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Breaking Down the AI server data center cost

Deploying AI-ready data centers involves far more than GPU server costs, which account for roughly 60% of total investment. Hidden expenses like advanced cooling, power upgrades, and specialized networking can double or triple budgets. AI workloads, driven by power-hungry servers like HPE XD685 and Dell XE9680, demand high-density racks, consuming 50-65 kW, necessitating liquid or immersion cooling systems costing $15K-$40K+ per rack. These reduce annual operating costs by over $10K per 50 nodes compared to air cooling. Capital expenses range from $337K for entry-level setups to $565K for enterprise configurations, with ongoing operational costs including energy, maintenance contracts ($15K-$40K per server), and software licenses. Retrofitting existing facilities saves upfront costs but risks downtime, while new builds optimize TCO, saving $150K per rack over four years. Strategic planning, hybrid stacks, and vendor partnerships can cut TCO by 25-40%, ensuring efficiency and scalability.

8 minute read •Information Technology

Avoiding Budget Overruns: Costs of AI Server Deployments

AI infrastructure can be a budget breaker if hidden costs go unchecked. This blog breaks down the real-world expenses of deploying AI servers—beyond just the hardware. From shipping and rack space to software licensing, network upgrades, and support contracts, every overlooked detail can add up fast. The NVIDIA H200 emerges as a strategic choice, offering superior performance, lower power draw, and greater memory bandwidth compared to the H100—all while reducing total cost of ownership (TCO). You'll learn why CapEx is just the tip of the iceberg and how ongoing OpEx—from cooling to SLAs—can quietly derail budgets. With practical tips on planning for scaling, emergency replacements, and service warranties, the blog equips enterprise teams to budget smarter and avoid overruns. The takeaway: Don’t just buy servers. Invest in scalability, reliability, and cost efficiency with H200-based systems—and build an AI infrastructure that works as hard as your ambitions.

6 minute read •Information Technology

NVIDIA at Computex 2025: Building the Ecosystem, Not Just the Chips

At Computex 2025, NVIDIA, led by CEO Jensen Huang, unveiled a bold vision transcending chip-making to orchestrate the AI economy. NVLink Fusion integrates third-party CPUs and accelerators with NVIDIA’s GPUs, ensuring ecosystem centrality despite competition from custom silicon. AI Factory Blueprints and DGX Cloud Lepton simplify scalable AI infrastructure, enabling enterprises to deploy without hyperscaler expertise. Hardware updates include the GB300 platform, RTX Pro AI Server for cost-efficient inference, and DGX Spark for edge AI. NVIDIA’s Taiwan strategy, including a supercomputer with TSMC and a new R&D office, strengthens supply chain resilience amid geopolitical tensions. The push into robotics via the Isaac platform targets physical AI, streamlining robot training and deployment. The NVIDIA H200 remains pivotal, offering cost-effective performance for AI factories and edge inference, reinforced by ecosystem synergy. NVIDIA’s strategy ensures it remains the backbone of AI’s future, from data centers to robotics.

19 minute read •Information Technology

Beyond Hopper: What NVIDIA’s Blackwell Architecture Means for the Future of AI

Today’s most powerful NVIDIA AI server solutions are built on Hopper architecture, with the H200 standing as its pinnacle. These systems deliver exceptional capabilities: 141 GB/s memory bandwidth with HBM3e, 700W power efficiency, and specialized FP8 support for AI workloads. They handle current large language models and complex simulations effectively.

8 minute read •Information Technology

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The Evolution of the Information Technology Industry

Most of us recognize the term information technology as a reference to a full spectrum of technologies, including networks, computers, hardware, software, and any device that uses or transfers data. But as a department of the enterprise, IT has a history that is longer than many people realize.Here’s how IT has changed its inception and what you can expect the future of IT to look like.

Innovating From the Start

IT has its roots in the communication technologies that most of us take for granted today. But arguably, the dawn of information technology as a discipline began during the first industrial revolution.

The electric telegraph was developed during the 1830s and 1840s by Samual Morse (1791-1872) and other inventors. By the early 1860s, the Western Union Telegraphy Company had laid the first transcontinental telegraph line. This new mode of communication made it possible for people to send messages across vast distances in minutes, whereas before it would have taken weeks or even months.

First entirely electronic computing machine

Realizing the value of this capability, businesses jumped on the opportunity and invested significant capital into their communications infrastructures. These investments eventually led to the deployment of business telephone systems, and then the earliest computers.

In 1943, IBM developed the first entirely electronic computing machine. At the time, the computer was big enough to fill an entire room, but it would eventually birth an entire generation of computers. In the 1950s and 1960s, IBM deployed many of the core technologies that make up the world of information technology as we know it today.

The birth of the IT industry

Naturally, organizations needed specialists to manage these technologies because most people weren’t familiar with how they worked and how they needed to be maintained. Furthermore, businesses needed guidance from experts, especially those who could provide an objective opinion about how to invest the company’s technology budget and how to leverage machines to generate value.Thus, the IT and IT consulting industries were born.

The Information Technology Industry Now

Artificial Intelligence and Machine Learning

Relatively simple forms of artificial intelligence (AI) are already widespread. Anyone who has spoken with an automated chatbot or asked Apple’s Siri to look up directions to a restaurant has experienced an AI.

Now, evolving generative AI technologies are bringing unprecedented power to virtually anyone, even nontechnical users. Organizations across sectors are
integrating generative AI with familiar technologies, existing workflows, and our everyday lives as well.

AI is already regularly used to analyze vast data sets, so organizations can more efficiently leverage data for insights and other capabilities. AI also has a noticeable presence in almost every industry on the planet, including transportation, manufacturing, healthcare, education, media, and customer service.

Edge Computing

Edge computing is an enterprise computing capability in which distributed computing and data storage systems are located closer to data sources to improve response times and save bandwidth. Although this may sound like a throwback to early on-premises enterprise computing systems, edge computing has only become feasible in recent years thanks to the smaller scale and affordability of computing infrastructures.

Rather than relying on a central location to run computations, such as a data center, organizations can use edge computing to analyze data at blinding speeds on location as soon as it originates. It will be an important capability for organizations that need to manage vast quantities of data at the edges of their network, such as manufacturing companies operating autonomous robots in factories.

Internet of Things (IoT) and Autonomous Robots

The internet of things describes a network of physical objects—typically, devices—that contain sensors and other technologies to allow them to connect to a broader network IoT technology is a necessary component for autonomous robotics, as it allows robots to communicate with each other so they can navigate physical spaces, receive updates from other machines, and complete tasks.

That said, the potential of IoT is almost limitless. It will be a foundational technology for tomorrow’s smart factories, but it will also enable organizations of all kinds to deploy and automate many of their core functions at scale, almost instantly.The number of connected devices in the world is expected to rise significantly in the next few years, and many of the latest technologies will be IoT technologies by default.

Quantum Computing

Quantum computing refers to the use of quantum mechanics phenomena to perform computations. A quantum computer can use the quantum superposition of particles—the features of a quantum system whereby particles exist in several separate quantum states at the same time—to perform computations simultaneously instead of chronologically.

It’s too soon to say how quantum computing will change our lives, but there’s little doubt that it could unlock capabilities that even the most innovative IT experts have never dreamt of before. After all, when IBM created its first room-sized computing system, no one predicted that the average consumer would eventually hold an exponentially more powerful computer in the palm of their hand. If the power of quantum computing is unlocked, the possibilities for the IT industry are endless.

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