Ryan Brown
Director, Product Management
AI infrastructure has a heat problem. But simply throwing more cooling at it isn’t the answer. As GPUs get hotter, racks get denser, and workloads become more dynamic, the traditional response has been predictable: lower coolant temperatures, increase flow, run fans harder, add chiller capacity.
In other words: cool everything more to protect the components that need it most.
It works. But at AI scale, it’s an increasingly expensive way to solve a highly localized problem.
The Thermal Problem Is Changing.
Today’s AI systems don’t generate heat uniformly.
A GPU hotspot, HBM stack, networking ASIC, or optical engine can become thermally constrained while the surrounding infrastructure remains well within its operating limits. Those conditions can also change extraordinarily quickly as workloads shift.
Yet legacy cooling infrastructure operates at a macro level—across servers, racks, loops, and facilities—and responds on very different timescales.
That creates a fundamental mismatch.
AI workloads move in milliseconds. Thermal infrastructure needs to start thinking in milliseconds, too.
That’s where dynamic thermal control changes the equation.
Instead of making the entire cooling system work harder, precision thermal control manages temperature directly at the components where thermal constraints occur.
At Phononic, our solid state Thermal Control Systems™ combine thermoelectric cooling, real-time telemetry, and software-defined control to respond dynamically to changing thermal conditions at the source.
That distinction matters.
What Happens When You Control Temperature At The Source?
You can begin operating the rest of the infrastructure differently.
In analysis highlighted in our recent Data Center Dynamics article, precision thermal control can enable supply water temperatures as high as 50°C (122°F) while reducing total data center energy consumption by as much as 14%.
Higher operating temperatures can dramatically reduce dependence on chillers and evaporative cooling. At hyperscale, the potential water savings can approach one million gallons per day.
But I think the most interesting implication is even bigger:
Every watt we don’t spend unnecessarily cooling infrastructure is a watt that can potentially be put toward compute.
In a power-constrained AI world, thermal efficiency becomes compute capacity.
From Cooling Infrastructure To An Intelligent Thermal Layer
This gets especially interesting when you stop thinking about a single hotspot.
Imagine precision thermal control deployed across GPUs, HBM, networking silicon, optical engines, and other thermally constrained components throughout an AI cluster.
Now those individual control points become something bigger.
Real-time telemetry tells us what’s happening. Software-defined controls determine what needs thermal intervention. Solid state devices respond in milliseconds—up to 600 times faster than conventional cooling infrastructure.
The result is the foundation for a workload-aware thermal layer that can dynamically manage temperature across AI infrastructure.
That’s where I believe thermal management is heading.
Not replacing facility or liquid cooling. Making it smarter.
Bulk cooling will continue doing what it does well: moving heat out of the data center.
Dynamic thermal control adds something different: the ability to precisely manage temperature where compute happens, as workloads change.
And that changes the question from:
How do we cool all this AI infrastructure?
How do we intelligently control temperature to get more from the infrastructure we already have and everything we’re about to build?
That’s a much more interesting problem to solve.
