complex cloud, on-premises, and agentic environments, protecting sensitive data is critical. Enterprises must also ensure that AI systems operate securely and consistently with their policies. Google Cloud Confidential G4 VMs powered by NVIDIA RTX PRO 6000 Blackwell GPUs provide a powerful foundation for privacy-preserving AI and AI operations. Confidential Core AI builds on Google Cloud to transform enterprise principles, policies, and operating requirements into real-time safeguards that monitor and govern AI agents, models, data, and tools. Together, these capabilities enable organizations to scale trusted AI on Google Cloud while maintaining control, confidentiality, and accountability.” **- Taher Behbehani, Founder and CEO, Confidential Core AI**\n\nAdditional benefits for enterprise AI workloads include:\n\n**End-to-end confidentiality and isolation:** Encrypt data and model parameters in hardware-enforced execution environments to shield intellectual property and customer inputs from host system administrators, hypervisors, and access by cloud providers.\n\n**Verifiable cryptographic trust:** Use hardware-rooted remote attestation to verify code, platform configuration, and environment integrity before releasing sensitive datasets and running workloads.\n\n**No code refactoring:** Deploy models out of the box using standard Ubuntu OS images, NVIDIA drivers, and NVIDIA CUDA-X libraries without application changes or custom builds.  \n\nFor Polaris Cloud, this ease of deployment was transformational.\n\n“Polaris builds workspaces for AI agents, and our customers need to know where an agent's work actually ran and that it was cryptographically isolated along the way. Google Cloud Confidential G4 has been a game changer for that. We get hardware-backed provenance from the NVIDIA RTX PRO 6000 Blackwell GPU, which we bind into the receipt customers get back with their results. Virtually nothing else changed: same G4 image, same NVIDIA driver, same container runtime, one flag at launch, same performance. Overnight it upgraded our customer promise from 'we run your agent' to 'every workload you run with us is verifiable.” **- Fred Esere, Founder and CEO, Polaris Cloud**\n\n**Multi-party data collaboration:** Run collaborative analytics and joint model training securely using Confidential Space. Multiple parties can combine sensitive datasets without revealing underlying private data to each other.\n\n“Enterprises need to put their most valuable data to work with AI while protecting it during processing. NVIDIA Confidential Computing brings hardware-backed protection to GPU-accelerated workloads, helping safeguard sensitive data and proprietary models while in use. Together with Google Cloud, we’re bringing NVIDIA Blackwell performance and these security capabilities to Confidential G4 VMs so organizations can scale production AI with high performance and verifiable protection.” **- Dave Salvator, Director of Accelerated Computing Products, NVIDIA**\n\nTo understand the operational impact of memory encryption, we analyzed Confidential G4 instances against standard, non-confidential G4 instances on the g4-standard-48 machine shape. Our testing focused on widely adopted open-weight models that customers frequently deploy on single-GPU instances for their own inference pipelines.\n\nBenchmarking across popular models including Llama 3, Llama 3.3, Resnet-50, and SDXL shows that **Confidential G4 delivers performance on par with standard G4 instances for AI inference workloads, with less than 3.5% degradation in throughput** (measured in tokens or images per second).\n\nNote: Performance results are based on specific testing conditions and configurations. Individual workload performance may vary based on system setup, hardware choice, and software environment.\n\nOur analysis can help you move fast while protecting sensitive data and meeting compliance requirements — a trade-off that real-world deployments are already validating.\n\n”We benchmarked Google Cloud's Confidential G4 against a standard G4 running identical software on our workloads. We found that for a modest 5.2% increase in end-to-end latency, we get a hardware-level assurance that ensures unauthorized users outside our users' attested environment cannot eavesdrop on their conversations. We'd make that trade every time; our users get the convenience of the cloud with the privacy of on-premises.” **- Andy Qin, CEO, [Vertebrae](https://vertebrae.ai/)**\n\nWith Confidential G4, improving enterprise security during model execution and agentic workloads doesn't require sacrificing performance, flexibility, or operational simplicity. By providing verifiable trust and robust hardware isolation with near-zero throughput degradation, organizations can securely deploy high-value models and protect critical data assets at cloud scale.\n\nGet started today by following the [Confidential VM with GPU documentation](https://docs.cloud.google.com/confidential-computing/confidential-vm/docs/create-a-confidential-vm-instance-with-gpu).", "url": "https://wpnews.pro/news/hardware-backed-security-at-global-scale-confidential-g4-with-nvidia-blackwell", "canonical_source": "https://cloud.google.com/blog/topics/ai-infrastructure/confidential-g4-gpus-are-ga/", "published_at": "2026-10-21 11:30:00+00:00", "updated_at": "2026-10-21 11:41:16.862581+00:00", "lang": "en", "topics": ["ai-infrastructure", "ai-safety", "ai-chips", "artificial-intelligence"], "entities": ["Google Cloud", "NVIDIA", "Confidential G4", "NVIDIA RTX PRO 6000 Blackwell Server edition", "AI Hypercomputer", "Confidential Space", "Compute Engine", "GKE"], "also_reported_by": [], "alternates": {"html": "https://wpnews.pro/news/hardware-backed-security-at-global-scale-confidential-g4-with-nvidia-blackwell", "markdown": "https://wpnews.pro/news/hardware-backed-security-at-global-scale-confidential-g4-with-nvidia-blackwell.md", "text": "https://wpnews.pro/news/hardware-backed-security-at-global-scale-confidential-g4-with-nvidia-blackwell.txt", "jsonld": "https://wpnews.pro/news/hardware-backed-security-at-global-scale-confidential-g4-with-nvidia-blackwell.jsonld"}}