Forget the AI Factory. The Real Battle Is Hybrid AI Infrastructure

Let’s be honest. If you’re a CIO right now, you’re drowning in noise about the “AI Factory.” Every keynote, every vendor slide deck—it’s all about massive GPU clusters and training foundation models from scratch. In that narrative, Dell and HPE are competing for the right to stand closest to NVIDIA, each trying to deliver racks fast enough to keep up with the next generation of GPUs.

But talk to actual enterprise IT leaders, and you’ll hear a very different story.

Most organizations aren’t training foundational models. They’re:

• Embedding copilots alongside existing apps
• Running RAG workflows over product documentation
• Deploying inferencing next to ERP, CRM, and call center data
• Integrating LLMs with SaaS systems, not replacing them

They’re not building AI Factories. They’re operationalizing AI inside hybrid environments, alongside the systems that already run the business. And that’s where the real infrastructure work is happening.

The Reality: AI Is Showing Up in the Middle of Everything

A Fortune 100 enterprise—major by every definition—is currently supporting 20,000 developers using AI-assisted coding tools. The entire workload runs on eight physical servers, none of which include NVIDIA H100s. That’s not a corner case. That’s what modern inference at scale actually looks like in the real world.

So why is the industry still obsessed with the AI Factory narrative?

Because that’s the story NVIDIA wants the world to tell.

Dell, HPE, Lenovo—any OEM that wants access to NVIDIA’s newest chips and to be seen as a preferred partner to the big foundational model companies—has to align its messaging with NVIDIA’s AI Factory blueprint. That means a lot of talk about training scale, GPU rack density, and CUDA pipelines. But you rarely hear about hybrid inferencing, CPU alternatives, or operational portability.

In plain terms: if you want to sell the chips, you have to tell NVIDIA’s story.

But that story doesn’t match what most enterprises need. For the 99% of companies focused on inferencing and integration—not training—the real challenge isn’t how fast you can ship an H100. It’s how well your platform supports hybrid, governed, and explainable AI operations.

Dell Private Cloud: Vertical Integration, Fast Time to Value

At Dell Tech World 2025, Dell’s strategy came into focus. Dell Private Cloud is now its platform of record, replacing APEX as the architectural centerpiece. And based on direct conversations with Dell executives, the company has strategically pivoted away from VxRail for new hybrid deployments. Try getting a quote for net new VxRail from your Dell rep). That’s a clear signal: Dell believes the future lies in a single, Dell-controlled operational plane.

The Private Cloud platform includes validated stacks for VMware, Red Hat OpenShift, and Azure hybrid environments. AI Factory 2.0, Dell’s tightly managed solution stack for inference workloads, is now bundled in—bringing NVIDIA and Intel Gaudi3 accelerators into a rack-level experience designed to sit adjacent to your traditional workloads.

It’s a strong option for CIOs who value speed and simplicity—a well-integrated system with a clear support model. But it’s still a hardware-first, vertically integrated approach. If you go this route, you’re aligning with Dell’s architecture, its automation layer, and its cadence for updates and extensions.

HPE GreenLake: Agentic AI for Hybrid Workflows

HPE took a different approach at Discover 2025. Yes, it expanded its NVIDIA-based Private Cloud AI offering. But the real innovation came from the unveiling of GreenLake Intelligence—a framework for embedding agentic AI into the fabric of hybrid operations.

Here’s where it gets interesting. HPE’s GreenLake Copilot, OpsRamp AI agents, and programmable governance tools aren’t just dashboard features. They represent a new operational layer—one that treats AI as a first-class actor in the infrastructure itself.

Imagine an OpsRamp agent detecting a performance anomaly in a VM. A GreenLake Copilot agent automatically reroutes traffic to a less-utilized server, avoiding downtime—all without human intervention. That’s agentic infrastructure in action.

This positions HPE as the vendor for organizations that see AI not as a sidecar workload, but as a cross-cutting concern that spans governance, performance, cost control, and resilience. And while the deepest features still favor HPE hardware, the overall model is far more control-plane oriented than box-driven.

📎 Sidebar: It’s Not About the GPU. It’s About Hybrid Data and Workflow Gravity.

Let’s break down a realistic RAG use case. Say you have 15,000 customer service documents, and you want to answer 300 inquiries per minute with an AI assistant.

Each request:

• Pulls ~1,300 tokens of context
• Generates ~300 tokens of output
• Total: ~1,700 tokens per request
• At 5 RPS: ~8,500 tokens/second

You can support that with 8–10 CPU-based servers or modest GPUs like A10s or L4s. You do not need an H100 SuperPod.

And again, a Fortune 100 company is already doing this today—20,000 developers on eight servers, no H100s required.

So why is this hard? Because the data is hybrid, and so is the workflow:

• Docs might be in SharePoint or Box
• The chatbot lives in Salesforce
• Auth flows through Okta or Entra ID
• Responses log into Zendesk

The GPU doesn’t solve that. The control plane, data orchestration, and governance layer do. This is where platforms like Dell Private Cloud or HPE GreenLake need to earn their keep—not in raw tokens per second, but in making hybrid work securely and observably across boundaries.

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Final Thought: Let the Hyperscalers Build AI Factories. You’ve Got a Business to Run.

The AI Factory makes for a dramatic keynote. But most enterprises aren’t buying racks of GPUs. They’re building AI into the fabric of their business—next to the data, next to the apps, and inside the governance and budget realities they already manage.

They’re asking infrastructure to do three things:

• Support hybrid data gravity
• Run inference and agentic workflows at the edge and in the core
• Govern AI like any other enterprise workload

Dell offers a well-packaged stack for CIOs who want standardization and speed, and are comfortable aligning tightly with one vendor’s architecture.

HPE offers a more flexible, agent-driven model for teams that need to reason across hybrid environments and weave AI into distributed operations.

Both vendors offer a path forward. But you need to ask yourself: Do you want a tightly integrated stack for AI, or a flexible, intelligent layer that works with what you’ve already got?

The answer to that question isn’t in a keynote.

It’s in your business, your budget, and your data center.

So, let the chip vendors sell the dream.

You’re here to deliver outcomes.