Why CPUs still matter in the age of AI agents

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When the conversation turns to AI infrastructure, it almost always lands on GPUs and TPUs. The New Stack sat down with Bhumik Patel of Arm and Mo Farhat of Google to talk about the chip that rarely makes the headlines anymore: the CPU, and why it’s getting more important, not less, as AI shifts from chatbots to agents.

Farhat, who leads product management for Axion and Arm-based virtual machines at Google Compute Engine, tells The New Stack, “The role, more or less, is of a CPU as an air traffic controller.”

In this episode, we discuss how the shift from conversational chatbots to autonomous agents is quietly turning into a CPU story.

“Today’s six- to eight-billion-parameter models are performing much better than they have in the past,” Farhat says. For some specialized workloads, he says, CPUs can deliver roughly 25 tokens per second, which can be enough for agentic workloads.

The workload shifted from answering to acting

Early chatbots returned a response, but agents can act on them. They perform tasks by calling tools and, when needed, create environments to execute the code they write.

“The orchestration harnesses themselves for agentic workloads are these always-on branching kind of control-flow logic that CPUs are great at,” Farhat says.

While large language models typically run on accelerators, CPUs also handle orchestration, data preparation, semantic search, and vector databases, Farhat says.

Patel, who drives Arm’s software ecosystem efforts for cloud and AI, says the company is focused on the software and infrastructure layers needed to run these …

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