AI in Data Centers – Challenges and Solutions

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The exponential rise in demand for AI-powered applications in recent years has necessitated a new approach to data center design, configuration and management.

Wall Street Journal estimates About 20% of global data center capacity is currently used for AI purposes.

However, with over 77% of companies If we are already using or researching AI technology, traditional data centers may become obsolete quickly.

The AI ​​stand-off

Due to their complex algorithms and models, AI applications typically require more power and computing resources than others.

For example, a simple search on ChatGPT requires almost 10 times as much electricity as it takes to process a quick Google search.

Traditional data centers are designed with an average density of five to ten kilowatts per rack, but this can increase to sixty or more kilowatts per rack to handle AI applications.

A larger workload and higher energy demands equal higher overhead costs.

Additionally, data centers must come up with alternative and advanced ways to deal with cooling issues, vulnerabilities, security challenges, and maintenance issues that may arise due to staff shortages.

Then there is the issue of environmental sustainability. Researchers estimate that GPT-3 generated more than 552 tons of CO2 before it was even released for public use in 2020.

This figure corresponds to the CO2 emissions that 123 petrol vehicles would produce over a full calendar year.

Unfortunately, unless these challenges are addressed strategically and dynamically, we could face an infrastructure tightrope similar to the GPU supply shortage.

The shortage of data centers fully equipped to meet the overwhelming demands of AI technology could ultimately slow growth, promote the monopolization of AI infrastructure, and have serious environmental consequences.

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Building for now and the future

To tackle these problems head on, many companies are already implementing new measures.

These include the use of co-located data centers to reduce operational costs, promote scalability and ensure the availability of competent on-site maintenance.

Data centers also use more advanced cooling techniques such as liquid cooling, direct-to-chip cooling and immersive cooling, as opposed to conventional air cooling systems.

Design is paramount for new centres. In 2022, for example, Meta halted construction of its $800 million data center in Texas to consider redesigning the 900,000-square-foot facility.

However, data centers can not only function as the infrastructure and computing powerhouse for AI-enabled applications and products, but can also leverage the same AI to optimize performance, control costs, and ensure operational efficiency in a variety of ways.

Let’s take a look at a few.

Workload management

AI and automation tools can more accurately predict and allocate workloads in data centers more efficiently, ensuring deployments match resource requirements.

This reduces waste by minimizing underutilization of computer hardware and reducing energy consumption. About 32% of cloud spend is largely wasted due to overstocking.

However, AI systems can reallocate resources to projects that need them most, optimizing performance and maximizing idle hardware.

Repetitive and routine tasks can be easily automated, saving time, energy and skilled manpower.

AI can also process data and performance metrics, enabling strategic, proactive measures to address potential workload management issues before they arise.

AI-powered cooling systems

In addition to introducing better cooling facilities, AI can play an important role in dynamically sensing and adjusting temperature.

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Instead of statically cooling the hardware in the data center, AI can analyze and respond to temperature data to deliver exactly the amount of cooling needed to each piece of hardware.

This can control humidity conditions for optimal performance, improve energy efficiency and extend equipment life.

Dynamic effectiveness of energy consumption

Real-time monitoring and predictive analytics by AI systems can provide important insights into energy consumption patterns and inefficiencies, allowing managers to make data-driven decisions and implement necessary energy management strategies.

While the objective fact remains that power requirements for data centers with AI workloads will always be higher than those of traditional data centers, the synergistic efforts of AI-driven management and data center design can have a significant impact.

Data centers can also minimize their carbon footprint and reduce environmental impact by prioritizing efficient energy management systems and implementing energy management techniques such as DVFS (dynamic voltage and frequency scaling).

Rounding up

The price for a highly advanced digital future lies at the core of the infrastructure.

Data centers must make physical, operational and software changes to keep pace with the evolving modern world and its AI demands.

Fortunately, AI challenges can also be addressed with AI solutions.

As the technology industry gradually adapts and technology improves, AI-driven workload management and optimization will become mainstream, leading to robust data centers equipped to power the future.

Innovation from other alternatives, such as decentralized computing infrastructure, will also create healthy competition and improve efficiency.


Daniel Keller is the CEO of InFlux technologies. He has over 25 years of IT experience in technology, healthcare and non-profit/charitable work. Daniel successfully manages infrastructure, bridges operational gaps and deploys technology projects effectively.

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