Phononic on Cooling the Hotspots That Limit AI Performance

This summer, TechArena has been asking the companies that build AI infrastructure how their requirements are changing as deployments scale.

For our latest conversation in this series, we sat down with Ryan Brown, Director of Data Center Product Management at Phononic, a company that puts cooling directly on the component, right where heat is generated. The approach matters more now, as rack densities climb and the list of thermally sensitive parts stretches well beyond the GPU.

We talked about what targeted, solid-state cooling makes possible inside a dense AI rack; how pairing it with liquid cooling changes both performance and economics; and why coolers that read voltage, current, and temperature at each hotspot turn thermal management into an operational tool. Here's what we learned.

Q1: Phononic puts solid-state cooling right on the hotspot, cooling a specific component on demand rather than the whole room or loop. What does that kind of targeted cooling make possible in a dense AI rack?

A: Traditional cooling systems remove heat from the rack or server level. Phononic’s solid state approach addresses heat exactly where it is generated—at the hotspot. By actively and dynamically controlling temperature at the component level, operators can maintain tighter thermal margins on the devices that matter most, even as rack densities continue to climb.

That targeted cooling enables higher compute utilization, more predictable performance, and greater design flexibility. Rather than overcooling an entire rack to protect a handful of critical components, operators can deliver cooling precisely where it is needed, helping unlock higher-density AI infrastructure while improving overall system efficiency.

Q2: Liquid cooling is the default now, and you describe solid-state as working with it, targeting hotspots so operators can run warmer loops and over-provision less. How does that pairing change the performance and the economics?

A: We do not see solid-state cooling as a replacement for liquid cooling—we see it as a complementary technology. Liquid cooling excels at removing large amounts of heat from the system, while solid-state cooling delivers millisecond-scale, precision temperature control at the most thermally sensitive locations.

That combination allows operators to run warmer coolant temperatures, reduce thermal variability across critical components, and minimize the need for excessive cooling headroom. The result is a better balance between performance and efficiency: improved utilization of expensive AI hardware, lower cooling-system complexity, and potentially reduced energy and infrastructure costs.

Q3: Everyone watches the GPU, but you cool hotspots across HBM stacks, voltage regulators, and the lasers inside high-speed optics, where you already ship tens of millions of coolers. As density climbs, which of these is becoming the real limit on performance?

A: The answer is increasingly “all of the above.” GPUs remain the focal point because they consume so much power, but AI systems are becoming constrained by a growing number of thermally sensitive components.

High-bandwidth memory is critical because memory performance directly affects AI throughput. Voltage regulators influence system stability and power delivery. Meanwhile, optical interconnects are becoming increasingly important as clusters scale, and laser temperature stability directly impacts optical performance and reliability.

As AI infrastructure becomes more tightly integrated, performance is increasingly determined by the weakest thermal link in the system. That is why component-level thermal management across compute, power, memory, and optics is becoming essential.

Q4: Your cooling also reads voltage, current, and temperature at each hotspot and feeds that back through standard APIs. What does that thermal intelligence let an operator do that cooling alone never could?

A: Cooling is only part of the story. When thermal systems become intelligent sensing platforms, operators gain real-time visibility into the health and operating conditions of critical components.

That data can support predictive maintenance, optimize workload placement, identify emerging failure risks, and enable dynamic thermal control strategies. Instead of reacting after performance degrades, operators can make proactive decisions based on actual device conditions.

In AI infrastructure, where uptime and utilization are paramount, thermal intelligence turns cooling from a supporting utility into an operational tool that helps improve reliability, efficiency, and asset management.

Q5: The industry has seen racks climb from 20 kilowatts to 100 and now toward a megawatt. Where does cooling have to go next, and what are you building for it?

A: The next generation of AI infrastructure will require a layered approach to thermal management. As rack power continues to rise, broad system-level cooling alone will not be enough. Operators will need precision thermal control at the component level alongside advanced liquid-cooling architectures.

Phononic is focused on enabling that future with scalable solid state cooling solutions that address the most challenging hotspots in compute, optics, memory, and power electronics. We believe the industry is moving toward cooling systems that are not just more powerful, but also more intelligent, localized, and responsive.