When we think of CERN, we imagine the Large Hadron Collider, enormous detectors, and scientists searching for the building blocks of the universe. But what is less visible, and equally extraordinary, is the computing infrastructure that makes all of this possible.
The collisions inside the LHC produce an unimaginable quantity of data — billions of events every second, each recorded by detectors that transform subatomic chaos into digital signals. Most of it is discarded within microseconds, filtered through layers of electronics and software, but the remaining fraction is still vast. Every year, CERN’s data centers receive tens of petabytes of new data, to be stored, distributed, and processed by thousands of servers.
What fascinated me most during my 13 years and Ph.D. there was not only the scale, but the efficiency of the system. Beneath the complexity lies a simple principle: data must move quickly, securely, and intelligently, from where it is generated to where it can be understood. At CERN, this meant connecting underground experiments to central computing halls through a dense fiber-optic network. In a sense, the data centers are the brain of the laboratory, and the fibers are its neural pathways.
Building and maintaining those pathways required precision worthy of the science they supported. Some of the optical fibers we installed had to resist radiation and harsh environments. The supply chain to achieve such performance was incredibly complex: the fibres were manufactured in Japan, cabled in Germany, terminated in China, and installed by Polish vendors in Geneva.
” I still fondly remember my meetings with suppliers, where we would speak five languages during a 30-minute discussion. What a headache! “
Each connection mattered, because behind every optical strand was a chain of information leading to potential discovery. The detectors could not pause, the experiments could not fail — uptime was sacred. In those tunnels and racks, one learns that science depends on reliability as much as on intelligence.
Edge Data Centers: A Familiar Pattern
What many people don’t realize is that CERN’s computing infrastructure, in its architecture, resembles today’s edge data networks. The experiments themselves host small computing nodes near the detectors, performing local processing to reduce the data volume before sending it to the main data centers.
It is the same philosophy guiding modern edge data centers: keep computation close to where data is generated, reduce latency, and save bandwidth. At CERN, this was done for physics; today, it is done for everything from autonomous cars to artificial intelligence.
The analogy goes further. CERN’s data centers must deal with enormous throughput, distributed workloads, and tight energy constraints. Cooling and power optimization became central topics, just like in the commercial data center world. In the past decade, CERN introduced high-efficiency cooling systems and sophisticated monitoring to lower its PUE to around 1.2 — the Power Usage Effectiveness, a metric now familiar to every operator in the industry.
” I still remember the first PUE readings, what an achievement”
The transition from underground caverns to the data hall is, in fact, a journey from chaos to order. Raw data from collisions are incomprehensible streams until they are reconstructed, filtered, and stored. The data center is where matter becomes meaning. And that process, though driven by physics, has its parallels in every modern technology sector. Whether it’s AI models learning from sensor networks, or edge nodes processing industrial telemetry, the pattern is identical: collect, filter, transmit, compute.
Over the years, CERN also became a pioneer in distributed computing through the Worldwide LHC Computing Grid, a network connecting hundreds of data centers across the world. Scientists could analyze data remotely, without ever touching the local servers. It was, in essence, a precursor to today’s cloud computing. The same principle now fuels n-connect network in our nLighten data center : instead of moving petabytes across the planet, you move the computation to the data.
Working inside that ecosystem changed the way I see data centers. They are not warehouses for machines; they are living systems where information flows like energy in a circuit. Every decision — a cable route, a power redundancy, a cooling setting — affects the flow. When you stand in front of a rack, you are not looking at infrastructure, but at an instrument. Each server contributes to the reconstruction of an event that might rewrite the laws of nature.
From Protons to Platforms: Shared Challenges
After more than a decade immersed in that world, I eventually shifted from science to technology, joining the modern data center industry in nLighten. At first glance, it might seem like moving from physics to business, from cosmic questions to commercial systems. But the connection is deeper. Today’s edge data centers face similar challenges to those at CERN: the need for reliable low-latency connectivity, sustainable operations, and intelligent data management. The objectives are different, but the engineering spirit is the same — designing systems that make sense of an overwhelming quantity of information.
What struck me most in both worlds is the convergence between performance and responsibility. Power efficiency, once a secondary concern, has become a defining factor. The same metrics that once guided CERN’s optimization efforts now shape every modern facility — from hyperscale operators to regional edge providers. The quest for a lower PUE or for renewable integration has become universal.
At CERN, the next frontier is the High-Luminosity LHC, expected to produce ten times more data than the current collider. The infrastructure will need to evolve again, with faster fiber links, denser compute nodes, and more intelligent data distribution.
In parallel, the commercial world is dealing with its own data explosion — driven by AI, IoT, and the digital economy. Both are trying to answer the same fundamental question:
How do we move and process data efficiently and responsibly?
