$10,000 cooler designed with AI keeps Core i9-14900KF chilly at 7.5 GHz

zohaibahd

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Pushing the limits: Enthusiasts are always looking for an edge in the world of overclocking and extreme cooling. In this wild test, the team sought to determine whether advanced GenAI and 3D printing could help them squeeze out more performance from today's processors. To answer this, they built a liquid nitrogen (LN2) container in a whole new way – and arrived at some interesting conclusions.

The project brought together experts from across the ecosystem – Skatterbencher who's renowned for overclocking prowess; Diabatix, specializing in generative AI for thermal solutions; 3D Systems for additive manufacturing; and finally ElmorLabs for overclocking gear.

The team took ElmorLabs' existing Volcano LN2 container as a reference point, then tasked Diabatix's ColdStream Next AI to generate an improved design. 3D Systems then brought that digital blueprint to life, 3D printing a prototype using oxygen-free copper powder. Shockingly though, the cutting-edge process commanded a steep $10,000 price tag – a far cry from the $260 cost of the original Volcano.

The AI/3D printed design showed promise in early testing, focusing on three key metrics: cool-down time from room temperature to -194°C, heat-up time from -194°C to 20°C under a 1250W load, and the lowest temperature achieved using 500mL of liquid nitrogen.

It blew past the Volcano in cool-down speed, chilling from 28°C to -194°C in under a minute compared to the Volcano's 3-minute pace. Heat-up performance was better too, with the AI container warming up 30% faster. Efficiency also favored the AI design – using 500mL of LN2, it hit -133°C, while the Volcano stopped short at -100°C.

However, since these tests do not represent real-world performance, the team decided to run three more using the Intel Core i9-14900KF Raptor Lake processor. First, they fired up Cinebench 2024 to find the most stable maximum CPU frequency.

"We find that both LN2 containers can handle the Core i9-14900KF with P-cores clocked to 7.4 GHz without any issue. It seemed the AI-generated design could perhaps hold 7.5 GHz just a tad longer. But that might just be run-to-run variation," they noted.

In the second test, they checked the CPU temperature deltas between the heat spreader and cooling container base to assess real heat transfer capabilities. There was also an all-out stress test, pushing over 600W through the chip for several minutes.

While the AI container did pull ahead a smidge, the gains were relatively muted compared to the theoretical test results. Temperature deltas between the CPU heat spreader and container base were tighter on the 3D-printed model, but not by an earth-shattering margin. Even the performance uplift in Cinebench was fairly modest, as seen above.

After crunching the numbers, the team determined that while technically impressive, the AI/3D printed design currently doesn't pencil out from a cost/benefit perspective for modest overclocking scenarios. Not with that $10,000 price tag.

However, they are not done yet. While "nothing concrete" is in hand, the team says they could look into performance and cost optimizations. The design of the LN2 container doesn't necessarily need to be circular, for example. They are also exploring new designs for higher-power CPUs like the Ryzen Threadripper or Intel's Xeon 6.

All in all, the feasibility study may have exposed some limitations, but it also proved generative AI has better uses than simply churning out six-fingered models.

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What a waste of time and money... or maybe they are earning money from the amount of views/clicks/ads??
 
I suspect that's a one-at-a-time, prototype cost. There is no way a copper sintered heat sink is worth 10k.
 
I think overclocking was "cool" in the early 2000s. Now its more like art than science.
Now it is almost nonexistent because of how hard the CPUs are pushed already and other limitations. I remember playing with the bus speed, voltages and multipliers back in the golden days of OCing :)
 
It would be nice to know what the AI determined was most efficient, and why. Why is its design better?

Look at the rendered demo model; it's a tree of cooling fins that supposed to be absorbed by the evaporating LN2.

With that it gets a small advantage over the reference - I'd say anything that can transfer more cold and faster would be a winner.

 
This is so stupid.... Ai didn't do anything here. We have ALWAYS known that heat capillaries are the best wat to exchange heat... there was just no way to make a pot using them. K|ngp|n & others were discussing this some 15 years ago in his mill shop.

This is more about 3D printing, than Ai...!
 
What is it, platinum? I mean copper's a bit pricey but not that high.

Impressive nevertheless.
 
I suspect that's a one-at-a-time, prototype cost. There is no way a copper sintered heat sink is worth 10k.

Pretty much this. Copper is still in its infancy for laser powder bed additive manufacturing and as such quite expensive to process. The price also reflects the engineering effort behind all of this, I would suspect this was over half the total cost, putting the actual part build and process anywhere around $2.5-3k (finger in the air but from some experience)
 
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It would be nice to know what the AI determined was most efficient, and why. Why is its design better?

Likely mis-labelled as AI, their solver can use a technique called topology optimisation which is an optimisation approach applied to the finite element method (typical of structures analysis but also used in some fluids solvers too). In laymans terms, you start off with a solid block and chip away at bits of it, assess the change in perfomance (good or bad), make changes based on that and progress until the objectives are acheived (or as close as whilst not breaking any constraints).
 
This is so stupid.... Ai didn't do anything here. We have ALWAYS known that heat capillaries are the best wat to exchange heat... there was just no way to make a pot using them. K|ngp|n & others were discussing this some 15 years ago in his mill shop.

This is more about 3D printing, than Ai...!
It is about optimisation (AI or otherwise) and 3D printing. You're likely right, you could probably get to within 75-80% of the performance in a human-led design, if you have prior knowledge of what works really well within the conforms of your manufacturing route. AI or numerical optimisation methods allow you to explore the art of the possible, without the constraints of our knowledge.
 
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