PARTNER CONTENT: As agentic AI systems scale across cloud and datacenter environments, CPUs remain the control plane coordinating performance and efficiency.
Modern AI infrastructure, particularly in the realm of agentic AI, is often discussed through the lens of accelerators, model sizes, and training clusters. However, making large-scale agentic AI systems functional relies heavily on the CPU within these pipelines.
As agentic AI deployments expand across cloud and enterprise environments, the CPU increasingly acts as the control plane for the entire system. It manages the coordinated movement of data between storage, memory and accelerators, ensures the secure isolation of workloads, and handles precise scheduling across distributed infrastructure. These are crucial tasks that underpin the efficiency and reliability of agentic AI operations. Without efficient and performant CPUs, even the most powerful accelerators cannot deliver their full value.
This is one reason why Arm's role in the datacenter has expanded rapidly in recent years. Long associated with mobile devices, Arm has become the core architecture for cloud and AI infrastructure.
The, built in close collaboration with Meta targets maximum rack-level density to scale up performance for agentic AI datacenters, it is built on the Arm Neoverse V3 platform and delivers high-performance cloud and AI deployments. The Arm AGI CPU also offers system efficiency, security features such as confidential computing, and the flexibility required for hyperscale environments. ---
Hyperscalers and leaders in AI, including AWS, Google Cloud, and Microsoft Azure, have each introduced multiple generations of custom Arm-based processors for their platforms, reflecting a broader move toward purpose-built silicon optimized for modern workloads.
Google's Axion processors are designed to support applications ranging from data analytics and microservices to AI inference. Testing has shown strong gains in performance and efficiency, while companies such as Spotify have reported significant improvements when evaluating the architecture for large-scale workloads. Similarly, AWS Graviton processors offer enhanced performance and cost-effectiveness, making them a competitive choice for various computational tasks.
Microsoft is taking a similar approach with its Azure Cobalt processors, which are built on the Arm's Neoverse compute platform and designed specifically for cloud-native environments. Early deployments have demonstrated measurable improvements in performance and infrastructure efficiency for real production services.
NVIDIA has used Arm Neoverse’s line of CPU designs in its Grace Hopper and Grace Blackwell chips, as well as the latest Vera Rubin NVL72 system..
These developments point to a broader architectural model sometimes described as the "converged AI data center." In this environment, compute, accelerators, networking, storage, and software are designed together as a unified system rather than as independent components. The CPU plays a central role in that model by coordinating how the rest of the stack operates.
For architects planning the next generation of AI infrastructure, the lesson is increasingly clear: accelerators may drive model performance, but the CPU remains the engine that keeps the entire system running.
To explore the architecture behind the Arm AGI CPU and Arm Neoverse in more detail, visit the Arm product page.
Sponsored by Arm.