For Orange CTO Laurent Leboucher, the main attraction of AI today lies in its potential to improve the efficiency of the radio access network (RAN). That helps explain Orange’s recent collaboration with Nokia and Nvidia. Orange already deploys Nokia’s purpose-built 5G products at mobile sites in France and other markets. Until recently, it had little obvious need for Nvidia, the US chipmaker best known for the graphics processing units (GPUs) used to train large language models. But Nokia and Nvidia became closely aligned last October, when the chipmaker took a 3% stake in the RAN vendor as part of a $1 billion investment. Nokia is now developing RAN software designed to run on GPUs.

Leboucher’s interest is driven in part by concerns over the cost of custom silicon — the application-specific integrated circuits (ASICs) used in purpose-built 5G networks. “It creates an opportunity to bring a general-purpose chipset instead of an ASIC implementation,” he told Light Reading at last week’s FutureNet World event in London. “I think we could, at some point, benefit from the economies of scale of new chipsets. That could be Nvidia.”

The rationale is much easier to understand than arguments about 5G for autonomous vehicles. Chip manufacturing is already expensive, and both Nokia and Ericsson expect component costs to rise further this year amid relentless AI demand. At the same time, the RAN market remains relatively small and has contracted. According to market research firm Omdia, telco spending fell from $45 billion in 2022 to $35 billion last year and is expected to stay at that level. In that context, it is increasingly difficult to justify designing high-cost chips with limited reuse outside telecom.

Image Credit: Orange

Last year, Nvidia spent about $18.5 billion on research and development, generated nearly $216 billion in revenue, and reported a gross margin of more than 70%. Its financial strength is not in question. If telecom operators can use its GPUs for RAN software, they may face less pressure to secure the long-term economics of 5G and 6G development. That alone could be enough to support the case for Nvidia. The counterarguments are cost and power consumption. By design, custom silicon is optimized for a specific workload and will always outperform a more general-purpose processor at that task. An Nvidia GPU in the RAN could therefore be seen as excessive — like using a crop duster to water a hanging basket.

Leboucher, believes that Nokia and Nvidia are developing something far more compact than a typical data-center deployment. “It is not a Blackwell GPU,” he said, referring to Nvidia’s current hyperscaler-class product line. “I have an understanding it’s something which is a little bit smaller.” One of the first GPU-based products is expected to come on a card that Orange can insert into an existing Nokia AirScale chassis.

He is also interested in replacing traditional RAN algorithms with AI to improve spectral efficiency and overall performance. Through trials with Nokia and Nvidia, Orange wants to determine whether a GPU is actually required to capture the full benefit. “We can completely rethink the way we are doing algorithms today, using AI for the radio Layer 1,” he said, referring to the most compute-intensive part of the RAN software stack. Some of the “AI-RAN” narrative still sounds “a little bit like science fiction,” Leboucher admitted. “But I think there are some very interesting ideas behind that. We want to understand where we are.”

This is not the first time the industry has debated a shift from ASICs to general-purpose processors for RAN equipment. Alongside its purpose-built 5G portfolio, Ericsson already offers cloud RAN products based on Intel CPUs. Samsung is now focused on Intel-based virtual RAN and has recently predicted the end of purpose-built 5G. Even so, cloud and virtual RAN still account for only a small share of live 5G deployments. Huawei and Ericsson, the two largest RAN vendors, remain committed to custom silicon development.

Nvidia’s entry into the market has clearly given Leboucher and his team more to evaluate as RAN technology becomes more sophisticated. “We are introducing new requirements for radio networks, typically for beamforming, and we have to consider the need for quite powerful chipsets,” he said. “Whether the best way to keep going is using ASICs or a general-purpose architecture – I think this is a good time to ask the question. Before, it was too early.”

The answer could shape Orange’s next major RAN decisions. The operator is preparing for what Leboucher describes as a “refresh” of RAN equipment across several countries ahead of the expected 6G launch in 2030. For the first time, he said, Orange will include cloud RAN as a “major option” in its request for proposal.

The concern around Intel as an alternative to Nvidia is its still-fragile financial position. Before December, Intel had been trying to spin off its network and edge group (NEX), which develops RAN chips. Those plans were later shelved, but the company’s net loss widened to about $4.3 billion in the most recent first quarter, from $887 million a year earlier, while revenue rose only 7% year over year to $13.6 billion. Cristina Rodriguez, who had led NEX, left this month to join Coherent, and Intel has not yet named a successor.  “The shares jumped 28% in after-hours trading, taking Intel firmly into meme-stock territory,” said Radio Free Mobile analyst Richard Windsor in a blog published after results came out on April 23. “I say meme-stock because there is no other way to describe it when the shares are on a 2026 PER [price-to-earnings ratio] of 137x, and its technology looks obsolete.”

Orange places significant value on separating hardware from software, allowing the same RAN software to run across multiple hardware platforms. Ericsson and Samsung both say the virtual RAN software they have built for Intel CPUs could, with relatively modest changes, be ported to AMD silicon using the same x86 architecture or to Arm-based CPUs.

By contrast, Layer 1 code written for Nvidia GPUs and the CUDA software stack would not be portable to other platforms, according to Ericsson. “I think the main challenge we see with that is we are trying very hard to keep our stack portable, to give hardware options,” Michael Begley, Ericsson’s head of RAN compute, told Light Reading at MWC Barcelona this year. “If you go all in on one, it’s great, but you’re all in on one, and you can’t offer those other options to the operators or the ecosystem.”

Leboucher acknowledges that risk. “The risk of lock-in exists, definitely,” he said. “We really want to stay open. At the same time, we know that benefiting from a very, very large-scale general-purpose architecture should improve the TCO [total cost of ownership]. At the end of the day, it will be a trade-off. But we would welcome an architecture where we have the capacity at some point to decide to swap if we need to swap.”

Nokia’s hope is that much of the Layer 1 software written for Nvidia GPUs will eventually be deployable on other GPU platforms. But Nvidia’s near-monopoly in that segment leaves the industry with few alternatives for now. There is also optimism inside Nokia that GPU-based code could later be adapted for capable CPUs, although Ericsson’s comments suggest that would be much harder. For telecom executives, the choices made over the next couple of years may be pivotal as 6G approaches.

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