SK Telecom has partnered with Arm and Rebellions to develop AI servers, which will be tested in SK Telecom's AI data centres. Rebellions' RebelCard is set for release in the third quarter. The partnership forms part of South Korea's Sovereign AI Foundation Model project, which aims to build a homegrown AI value chain and reduce reliance on foreign technology. Rebellions has raised over $850 million from investors including Arm, Samsung and SK Hynix, and receives government backing through the K-Nvidia Nurturing Project. The alliance represents a systems-level challenge to Nvidia, offering complete AI computing systems including rack-scale products like RebelPod. Rebellions claims its systems can connect to data and run inference within 48 hours of delivery. SK Telecom plans to export this sovereign AI model to other countries seeking domestic technology stacks.

Arm and Meta partner on new class of CPUs to support data centres and large-scale AI deployments. - Content Director Published: Apr 9, 2026 Chip architecture company Arm Holdings has partnered with Meta to develop a new processor for AI data centres, marking the first time Arm has designed and delivered production silicon in its history The Arm and Meta partnership. The collaboration centres on the Arm AGI CPU, a processor designed to support emerging agentic AI workloads and large-scale AI infrastructure. Meta served as the lead partner and co-developer for the chip, working with Arm to optimise infrastructure for its family of applications. "Delivering AI experiences at global scale demands a robust and adaptable portfolio of custom silicon solutions, purpose-built to accelerate AI workloads and optimize performance across Meta's platforms," said Santosh Janardhan, Head of Infrastructure at Meta. "We worked alongside Arm to develop the Arm AGI CPU to deploy an efficient compute platform that significantly improves our data centre performance density and supports a multi-generation roadmap for our evolving AI systems." Meta collaboration anchors Arm's first silicon launch. The partnership with Meta comes as Arm expands its compute platform beyond licensing processor designs. For the first time, the company is introducing Arm-designed production silicon, giving partners more ways to deploy Arm technology across AI infrastructure. "AI has fundamentally redefined how computing is built and deployed. Agentic computing is accelerating that change," said Rene Haas, CEO of Arm Holdings. "Today marks the next phase of the Arm compute platform and a defining moment for our company. With the expansion into delivering production silicon with our Arm AGI CPU, we are giving partners more choices all built on Arm's foundation of high-performance, power-efficient computing, to support agentic AI infrastructure at global scale." The Arm AGI CPU for agentic AI infrastructure. The Arm AGI CPU is designed to support the growing demand for compute power driven by AI agents - systems that continuously reason, plan and act. According to Arm, the rise of these workloads is increasing the volume of tokens generated across AI systems and driving a need for more CPUs to manage reasoning, coordination and data movement. The processor offers several capabilities designed for AI data centres. Performance details. The chip can include up to 136 Arm Neoverse V3 cores per CPU, delivering performance per core, system-on-chip, blade and rack. It also delivers 6GB/s memory bandwidth per core with sub-100ns latency. Infrastructure scale. The CPU operates at 300-watt TDP and includes a dedicated core per program thread, which Arm says enables deterministic performance under sustained workloads. The architecture supports 1U server chassis deployments, including: * Air-cooled systems with up to 8,160 cores per rack * Liquid-cooled systems delivering more than 45,000 cores per rack Arm says the processor can deliver more than 2x performance per rack compared with x86 CPUs, potentially enabling up to $10 billion in capital expenditure savings per gigawatt of AI data center capacity. Expanding the Arm Compute platform. For more than three decades, the Arm compute platform has enabled scalable and power-efficient computing across hundreds of billions of devices. As AI reshapes computing infrastructure, Arm says customers are looking for ways to deploy Arm technology more quickly and at greater scale. The company is expanding its platform strategy beyond Arm IP and Arm Compute Subsystems (CSS) to include Arm-designed silicon products. This approach allows partners to choose between licensing Arm designs, adopting compute subsystems, or deploying Arm's own silicon. Broad ecosystem support for the Arm AGI CPU. In addition to Meta, Arm says a range of companies across the AI and cloud ecosystem plan to deploy the Arm AGI CPU. These include: * Cerebras * Cloudflare * F5 * OpenAI * SAP * SK Telecom These organisations are expected to use the processor for workloads including accelerator management, control plane processing, and cloud and enterprise-based API, task and application hosting.

IBM and Arm partner on dual-architecture computing to redefine mainframes for AI. Analyst(s): Brendan Burke Publication Date: April 7, 2026 IBM and Arm have announced a strategic collaboration to develop dual-architecture enterprise platforms that enable Arm-based workloads to run on IBM Z systems. The move aims to expand software compatibility, improve flexibility, and support AI and data-intensive workloads in regulated, mission-critical environments. What is covered in this article: * IBM and Arm are collaborating to develop dual-architecture hardware enabling Arm-based workloads on IBM Z and LinuxONE systems * The initiative focuses on virtualization, security, data sovereignty, and expanding software ecosystem compatibility * The collaboration targets AI and data-intensive workloads, particularly in regulated environments where data cannot move to the cloud * Arm's ecosystem and efficiency advantages are being extended into enterprise mainframe environments * The move reflects IBM's continued investment in mainframe AI infrastructure, including Telum II and Spyre Accelerator The News: IBM announced a strategic collaboration with Arm to develop dual-architecture hardware that allows Arm-based software environments to run on IBM Z and LinuxONE systems. The initiative is designed to support AI and data-intensive workloads by expanding software compatibility, enabling virtualization of Arm environments, and maintaining enterprise requirements such as security, reliability, and data sovereignty. The collaboration focuses on three areas: enabling virtualization technologies for Arm workloads on IBM platforms, supporting the performance and efficiency needs of modern applications, and building shared technology layers to expand software ecosystems. IBM stated that the effort targets enterprises running regulated workloads that cannot move to the cloud, while also aiming to improve system flexibility and expand infrastructure choice without disrupting existing mission-critical environments. Analyst Take: By partnering with IBM's mainframe business, Arm can build on its clear intent to bring cloud price-performance to the on-premises data center. Arm's recent AGI CPU announcement allows enterprises that use custom Arm CPUs in cloud environments to repatriate their workloads to their local data centers. A missing link in this strategy was the mainframe, a category that has experienced a resurgence due to AI. IBM's Z mainframe product reached its highest fourth-quarter revenue in two decades in Q4 2025, contributing to $5.1 billion in infrastructure revenue, indicating that sensitive workloads are merging with AI-native processors. This partnership combines IBM's strength in mission-critical systems with Arm's power-efficient architecture and broad software ecosystem. Virtualization as the primary mechanism for software expansion. The collaboration expands virtualization technologies to allow Arm-based software environments to operate within IBM enterprise platforms. This approach is intended to eliminate the need to port applications natively to IBM Z architectures, which is costly, time-consuming, and uncommon in modern development environments. By enabling Arm workloads to run via virtualization or emulation, IBM aims to expand software compatibility while simplifying how developers bring applications into mission-critical systems. However, IBM has not disclosed whether this will be implemented at the hypervisor level, through PR/SM partitioning, or via container-based approaches, leaving a key technical gap for enterprise architects. This reliance on virtualization highlights a practical pathway to software portability but also introduces unanswered questions about implementation specifics and operational trade-offs. Extending Arm into regulated and mission-critical environments. The collaboration explicitly targets enterprises running regulated workloads that cannot be moved to the cloud, with a focus on security, data residency, and high availability. IBM's mainframe platforms are primarily deployed in repositories of critical data, including financial systems, government databases, and high-value transactional engines. By enabling Arm workloads to run closer to these systems of record, the approach reduces latency, minimizes the need for data replication, and addresses compliance risks associated with moving data across external platforms. At the same time, Arm's ecosystem, which already contributed 50% of server CPUs for top hyperscalers in 2025, is being extended into these enterprise environments. This positioning reflects a targeted expansion of Arm beyond cloud-native use cases into sovereign and air-gapped markets, reinforcing the role of mainframes as controlled execution environments for modern workloads. Balancing flexibility with performance trade-offs. While the collaboration expands flexibility and software choice, it is not positioned as a performance-driven solution for all workloads. Running Arm workloads on IBM Z through virtualization or emulation introduces performance penalties, and the model is not intended for performance-intensive applications. Instead, the focus is on total cost of ownership, operational stability, and risk mitigation, which are identified as primary decision factors for enterprise customers. IBM's hardware investments, including the Telum II processor with eight cores running at 5.5GHz and a 40% larger 360MB cache, and the Spyre Accelerator with 32 compute cores and up to 1TB of memory per IO drawer, support AI workloads at mainframe scale but do not eliminate architectural trade-offs. This highlights that the IBM Arm dual-architecture approach prioritizes integration and operational continuity over raw performance, making it suitable for specific enterprise scenarios rather than broad replacement of existing AI infrastructure strategies. Positioning within broader enterprise infrastructure strategies. The collaboration reflects a broader effort to reposition the mainframe within modern enterprise infrastructure strategies, particularly as AI adoption increases. At first, Arm can expand mainframe use cases and make the platform more attractive to CIOs by enabling cloud repatriation without requiring code changes. Going forward, IBM is pursuing parallel AI infrastructure strategies, including its expanded collaboration with NVIDIA for GPU-based analytics and AI deployments. The alignment of AI workloads with mainframe computing places the IBM Arm dual-architecture approach as part of a broader multi-architecture strategy rather than a standalone solution. What to watch: * Lack of clarity on virtualization implementation may delay enterprise adoption decisions * No defined timeline or technical specifications for dual-architecture systems, with development potentially extending over multiple years * Performance limitations of virtualization could restrict adoption for high-performance AI workloads * Continued reliance on GPU-based infrastructure, including IBM's NVIDIA collaboration, may limit the role of Arm in large-scale AI deployments * Enterprise adoption will depend on the ability to integrate Arm workloads without disrupting existing mainframe operations See the complete press release about the IBM and Arm strategic collaboration to develop dual-architecture enterprise platforms on the IBM website. Declaration of generative AI and AI-assisted technologies in the writing process: This content has been generated with the support of artificial intelligence technologies. Due to the fast pace of content creation and the continuous evolution of data and information, The Futurum Group and its analysts strive to ensure the accuracy and factual integrity of the information presented. However, the opinions and interpretations expressed in this content reflect those of the individual author/analyst. The Futurum Group makes no guarantees regarding the completeness, accuracy, or reliability of any information contained herein. Readers are encouraged to verify facts independently and consult relevant sources for further clarification. Disclosure: Futurum is a research and advisory firm that engages or has engaged in research, analysis, and advisory services with many technology companies, including those mentioned in this article. The author does not hold any equity positions with any company mentioned in this article. Analysis and opinions expressed herein are specific to the analyst individually and data and other information that might have been provided for validation, not those of Futurum as a whole. Image credit: Arm. Brendan is Research Director, Semiconductors, Supply Chain, and Emerging Tech. He advises clients on strategic initiatives and leads the Futurum Semiconductors Practice. He is an experienced tech industry analyst who has guided tech leaders in identifying market opportunities spanning edge processors, generative AI applications, and hyperscale data centers. Before joining Futurum, Brendan consulted with global AI leaders and served as a Senior Analyst in Emerging Technology Research at PitchBook. At PitchBook, he developed market intelligence tools for AI, highlighted by one of the industry's most comprehensive AI semiconductor market landscapes encompassing both public and private companies. He has advised Fortune 100 tech giants, growth-stage innovators, global investors, and leading market research firms. Before PitchBook, he led research teams in tech investment banking and market research. Brendan is based in Seattle, Washington. He has a Bachelor of Arts Degree from Amherst College. Related Insights