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Ericsson Pursues AI-RAN Without Nvidia to Achieve 5G Silicon Independence

Summarized by NextFin AI
  • Ericsson has launched its AI-RAN products that utilize its own silicon, aiming to enhance spectral efficiency and beamforming by up to 10% without relying on Nvidia's GPUs.
  • The global RAN market stabilized at approximately $35 billion in 2025, with Ericsson avoiding vendor lock-in by not following Nokia's investment in Nvidia for GPU compatibility.
  • Ericsson's R&D spending reached approximately 48.9 billion Swedish kronor ($5.4 billion) in 2025, focusing on in-house silicon to bridge traditional and virtualized networks.
  • By 2030, virtualized RAN is expected to account for only 15% to 20% of the market, indicating a continued demand for specialized silicon over general-purpose hardware.

NextFin News - In a decisive move to safeguard its technological sovereignty, Ericsson has officially launched its initial wave of AI-Radio Access Network (AI-RAN) products designed to operate on its own purpose-built silicon rather than relying on Nvidia’s graphics processing units (GPUs). During a pre-MWC briefing held in London on February 19, 2026, Ericsson Chief Technology Officer Erik Ekudden and Mobile Networks head Per Narvinger detailed how the company is integrating AI capabilities directly into its baseband and radio-unit silicon. This strategy is intended to improve spectral efficiency and beamforming by up to 10% without the power-hungry overhead associated with general-purpose AI chips. By prioritizing its internal Application-Specific Integrated Circuit (ASIC) development, Ericsson is positioning itself as a champion of "silicon freedom," contrasting sharply with competitors who have deepened their ties to the Nvidia ecosystem.

The timing of this announcement is critical, as the global RAN market remains under pressure, having stabilized at approximately $35 billion in 2025 after a significant post-5G-peak contraction. According to Light Reading, Ericsson’s refusal to follow Nokia’s lead—which recently accepted a $1 billion investment from Nvidia to overhaul its 5G and 6G software for GPU compatibility—stems from a desire to avoid vendor lock-in. Narvinger emphasized that while Ericsson supports multiple hardware platforms in prototype stages, including AMD and Arm, its commercial focus remains on a "one software track" approach that can eventually be deployed across various silicon architectures through abstraction layers like BBDev, rather than being natively tied to a specific proprietary platform like Nvidia’s CUDA.

This pursuit of independence is driven by the volatile nature of the semiconductor industry. Just last year, Intel, a primary supplier for Ericsson’s cloud RAN portfolio, faced significant financial turbulence and considered divesting its network and edge group (NEX). Although U.S. President Trump’s administration has emphasized domestic semiconductor stability, and Intel eventually shelved its divestment plans following a $7 billion injection from SoftBank and Nvidia, the episode underscored the risks of over-reliance on a single partner. Ericsson’s response has been to increase its R&D spending, which reached approximately 48.9 billion Swedish kronor ($5.4 billion) in 2025, with a substantial portion dedicated to in-house silicon that bridges the gap between traditional and virtualized networks.

From a technical perspective, Ericsson argues that AI-RAN is achievable without GPUs by utilizing specialized neural network accelerators integrated into the radio unit. These accelerators are designed to handle specific Layer 1 tasks—the most computationally demanding slice of RAN software—more efficiently than general-purpose hardware. While Nvidia’s "inline" acceleration approach puts the entire Layer 1 on the GPU, Ericsson’s "lookaside" strategy offloads only specific problematic functions, such as forward error correction (FEC), to discrete accelerators. This allows the bulk of the network software to remain portable across different CPU architectures, including Intel’s Xeon 6 (Granite Rapids) and potentially future Arm-based solutions.

The industry remains divided on the economic viability of these competing paths. Analysts at Dell’Oro Group have noted that while common off-the-shelf (COTS) servers are improving, they still lag behind custom silicon in terms of performance-per-watt and total cost of ownership (TCO). Ericsson’s Ekudden contends that unless a telco is co-hosting non-RAN AI workloads at the edge, the cost of deploying GPUs solely for network functions is difficult to justify. By 2030, virtualized RAN is expected to account for only 15% to 20% of the market, suggesting that the era of specialized silicon is far from over.

Looking forward, Ericsson’s strategy represents a high-stakes bet on the modularity of 6G. If the company succeeds in creating a truly hardware-agnostic software stack that performs optimally on its own efficient ASICs, it will have successfully insulated itself from the margin-squeezing power of the global chip giants. However, if the industry shifts toward a unified AI-on-everything model where GPUs become the standard for edge computing, Ericsson may find itself maintaining an expensive, isolated hardware island. For now, the Swedish vendor is banking on the fact that in the world of telecommunications, efficiency and independence are the ultimate currencies.

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Insights

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What motivated Ericsson to develop its own silicon for AI-RAN?

What is the current market situation for the global RAN industry?

What user feedback has been received regarding Ericsson's AI-RAN products?

What are the latest developments in Ericsson's AI-RAN strategy as of 2026?

What policy changes in the semiconductor industry affect Ericsson's strategy?

How might Ericsson's AI-RAN impact the future of telecommunications?

What are the potential long-term effects of Ericsson's silicon independence?

What challenges does Ericsson face in achieving silicon independence?

What controversies surround Ericsson's decision to avoid Nvidia's GPUs?

How does Ericsson's approach to AI-RAN compare to Nokia's strategy?

What historical precedents exist for companies pursuing silicon independence?

What specific technologies are crucial for the success of Ericsson's AI-RAN?

What are the expected market trends for RAN technology by 2030?

How does Ericsson's R&D investment compare to its competitors?

What role do neural network accelerators play in AI-RAN's performance?

What are the implications of Ericsson's 'lookaside' strategy in RAN?

How is Ericsson addressing the risks of vendor lock-in in its strategy?

What factors contribute to the economic viability of Ericsson's silicon strategy?

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