DISCALIMER: The views and opinions expressed in this blog are solely my own and do not reflect those of my employer, Fraunhofer or any of its affiliates. All content is based on my personal insights, experiences, and research, and should not be construed as representing official positions or policies of Fraunhofer.

TLDR; AI Factories are Europe’s bet to centralize AI resources and boost innovation. Acting as one-stop shops, they offer supercomputing power, datasets, and expertise to startups, SMEs, and researchers. The initiative is funded up to 50% by the EU and split into three tracks: appointing, upgrading, or building AI-optimized supercomputers. Factories integrate with existing EU initiatives like EDIHs, EDICs, and TEFs. Challenges remain in coordination and fostering healthy competition, but success could transform Europe’s AI ecosystem.

Introduction

Here we go again! Another deep dive into the ever-evolving EU AI landscape.

This time, I set out to understand AI Factories, a new initiative that aims to scale Europe’s AI capabilities and innovation. And what better way to figure it out than by writing a blog post?

Why now? Well, the EU recently announced the selection of seven brand new AI Factories across Europe, marking a significant step forward in building state-of-the-art infrastructure to support AI innovation. It seemed like the perfect time to investigate what these factories are, what they’re supposed to do, and, of course, what this all means for Europe’s AI ambitions.

We briefly touched on AI Factories in the first post of this series. Now that we have real-world progress to discuss, let’s focus on what AI Factories actually are, what they’re supposed to achieve, and why this matters for Europe’s AI future.

For the curious reader, my sources include:

• The official Call for Expression of Interest.
• The 2021 regulation (2021/1173) that established the High Performance computing Joint Undertaking (EuroHPC JU).
• The 2024 amendment (2024/1732) that introduced the AI Factories as a priority.
• And, of course, the newly announced list of accepted proposals

So, let’s dive in! 🚀

Current Status: Europe’s Seven AI Factories

If you’ve been following AI developments over the past 4–5 years, you’ll know we’re living in a pivotal moment. AI has brought both hope – with advances in medicine, science, and productivity – and concern, particularly around growing inequality. Economically, though, the impact is undeniable. By 2030, PwC estimates AI will contribute $15.7 trillion to the global economy and boost GDP by 26% in some regions.

Europe, however, has struggled to keep up. Despite its strengths in regulatory leadership and ethical AI, it lags in terms of AI companies and funding. To put it into perspective:

• Europe accounts for only 14.5% of global AI companies compared to 36.5% in the US and 26.8% in China.
• AI spending in the EU stands at €45 billion annually, far behind the €300 billion in the US and €91 billion in China.
• Programs like Horizon Europe have been crucial, but their average **€3 million contributions **haven’t been enough to ignite large-scale AI industry adoption (check out my full analysis here).

To address this, the European Commission announced the first seven AI Factories on December 10, 2024, with €1.5 billion in investments. These Factories are located in:

• Finland: LUMI AI Factory in Kajaani.
• Germany: HammerHAI in Stuttgart.
• Greece: Pharos in Athens.
• Italy: IT4LIA in Bologna.
• Luxembourg: L-AI Factory in Bissen.
• Spain: BSC AI Factory in Barcellona.
• Sweden: MIMER in Linkǒping.

The goal? To create hubs of innovation where startups, SMEs, researchers, and industry can collaborate, access critical infrastructure, and develop AI solutions.

This is Europe’s answer to scaling up its AI ecosystem. Instead of relying on scattered small-scale funding, the AI Factories aim to centralize resources and close the gap with global leaders like the US and China.

What’s an AI Factory?

Let’s start with the easy part: What’s an AI Factory (AIF for short)?

At first, I’ll admit, I had a completely different idea of what an AI Factory would look like. I imagined something more project-oriented – like a company such as OpenAI or Anthropic – spitting out generative AI models that are bigger, shinier, and fancier than anyone else’s¹. Turns out, I was wrong.

The “Factory” Analogy

What I was missing was the “factory” part of AI Factory. Think of it like this (thanks to ChatGPT, which I’m shamelessly stealing this from):

• Workers: Researchers, companies, and developers.
• Machinery: Supercomputers and high-performance infrastructure.
• Raw Materials: Datasets, algorithms, and software tools.
• Production Process: AI training, fine-tuning, and experimentation workflows.
• Final Product: AI applications, solutions, and models ready to be deployed.

Easy, right? Well… ish.

It’s a Physical Place

What really escaped me at the start was the sheer physicality of an AI Factory. This isn’t just some software program in the cloud or a collection of models sitting on a server. It’s a real, tangible place – a building you can walk into, touch the walls, and open doors too.

And with that physicality comes real-world problems – things you never think about when you’re running an AI model on your laptop. For example, a factory needs (long list incoming):

• Land : Either build a brand-new facility (think 2300 square meters of high-tech centers), or retrofit an existing structure. That’s offices, server rooms, restrooms, meeting spaces – the whole package.
• Infrastructure: Infrastructure: Cooling systems (water and air), enough electricity to power your supercomputers, internet connectivity (at least 100 Gbit/s to connect to other HPC systems), roads for employees, and backup diesel generators for when the power grid fails.
• Personnel:
  • Security teams to keep systems safe (and shoot down anyone who tries training on copyrighted data kidding, obviously).
  • Engineers to install and maintain hardware.
  • System admins to keep everything running smoothly.
  • Cleaning staff (because, you know, people work there).
• Technical Equipment:
  • Supercomputers and GPUs (the sweet, sweet GPUs).
  • Storage systems for enormous datasets.
  • High-resolution monitors, servers, and all the peripherals you need.
  • And as Italians say, “chi più ne ha più ne metta” – “the more, the better.”

And that’s just the physical setup. We haven’t even started talking about what an AI Factory actually does!

What is it supposed to do?

Good question! To answer that, let’s take a step back and look around. 👀

What do we see? Oh, look, it’s an LLM. It can process text and images? It speaks and understands speech?? OMG, it’s basically agentic now!

AI’s capabilities have exploded in the past few years, and that growth has sparked a need for something bigger – something centralized, powerful, and accessible.

Enter the AI Factory.

The Purpose of an AI Factory

According to the call for proposals, an AI Factory “shall create a one-stop shop for the users, including startups, small and medium-sized enterprises, and scientific users, to facilitate access to its support services.”

Let’s break that down.

This isn’t just about putting a big shiny supercomputer somewhere. An AI Factory is where AI innovation meets its users and turns ideas into solutions. It connects resources, tools, and expertise under one roof, enabling users to:

• Access foundational models and specialized datasets.
• Train and fine-tune AI systems on high-performance computing (HPC) infrastructure.
• Collaborate with AI and HPC experts to solve technical challenges.

What makes these factories unique is their emphasis on integration. They aren’t isolated projects; they are part of a larger ecosystem. To succeed, AI Factories need to plug into existing EU initiatives like the European Digital Innovation Hubs (EDIHs), European Digital Infrastructure Consortiums (EDICs), the Common European Data Spaces (CEDS) and the Testing and Experimentation Facilities (TEFs)². These connections ensure that AI Factories don’t just exist – they thrive, align with national strategies, and drive innovation across the continent.

Now, I know this still sounds abstract. So let’s bring it to life with a story.

Lil’Bits: A Story of AI Innovation

You are the proud CEO of Lil’Bits, a cozy local restaurant that serves extremely tiny portions (you call it “artisanal minimalism,” others call it “eating air with a spoon.”). One day, you hear about an EU Innovation Conference happening nearby³ and decide to check it out. Why not? Maybe AI can help you make those portions even smaller.

The conference is buzzing. Talks about AI’s potential are everywhere: efficiency this, accuracy that. By the end of the day, your brain feels like mush, but you’re intrigued. Over coffee, you strike up a conversation with someone from an EDIH – a regional hub for AI adoption – who’s thrilled to hear about Lil’Bits. He claims AI can revolutionize your business. “We can make your portions so small they’re invisible!” he says, only half-joking.

He then introduces you to a colleague from the local AI Factory, who assures you they have everything you need: HPC resources, AI expertise, and access to consortium partnerships. You nod politely, still skeptical, until the magic words hit: “AI-reduced culinary portions”. Your eyes light up.

A Trip to the AI Factory

Fast forward to a sunny spring morning. You find yourself at the gates of the AI Factory campus. It’s bustling with activity – developers in hoodies, researchers in lab coats, and businesspeople who look like they just stepped off a plane. You think to yourself: this is where the future happens.

Your EDIH contact greets you and leads you through the facility. It’s a maze of rooms, each with its own purpose:

• Server rooms hum with the sound of supercomputers crunching data.
• Co-working spaces bring together developers, industry experts, and academics in lively discussions⁴.
• Student areas feel like modern dorms, with young talent working on exciting projects⁵.

Finally, you reach a sleek conference room, where a team of experts is waiting. You pitch your vision: “I want to make food portions even smaller – smaller than anyone thought possible.”

They love it. A woman from business model development explains how you could turn Lil’Bits into a global chain⁶. Another expert from the Agri-food EDIC⁷ nods enthusiastically: “Micro-portions are revolutionary!” She mentions the ShrinkiFood Innovation Hub and how they specialize in AI solutions for food minimization.

You pause. “But do you even have the data to train a model for shrinking food?”

The expert grins. “Funny you ask. The AI Factory has access to a Common European Data Space⁸ for food innovation, where researchers and companies contribute high-quality datasets⁹. Your dream of micro-sized dishes? The data you need is right here, sitting in our server room.”

The Inevitable Question

It all sounds too good to be true. You’re ecstatic, but doubts creep in. “What about regulations? I’ve heard about the AI Act, and I’m pretty sure invisible food might break some rules…”

The room goes quiet. Finally, a representative from the TEF for Agrifood Innovation speaks up. He assures you they have facilities to test and validate AI systems under simulated, real-world conditions¹⁰. “We’ll make sure your AI model meets all ethical and trustworthiness standards,” he adds confidently¹¹.

The Future is Small

You leave the meeting overwhelmed but inspired. With the help of the AI Factory, you can finally make your grandfather’s dream come true: serving food portions so small they’re practically theoretical. You shake hands with the team and walk out with a smile, imagining millions of satisfied customers worldwide eating a meal they swear they saw, if only for a moment¹².

Overview and Takeaways

Hopefully, the Lil’Bits story was entertaining (and absurd) enough to keep you reading, but more importantly, it illustrated a key point: an AI Factory thrives on connections.

The purpose is to create a space where startups, researchers, SMEs, public institutions, and industry players can collaborate and innovate. These factories are designed to integrate into existing EU initiatives – like EDIHs, EDICs, CEDS, and TEFs – to ensure no one has to tackle AI challenges alone.

While the story focused on SMEs (because who doesn’t want AI-optimized culinary portions?), the AI Factory ecosystem serves a much broader range of users:

• Researchers and Academics can leverage HPC resources, datasets, and training to drive AI research.
• Public Sector Organizations benefit from AI adoption expertise offered through EDIHs.
• Startups and SMEs gain access to business accelerators (like the EIC), data, and infrastructure to scale their AI solutions.
• Large Industry Players can collaborate with experts and access advanced resources for AI development and training.

The key takeaway? AI Factories bring these groups together under one roof, providing the tools, knowledge, and physical space needed to turn ideas into real-world solutions.

And let’s not forget: skills and education are at the heart of this ecosystem. AI Factories will play a critical role in training the next generation of AI developers and researchers, ensuring that Europe remains competitive in the global AI race.

Types of Factories Tracks

Now that we’ve covered what an AI Factory does and why it matters, let’s look at how they’re actually implemented. Because, as impressive as these hubs of innovation sound, not all AI Factories are built from scratch. The EU proposal splits AI Factories into three tracks, depending on how “ready-to-go” your infrastructure already is.

You can think of it like renovating a house:

• Track 1: Use what you’ve got – a quick clean-up and some new furniture.
• Track 2: Give the place a serious upgrade – new floors, walls, and appliances.
• Track 3: Build from the ground up – lay the foundation, choose a plot of land, and go full architect mode.

Here’s a quick breakdown:

Track	Description	EU Contribution
(1) AI Factories Track	Use existing EuroHPC systems.	Partial funding for costs.
(2) Upgraded Supercomputer Track	Upgrade existing EuroHPC infrastructure.	Covers upgrade costs.
(3) New AI Supercomputer Track	Build new AI-optimized infrastructure.	Highest funding allocation.

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“AI Factories” Track

This track is the fastest and least construction-heavy option. If you already have a EuroHPC supercomputer humming away in your facility, you can apply to have it appointed as an AI Factory.

The requirements are relatively straightforward: you need to prove that your system has enough computing resources to handle the train general-purpose AI models.

The call doesn’t spell out strict benchmarks, but we can make some educated guesses:

• Computing Power: Your system will likely be judged on existing benchmarks like HPL-MxP or MLPerf Training, which measure HPC performance.
• Time-to-Solution: Another measure is the time it takes to train a specific large language model, for example, training a 10-billion parameter model within 45 days.

If you meet these requirements, congratulations – you’ve got yourself an AI Factory. Just make sure you cover all the activities we discussed earlier (data, models, expertise) to keep the “factory” running smoothly.

Upgraded AI Optimised Supercomputer Track

If you already have a EuroHPC supercomputer, but it’s not quite AI Factory-ready, this is your track. Here, you don’t just appoint a system – you** upgrade it** with enhanced AI capabilities.

This track adds a little more work to the mix. In addition to meeting all the usual AI Factory requirements, you need to:

1. Specify the Upgrades: Show exactly what improvements are needed – for example, new GPUs, additional storage, or faster networking.
2. Prove Eligibility: Outline how these upgrades will make the system capable of hosting AI Factory activities.

AI Optimised Supercomputer Track

Finally we get to the big one. This track involves acquiring a brand-new AI-optimized supercomputer and setting up the infrastructure to host the factory. It’s also the most complex, for obvious reasons:

• You need to find the perfect location – balancing factors like cooling efficiency, energy availability, and connectivity¹³.
• If you’re working with a consortium (which you probably are), get ready for a tug-of-war between partners, since everyone will want the factory in their country. It brings prestige, jobs, and economic benefits – but also hefty responsibilities.

Budgets: What Does the EU Cover?

Now that we’ve covered the types of AI Factory tracks and their activities, let’s take a closer look at how the EU plans to fund them.

First things first, the AI Factory initiative draws on significant funding from the multiannual financial framework (2021-2027), which prioritizes the development and deployment of digital technologies, including AI.

Here’s how funding is structured:

• Horizon Europe: Focuses on R&D&I projects and scaling up AI startups.
• Digital Europe Programme: Expected to be the primary framework supporting AI Factories by funding technology deployment and training.
• EuroHPC: As part of the AI Innovation Package, the amendment to the EuroHPC regulation has made AI Factories a new priority in terms of investments.

Funding will also come from member states and private companies to complement EU contributions.

In summary, the EU covers up to 50% of acquisition, operation, and upgrade costs for AI Factories, while member states and private partners fund the remainder.

This also translates in different allocation for the tracks:

Track	EU Contribution	Hosting Entity Responsibility
Existing EuroHPC Supercomputing Systems as AI Factories Track	Up to 50% of operational costs	Remainder of operational costs
Upgraded AI Optimised Supercomputer Track	Up to 50% of acquisition costs for upgrades (depreciated lifetime), Up to 35% of additional operating costs	Remainder of acquisition and operating costs
New AI-Optimized Supercomputer Track	Up to 50% of acquisition costs for new supercomputers, Up to 50% of operating costs	Remainder of acquisition and operating costs

National Strategy and Networking

To maximize their impact, AI Factories must align closely with the national AI strategies of the hosting country or consortium of countries. Applicants are expected to outline:

• National AI Strategy Linkage: Describe how the AI Factory integrates into and advances the objectives outlined in the hosting country’s national AI strategy.
• National Data Policies: Provide an overview of the current national data policies, including access to large datasets and knowledge corpora, with a focus on open, FAIR (Findable, Accessible, Interoperable, and Reusable) data¹⁴.
• National Access Policy for AI Community: Applicants should develop user-friendly access policies for allocating national computing time on EuroHPC supercomputers.

The task is no small feat. It requires knowing the **national AI landscape **inside out and finding ways to integrate the factory’s goals with the broader ecosystem. Done right, the AI Factory becomes the nucleus for AI innovation – building momentum, fostering collaboration, and benefiting everything around it.

AI Factory coordination

So, let’s say you’ve set up your AI Factory. You’ve got the hardware, the datasets, and a steady stream of users. But there’s still one big question: how do these factories coordinate with each other?

The call for proposals and its amendment outline several strategies for collaboration:

• Knowledge Sharing: Factories are expected to share best practices, lessons learned, and technical expertise, ensuring everyone benefits from what works (and what doesn’t).
• Specialization: Each AI Factory should carve out a specific domain or expertise – this avoids duplicating efforts and maximizes efficiency.
• Asset Reutilization: Sharing tools, datasets, or even specific resources across factories to make the most of existing investments.
• Support and Training: Factories will collaborate on programs to train AI professionals, organize workshops, and upskill users.
• Staff Exchange: Facilitating staff exchanges to promote knowledge transfer, collaboration, and cross-pollination of ideas.
• Homogeneous End-to-End User Experience: For projects spanning multiple factories, the experience should be seamless and consistent for users, no matter where they access resources.

These are all great strategies on paper. They encourage cooperation, reduce redundancy, and create a networked AI ecosystem where everyone shares the benefits. But here’s where I get a little skeptical.

The EuroHPC Joint Undertaking (JU) is tasked with overseeing these coordination efforts – facilitating networks, developing standards, and ensuring alignment between factories. Don’t get me wrong, this is a well-thought-out approach. But the EuroHPC JU already has a ton of responsibilities on its plate, from managing supercomputing projects to supporting HPC innovation across Europe. Adding “coordinate the AI Factories” on top of that feels like it might stretch their capacity thin.

This is just my take, of course, but I can’t help wondering: is this enough? Later on, I’ll suggest a few alternatives.

Conclusion

Here we are, at the end of another blog post. This one has been especially close to my heart because it touches on something fundamental: centralizing AI resources while fostering innovation across Europe. The AI Factories initiative is ambitious, and if done right, it could be a game-changer for Europe’s AI ecosystem.

For those who skipped straight to the bottom (no hard feelings), here’s a quick summary. Here’s the final, polished version of the Conclusion, complete with a filled-out summary and improved flow. I’ve kept your style intact, added clarity, and ensured the section ties everything together neatly.

Summary

The AI Factories are Europe’s bold bet on scaling up AI infrastructure and innovation:

• They act as one-stop shops for startups, SMEs, researchers, and public-sector users to access supercomputing resources, datasets, and expertise.
• They are designed to be interconnected with existing EU initiatives, such as European Digital Innovation Hubs (EDIHs), European Digital Infrastructure Consortiums (EDICs), Common European Data Spaces (CEDS), and Testing and Experimentation Facilities (TEFs), ensuring alignment and collaboration across the continent.
• Factories are implemented through three tracks: appointing existing EuroHPC systems, upgrading current infrastructure, or building new AI-optimized supercomputers.
• Funding comes from Horizon Europe, the Digital Europe Programme, and the EuroHPC Joint Undertaking, with up to 50% of costs covered by the EU.
• AI Factories must align with national AI strategies and integrate into broader EU initiatives like EDIHs, EDICs, and TEFs to maximize their impact.
• Coordination between factories remains a challenge, as the EuroHPC JU is tasked with managing this, despite its already heavy workload.

While the initiative has tremendous potential, a few questions remain:

• Who ensures effective coordination?
• Can competition between factories drive even more innovation?
• How can secure and trustworthy data/model access be implemented?

Open Questions and Suggestions

Coordination Between Factories

One of the main concerns I have is the coordination challenge. The EuroHPC JU is currently tasked with supervising and connecting AI Factories, but this feels like adding too much weight to an already full plate.

So, what could be done differently?

1. Factory-Level Coordination Groups:
   Each AI Factory could be required to establish a dedicated coordination group responsible for networking with other factories. Their progress could be evaluated through specific KPIs (Key Performance Indicators), ensuring that collaboration and knowledge sharing don’t fall through the cracks.

   While this decentralized approach gives factories more autonomy, it’s not explicit in the current setup. Making it a formal requirement could make a big difference.

2. A Centralized Coordination Entity (CERN4AI):
   Alternatively, I remain a firm believer in creating a superseding entity – call it CERN for AI, if you like. The AI Office is currently the closest thing to this idea, but it’s stretched thin with responsibilities like developing standards and aligning with the AI Act.

   A dedicated entity focused solely on coordinating AI Factories could provide the oversight and direction needed to ensure these hubs work together effectively. This could be the missing piece to make Europe’s AI efforts truly cohesive.

What About Competition?**

It’s well-documented that competition fuels innovation¹⁵. But looking at the current setup, we don’t see much competition between AI Factories.

Here’s an idea: introduce healthy competition through challenges.

• Factories (or groups of factories) could compete on specific AI-related projects with clear, measurable KPIs.
• To ensure fairness, introduce one or two secret benchmarks to evaluate outcomes.
• Periodic evaluations could filter out underperforming projects while rewarding the best ones.

The rewards? Successful factories could receive additional funding, favorable conditions from EuroHPC JU, or even priority access to resources. This would drive innovation, foster collaboration, and create incentives to push boundaries.

Competition doesn’t have to mean isolation. In fact, it could encourage factories to form alliances, share expertise, and strive for excellence. After all, who doesn’t love a bit of friendly rivalry?

Ensuring Connectivity and Data Access

The proposal and amendment require that AI Factories be connected via two networks:

• EuroHPC Hyper-Connectivity Network: This ensures high-speed data transfer and efficient communication between AI Factories across Europe.
• GEANT Network: As part of the pan-European research and education network, this connection allows AI Factories to integrate closely with academic and research institutions.

While these connectivity measures are clear, the documents remain vague on how data access should be implemented. Factories are expected to retrieve data from Common European Data Spaces or secure, trusted repositories. On one hand, this flexibility avoids imposing a one-size-fits-all solution. On the other, it raises questions about standardization and interoperability.

This is where frameworks like Gaia-X could play a significant role. Gaia-X promotes federated access to shared resources, prioritizing privacy, security, and trustworthiness – all fundamental requirements for AI Factories.

I believe implementing a standardized yet flexible approach inspired by Gaia-X would greatly benefit AI Factories.

While it’s still too early to say if this will happen, I’m optimistic. We’ve seen the rise of federated access systems across the EU, and AI Factories are the perfect opportunity to adopt such standards. By doing so, Europe can ensure its AI infrastructure is not only powerful but also secure, interoperable, and future-proof.

Final Thoughts

The AI Factories initiative is an ambitious move to centralize Europe’s AI efforts, address resource fragmentation, and provide real opportunities for innovation. It’s a bold bet – and while the framework is promising, there are still challenges to overcome.

From improving coordination to exploring competition as a driver of innovation, there’s room for improvement. But if Europe gets this right, AI Factories could become the cornerstone of its AI strategy, transforming ideas into solutions that benefit everyone.

Let’s keep an eye on how this unfolds – I, for one, will be watching closely.

Footnotes