TL;DR:
- Sovereign AI means building and deploying AI using a nation’s own infrastructure, data, and workforce.
- Governments are shifting from AI consumers to producers to ensure national security and economic independence.
- Key ingredients: local data centers, domestic AI capabilities, and AI-ready infrastructure (aka “AI factories”).
- Practical deployment requires secure, compliant, and auditable full-stack platforms.
- HPE and NVIDIA are co-developing turnkey sovereign AI solutions tailored for public and private sectors.
What is sovereign AI, really?
“Sovereign AI” isn’t just a buzzword. It’s a policy imperative. As Shilpa Kolhatkar, Global Head of AI Nations @ NVIDIA, explained in the webinar, sovereign AI is about enabling governments to produce and deploy AI using their own infrastructure, local datasets, and workforce. The goal: control and accountability.
Nations now view data like a natural resource. Local ownership and stewardship are critical not just for privacy and compliance, but for unlocking economic and strategic value.
“AI has gone mainstream. Every nation is part of the AI revolution. But the real question now is: who is producing it?” — Shilpa Kolhatkar (04:32)
The shift from consumption to creation is underway globally. National investments are increasingly focused on building end-to-end AI ecosystems—data centers, AI factories, LLMs, talent pipelines, and startups.
Why it matters: Security, compliance, and economic resilience
Data localization, privacy laws, and regulatory sovereignty aren’t new, but the complexity and stakes have grown dramatically with generative AI. Bouvena Gdad, Chief Technologist, WWNS AI Services @ HPE, broke it down:
- Digital sovereignty: Where and how data is stored and processed.
- Operational sovereignty: Who controls IT operations, audits, and monitoring.
- Technological sovereignty: Full-stack control from infrastructure to models.
Many organizations are discovering that even storing data locally isn’t enough. If model inference, monitoring, or incident response crosses borders, you’re still exposed.
“Customers say, ‘I can’t run inference on a model stored in the cloud.’ They need on-prem models, data, and inference to comply.” — Bouvena Gdad (21:17)
Infrastructure matters: The rise of AI factories
The concept of the “AI factory” is gaining traction. In this model, raw data enters, and out comes intelligence in the form of tokens and predictions—driving economic growth, innovation, and public services.
Examples like Indonesia’s partnership with telco IOH show how existing national infrastructure (telcos, energy providers, regional CSPs) can serve as the foundation for sovereign AI clouds.
HPE and NVIDIA are leading the charge with co-engineered solutions:
- Based on NVIDIA OVX and HPE Cray EX platforms
- Support for hundreds of GPUs per rack
- Turnkey, air-cooled, rack-based deployments
- Integration with NVIDIA NIMs and HPE AI Essentials software
“From loading dock to deployment-ready in 24 hours. We can show a RAG chatbot on your data on day one.” — Steve Heibein, Federal AI Chief Technologist @ HPE (34:52)
What makes deployment so hard?
Even with the right hardware, practical challenges persist:
- Lack of local talent: AI skills are in short supply, especially in secure environments.
- Data sensitivity: Organizations may restrict real data access, even during model testing.
- Integration burden: New stacks must work with legacy infrastructure and applications.
- Regulatory churn: Requirements evolve fast and vary by region.
HPE offers services across every layer—from pre-deployment consulting to continuous compliance monitoring. For air-gapped environments, they’ve delivered sovereign AI deployments without internet access, using fully vetted software images and in-person implementation.
Questions answered in this session
- What is sovereign AI and why is it critical for governments?
- How are countries building AI infrastructure that respects data sovereignty?
- What are the challenges of deploying LLMs in sovereign environments?
- How do private cloud AI solutions from HPE and NVIDIA support compliance?
- How does TCO compare to commercial cloud models?
Key takeaways
- Sovereign AI is about producing, not just consuming AI.
- Control over data, infrastructure, and models is now a national priority.
- Turnkey solutions from HPE and NVIDIA reduce complexity and accelerate deployment.
- Compliance, auditability, and security must be designed into every layer.
- The skills gap and legacy integration are real bottlenecks to scale.
For public sector leaders, regulators, and infrastructure architects, this webinar unpacked how to align AI innovation with national security and compliance needs—without sacrificing performance or agility.
Watch the webinar: Sovereign AI: Keeping Your Data Secure and Controlled
Speakers:
- Shilpa Kolhatkar, Global Head of AI Nations @ NVIDIA
- Bouvena Gdad, Chief Technologist, WWNS AI Services @ HPE
- Steve Heibein, Federal AI Chief Technologist @ HPE
- Hosted by Brian Mink, Co-founder and President @ Data Science Connect
