Training Cluster

Before an AI system like a chat program can answer questions, it has to learn. This learning process is called training: the system works through gigantic amounts of text or images, adjusting its internal settings along the way. This requires an unimaginable number of computational steps — more than a single computer could accomplish in many years. A training cluster is therefore not a single machine, but a connected network of thousands to tens of thousands of computing machines. They stand in a hall, are linked by extremely fast cables, and work on the same task. Ideally, for the software, this network looks like a single, extremely large computer.

Why clusters determine the AI ranking

How good an AI system becomes depends heavily on how much computing power was put into its training. More computing power allows for larger systems and more training data. That’s why access to a large training cluster is almost as important for AI companies as good researchers. Without one, you simply can’t compete for the largest systems.

This explains the enormous investments that regularly show up in business news. A single modern computing chip for AI costs several tens of thousands of euros. Take twenty thousand of these, add buildings, cooling, and power infrastructure, and you quickly end up in the billions. Corporations like Microsoft, Google, Meta, or Amazon spend double-digit billions annually on such facilities.

Then there’s the power demand. Large clusters need as much electricity as a small city. That’s why they are often built where power is cheap and plentiful. Some operators even plan their own power plants right next door.

How ten thousand chips work on one task

The actual computing work is usually not done by regular processors, but by graphics chips, known as GPUs. These chips were originally designed for computer games. They can perform very many simple calculations simultaneously — exactly what’s needed for AI training. Several such chips sit in one housing, many housings in one rack, many racks in one hall.

The real difficulty lies in dividing up the work. All chips are learning the same AI system, so they must constantly synchronize their results. After each computational step, they exchange their adjustments with each other. If the connection is too slow, expensive chips sit idle waiting for data. That’s why a cluster involves almost as much engineering effort in the network as in the chips themselves.

A comparison makes this vivid: ten thousand people can’t write a book faster if they constantly have to coordinate with each other. This exact coordination problem limits the size of clusters. On top of that, there’s wear and tear: with tens of thousands of chips, one fails almost every day. The software must bridge such failures without losing weeks-long training runs.

Training clusters in the news and stock prices

In everyday life, you never see a training cluster, you only use its results. Every answer from an AI chat comes from a system that previously spent months learning on such a facility. When actually being used, the AI then runs on other, smaller computers — this operation is called inference and is considerably less resource-intensive than training.

In financial news, however, the term appears almost daily. Reports about new data centers, chip delivery times, or export bans to China are essentially all about training clusters. Chipmaker Nvidia became one of the most valuable companies in the world largely because of this. Disputes over electricity prices and water consumption for cooling also have their origin here.

A common misconception is that a bigger cluster automatically produces a better AI. Computing power is only one of several requirements. Without good training data and a sensible concept, even the most expensive facility is of little use. Conversely, smaller teams have shown that with clever methods, remarkable results can be achieved with fewer chips as well.