Nvidia Corp. is no longer just a hardware company — it has evolved into a full-stack AI platform delivering complete AI systems.

Last week’s Nvidia GTC event was a showcase for Nvidia’s numerous partnerships with Dell Technologies Inc., Adobe Inc., Vast Data Inc. and more. While the release of Vera Rubin chips shows that powerful hardware remains Nvidia’s bread and butter, the company has its fingers in many pies, including physical AI, simulations and AI-powered creation.

“Being able to represent physical things inside a computer and do iterations on it … is a superpower,” said Rev Lebaredian (pictured, right), vice president of Omniverse and simulation technology at Nvidia, in an interview with theCUBE. “Without it, we actually don’t really have a chance at creating the complex products that we’re creating today and into the future.”

Lebaredian spoke with theCUBE’s John Furrier at Nvidia GTC 2026, where theCUBE, SiliconANGLE’s livestreaming studio, provided exclusive video coverage of the event. Furrier, along with co-hosts Gemma Allen, Dave Vellante and Bob Laliberte spoke with industry experts about Nvidia’s evolving AI system, its new partnerships with Adobe and Dell, and how the industry is solving the energy bottleneck.

Here are three key insights you may have missed from Nvidia GTC 2026:

Insight #1: Digital simulation is a big part of AI’s future.

The next step in Nvidia’s AI technology is creating simulations of the physical world, primarily through Nvidia Omniverse, where developers and artists can visualize and generate 3D models for their products.

The applications are wide-ranging. At GTC, Nvidia and Adobe announced a strategic partnership that combines Adobe’s creative workflows with Nvidia’s AI models and specialized hardware into a cloud-native 3D digital twin solution. The collaboration allows customers to convert a product’s digital identity from the design phase to campaign production using simulation-ready digital twins.

“The need for content is going to go up by 5x over the next two years,” Varun Parmar (left), general manager at Adobe, told theCUBE. “We know generative AI is the solution for content scaling. However, there are certain industries where identity preservation is really important, where you want to make sure that there are exact pixels of the product that are represented and there’s no hallucination.”

The partnership positions Nvidia Omniverse as a simulation infrastructure that can help maintain digital representations of products in a diverse set of fields, from marketing to the automation industry. In speeding up campaign asset production, Nvidia enables brands to begin campaigning before the physical product even ships.

“It’s really important that you use the actual digital twin,” Lebaredian said. “As great as AI is at creating content … it’s really important when you represent your product that what you’re showing is exactly what it’s going to be like when people interact with it in the physical world. You can’t really promise that with a pure AI-generated image.”

Here’s theCUBE’s complete video interview with Lebaredian and Parmar:

Insight #2: Nvidia’s hardware is packaged with an integrated AI system.

Nvidia recently announced Vera Rubin, a multi-rack platform that is 35 times faster than Grace Blackwell, the previous iteration, and engineered for agentic AI business models. The guiding strategy behind the release is the DSX AI Factory reference design, which uses dynamic power provisioning to reduce wasted energy.

Vera Rubin also represents Nvidia’s shift toward a coordinated AI system that incorporates compute, networking, storage and power management, allowing customers to maintain software compatibility while getting more efficient use of their stack.

“On average [in] today’s data center, if you want a gigawatt of provision power, you’re probably only using 600 megawatts,” said Charlie Boyle, VP of DGX at Nvidia. “With this new technology and this new rack architecture, we’re making it so much more efficient, but also bringing AI into that infrastructure. It’s not humans and phone calls to turn knobs. It’s agents turning those knobs, making [power] 100% utilized in that data center, but doing it safely.”

Nvidia’s partnerships address the ongoing energy bottleneck that is an impediment to widespread AI adoption. Zededa Inc. recently launched its Edge Intelligence Platform, which simplifies AI deployment across distributed environments by leveraging Nvidia’s AI system. Nvidia recently enabled greater AI performance at the edge with the release of its IGX Thor platform.

“With Thor, you can now run a very powerful LLM, VLM model at the edge in an industrial location,” said Padraig Stapleton, senior VP at Zededa. “You can marry that with agentic AI software and capability, which allows you to run different types of use cases, everything from inspection on widgets going down the line to safety … applications on an oil rig or a platform.”

A key hardware partner for Nvidia is Texas Instruments Inc., a global semiconductor company whose chips and racks are designed to ensure reliability on high voltage systems. The goal is to create a sustainable power architecture that can manage AI workloads.

“Reliability becomes supremely important, and that’s where the blast radius becomes a problem, because … in AI data centers, building redundancy into your infrastructure is a lot more difficult than it used to be before,” said Kannan Soundarapandian, VP and GM of high-voltage power at Texas Instruments. “If there’s a failure in one power converter somewhere that’s in the pathway of power to the GPU, you lose an entire workload.”

Here’s theCUBE’s complete video interview with Kannan Soundarapandian:

Insight #3: Nvidia is solving ‘the data problem’ with its partners.

The other component of any integrated AI system is, of course, the data. Nvidia’s Dell-Elastic collaboration harnesses Elastic’s hybrid search and vector database technology within the Dell AI Data Platform, helping customers better manage unstructured data for AI workloads.

“I think even [Nvidia Corp.] — and everybody else — has started to realize [that the] way to get AI factories into production at enterprises has to start with solving the data problem,” said Vrashank Jain, director of product at Dell. “It requires an entire ecosystem of companies and tools coming together to actually make it happen.”

Nvidia has also partnered with Vast Data, collaborating on the Dynamo inference engine, an open-source, modular inference framework. Its architecture builds on Vast Data’s work with Nvidia on cache offloading, which shifts stored model context out of GPU memory to open up compute capacity.

“We see a 10X improvement in inference capability out of a single GPU server,” said Andy Pernsteiner, field chief technology officer of data infrastructure company Vast Data. “If a GPU isn’t busy having to recalculate previously computed session data, then it can easily service another request and asynchronously fetch that session data. That’s the work that we’ve been working with Nvidia on.”

Here’s theCUBE’s complete video interview with Vrashank Jain and Steve Kearns, VP and GM of search at Elastic:

To watch more of theCUBE’s coverage of Nvidia GTC 2026, here’s our complete event video playlist:

(* Disclosure: TheCUBE is a paid media partner for Nvidia GTC. Sponsors of theCUBE’s event coverage do not have editorial control over content on theCUBE or SiliconANGLE.)

Photo: SiliconANGLE