AI is often discussed in terms of models – larger architectures, better training methods, and improved accuracy. But a key part of its progress lies elsewhere: in the computing infrastructure that made these models usable at scale.
A major shift came when deep neural networks were trained on GPUs, showing that massively parallel hardware could significantly improve performance. This marked a turning point, enabling the transition from theoretical potential to practical applications.
As AI workloads continued to grow, computing systems evolved alongside them. Specialized hardware began to emerge, designed specifically to handle machine learning tasks more efficiently. At the same time, training large models moved beyond single machines. Instead, it now relies on multiple accelerators working together.
Why Infrastructure Matters
Training a modern AI system depends on how well different components can communicate with each other.
When models are distributed across multiple devices, they must constantly exchange data, gradients, and parameters. In this context, networking and interconnect technologies become just as important as raw computation. Without efficient communication, performance quickly becomes limited.
This shift highlights a broader point: AI is not only a model problem, but also an infrastructure problem.
From Systems to Skills
Understanding how these systems work together requires a broader perspective that goes beyond individual tools or techniques. It involves viewing cloud platforms, high-performance computing environments, and networking as a single system.
This is the approach taken within ACHIEVE, where these elements are studied together rather than in isolation.
The Double Degree Master’s program in “Cloud and Networking Infrastructure and HPC”, part of the EIT Digital Master School, is designed around this systems-level view.
Students study in two different European countries, beginning with a first year at HPC Engineering – Politecnico di Milano, where they cover topics such as datacenter design, network infrastructure, and parallel and distributed programming, before moving to a partner university to specialize.
Within this collaboration, Evolutionary Archetypes Consulting SL contributes to strengthening employability-focused learning approaches, while 28DIGITAL, at the program level, supports alignment with broader European digital skills priorities.
Looking Beyond the Models
Understanding AI today means understanding the full system behind it. Models are only one part of the equation. The infrastructure that supports them plays an equally important role in making large-scale AI possible.
Learn more about ACHIEVE: https://28digital.eu/eu-collaborations/achieve
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Disclaimer: Co-Funded by the European Union. Views and opinions expressed are however those of the author(s) only and do not necessarily reflect those of the European Union or HADEA. Neither the European Union nor the granting authority can be held responsible for them.