Research Note: HPE NVIDIA Launch AI Factory Lab in France to Boost European AI Autonomy

Key Highlights:

The AI factory model, utilizing dedicated infrastructure like HPE Private Cloud AI and NVIDIA accelerated computing, transforms complex AI development into an efficient, scalable, and automated process, accelerating time-to-value while improving resource utilization through features like GPU fractionalization (MIG).

HPE Private Cloud AI has been significantly enhanced to meet data and operational sovereignty demands in Europe, featuring new hardware options (Blackwell GPUs), STIG-hardened/FIPS-enabled NVIDIA AI Enterprise for security, and country-specific reference architectures for regulatory compliance.

The new Alletra Storage MP X10000 prepares, organizes, and delivers high-throughput data for NVIDIA-aligned AI factories, with inline metadata services and scalable performance.

HPE has partnered with CrowdStrike and Fortanix to create a unified and sovereign AI security platform, integrating CrowdStrike for end-to-end protection and leveraging Fortanix Armet AI with NVIDIA Confidential Computing for highly regulated, secure, agentic AI workloads.

The primary driver for the shift toward private and sovereign cloud environments is the need for enhanced security, greater control over hardware and data residency, and predictable cost/performance to manage sensitive, data-intensive AI projects, especially for regulated industries adhering to laws like GDPR and the EU AI Act.

The News

At HPE Barcelona 2025, HPE announced an expansion of the NVIDIA AI Computing by HPE portfolio, introducing new solutions for secure and scalable AI factories, new AI datacenter interconnect to optimize AI workload performance across clusters operating over long distances or within multiple clouds, a new data intelligence storage tier, and a breakthrough AI factory lab in the European Union (EU) for customers worldwide to test and validate their AI deployments. These updates indicate HPE's intent to deliver a full-stack, sovereign-ready operating model built around validated architectures, accelerated networking, and integrated security controls. The announcement details are available in HPE's official press release.

Analyst Take

HPE and NVIDIA are collaborating to establish a new AI Factory Lab in Grenoble, France, offering a sovereign, air-cooled environment where customers can test and refine their AI workloads. This lab is fully equipped with advanced technology, including HPE servers and Alletra storage, HPE Juniper Networking PTX and MX Series routers, as well as NVIDIA accelerated computing and Spectrum-X Ethernet networking. Crucially, it features the latest NVIDIA AI Enterprise government-ready software. By hosting this infrastructure within the EU, the facility directly addresses the growing demands of global enterprises for data sovereignty, regional regulatory compliance, and validated performance for scaled AI deployments across the EU.

In a separate but related move to accelerate enterprise AI adoption, HPE is also partnering with Carbon3.ai to launch a Private AI Lab in London. This UK-based lab is specifically designed for UK enterprises and is built upon the HPE Private Cloud AI platform. It uses the NVIDIA AI Enterprise software suite and is powered by underlying NVIDIA AI infrastructure, providing a dedicated environment to help local businesses adopt and deploy private AI solutions.

We see that the HPE NVIDIA partnership is capitalizing on the growing demand for AI factories because they offer a purpose-built, automated, and scalable infrastructure that transforms the complex, experimental nature of AI development into an efficient, industrial process. By integrating the entire AI lifecycle, from data ingestion and model training on powerful GPU clusters to high-volume, real-time inference and a continuous feedback loop, they can accelerate the time-to-value for enterprises.

The factory model enables organizations to overcome the challenges of traditional IT, such as high cost and slow deployment, enabling rapid iteration, improved resource utilization through technologies such as GPU fractionalization, and, crucially, helping businesses in regulated sectors meet increasing demands for data and operational sovereignty by keeping their sensitive data and models secured on-premises or within their region.

Notably, the new Alletra Storage MP X10000 demonstrates a strategic shift in how HPE wants storage to participate in AI infrastructure. HPE is intent on making the data layer an active contributor to AI throughput, data readiness, and operational consistency across GPU pipelines. Pairing the X10000 with NVIDIA GB200 systems and Spectrum-X networking builds on the AI factory blueprint and moves the company toward a more integrated execution model across compute, data movement, and metadata services. In our view, this direction aligns with what many customers are discovering: AI performance is increasingly determined by how quickly, cleanly, and consistently data can be prepared and delivered to accelerators. HPE is offering a cohesive answer to that problem, and the X10000 is now positioned as a core part of that story.

HPE Private Cloud AI Expands Capabilities for Data and Operational Sovereignty

In response to European markets' heightened focus on data and operational sovereignty for secure and private AI adoption, HPE Private Cloud AI has been enhanced with significant new features and configurations. Key additions include the availability of NVIDIA RTX PRO 6000 Blackwell Server Edition GPUs alongside NVIDIA Hopper, offering customers flexible choices for various workloads. Security is significantly strengthened by the integration of STIG-hardened and FIPS-enabled NVIDIA AI Enterprise for air-gapped environments, ensuring compliance with numerous security standards.

HPE's Sovereign AI factory solutions now feature new, country-specific system designs and engineering-validated reference architectures that simplify local regulatory compliance, complemented by comprehensive cybersecurity advisory and professional services that align with regulated industry needs.

From our viewpoint, organizations are increasingly adopting private clouds to support their AI implementations and strategic goals, recognizing the critical advantages they hold over public clouds for complex, data-intensive, and sensitive AI workloads. The primary drivers for this shift are enhanced security and compliance, greater control and customization over infrastructure, and predictable cost and performance characteristics.

For AI projects dealing with proprietary or highly sensitive data, especially those involving Generative AI, security and regulatory compliance are paramount concerns. A private cloud is a single-tenant environment, offering complete data isolation because the compute, storage, and networking resources are dedicated solely to the organization. This dedication minimizes the risk of data breaches and performance interference from other tenants, ensuring proprietary AI models and training data remain securely behind the organizational firewall.

Additionally, in highly regulated sectors like finance and healthcare, private clouds allow organizations to maintain strict control over data residency and sovereignty, enabling them to perfectly align their security protocols and infrastructure to meet stringent laws such as GDPR and HIPAA.