Arm Set to Dominate AI Server Market with 90% Share by 2029, Leaving x86 and RISC-V Behind

The AI server landscape is undergoing a seismic shift that will fundamentally reshape the semiconductor industry over the next five years. According to new research from Counterpoint, Arm-based custom processors are poised to capture 90% of the AI server market by 2029, leaving traditional x86 architectures and emerging RISC-V designs fighting for the remaining 10%.

This transformation represents one of the most significant architectural changes in data center computing since the widespread adoption of x86 processors decades ago. The driving force behind this shift is the unique requirements of AI workloads, which demand specialized hardware optimization that general-purpose processors simply cannot match.

The Rise of Custom Silicon

Virtually all major hyperscale cloud service providers have launched their own custom silicon programs in recent years. These initiatives go beyond just developing AI accelerators – they encompass custom general-purpose CPUs based on the Arm instruction set architecture (ISA). Companies like AWS, Google, Microsoft, and Meta are all investing heavily in proprietary Arm-based processors designed specifically for their AI workloads.

AWS has been particularly aggressive with its Graviton processor line, expanding its role across Trainium-based systems while maintaining x86 compatibility in certain configurations. Google's next-generation TPU infrastructure relies entirely on its Axion Arm CPU, while Microsoft has paired its Azure Cobalt Arm CPU with Maia accelerators from day one to create a vertically integrated AI infrastructure. Meta is preparing to deploy Arm's own AGI CPUs in the near future.

Why Arm Is Winning the AI Race

The economics and technical advantages of Arm-based custom CPUs are compelling for AI workloads. These processors offer superior cost and power efficiency compared to traditional x86 designs, primarily because they can be tailored specifically for data-intensive AI operations. Unlike general-purpose computing where backward compatibility is crucial, AI workloads represent emerging use cases where starting fresh with an optimized architecture makes perfect sense.

Neil Shah, vice president of research at Counterpoint Research, emphasizes that this transition is happening methodically rather than through a sudden switch. "The transition from x86 to Arm in AI servers is not a single switch," Shah explains. "It has played out generation by generation, configuration by configuration."

This gradual approach allows hyperscalers to write compatible and interoperable software while carefully evaluating the economics of each deployment. The transition is expected to accelerate meaningfully in the second half of 2026 as next-generation ASIC platforms roll out across major cloud providers.

The Current State and Future Trajectory

Today's AI server market still predominantly relies on x86 processors from AMD and Intel. However, this balance is shifting rapidly. Counterpoint's analysis projects that Arm-based CPUs will account for at least 90% of host CPU deployments in custom AI ASIC servers by 2029, up from approximately 25% in 2025.

This represents a structural shift driven by the accelerating rollout of in-house Arm CPU programs across hyperscalers. The research firm notes that while loads of AI servers will continue to rely on off-the-shelf EPYC and Xeon processors from traditional suppliers, the broad adoption of Arm by hyperscalers for their custom silicon programs should serve as a wake-up call for AMD and Intel.

The x86 Response

Both AMD and Intel are not standing still in this evolving landscape. AMD has developed its own vertically integrated AI platforms featuring x86 EPYC processors, Instinct MI-series AI accelerators, Pensando DPUs, and Pensando NICs. This comprehensive approach suggests these CPUs are being tailored for AI workloads, even within the x86 ecosystem.

Intel is taking a different but equally strategic approach by developing custom Xeon processors for Nvidia's next-generation AI platforms. This collaboration indicates that these processors will be optimized primarily for AI workloads, even as Intel works to maintain its x86 dominance.

What This Means for the Industry

The projected 90% market share for Arm in AI servers doesn't spell the end for x86, but it does represent a fundamental rebalancing of the data center ecosystem. x86 will continue to command a sizeable share of the overall server market, particularly for traditional enterprise workloads and applications where compatibility and established software ecosystems remain paramount.

For RISC-V, the outlook is even more challenging. Despite significant investment and development efforts, RISC-V appears to be on the outside looking in when it comes to AI server dominance. The technology may find success in other niches, but the AI server market appears to be shaping up as an Arm versus x86 battle.

The Broader Implications

This shift toward Arm-based custom processors in AI servers has several important implications for the broader technology industry:

Supply Chain Diversification: As hyperscalers develop their own silicon, the traditional relationships between chip manufacturers and server vendors are being disrupted. This could lead to more direct relationships between silicon designers and cloud providers.

Software Ecosystem Evolution: The move toward custom architectures will accelerate the development of cloud-native software that's less dependent on specific instruction set architectures, potentially making future migrations easier.

Manufacturing Dynamics: The demand for advanced process nodes to manufacture these custom Arm processors could reshape semiconductor manufacturing capacity allocation and pricing.

Innovation Acceleration: Competition between different architectural approaches could drive faster innovation in areas like power efficiency, performance per watt, and specialized AI operations.

Looking Ahead

The next five years will be critical in determining whether Arm can deliver on its projected 90% market share. Success will depend on continued execution by hyperscalers in developing and deploying their custom silicon programs, as well as the ability of the broader Arm ecosystem to support these specialized workloads.

For AMD and Intel, the challenge will be to make their custom CPU programs more appealing to customers while maintaining their strong positions in the broader server market. This may involve developing more specialized offerings for AI workloads or finding new ways to differentiate their x86-based solutions.

As AI continues to transform every aspect of computing, the battle for dominance in AI servers represents a crucial front in the broader semiconductor industry competition. The outcome will shape not just who builds the servers that power our AI future, but also the fundamental architecture of computing itself.

Image credits: Micron