NVIDIA
NVIDIA is a computing company founded in 1993, known for GPUs and accelerated computing, with products spanning PC gaming, computer graphics, AI, industrial digitalization, and data-center-scale infrastructure.
Europe’s AI Ambition Hinges on Building, Not Just Using, AI
At GTC Berlin, NVIDIA CEO Jensen Huang argued that Europe’s AI economy depends on producing AI, not simply adopting it. He cast the region’s engineering strengths and companies being built around AI from the outset as foundations for that effort, and pointed to 14 startup investments announced during a week of travel across Europe. His case was that AI could reshape existing industries while creating new ones.
NVIDIA Halos SAFE Connects Road Tests, Simulation, and Hardware for AV Evaluation
NVIDIA argues that autonomous-vehicle safety evaluation should connect measured road tests to repeatable simulation and hardware checks, rather than treat them as separate tests. Its Halos SAFE framework reconstructs captured scenarios with Omniverse NuRec, checks simulated results against real-world measurements and vehicle hardware, then uses generated variations to retest software releases. NVIDIA says the loop can produce evidence for safety targets and certification across markets.
Digital Twin Simulation Aims to Cut Months From Audi Logistics Testing
Audi plans to use a digital twin built in Dassault Systèmes’ DELMIA to test logistics tasks for robots before deploying them in production. The proposed system combines factory knowledge from the 3DEXPERIENCE platform with NVIDIA Omniverse libraries for simulated perception and motion, creating a virtual training environment for robots from Agile Robots and idealworks. NVIDIA says scaling tests to multi-robot fleets could help assess safe operation and save months in future logistics testing.
AI Factories Are Being Built to Run Agents, Not Just Models
NVIDIA CEO Jensen Huang argues that AI infrastructure is shifting from systems built to run models to AI factories designed to run agents, which he describes as the basis of a new industrial economy. In his GTC Berlin keynote, he extended that case to physical AI, presenting shared platforms and reference systems for vehicles and robots as part of the effort to build Europe’s AI economy.
AI Agents Build Robot Training Worlds From CAD Models and Real-World Motion
NVIDIA presents Agent Astra as a way to automate parts of robot development, turning a prompt, CAD assets and real-world motion data into a simulation for training and testing skills. In the demonstration, Astra builds the world, replays motion and tunes its physics; Isaac Lab and Newton handle training, while Isaac Lab-Arena evaluates skills locally on DGX Station. NVIDIA positions Agile ONE SN, built on Isaac GR00T and powered by Jetson Thor, as a reference robot for bringing trained skills into the physical world.
NVIDIA Links Road Tests, Simulation, and Vehicle Hardware in One Safety Loop
NVIDIA presents Halos SAFE as a framework for evaluating autonomous-vehicle safety by linking road tests, simulation, fleet data and vehicle hardware in a repeatable testing loop. Its central claim is that measured driving scenarios can be reconstructed with Omniverse NuRec, varied across conditions with Cosmos, and reused to test software changes and vehicle responses against safety targets. NVIDIA says the approach is intended to support evaluations from driver assistance through L4 robotaxis and provide evidence for certification across markets.
AI-RAN Puts Network Functions and AI Services on Shared Infrastructure
Nokia and NVIDIA argue that telecom operators can turn network infrastructure into a shared platform for radio access and AI workloads, creating a path from programmable 5G toward 6G. Their AI-RAN collaboration pairs Nokia software with NVIDIA accelerated computing; the companies say it can improve network performance, support AI services at the network edge and help automate network operations.
Europe’s AI Strategy Depends on Building Local Compute and AI Factories
In his GTC Berlin keynote, NVIDIA CEO Jensen Huang argued that Europe’s AI economy will depend on producing intelligence, not just adopting AI services. He called for investment across the stack—from energy and computing to AI factories, models and applications—and presented NVIDIA’s hardware, software and partner ecosystem as infrastructure for that production. His case extended from industrial agents and robotics to secure runtimes and local workstations: AI, he said, requires systems built to run models, connect them to knowledge and tools, and put them to work.
Connected AI Tools Link Patient Reports, Clinical Plans, and Recovery Support
The article argues that connected digital systems are intended to carry a patient’s account from one setting into the next decision, while equipment and supplies support care teams where they are needed. In its consultation example, a transcription tool records the patient’s account, the clinician’s assessment and the follow-up plan; it supports the clinician’s work, rather than making the decision.
AI Models and Robotic Labs Narrow Candidates for B-Cell Lymphoma Therapy
Lila Sciences is connecting AI agents to an autonomous laboratory to search for candidate therapies intended to help a patient’s own immune cells fight B-cell lymphoma. The company’s workflow uses Iris, built on NVIDIA Nemotron and Agent Toolkit, to turn a research goal into an experimental plan; models built with NVIDIA BioNeMo then run millions of virtual experiments to narrow candidates before robots make and test them. Lab results feed back into the next round of designs, though the source presents a research process, not a finished therapy.
NVIDIA’s OpenShell Enforces Agent Access Outside the Model
NVIDIA’s OpenShell reference architecture argues that model alignment and instructions are not enough to secure autonomous agents: the systems around them must enforce limits on access. OpenShell applies operator-defined policies at the runtime boundary, mediating agents’ system, file and network access; an optional layer called Sentry monitors activity outside the host and can cut an agent’s connection to its model. NVIDIA presents the two controls as complementary safeguards for agents that use tools and data.
NVIDIA’s GTC Berlin Keynote Frames Tokens as a Path From Data to Action
NVIDIA CEO Jensen Huang’s GTC Berlin keynote opening video presents tokens as the building blocks that turn data into knowledge and action. Its examples range from treatment planning and crowd forecasts to robots carrying medical samples and inspecting hazardous sites, connecting AI to decisions and physical work. The video places those examples alongside computing infrastructure and closes with a shared ambition for a future built for all humankind.
AI Agents Link Virtual Experiments to Lab Tests, Factory Plans to Floor Conditions
NVIDIA’s GTC Berlin presentation argues that industrial AI agents need more than a model: they must connect to company knowledge and domain-specific tools, with controls over what they can access and execute. It illustrates that architecture through Lila Sciences’ experiments for therapy design, Siemens’ production planning and Dassault Systèmes’ vehicle-design workflow. Lila feeds physical test results into another round of designs and experiments; in Dassault Systèmes’ example, simulation results prompt refinement before an engineer selects the final design.
Micro Duck’s Walking Policy Trains in the Cloud, Then Returns to Local Simulation
The workflow uses NVIDIA RTX Spark for scene creation and policy evaluation, while cloud compute handles parallel training in Isaac Lab; the trained policy then returns to the local system before deployment to Micro Duck. The operator says Astra uses tools including FreeCAD, Blender and Isaac Sim to build the course and robot model, and that Isaac Lab trains thousands of Micro Ducks in parallel. The account shows the robot walking after deployment but reports no separate measurement of its real-world performance.
Factory-Tested AI Pods Could Cut On-Site Deployment From Months to Days
NVIDIA presents DSX as a full-stack reference architecture for designing AI factories as coordinated systems, with compute, networking, power and cooling validated in a digital twin before construction. The company says its DSX Max LPS can deliver the same output with 30% less power, while closed-loop cooling and outdoor dry coolers can save up to 54 million liters of water a day at a 2-gigawatt scale in suitable climates. Its DSX POD packages pre-integrated, factory-tested equipment to cut on-site deployment from months to days once utilities are ready.
NVIDIA Frames AI Compute as Revenue-Producing Infrastructure
NVIDIA’s GTC Berlin 2026 keynote closing video presents AI computing as industrial infrastructure with the potential to generate revenue, linking GPUs and data centers to a wider stack of models, software and robotics. In a musical recap performed by robot characters, the company also calls for agents to act within security limits and urges Europe to build across that stack. The performance makes these points through slogans rather than technical or financial evidence.
RTX Spark Puts Gaming, Creative Work, and Local AI on One Laptop
NVIDIA’s RTX Spark campaign presents the laptop as a single PC for gaming, creative work and building AI locally. Its argument is that a computer once used to enter other people’s worlds can also help users imagine and make their own, without giving up play. The campaign establishes that broader role through its imagery, but does not detail the hardware or performance behind the claim.
AI Is Linking Semiconductor Design, Fab Operations and Inspection
NVIDIA argues that accelerated computing and AI can help semiconductor manufacturers manage growing design and fab complexity by linking simulation, digital twins, engineering support, robotics and inspection. Its account centers on a feedback loop: simulation and production data inform models, while inspection results feed back into them. NVIDIA reports chamber simulations up to 35 times faster and more than 96% accuracy in wafer-map defect classification, figures it presents as examples of the approach rather than measures of overall fab performance.
DOCA Skills Cut BlueField-3 RDMA Code by 73%
In a BlueField-3 comparison, two coding agents built Go applications that used NVIDIA DOCA to send RDMA traffic and verify its arrival on the hardware. The demo argues that NVIDIA’s DOCA skills changed the route, not the result: both agents succeeded, but the agent with curated guidance reused NVIDIA’s sample and wrote about 73% less code while making roughly half as many hardware commands.
AI Factories Shift From Peak Power Design to Dynamic Capacity
NVIDIA argues that AI-factory capacity is constrained by the facility as a whole—grid connection, power delivery and cooling—not GPUs alone. Its DSX AI Factory Platform combines grid-responsive demand management, DSX MaxLPS power controls, 800 VDC distribution and 45°C liquid cooling to direct more of a fixed power budget to compute. NVIDIA says MaxLPS can enable up to 40% more compute within the same power budget.
Taiwan’s Semiconductor Ecosystem Is Building the Infrastructure for AI Factories
NVIDIA argues that scaling AI is not only a matter of better models or processors, but of building “AI factories” that combine chips, servers, power, cooling and data-center infrastructure into installed computing capacity. In its SEMICON Taiwan 2026 film, the company presents Taiwan’s semiconductor and manufacturing ecosystem—from TSMC and component suppliers to Foxconn, Wistron and Pegatron—as the industrial base that turns its designs into deployable AI systems.
Firebird Plans 250 Megawatts of AI Infrastructure Across Armenia and Kazakhstan
NVIDIA says Firebird has launched the CIS region’s largest AI factory in Armenia and plans to deploy 250 megawatts of NVIDIA AI infrastructure across Armenia and Kazakhstan within 12 months. Jensen Huang argues that broadly available AI services do not eliminate the need for countries to build local computing capacity: “No nation can outsource all of its intelligence.” He presents the buildout as infrastructure for domestic researchers, companies and governments, while seeking to attract global firms to develop and run AI in the region.
Enterprise Agents Route Tasks Across Specialized And Frontier Models
NVIDIA argues that enterprise AI agents should route work across multiple models rather than rely on a single general-purpose system. In its example of Glean, a specialized model gathers context from enterprise sources and handles straightforward requests, while frontier models are used for complex analysis involving tools, multiple sources, or deeper reasoning. NVIDIA says this division can reduce latency and token use while preserving answer quality, though the performance figures are presented as Glean claims.
Autonomous AI Agents Target Fewer Iterations Across Chip Design
NVIDIA argues that autonomous AI agents can reduce the repeated design cycles that slow development of increasingly complex AI-factory chips and their surrounding systems. The company positions Cadence, Synopsys and Siemens EDA tools across a connected workflow from RTL creation and verification to physical signoff, packaging, board design and thermal modelling, while retaining human supervision in Synopsys’s RTL-to-GDS flow. Its measure of progress is not AI-generated code alone, but whether designs require fewer iterations to meet performance, power, area and physical constraints.