IBM and Arm have announced a plan to develop hardware that can run both IBM and Arm-based workloads, to let Arm software run on IBM mainframes.
The two companies plan to work on three things: building virtualization tools so Arm software can run on IBM platforms; making sure Arm applications meet the security and data residency rules that regulated industries must follow; and creating common technology layers so enterprises have more software options across both platforms, IBM said in a statement.
IBM has not said whether the virtualization work will happen at the hypervisor level, through its existing PR/SM partitioning technology, or via containers — a question enterprise architects will need answered before they can assess the collaboration’s practical value.
IBM described the effort as serving enterprises that run regulated workloads and cannot simply move them to the cloud, the statement said.
Where Arm stands today
IBM’s mainframe customers have so far been unable to tap into the Arm efficiency gains that cloud users already enjoy, the statement said. Arm says close to half of all compute shipped to top hyperscalers in 2025 runs on Arm chips, with AWS, Google, and Microsoft deploying their own Arm silicon through Graviton, Axion, and Cobalt, respectively.
AWS says more than half of all new CPU capacity it has added for the third consecutive year runs on Graviton. Independent benchmarking by Signal65 found Graviton4 delivered up to 168% better LLM inference performance and 220% higher price-performance than comparable AMD x86 chips.
That gap is precisely what the collaboration is intended to address, said Rachita Rao, senior analyst at Everest Group. “This is a mainframe adjacency play,” she said. “The intent is to extend IBM Z and LinuxONE environments by enabling Arm-compatible workloads to run closer to systems of record. While hyperscalers use Arm to lower their own internal power costs and pass savings to cloud-native tenants, IBM is targeting the sovereign and air-gapped market.”
For banks and insurers specifically, the collaboration is as much about people as it is about technology, Rao said. “These organizations are hesitant to change architectures due to the risk of breaking the ledger, but they face a shrinking pool of legacy specialists,” she said. “It doesn’t change the procurement cycle today, but it de-risks the long-term viability of the LinuxONE or the Z platform as a modern internal cloud.”
The chips behind it
The collaboration involves two hardware platforms built by IBM to handle AI workloads at mainframe scale: the Telum II processor and the Spyre Accelerator.
The Telum II processor, announced at Hot Chips in August 2024, has eight cores running at 5.5GHz, a 40% larger on-chip cache at 360MB, a built-in AI accelerator for real-time transaction inferencing, and a new data processing unit for IO tasks, IBM said.
The Spyre Accelerator is now shipping as part of the IBM z17 and LinuxONE 5 platforms. It connects via PCIe, has 32 compute cores, up to 1TB of memory per IO drawer, and draws no more than 75 watts per card, IBM said. The two chips work together to run ensemble AI, where multiple AI models are combined to produce more accurate results, according to IBM.
No timeline yet
IBM gave no shipping date and no technical specs for the planned dual-architecture systems. Statements on future direction “represent goals and objectives only” and are subject to change, the announcement said.
Enterprises should plan for a three-year development horizon based on how long IBM’s previous hardware cycles have taken, Rao said. While IBM revealed the Telum II and Spyre at Hot Chips in August 2024, Spyre is only now reaching general availability, roughly 12 to 18 months later.
IBM is also pursuing other AI infrastructure partnerships at the same time. In March 2026, the company announced an expanded collaboration with Nvidia at GTC 2026 covering GPU-based data analytics, document processing, on-premises and regulated infrastructure, and consulting. IBM plans to offer Nvidia Blackwell Ultra GPUs on IBM Cloud in early Q2 2026 for large-scale AI training, inferencing, and reasoning, the company said.
That parallel move signals where IBM sees its primary AI bets, Rao said. “IBM’s Nvidia expansion is clearly about GPU-native analytics and AI deployments across cloud and on-prem regulated infrastructure,” she said. “That tells you IBM itself is not treating Arm compatibility as the primary answer to enterprise AI scale. Large-scale AI is still moving to GPU-heavy environments. Arm compatibility could help bring newer application stacks or services closer to the mainframe, but it will not redefine the primary AI infrastructure strategy.”
