Nvidia's AI Expansion Reshapes Hyperconverged Edge

Telecom infrastructure, enterprise networking, and compute architecture converge as Nvidia pushes AI beyond data centers

Published on Feb. 13, 2026

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Nvidia's push to extend artificial intelligence beyond centralized data centers is accelerating the convergence of telecom infrastructure, enterprise networking, and compute architecture at the hyperconverged edge. Companies like Veea are aligning their architectures to capitalize on this shift, which could allow telecom operators to evolve from bandwidth providers to edge compute orchestrators.

Why it matters

The hyperconverged edge is where AI factories collide with wireless networks, forcing infrastructure, networking, and compute to converge in new ways. This presents a major opportunity for telecom operators to move beyond 'dumb pipe' services and provide value-added use cases at the edge, but they must adapt their architectures and strategies to take advantage.

The details

Nvidia's Open RAN work and alignment with Nokia signal that AI acceleration is moving directly into the radio access network, embedding compute into the network fabric itself. This is critical as higher frequency bands and distributed radios demand equally distributed compute to power real-time applications. Networking, AI acceleration, security, and orchestration are converging into a single architectural stack at the edge, where AI agents must be trusted participants in operational systems.

  • Nvidia developed an Open RAN architecture roughly 4 years ago that was first tested in Las Vegas.
  • Nvidia discussed its edge computing vision and Open RAN strategy on stage with Nokia at a recent GTC event in Washington, D.C.

The players

Nvidia

An American multinational technology company that designs graphics processing units (GPUs) for the gaming and professional markets, as well as system on a chip units (SoCs) for the mobile computing and automotive market.

Veea Inc.

A company aligning its architectures to capitalize on the convergence of telecom infrastructure, enterprise networking, and compute at the hyperconverged edge.

Allen Salmasi

The chairman and chief executive officer of Veea Inc.

Nokia

A Finnish multinational telecommunications, information technology, and consumer electronics company.

Vapor

A company that was involved in the initial testing of Nvidia's Open RAN architecture in Las Vegas.

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What they’re saying

“I really believe that this is a huge opportunity for telcos to effectively come out of this dump pipe type of service offering and really provide for a lot of value added into the use cases that are running at the edge.”

— Allen Salmasi, Chairman and CEO, Veea Inc.

“When you get into terahertz range of frequencies, now effectively every room, every small room is going to have some type of a radio head. It's fully distributed in terms of radio communications, but that has to be aligned with, completely in sync with, a distributed compute type of capability. Our architecture supports a compute mesh, as well as microservices mesh on top of this radio.”

— Allen Salmasi, Chairman and CEO, Veea Inc.

“You have communications and cybersecurity fully intertwined and integrated as one fabric. It's totally embedded into all of the connections that you make on a zero-trust basis. Because, frankly, unless you can trust the AI agents that you're connecting to, you are not going to really have physical AI introduced at the edge.”

— Allen Salmasi, Chairman and CEO, Veea Inc.

What’s next

Telecom operators must evolve their architectures and strategies to capitalize on the convergence of AI, networking, and edge computing, leveraging their existing facilities, power, and aggregation points to become edge compute orchestrators.

The takeaway

The hyperconverged edge is reshaping the future of AI, telecom infrastructure, and enterprise computing, presenting a major opportunity for telecom operators to move beyond 'dumb pipe' services and provide value-added use cases - but only if they can adapt their architectures and strategies accordingly.