The AI revolution: Why hardware matters more than ever for government

AI has come to dominate IT conversations much like, and more so in many regards, than cloud computing conversations did nearly two decades ago. Clearly, AI’s rapidly developing capabilities promise to revolutionize everything from healthcare and education to defense and intelligence.

However, for government agencies, the AI revolution is about more than harnessing software capabilities; it’s about ensuring agencies have the proper hardware foundation to leverage this transformative technology securely and efficiently.

It’s critical to recognize that the pace of AI applications is accelerating dramatically, and therefore, so must our thinking about hardware. Failing to adapt to the unique demands of AI workloads will leave agencies lagging behind in a world where agility and intelligent automation are increasingly critical to success.

Where to start: Understanding the AI spectrum

Whether you are a mission owner, a business partner or a technologist, the first step in navigating the AI revolution is recognizing that AI applications exist across a broad spectrum. I often describe it as spanning from “augmented to autonomy.”  On one end, we have “light AI” that augments human tasks, such as automating routine processes like data entry or document review. On the other end of the spectrum, we have full-scale automation involving robotics, complex decision-making, and even autonomous systems. This could revolutionize areas like logistics, manufacturing, and even national defense, enabling faster, more precise, and more resilient operations.

It’s crucial for government agencies to honestly assess where they are on their AI journey and where they want to be. Identifying your agency’s position and mapping out an AI roadmap will be increasingly crucial to guiding your hardware investment strategy and ensuring your agency is prepared for the future, not just the present. One thing will become clear: A one-size-fits-all approach to hardware simply won’t cut it in the AI era.

The second thing to look at is AI in the context of your agency’s ongoing digital modernization. You don’t have to make your AI decisions now. Still, it is essential to make infrastructure modernization procurements now that are AI-ready and AI-aware so that when you start taking incremental AI steps forward, you have an infrastructure ready to receive them.

A new imperative: Sovereignty and security in the AI

Cloud providers have undoubtedly played a crucial role in democratizing access to AI capabilities, offering readily available infrastructure and pre-trained models. However, the reliance on external infrastructure raises critical concerns for government agencies, particularly regarding data sovereignty and security. AI thrives on data. The sensitive nature of government data — including classified information, personally identifiable information (PII), and critical infrastructure data — necessitates a more nuanced and controlled approach. Simply put, not all data is suitable for the public cloud, where control and visibility are often limited.

Moreover, the increasing sophistication of cyber threats demands a renewed focus on hardware security. Nation-state actors and adversaries are actively seeking vulnerabilities in AI systems, aiming to manipulate data, disrupt operations, or steal valuable intellectual property. Controlling the provenance of your hardware becomes paramount in mitigating these risks. To state it plainly: Hardware matters. This means knowing where your chips are made, understanding their supply chain and where they’re deployed, and ensuring they are free from potential backdoors or vulnerabilities.

One reason investing now in the latest Intel chips for your on-premises computing has become especially important is all of the extensions to the silicon that we build into them. These extensions ensure secure communication and homomorphic encryption, which allows data to remain encrypted while being processed, protecting it from unauthorized access even by cloud providers or system administrators. It also allows enterprises to envision data as a stream rather than a state that it lives in, opening up new possibilities for secure data sharing and collaboration. Combined with a robust on-premises or hybrid cloud strategy, these technologies can provide the level of security and control that government agencies require in the AI era.

Acceleration: Rethinking the CPU vs. GPU debate

The conversation around AI hardware often centers on the perceived dominance of GPUs. While GPUs excel in certain AI tasks, particularly the computationally intensive process of training large AI models, they are not a one-size-fits-all solution. Most AI workloads, especially in deployment, where trained models are used to make predictions or decisions in real-time, reside on CPUs. Intel’s latest generation of Xeon processors have integrated AI accelerators directly into the CPU, offering significant performance gains for a wide range of AI tasks without requiring specialized GPU hardware. This means agencies can leverage their existing CPU infrastructure for many AI applications, maximizing their investment and simplifying deployment.

Furthermore, purpose-built accelerators like Intel’s Gaudi3 address the growing need for efficient inference at the edge. Edge computing, where data is processed closer to its source, is becoming increasingly important for AI applications that require low latency and high bandwidth or operate in environments with limited connectivity.

Whether analyzing incoming cargo manifests at a port, processing intelligence data in a remote location, or enabling real-time decision-making in autonomous vehicles, these accelerators offer a compelling alternative to power-hungry GPUs. They deliver both cost and energy efficiency. They are specifically designed for the demands of inference workloads, providing optimal performance in power-constrained and thermally challenging environments.

They’re also making inroads in the workplace. A great example is taking shape at the Air Force Materiel Command, where we’ve helped their procurement team train an AI model on a PC designed to ingest RFPs and compare them against core RFP requirements. The goal is to improve the down-select process that involves choosing more compliant suppliers over those less compliant with the requirements. What typically took four and a half months to do, the AI model accomplished in less than 30 minutes on a PC, and they reached the same conclusion.

Modernizing holistically for the AI Future:

The AI revolution is no longer on the horizon; it’s happening now. Traditional refresh cycles, where hardware gets upgraded every few years, are no longer sufficient in an age when software gets upgraded almost constantly. This requires not only a more agile hardware roadmap but a closer partnership between acquisition and mission teams to ensure that hardware procurement strategies align with the future demands of ever-increasing AI workloads.

It also necessitates a shift in mindset, recognizing that hardware matters more than ever in a world where data is the essential fuel for AI-enabled systems. This is not simply about buying more powerful chips or keeping up with the latest technology; it’s about building a flexible, resilient, and secure foundation for the future of government operations.

By embracing a holistic approach to AI infrastructure and working with OEMs like Intel and HPE, which uniquely understand the large-scale computing demands of government enterprises, agencies can position themselves to lead the way in this new era of intelligent automation, driving innovation, improving citizen services, and strengthening national security.

Learn more about how Intel and Hewlett Packard Enterprise can help your organization modernize more effectively for the AI era.