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Home AI Building a Dense Agentic AI CPU Rack Today
AI<br>Server<br>Server CPUs
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Dell PowerEdge R7725 Dual SP5
Server CPUs have gone from the doghouse to becoming ultra-important pieces of infrastructure, and agentic AI is the reason. This is one of those topics that I have been talking about with organizations for months, and I thought I might just put a broader discussion piece. Right now, much of the online discussion is simply on running agents as a new class of workload, and for good reason. It is net new demand on the compute infrastructure. Still, that is at best a part of the equation. On June 3, 2026, Cloudflare CEO Matthew Prince said that AI bot traffic has eclipsed human traffic on the Internet. You can pretend that trend is not real, but it impacts everyone who runs servers, and it is only going to get worse as agentic platforms become part of everyday workflows. Server CPUs are heating up for a reason, and the companies that get ahead now will have a real advantage. Since we have been doing server CPUs for a decade and a half-plus, I figured that I should give folks a broader framework to use.
We have a video for this one. We are going to use AMD EPYC and Dell servers here. AMD sent the CPUs. Dell paid for my travel to Dell Tech World. We have to say this is sponsored. Still, if you read the STH Substack, it is pretty clear why we will be talking a lot about AMD EPYC in the next year and change.
Why Agentic AI is a CPU Story
In the data center, CPUs are everywhere. They sit alongside GPUs to process data and attach extra memory pools to those accelerators. They run storage nodes, control planes, Kubernetes workloads, network switches, and even some network adapters. Whenever you build a cluster, CPUs are the common denominator.
STH Build A Cluster And The CPUs Are Everywhere<br>Agentic AI changes how those CPUs get used. Platforms like OpenClaw, Hermes, and similar agent frameworks do not run on GPUs. These agent frameworks run on CPUs, and they need to stay alive and responsive so they can react whenever something happens. OpenClaw makes that straightforward to set up currently with just:
curl -fsSL https://openclaw.ai/install.sh | bash
openclaw onboard --install-daemon
Then you are off and ready to go.
OpenClaw Control Overview<br>From there, you add security layers and manage access. For company deployments, most guides online cover setting up OpenClaw as a personal assistant. When you deploy at a company, think about it more like bringing on a contractor. Grant constrained access to data and services, not open all access privileges. The "let Hermes or OpenClaw have access to everything" is what I have been calling a YOLO AI agent.
STH Mental Model Of AI Agent As A Contractor Instead Of A YOLO Install<br>LLM inference usually runs over APIs to GPUs. CPU-side infrastructure handles everything else. If you want deterministic, reproducible results, ask the LLM to generate scripts that run tasks deterministically on the CPU rather than relying on raw LLM output to execute commands directly.
STH Use LLMs To Build Scripts Not Format Commands Every Time To Shift From Probabilistic...