NVIDIA expands global AI Cloud network to support sovereign and agentic AI

NVIDIA has announced a major expansion of its AI Cloud ecosystem, supporting the rapid global deployment of AI factory infrastructure designed to meet growing demand for agentic AI, physical AI, sovereign AI and large-scale inference workloads.

The initiative aims to expand access to high-performance computing resources for enterprises, startups, governments, researchers and AI developers worldwide.

According to NVIDIA, the ecosystem now spans six continents, with new partners expanding AI Cloud infrastructure across multiple regions. The company said the expansion is intended to bring AI computing resources closer to users, industries and national AI initiatives while supporting regional and sovereign AI requirements.

Several cloud providers are expanding infrastructure to support advanced AI applications, including model training, fine-tuning, inference and AI agent development. Companies including CoreWeave, Firmus, Nebius and others are deploying new AI factories capable of supporting model training, fine-tuning, inference and AI agent development.

The expansion also includes support for emerging physical AI and robotics workloads through platforms such as NVIDIA Cosmos and Isaac.

NVIDIA also highlighted growing adoption of its DSX platform, which is designed to help cloud providers deploy and manage AI factories more efficiently. The company said AI infrastructure is increasingly being assessed using metrics such as cost per token, energy efficiency and infrastructure utilisation, rather than raw computing capacity alone.

Why does it matter?

The expansion highlights the growing importance of AI infrastructure as governments and companies compete to secure the computing resources needed for advanced AI systems. Access to large-scale computing capacity is increasingly viewed as a strategic asset, particularly as countries pursue sovereign AI initiatives and seek greater control over critical digital infrastructure.

The announcement also reflects a broader shift in the AI industry, where demand is expanding beyond model training to include inference, autonomous agents and robotics applications, placing new emphasis on infrastructure efficiency, energy use and geographic distribution of computing resources.

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