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Data Center Thermal Management Series-Part 1 of 3

The biggest driver in today’s global economy is the data center, and in the process, data centers are generating and taking more heat than ever before. Heat — the generation of it and public concerns about it — is the most pressing issue facing the data center industry. In response, data center managers and engineers are viewing […]

The biggest driver in today’s global economy is the data center, and in the process, data centers are generating and taking more heat than ever before. Heat — the generation of it and public concerns about it — is the most pressing issue facing the data center industry. In response, data center managers and engineers are viewing the problem in new ways, devising innovative, next-generation thermal control strategies that manage the problem more effectively.

In the minds of IT managers and the public, data centers and AI are joined at the hip, along with increasing power demands and associated issues of environmental impact, water usage, and higher utility costs. As community activists, political leaders, and now even some prominent AI industry CEOs call for limits on AI development and data center construction, it’s imperative that the data center industry better manage the heat they’re producing — and taking.

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Texas Instruments’ portfolio of data center thermal management systems spans the full coolant path. It’s been under active development at TI for decades and has evolved in response to the rising power, computing demands, and complexity of advanced data centers.

Traditional thermal management techniques can’t keep up with AI’s intense computing requirements. For decades, air cooling was sufficient. Fans blew cool air across hot components and carried heat away, keeping data centers and AI servers operating reliably. But with AI training and inference pushing rack power beyond 20kW to 40kW, air alone can no longer remove heat quickly enough. In addition, air cooling is noisy and consumes too much energy. AI server racks are coming online now that draw 100kW of power, and they’re on their way to more than a megawatt in a few years. Each generation of servers grows denser, more powerful, and hotter as they move and compute massive volumes of data from memory to large language models to processors.

So what’s to be done? Data center engineers increasingly are taking on a holistic view of the problem, attacking it across multiple fronts simultaneously, from the facility level through to the server tray. What’s needed is a portfolio-based solution that spans the full coolant path, from the heating, ventilation and air-conditioning (HVAC) systems that cool an entire data center facility, to the cooling distribution unit (CDU) that feeds the racks, to the individual server tray. It’s a comprehensive approach that’s proving to be highly effective.

It starts with liquid cooling, which is more energy-efficient and quieter than air cooling. Rather than cooling the air in and around a server, liquid cooling removes the heat at its source. A cold plate sits directly against the chip in a server tray, allowing liquid to remove heat.

Liquid cooling, in earlier, less-developed forms, has been around for decades. The technique picked up steam about 15 years ago in the HPC industry, cooling clusters of high-performance servers conducting supercomputing-class scientific workloads. Today, liquid cooling in the supercomputing and AI data center sectors has been proven safe and cost-effective; it’s become the norm.

Newer liquid cooling techniques from TI are applied across entire data center facilities, from the coolant distribution units (CDUs), which circulate coolant between server racks and the external facility infrastructure, to the individual trays. The CDU houses pumps and fans that form the mechanical heart of the loop, circulating coolant through the system. Motor drivers control these pumps and fans, with built-in protection against failure. Valves regulate how much coolant reaches each tray, opening or closing based on real-time demand. 

There’s an irony here: in the face of public concerns about data center water consumption, advanced liquid cooling techniques can cut data center water consumption by 90 percent or more, whereas traditional air cooling combined with evaporative chillers can consume millions of gallons per megawatt-year.

And while air cooling has lower initial CapEx costs, liquid cooling generates lower long-term operating expenses (OpEx), with up to 40 percent energy savings and a breakeven timeline of two years or less.

Liquid cooling offers another advantage: it reduces thermal stress on servers and other computing hardware, resulting in a longer mean time between failures (MTBF) of up to 65,000 hours versus 40,000 hours for air cooling.

As an example, a new, super-sized data center in Wisconsin with a closed-loop coolant system reports that its annual water use is roughly that of a typical restaurant or what an 18-hole golf course consumes per week in peak summer.

But bringing liquid this close to electronics can be expensive, and this calls for TI’s ecosystem of components that can move, monitor, and control coolant. Monitoring liquid coolant is a critical aspect for the reliable operation of the entire thermal management system. A big part of data center engineers’ jobs is maintaining a vigilant watch, tracking temperature and humidity, along with any signs of a leak or failed components, for any sign that something isn’t right.

Microcontrollers tie everything together, processing sensor data in real time and adjusting the system accordingly.

Downtime is anathema to data center operators and customers, and that means reliability is paramount even as challenges rise with rack power increasing to the megawatt level. With this in mind, TI believes that the next frontier of reliability in data center cooling combines multiple sensors through edge AI and sensor fusion. Today’s systems often rely on a single sensor to catch a leak. Tomorrow’s systems will analyze a web of real-time signals — motor current, compressor speed, humidity, pressure, and temperature — to recognize subtle patterns beyond the detection of a single sensor. This is where edge AI comes in, delivering fast response time and low latency. Correlating those signals is too complex for traditional rule-based software.

“We’re not starting from scratch,” said Patrick Zeng, general manager of data center thermal management at TI. “We’re building on technologies in commercial heating, ventilation, and air-conditioning (HVAC) systems that we’ve used for years in building automation and appliances. That means faster time to market because the foundation is already there. And it means reliability, because this is proven technology.”

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Data Center Thermal Management Series-Part 1 of 3

The biggest driver in today’s global economy is the data center, and in the process, data centers are generating and taking more heat than ever before. Heat — the generation of it and public concerns about it — is the most pressing issue facing the data center industry. In response, data center managers and engineers are viewing the problem in new ways, devising innovative, next-generation thermal control strategies that manage the problem more effectively. In the minds of IT managers and the public, data centers and AI are joined at the hip, along with increasing power demands and associated issues of environmental impact, water usage, and higher utility costs. As community activists, political leaders, and now even some prominent AI industry CEOs call for limits on AI development and data center construction, it’s imperative that the data center industry better manage the heat they’re producing — and taking. Sponsored Resources: Texas Instruments’ portfolio of data center thermal management systems spans the full coolant path. It’s been under active development at TI for decades and has evolved in response to the rising power, computing demands, and complexity of advanced data centers. Traditional thermal management techniques can’t keep up with AI’s intense computing requirements. For decades, air cooling was sufficient. Fans blew cool air across hot components and carried heat away, keeping data centers and AI servers operating reliably. But with AI training and inference pushing rack power beyond 20kW to 40kW, air alone can no longer remove heat quickly enough. In addition, air cooling is noisy and consumes too much energy. AI server racks are coming online now that draw 100kW of power, and they’re on their way to more than a megawatt in a few years. Each generation of servers grows denser, more powerful, and hotter as they move and compute massive volumes

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LiquidStack unveils liquid-cooling platform targeting AI data centers

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From Coal to Compute: How Pennsylvania Is Rebuilding Power for AI Data Centers

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OpenStack Hibiscus adds DNS security features and confidential computing to the open-source cloud platform

Type-5 support is aimed at data center integration. “It lets the tenant network prefixes be advertised directly into the physical fabric, so that really that’s basically how modern data center networks are built,” Carrez explained. “So really helps OpenStack fit into those environments without extra gateway layers that we’ve seen in use before.” Routable tenant addresses: The OVN BGP integration gains a route leaking option. An operator turns it on with the leak_routes attribute of a subnet. The extended OVN features follow the same approach. “OVN BGP features that let the tenant addresses be routable directly from the underlay, and that again is exposing how modern data centers are built directly into OpenStack,” Carrez said. Lower memory use: In deployments that use Open vSwitch, a monitoring daemon tracked keepalived state changes for HA routers. Hibiscus replaces the daemon with a shell script. The change applies to every HA router, so the savings add up across a deployment. “The 15 times reduction in memory footprint for the high availability router monitoring is, I think, really interesting,” Carrez said.

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DCF Trends Summit: ON.energy’s Asser Elsamahy – Using AI UPS Systems to Tame AI Load Swings

The power challenge surrounding artificial intelligence is increasingly about more than finding enough megawatts. AI data centers can also introduce rapid changes in electricity demand as large clusters of accelerators ramp workloads up and down. Those swings create a different kind of infrastructure problem: how to serve highly dynamic compute loads without passing that volatility directly onto the electric grid. That challenge is helping move battery energy storage deeper into data center power architecture. In an onsite podcast interview recorded live at the Data Center Frontier Trends Summit 2026, DCF Contributing Editor Doug Black spoke with Asser Elsamahy, P.E., vice president of engineering at ON.energy, about the emerging role of battery-based power quality infrastructure for AI data centers. Elsamahy said battery power systems themselves are hardly new. Energy storage has been deployed at gigawatt scale around the world for roughly two decades. What is new is the way the technology is being adapted to the operating characteristics of large AI facilities. “They’re new to the data center industry, but they’re not necessarily new in the market,” Elsamahy said. “They’ve been deployed at gigawatt scale already, multiple gigawatts all over the world.” The difference now is the load. Major swings in AI computing demand can create additional stress for grid operators already confronting rapid growth in large-load interconnection requests. Elsamahy said that dynamic is accelerating interest in energy storage as a way to manage the interface between AI infrastructure and the grid. From Battery Storage to an “AI UPS” ON.energy’s approach is built around what the company calls an AI UPS, or medium-voltage uninterruptible power supply. The architecture differs from the parallel battery energy storage system, or BESS, configuration commonly used for standalone grid storage. ON instead uses a double-conversion design with two sets of inverters. One inverter set faces the

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Microsoft will invest $80B in AI data centers in fiscal 2025

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John Deere unveils more autonomous farm machines to address skill labor shortage

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2025 playbook for enterprise AI success, from agents to evals

Join our daily and weekly newsletters for the latest updates and exclusive content on industry-leading AI coverage. Learn More 2025 is poised to be a pivotal year for enterprise AI. The past year has seen rapid innovation, and this year will see the same. This has made it more critical than ever to revisit your AI strategy to stay competitive and create value for your customers. From scaling AI agents to optimizing costs, here are the five critical areas enterprises should prioritize for their AI strategy this year. 1. Agents: the next generation of automation AI agents are no longer theoretical. In 2025, they’re indispensable tools for enterprises looking to streamline operations and enhance customer interactions. Unlike traditional software, agents powered by large language models (LLMs) can make nuanced decisions, navigate complex multi-step tasks, and integrate seamlessly with tools and APIs. At the start of 2024, agents were not ready for prime time, making frustrating mistakes like hallucinating URLs. They started getting better as frontier large language models themselves improved. “Let me put it this way,” said Sam Witteveen, cofounder of Red Dragon, a company that develops agents for companies, and that recently reviewed the 48 agents it built last year. “Interestingly, the ones that we built at the start of the year, a lot of those worked way better at the end of the year just because the models got better.” Witteveen shared this in the video podcast we filmed to discuss these five big trends in detail. Models are getting better and hallucinating less, and they’re also being trained to do agentic tasks. Another feature that the model providers are researching is a way to use the LLM as a judge, and as models get cheaper (something we’ll cover below), companies can use three or more models to

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OpenAI’s red teaming innovations define new essentials for security leaders in the AI era

Join our daily and weekly newsletters for the latest updates and exclusive content on industry-leading AI coverage. Learn More OpenAI has taken a more aggressive approach to red teaming than its AI competitors, demonstrating its security teams’ advanced capabilities in two areas: multi-step reinforcement and external red teaming. OpenAI recently released two papers that set a new competitive standard for improving the quality, reliability and safety of AI models in these two techniques and more. The first paper, “OpenAI’s Approach to External Red Teaming for AI Models and Systems,” reports that specialized teams outside the company have proven effective in uncovering vulnerabilities that might otherwise have made it into a released model because in-house testing techniques may have missed them. In the second paper, “Diverse and Effective Red Teaming with Auto-Generated Rewards and Multi-Step Reinforcement Learning,” OpenAI introduces an automated framework that relies on iterative reinforcement learning to generate a broad spectrum of novel, wide-ranging attacks. Going all-in on red teaming pays practical, competitive dividends It’s encouraging to see competitive intensity in red teaming growing among AI companies. When Anthropic released its AI red team guidelines in June of last year, it joined AI providers including Google, Microsoft, Nvidia, OpenAI, and even the U.S.’s National Institute of Standards and Technology (NIST), which all had released red teaming frameworks. Investing heavily in red teaming yields tangible benefits for security leaders in any organization. OpenAI’s paper on external red teaming provides a detailed analysis of how the company strives to create specialized external teams that include cybersecurity and subject matter experts. The goal is to see if knowledgeable external teams can defeat models’ security perimeters and find gaps in their security, biases and controls that prompt-based testing couldn’t find. What makes OpenAI’s recent papers noteworthy is how well they define using human-in-the-middle

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