From the AI Factory to the Workplace
What NVIDIA GTC Berlin 2026 Means for Businesses
What NVIDIA GTC Berlin 2026 Means for Businesses
Auf der NVIDIA GTC Berlin 2026 hat Jensen Huang eine klare Botschaft mitgebracht: KI ist kein Experiment mehr, sondern wird zur Kerninfrastruktur von Unternehmen. Im Zentrum seiner Keynote stand die Idee der „KI-Fabrik" – KI nicht als einzelnes Modell, sondern als produzierendes System aus Rechenleistung, Netzwerk, Speicher, Software und Datenmanagement. Die zentrale Erkenntnis: Infrastruktur muss vorhanden sein, bevor Modelle entstehen – ähnlich wie Stromnetze vor der Elektrifizierung.
AI sovereignty was one of the key topics. Huang emphasized that sovereignty means controlling one’s own data and retaining the ability to act. Europe—and Germany in particular—should not only consume AI but also produce it itself. To that end, NVIDIA announced investments in European AI initiatives and highlighted partnerships with the German government as well as companies such as Siemens and Mercedes-Benz.
A second focus was on agent-based AI systems. These are systems that combine models with tool usage, planning logic, and feedback loops to independently execute multi-step tasks. For companies, this means that AI agents are moving closer to productive processes—and thus bringing issues of security and control to the forefront.
Security was addressed as a technical engineering issue, not merely a policy matter. With the OpenShell Reference Architecture and NVIDIA Sentry on BlueField-4 DPUs, companies gain runtime control over autonomous agents. The division of roles is clear: agents “think” on Rubin GPUs, “act” via harnesses on Vera CPUs, and are monitored on BlueField-4 DPUs—Sentry can quarantine agents in milliseconds if they exceed their software boundaries. OpenShell relies on kernel-level sandboxing and is supported by over 100 partners, including Red Hat, Canonical, SUSE, CrowdStrike, SAP, and ServiceNow.
In the telecommunications sector, Deutsche Telekom has validated Nokia and NVIDIA’s commercial AI-RAN platform in Bonn and Oulu, including AI-enhanced uplink channel estimation; field tests are planned for 2027. Other operators include Fastweb and Vodafone in Italy, Kazakhtelecom, Orion in Serbia, and Cassava in Africa.
The keynote highlighted a clear trend: AI is becoming physical—in robotics, manufacturing, and industrial facilities. For companies, this means a growing need for computing power for simulation and training, such as for transferring simulated models into the real world.
The NVIDIA DGX Spark is already part of our portfolio. It enables development teams to run AI workloads on-premises—without relying on the cloud, with full control over their data, and without ongoing cloud costs. The keynote reaffirms this very approach: sovereign AI begins when teams retain control over their data and infrastructure.
Our customers in regulated industries—healthcare, manufacturing, and the public sector—need solutions that run AI in their own data centers and allow them to retain control over their data. As a vendor-neutral provider (NVIDIA, Gigabyte, Supermicro), we can deliver exactly that: hardware integration and on-site support from a single source.
The keynote highlighted a clear trend: AI is becoming physical—in robotics, manufacturing, and industrial facilities. For companies, this means a growing need for computing power for simulation and training, such as for transferring simulated models into the real world. This also touches on our core topic: HPC infrastructure. We have been working for years on the design and operation of computationally intensive systems—a foundation that is likely to be increasingly in demand in the realm of physical AI.
The “five-layer cake” of AI infrastructure can also be seen as a story of scalability: from the DGX Spark for entry-level use, to gigabyte-class workstations at the departmental level, all the way to HGX B200 servers for clusters and AI factories. We help customers choose the right scale—and grow along with them.
A photo that caused a stir at GTC Berlin 2026: Jensen Huang stands on stage in front of an entire “robot parade.” In the background are humanoid robots from Agility Robotics and Enchanted Tools; in the foreground—barely larger than a laptop—is Microduck, the tiny robot duck. Microduck is an open-source robot from Hugging Face, measuring just 25 centimeters tall. It can walk, sit down, and pick up objects with its beak. What might sound like a toy at first is actually a strategic move for the AI world: With such affordable and accessible hardware, developers worldwide gain a low-threshold entry point into the Sim2Real pipeline—that is, the transfer of simulated training into the real physical world. To this end, Hugging Face provides simulation environments, training data, and scripts free of charge on its platform. For us at sysGen, this development is more than just a nice anecdote. It vividly illustrates just how broad the landscape of physical AI has become: from ecosystem entry (Microduck) to commercial production (Agility Digit) to service interaction (Enchanted Tools). And it highlights what all these systems have in common—they require a robust infrastructure. Whether it’s local training on the DGX Spark, departmental workstations, or HGX-B200 servers for large-scale model development: the journey from the “duck” to a productive AI application begins with the right hardware and data control.