Why AI Data Centres Should Think More Like Formula One Teams

Updated October 2026: This article has been updated to reflect the latest test results for Conflux single-phase cold plates. The original version cited 0.0076°C/W at 6 LPM across a 2,500 mm² footprint (tested as of August 2026).

As AI infrastructure scales, most conversations around data centre performance still focus on compute capacity: faster GPUs, denser racks and increasing power availability.

However, a critical constraint is quietly emerging alongside these advances: cooling efficiency.

While the industry has largely acknowledged that liquid cooling is necessary to support next-generation hardware, less attention has been paid to how efficiently those systems operate at scale.

That distinction, between simply cooling hardware and engineering an efficient thermal system, is one Formula One has been refining for decades.

Engineering That Never Stands Still

Perhaps Formula One’s greatest lesson isn’t simply how to engineer an efficient cooling system, but how to engineer one that can evolve.

Every race weekend brings new components, revised aerodynamic packages and evolving thermal demands, leaving engineers with little time to redesign, test and validate solutions.

That relentless cycle has fostered an engineering mindset built around rapid iteration, where cooling systems are expected to evolve alongside the hardware they support.

AI data centres are now entering a similar phase.

Successive generations of GPUs are arriving faster than ever, each with different thermal profiles, power densities and packaging constraints, meaning cooling solutions can no longer remain static while the technology around them advances.

From Thermal Capacity to System Efficiency

That evolution also changes the engineering challenge. It is no longer enough for cooling systems to remove the required amount of heat. They must do so with maximum efficiency.

AI workloads are pushing rack power densities to unprecedented levels, and simply meeting thermal requirements does not guarantee an optimal system. Instead, operators must consider how much energy is required to deliver that cooling capacity.

This shifts the focus from peak performance to system efficiency, a change that has significant implications for both operational cost and infrastructure design.

The Hidden Cost of Pressure Drop

One of the most important, and often overlooked, aspects of cooling performance is pressure drop.

In liquid cooling systems, pressure drop has a significant influence on the energy required to move coolant through the system. While small, perhaps even negligible, at the component level, its impact becomes significant when multiplied across thousands of servers.

Inefficient flow paths can increase pump power requirements, raise energy consumption and reduce overall system flexibility. In extreme cases, they can become a limiting factor in scaling infrastructure.

Why Component-Level Thinking is No Longer Enough

Components such as 콜드 플레이트 are often assessed in isolation, focusing on metrics like heat transfer performance and flow rate.

However, as systems become more complex, this approach can be misleading. Formula One engineers have long understood that a component delivering impressive standalone performance can still compromise the performance of the car as a whole.

A component that delivers strong thermal performance but induces high hydraulic resistance may degrade overall system efficiency.

The net result is higher energy use, even if individual components appear optimised.

To address this, cooling must be treated as a system-level engineering challenge, where thermal and hydraulic performance are considered together rather than separately.

Balancing Competing Requirements

Effective liquid cooling requires managing a fundamental trade-off between improving heat transfer and maintaining low resistance to flow.

Design approaches that increase turbulence or surface area can enhance thermal performance, but they often do so at the cost of higher pressure drop. The challenge lies in achieving both low thermal resistance and low hydraulic resistance at the same time.

This balance demands precision engineering at both macro and micro levels. The most effective solutions focus on directing coolant exactly where it is needed, rather than applying uniform cooling across an entire component.

At Conflux, for example, our latest single-phase cold plate designs achieve thermal resistance as low as 0.006°C/W* at 6 LPM across a 2,500 mm² footprint, while maintaining exceptionally low pressure drop, demonstrating that high thermal performance and hydraulic efficiency can be achieved together, rather than traded off against one another. *Tested as of October 2026.

It’s the same engineering philosophy that underpins Formula One: the best solution isn’t the one that excels in a single metric, but the one that delivers the greatest overall system performance.

The Formula One Engineering Mindset

One of Formula One’s defining characteristics is that engineers do not evaluate components in isolation.

Every design decision is based on its impact on overall system performance. A component that performs exceptionally well in isolation can still reduce the overall performance of the car if it compromises weight, reliability or aerodynamic efficiency.

This principle is directly relevant to data centres, where cooling systems must balance competing objectives under tight constraints.

This is why cooling design is increasingly moving beyond component optimisation towards system-level engineering, where every design decision is judged by its impact on overall performance.

Delivering that level of optimisation demands equally sophisticated design tools and manufacturing techniques.

The Role of Advanced Design and Manufacturing

Achieving high levels of efficiency in liquid cooling begins with how fluid flow is controlled within components.

Modern engineering approaches use simulation tools such as computational fluid dynamics to analyse and refine internal geometries. These tools make it possible to optimise coolant distribution, minimise losses and improve heat transfer where it matters most.

포뮬러 원에서 친숙한 이러한 접근 방식은, 고급 시뮬레이션과 신속한 제조를 통해 엔지니어들이 미세한 성능 향상을 추구하며 부품을 지속적으로 개선할 수 있게 해줍니다. 액체 냉각 분야에서도 이와 동일한 원리들이 고도로 최적화된 차세대 콜드 플레이 설계를 가능하게 하고 있습니다.

적층 팹은 기존 제조 방법으로는 제작하기 어렵거나 불가능한 복잡한 내부 채널과 형상을 구현함으로써 이를 가능하게 합니다. 이를 통해 엔지니어는 냉각재 흐름을 훨씬 더 정밀하게 제어할 수 있으며, 열 성능과 압력 강하를 서로 타협하는 것이 아니라 동시에 최적화할 수 있습니다.

또한 이를 통해 콜드 플레이트를 여러 부품의 조립품이 아닌 단일한 일체형 부품으로 제작할 수 있습니다. 접합된 계면을 없앰으로써 잠재적인 누출 경로가 제거되는 동시에 구조적 무결성이 향상되며, 이는 신뢰성이 성능만큼나 중요한 미션 크리티컬 데이터 센터 환경에서 매우 중요한 장점입니다.

냉각의 역할 재고하기

포뮬러 원은 언제나 사소한 이점들이 모여 의미 있는 경쟁 우위를 만들어내는 미세 조정의 스포츠였습니다. AI 데이터 센터도 이와 유사한 시대에 진입하고 있습니다.

프로세서의 성능이 계속 향상됨에 따라, 더 이상 단순히 냉각 시스템이 충분한 열을 제거할 수 있는지가 문제되지 않습니다. 그것은 시스템 전체에 걸쳐 열 성능, 압력 강하, 에너지 소비 및 신뢰성의 균형을 유지하면서 최대 효율로 그 역할을 수행할 수 있는가의 문제입니다.

결과적으로, 데이터 센터 혁신의 다음 단계는 더 빠른 프로세서뿐만 아니라 이를 냉각하는 더 스마트하고 효율적인 방법에 의해 정의될 수 있습니다.