Author’s note: Our executive interview series continues with Mohamed Awad, Executive Vice President of the Cloud AI Business Unit at Arm. Arm entered the merchant CPU market this year with AGI CPU, a part designed for agentic workloads, and Mohamed is the executive responsible for making that bet work. The full video is embedded below for subscribers, followed by an edited transcript for those who prefer to read.
Late last year, when nearly every conversation in the industry was about GPUs, we started making the case that the CPU was about to become strategic again. We formalized that view in March with Secret Agent CPU and built out the full framework in September with The Agentic CPU Guidebook. The simplest way we describe it is that the GPU does the thinking and the CPU goes and does the actions. An agent calls tools, runs code, queries databases, reads what comes back, and decides on its next step, and all of that execution lands on CPUs. Our view came from studying the workload, which is how we approach every compute cycle. And yet, a year in, we still get asked what people are missing about this demand, so that is where we started with Mohamed.
His answer lines up closely with how we model the market. He breaks the AI data center into three classes of CPU: the head node that feeds the accelerator, the cloud-native CPUs hyperscalers have run for the better part of two decades, and a new class of agentic CPUs he thinks of as virtual users. Those are the same three segments in our Server CPU forecast, which we call general purpose, accelerator hosts, and agent execution. The head node is the one everyone already understands, and the more interesting part is how he describes the third class. An agent works on a person’s behalf the way that person would at a terminal, making database calls, researching a purchase, booking travel, or writing a script, except the agent never sleeps. A workload that runs around the clock changes what an operator needs from the compute platform, and it is the main reason we expect this segment to grow much faster than the rest of the CPU market.
Our Server CPU model puts the market at $62 billion in 2026 and $251 billion in 2030 in our Base case, with a range of $195 billion to $298 billion across our Low and Stretch cases. Price does as much of the work as units in that forecast, because we expect manufacturing capacity to stay tight, and as long as it does, the CPU suppliers keep their pricing leverage. The part we think the market has not caught up to yet is power. Our current thinking is that individual agentic compute alone needs somewhere between 25 and 35 gigawatts of additional capacity by 2030, on top of what the accelerators draw. That range comes from our demand model, and it assumes half of the global internet user base uses a personal AI agent in some fashion. Put another way, that is new data center capacity built and powered for agent execution by itself, and we do not think most have it in their numbers.
Several parts of the conversation map directly to how we built that model. Mohamed says agentic CPUs mostly sit outside the scale-up domain, and his reasoning is that agent execution is a scale-out problem. You have lots of independent agents each running their own path, and in the case of Meta Muse and OpenAI’s dots, every user gets their own virtual machine. That fits how we size the segment, as dedicated agentic CPU racks attached to accelerator racks, instead of a fixed CPU-to-GPU ratio inside the accelerator system. It also means those CPU pods need their own scale-out network, to each other and to the GPU cluster, and that is a topology decision operators have not had to make before.
On design, Arm built AGI CPU around a balance of single-threaded performance, scalability, and efficiency, and Mohamed was clear that leaning too hard on any one of the three leaves you with a compromised product. That is close to the wide, fast, and fed framework in our guidebook, where every design trades cores, per-core speed, and memory against a fixed budget of power and area. He also pushed on two areas we think investors are underestimating. The first is security. Hyperscalers that run only their own workloads never needed the isolation the cloud providers built with DPUs, because they trusted every workload. Agents change that, since an operator can no longer know whether a given agent is nefarious, so those techniques are now spreading to anyone running a data center. DPUs sit outside our CPU number, and our Networking model builds DPU demand from accelerator servers, so DPUs attached to agentic CPU racks would be additive. The second is inference. Mohamed’s point is that whether work stays on the CPU or moves to an accelerator comes down to the quantum of compute, and when the job is small, shipping it off costs more than running it locally. As better CPUs reach the installed base, models can make different choices about where work runs. That is a real possibility, but we want to see evidence of the shift before we count it.
The question we hear most from investors on Arm is about supply. Arm has never had to secure memory, substrates, and boards before, and it is doing it for the first time in a market where nearly everything is short. We closed the interview on that question, and Mohamed did not pretend it is easy. He said Arm is comfortable it will hit its numbers, and he pointed to decades-long relationships with the fabs and memory makers, plus executives Arm has brought in from NVIDIA, Broadcom, and Marvell. That is fair, and we would add one thing. Being in the supply chain means Arm is now having the same conversations its customers are having, and we see that as a positive. Our models do not see capacity easing soon, and that is the environment where Arm’s new supply chain muscle gets tested.
In the full interview:
Why Mohamed separates the AI data center into head nodes, cloud-native CPUs, and agentic CPUs, and why he thinks of agents as virtual users.
What Arm optimized AGI CPU for, and why it balanced single-threaded performance, scalability, and efficiency.
Where agentic CPU racks sit relative to the GPU scale-up domain, and what that means for networking.
When inference stays on the CPU and when it moves to the accelerator.
How DPUs went from a cloud provider tool to the control plane for the AI data center.
How Arm thinks about selling silicon, compute subsystems, IP, and architecture licenses to the same customers.
How Arm is handling the supply chain as a first-time merchant silicon vendor.
Go deeper with Atlas: Subscribers with CS Atlas access can work through the assumptions behind our Server CPU forecast, compare CPU vendor approaches from the guidebook’s technical companion, and test the agentic rack ratio against their own view. A few questions to start with:
What assumptions get us to a $251 billion server CPU market in 2030, and what would change that forecast?
How many agentic CPU racks does each accelerator rack need in our model, and how sensitive is the forecast to that ratio?
How do Arm, NVIDIA, AMD, and Intel differ in their approach to agentic CPUs?
When do agentic workloads need more cores versus faster cores or more memory?
How do DPUs fit into agent isolation and security, and who supplies them?
The executive interview video archive also lives in CS Atlas under the Videos link on the left-hand bar.
Companies covered in this report: Arm Holdings (ARM); NVIDIA (NVDA); Advanced Micro Devices (AMD); Intel (INTC); Amazon (AMZN); Alphabet (GOOGL); Meta Platforms (META); OpenAI (private).
Paid subscribers get the full video interview with Mohamed Awad and the complete edited transcript below, along with every Diligence Stack report and CS Atlas access to our Server CPU forecast and the Agentic CPU Guidebook companion. If you are not yet a paid subscriber, upgrade to keep reading.
