Modernize Your Data Center for AI—Without Slowing the Business Down

The security landscape changes week by week, and the attack surface is shifting from familiar endpoints to memory, networks, and data streams across cloud, on‑prem, and edge. This requires architecture that protects data where it lives, while still enabling sharing and multi‑party analytics when business value calls for it.

“We are seeing more and more attacks below the OS, targeting the hardware such as the memory directly, so we’re building in extra protection and a hardened ecosystem that makes it possible to run AI on sensitive data without compromising security,” explains George Murphy, Sales Development Manager for Intel and Dell in Northern Europe.

At the same time, IAPP estimates that around 79.3 percent of the world’s population is now covered by national privacy legislation, which raises the bar for data processing and governance.

The Edge Is Driving Data Growth—and the Architecture Must Follow

By the end of 2025, about three quarters of enterprise data will be generated and processed outside traditional data centers and the public cloud, according to Gartner. This shifts both capacity and decision‑making closer to where the data originates and makes latency, bandwidth, and efficiency decisive dimensions of the architecture.

Sustainability is also a leading factor: higher rack density, lower power consumption, and circular product design contribute to lower TCO and reduced emissions—without compromising performance.

“x86 offers fantastic software capabilities and a rich vendor ecosystem, so you can be confident your applications will run optimally on Xeon 6 both in the data center and out at the edge,” says Murphy.

CPU First, Accelerators Where They Matter Most

GenAI is notoriously memory‑hungry, but much of the AI used in enterprises—from classical machine learning and vector databases to small and medium‑sized language models and computer vision—can run efficiently on CPUs. Intel Xeon 6 is built to modernize existing environments with higher memory bandwidth, more cores, and built‑in AI instructions such as Intel AMX. This delivers better performance per watt, 1S–8S scalability, and the ability to consolidate your server fleet.

With Xeon 6, Intel documents an average 5:1 rack consolidation versus 2nd‑generation Xeon and up to 9.9× performance improvement per server in measured scenarios. When dedicated acceleration is the right choice, Intel Gaudi 3 delivers roughly four times the AI compute performance on Llama 3.3 BF16 compared with Gaudi 2, and is used by Dell in the PowerEdge XE7740 and XE9680.

The network layer is just as important: each Gaudi 3 accelerator has 24 ports of 200 Gb Ethernet with RoCE, enabling scalable, open clusters without proprietary high‑speed interconnects.

TCO, Sustainability, and Performance Go Hand in Hand

Performance today is measured in system throughput, response time, and total cost of ownership. By consolidating many older nodes onto fewer, more powerful servers, both power and cooling costs drop, and licensing models can be optimized.

Modern PowerEdge 17G systems with Xeon 6 deliver better performance per watt and built‑in acceleration that cuts the time required for typical AI and analytics jobs.

“With Xeon 6, we are in practice seeing consolidation rates of up to 10 to 1. You free up space, cut power consumption and licensing costs, and at the same time gain more capacity for new workloads,” says Murphy.

Meanwhile, ISA consistency and compatibility across the software ecosystem ensure the upgrade doesn’t turn into a heavy migration project, and that the same platform covers AI, HPC, databases and analytics, networking and security, as well as storage and content delivery.

From Hype to Consistent Delivery with Dell AI Factory

Many AI projects stall at the intersection of a complex tool stack and a lack of operational maturity. RAND points out that around 80 percent of AI projects fail due to complexity, a point echoed by several industry sources. A validated, API‑driven platform dramatically reduces risk.

Dell AI Factory brings together validated designs, orchestration, accelerated programming, and foundational AI software on infrastructure built on Intel Xeon and Gaudi. The result is that models can be put into production in hours, not weeks, with autoscaling, built‑in authentication, and API management.

Dell and Intel provide global teams that support you from capacity and power planning to model selection, data strategy, and operations. Add to that standards‑based Intel Ethernet controllers and adapters that ensure high‑performance connectivity, and you get an end‑to‑end solution where networking, compute, and acceleration pull in the same direction to get AI reliably into production.

The Market Demands Scalability—Without Lock‑In

The pace of investment is high and rising. IDC estimates that global AI spending will surpass $630 billion in 2028, with generative AI growing to roughly one‑third of that. At the same time, the cost of building your own platforms can quickly exceed $100 million when infrastructure, data, and skills are factored in. That’s why price‑performance and flexible scaling models are critical.

Gaudi 3 has been developed with an open software stack, tight integration with PyTorch, and an optimized model library on Hugging Face, and it ships in validated PowerEdge solutions that let you start fast and scale predictably—without vendor lock‑in.

Ready for the Next Step? Ask the Simple, Important Questions

The road ahead starts with an honest assessment: Which generation of Intel processors are you running today, and which workloads must be supported now and in two years? Where are the bottlenecks, and do any applications need dedicated acceleration? Do you have licenses approaching EOL, and what do your power and space budgets look like?

Once these answers are on the table, Dell and Intel will help you plan a target architecture that combines CPU‑driven AI with targeted acceleration, using open networks and proven price‑performance.

AI is no longer a one‑off project; it affects the entire business. When security is built in, the architecture is open and scalable, and the data center is modernized for both CPU‑driven AI and targeted acceleration, your teams can focus on creating value—not maintaining infrastructure. That’s how you make AI your own: simple, predictable, and on your terms.