Verizon is tapping into Nvidia’s robust artificial intelligence (AI) push to power enterprise AI services and digital transformation efforts running over the carrier’s 5G private network and mobile edge compute (MEC) infrastructure.

The offering stacks those components into a multitenant and scalable package designed to support enterprise-focused third-party applications. These include generative AI (genAI) large language models (LLMs) and vision language models; video streaming; broadcast management; computer vision; all the realities – augmented, virtual, extended (AR, virtual reality (VR), XR); autonomous vehicles and robots; and IoT.

Customers can access applications using Nvidia’s NIM microservices architecture and tap into those services remotely via a portable private network service or on-premises through a permanent private network deployment.

Verizon pointed to several consultancy surveys that showed enterprises are looking toward AI services to grow business opportunities. This included a PwC Global Artificial Intelligence Study that showed a vast majority of business executives “considered AI a business advantage and were either already using it or planning to do so.”

Verizon CEO Hans Vestberg, who has been a long-time proponent of the operator being in a strong position to power edge computing services, continues to pump up that potential despite a lack so far of a significant return.

“Our portfolio of high-performance spectrum, the capacity of our fiber, and our ability to deploy and support mobile edge compute make us as the backbone of the AI economy and the partner of choice for players in the space,” Vestberg said during an earnings call earlier this year. “We will power the best AI services for our customers. What set us apart with AI is our network’s mobile edge computing capabilities and deep fiber footprint. By processing data closer to the source we enable real-time AI application that requires security, ultra-low latency, and high bandwidth. This is where our network shines, opening up possibilities that simply weren’t feasible before.”

Adam Koeppe, SVP of technology planning at Verizon, in a recent interview with SDxCentral touted the carrier’s architecture and its ability to support AI-derived use cases.

“I think it's really important to look at what these capabilities exist within the architecture, and how can AI, true AI, augment things that are already being done or create things that are brand new,” Koeppe said. “Where I see our evolution occurring is when you have an advanced cloud platform, as we do, you have an orchestration layer on top that we already have, and you then find ways to incorporate new AI capabilities on top of that. That's going to allow your engineers and your operators to interface differently.”

These new AI interface opportunities are expected to push further edge investments.

“As the focus of AI shifts from training to inference, edge computing will be required to address the need for reduced latency and enhanced privacy,” Dave McCarthy, research VP for cloud and edge services at IDC, wrote in a recent report. “This trend not only optimizes operation efficiencies but also fosters new business models that were previously not possible with centralized infrastructure. Distributing applications and data to edge locations enables faster decision-making with reduced network congestion.”

Carriers like Nvidia for the edge Verizon’s work with Nvidia follows on the heels of the chip giant striking a deep AI development partnership with T-Mobile US. That deal has those two working with Ericsson on an AI innovation center focused on tying the radio access network (AI-RAN) into cloud-based RAN and AI development.

The carrier noted in a presentation that the goal of the partnership is to integrate cloud-based RAN and AI using unified infrastructure that can scale to serve millions of mobile users at once.

“AI-RAN will enable new AI algorithms to unlock the full potential of wireless networks,” T-Mobile US’ presentation noted. “These AI algorithms would be rapidly developed with software-defined RAN, trained on AI data centers, and fine-tuned with physically accurate digital twins. This will lead to dramatic improvements in spectral and energy efficiencies.”

This architecture plan will see operators update their RAN equipment deployed at either each cell site or at a central location that can serve several cell sites. These updates will turn those locations into edge data centers or cloud connections that can in turn better serve AI workloads.

“These kinds of AI workloads will increasingly demand that processing have happen on the device, which we’re starting to see but there are obvious limitations there, or on a cloud near the device, and that’s a business opportunity that we see in the future,” T-Mobile US CEO Mike Sievert said.

Verizon’s private 5G momentum Verizon’s Nvidia agreement could also propel the carrier’s private 5G business opportunities. The carrier has touted recent momentum around that slowly evolving space.

“We were doing a couple of private networks a month, we are doing a lot more than that,” Sowmyanarayan Sampath, CEO for Verizon’s Consumer Unit, told an audience at the MoffettNathanson Media, Internet and Communications Conference 2024.

This echoed comments from Vestberg who told investors during the carrier’s second-quarter earnings call that progress was indeed at hand and helping to bolster other opportunities.

“On the 5G use cases, now we start talking more and more about private networks because the number of them are many, then the value of them are still fairly small,” Vestberg said. “But when we build that base of private network, managed spectrum for enterprises, that, over time, is going to be a great opportunity for our enterprise sales force to add in, do the mobile edge compute.”