Artificial intelligence (AI) refers to computer systems that perform tasks typically associated with human intelligence, such as understanding language, recognizing images, or making decisions. Unlike traditional software, which follows predefined rules, modern AI systems learn from examples by deriving their behavior autonomously from large volumes of data. The most important subfield is machine learning, with deep learning—based on artificial neural networks—representing its most powerful and advanced form.

AI is usually visible only in its final output: a chatbot that answers questions, an image generator that creates visuals, or a diagnostic system used in healthcare. However, before an AI model can produce even a single response, it must undergo an extremely compute-intensive process known as AI training. Once training is complete, the model enters its operational phase, called inference. Running AI reliably, at scale, and in production therefore requires specialized hardware, high-speed networks, efficient cooling, and an infrastructure designed to support these demanding workloads.

AI training is the process in which a model learns from data. With modern methods of deep learning this means: an artificial neural network with millions to billions of parameters processes vast amounts of data, compares its predictions with the desired result, and adjusts its internal weights millions of times until it reaches the desired accuracy. AI training is thus a special case of machine learning – the subfield of AI in which systems are not explicitly programmed but learn patterns from data.

In practice, an AI model goes through three phases that place different demands on the infrastructure. In training, the model is built from scratch – by far the most compute-intensive step, which depending on model size can take days to weeks on large compute clusters. In fine-tuning, an already-trained base model is adapted using additional, often company-owned data; this is considerably cheaper but still requires specialized accelerators. Finally, in inference, the finished model is deployed in production – here maximum compute power over weeks is not as important, instead the focus is low latency and efficiency in continuous operation.

The enormous compute demand of AI training stems from three factors: the number of model parameters, the volume of training data, and the number of training runs. Large language models (LLMs) consist of billions of parameters that are recalculated in every iteration. This involves an enormous number of matrix operations – mathematically simple, but on a scale that a single server could never handle in a reasonable amount of time.

This is exactly where AI training becomes a classic High Performance Computing (HPC) challenge. The solution is parallelization: a large task is broken down into thousands of smaller subtasks that are processed simultaneously on many computing units. This is the same principle with which supercomputers have been running climate models and performing crash simulations for decades. The AI wave has not replaced HPC – it has become one of its biggest drivers. HPC and AI are merging: the same architectures that enable scientific simulations are today training the largest AI models.

The heart of every training infrastructure is the GPU. While a CPU is optimized for a few sequential tasks, a GPU has thousands of smaller cores that perform parallel computations – ideal for training neural networks. Modern accelerators from NVIDIA (such as the B200, B300, and Rubin generations) and AMD (Instinct series) feature specialized tensor compute units that execute the matrix operations typical of AI at high speed and energy efficiency.

A single GPU, however, is not enough for serious AI training. Several accelerators are bundled into compute nodes, and many nodes into aGPU cluster. What is then decisive is the interplay: for hundreds of GPUs to compute on a single model, they must communicate with minimal latency. High-speed interconnects such as InfiniBand handle this, complemented by parallel file systems (such as Lustre or BeeGFS) and fast flash storage, so that the accelerators never have to wait for data. Preconfigured systems such as the NVIDIA DGX Platform demonstrate this principle on a small scale; at larger scale, the same principle is used in custom-built clusters such as those built by MEGWARE as an NVIDIA Partner for research and industry.

A training infrastructure is therefore never “just a GPU”. It is a finely tuned system of accelerators, processors, network, storage, cooling, and software – and its performance depends on the weakest link.

Once a model is trained, the load shifts from training to inference – the production application of the model. The two phases have unique demand profiles. Training runs in long bursts at maximum compute power and is tolerant of brief interruptions. Inference, by contrast, runs continuously, must respond within milliseconds, and is often queried thousands of times in parallel. The economic focus shifts: over the lifetime of a model, the power consumption of inference can significantly exceed that of training.

This has given rise to a distinct discipline of infrastructure planning. Inference servers are designed for throughput, low latency, and energy efficiency rather than raw peak performance. Methods such as Retrieval Augmented Generation (RAG), in which a language model accesses its own knowledge base at runtime, shift part of the work from the GPU to upstream data and search systems – which once again changes the demands on storage and connectivity. Anyone deploying AI seriously therefore plans training and inference separately, but in coordination with each other.

Perhaps the most important strategic question is not “which GPU?” but “where does the system run?”. Three models are available – with clear differences:

For many research institutions and companies with sustained utilization or sensitive data, there is a strong case for on-premise AI – that is, AI systems in their own data center. The reasons are control, data protection and, in continuous operation, often the lower total cost of ownership. The prerequisite, however, is keeping the largest operating cost under control: energy.

Because AI clusters are power-hungry, and most of that energy turns into heat. Conventional air cooling reaches its limits at the power density of modern GPU systems. This is where the EUREKA platform from MEGWARE comes in: a direct warm-water cooling system (Direct Liquid Cooling) that cools all components of a server with supply temperatures of up to 50 °C. This enables year-round free cooling without energy-intensive chillers – and the resulting waste heat can be used for in-house heating or district heating networks. Efficient cooling is therefore not a side issue but a central cost and sustainability lever of any AI infrastructure.

The question of location is part of a larger issue: sovereign AI. This refers to the ability of a country or organization to develop and operate AI with its own infrastructure, its own data, and under its own legal control – rather than depending on non-European cloud providers. The drivers are data protection (GDPR), the EU AI Act, and the strategic interest in processing sensitive data on European soil.

Europe is therefore investing massively in its own computing capacity. As part of EuroHPC, so-called AI factories are emerging – central ecosystems that pool compute power, data infrastructure, and expertise and also open up access to high-performance computing for smaller companies. With JUPITER at the research center Jülich, the first European exascale system went into operation, multiplying AI compute power in Germany. Such facilities are proof that digital sovereignty requires owned HPC infrastructure – and that the hardware level is becoming a strategic question.

Nowhere is the connection between AI and HPC more apparent than in science. Researchers train AI models to forecast weather, simulate climate developments, analyze genomes, design new materials and battery technologies, or, in chemistry, identify the most promising candidates from millions of possible compounds. In all these fields, AI accelerates research – but only because supercomputers deliver the necessary computing power in the background.

MEGWARE builds exactly these research systems. The GPU cluster Helma at the University of Erlangen, equipped with modern NVIDIA accelerators and a multi-petabyte all-flash storage system, ranks 51st on the global TOP500 list. The Capella system at TU Dresden ranks 6th on the Green500 list of the most energy-efficient supercomputers – proof that peak performance and efficiency can go together. In total, MEGWARE is represented with numerous systems on the current TOP500 list. For research institutions that train AI models, this is relevant: the choice of infrastructure helps determine how fast and how sustainably insights are generated. 

This expansion is continuing: as part of BayernKI the largest AI computing infrastructure in the German higher-education landscape is currently being built at NHR@FAU in Erlangen. The Free State of Bavaria is investing €54.5 million in a new AI supercomputer with 1,024 additional NVIDIA B200 GPUs – the technical implementation is again being handled by MEGWARE. From autumn 2026, around 1,400 GPUs will thus be available for the Bavarian AI base model “Blue Swan”, and prospectively up to 1,700.

Planning an AI infrastructure means balancing several variables at once: sizing compute power, network, and storage to match the workload (training, fine-tuning, or inference); energy efficiency, measured for example by the Green500 logic of performance per watt; and the total cost of ownership over the operating life, which includes acquisition costs as well as power, cooling, and maintenance. An oversized facility burns budget; an undersized one slows work down.

As a specialist based in Chemnitz since 1990, developing and manufacturing entirely in Germany, MEGWARE supports such projects holistically – from needs analysis through system architecture to operation and service. The cluster-management software ClustWare® simplifies the management of large systems, from provisioning through job scheduling to monitoring; with XBAT applications can be benchmarked directly in the cluster; and in the Benchmark Center workloads can be tested on real hardware before the investment. Organizations planning their first AI infrastructure project will find an overview. of the possibilities in MEGWARE’s solutions forartificial intelligence and High Performance Computing.

What is the difference between AI training and inference?
Training is the one-time, very compute-intensive process in which a model learns from data. Inference is the subsequent production use of the finished model. Training needs maximum computing power over hours to weeks; inference needs low latency and efficiency in continuous operation.

What does inference mean in AI?
Inference refers to the “reasoning” of an already-trained model: it receives a new input and generates an output from it – for example an answer, a classification, or an image. Over the lifetime of a model, the larger share of energy consumption often falls on inference.

How much compute power does training an AI model require?
That depends on the model size and the volume of data. Small models can be trained on a few GPUs; large language models require GPU clusters with hundreds of accelerators and a high-speed interconnect – in other words, HPC infrastructure. What is decisive is not just raw performance, but the coordinated interplay of compute nodes, network, and storage.

What is on-premise AI – and when is it worthwhile?
On-premise AI means that training and/or inference run in your own data center rather than in the cloud. It is especially worthwhile with sustained utilization, with sensitive or regulated data, and when the total cost of ownership over several years is lower than ongoing cloud rentals.

What is sovereign AI (Sovereign AI)?
Sovereign AI is the ability to develop and operate AI with one’s own infrastructure, one’s own data, and under one’s own legal control. It reduces dependence on non-European providers and makes it easier to comply with GDPR and the EU AI Act.

What does AI have to do with data protection?
During training and inference, personal or confidential data is often processed. Where this data is stored and computed determines compliance with data-protection law. On-premise or sovereign solutions keep the data within one’s own area of responsibility.

What role does HPC play in AI research in Germany?
A central one: from climate models to drug discovery, scientists train their AI models on supercomputers. Systems such as JUPITER (Jülich) or the clusters Helma (FAU Erlangen) and Capella (TU Dresden) built by MEGWARE deliver the compute power required for this.

Which German company builds AI supercomputers?
MEGWARE from Chemnitz is among the leading European supercomputing specialists, develops and manufactures in Germany, and is represented with numerous systems on the current TOP500 list – including several GPU clusters for AI and research workloads.