Policy Brief
Built for Purpose?
Demand-Led Scenarios for Europe's AI Gigafactories
Authors
Dr. Felix Sieker
Programmes
Published by
interface and Bertelsmann Stiftung
October 22, 2025
in Cooperation with
Executive Summary
The AI Continent Action Plan, published in April 2025, outlines the EU’s ambition to become a ‘leading AI continent,’ primarily through a major expansion of compute infrastructure. Central to this effort is the creation of a network of 19 AI Factories across Europe, each equipped with up to 25,000 H100 GPU equivalents. These facilities are designed to provide small and medium-sized enterprises (SMEs), researchers, and startups with access to the computing power needed to develop and test AI systems. In addition, the EU plans to establish five GPU clusters that are four times larger than the AI factories. These large-scale facilities, known as AI Gigafactories (AIGFs), will each host at least 100,000 H100 GPU equivalents and are intended for training and deploying frontier AI models. To support this initiative, a dedicated €20 billion fund under InvestAI will cover approximately one-third of the capital expenditures for each site.
In this policy brief, we analyse the feasibility of the AIGF initiative by taking into account the private sector dynamics that can be observed around AI compute buildouts. We argue that by placing so much weight on expanding compute infrastructure, the European Commission’s proposals seem to assume that inadequate compute is the primary reason Europe has yet to realise its AI potential. This supply-side focus overlooks the factor that will ultimately determine whether billions of euros deliver meaningful outcomes: demand.
Drawing on our mapping of existing datacentre buildouts and the landscape of compute providers and users, we suggest two plausible operating models for the AIGFs that factor in demand:
(1) Anchor customer model: Secure one or a few anchor customers with very high compute demand, as seen in the United States and China.
(2) Multi-client model: Serve a broader set of clients with low to moderate AI workloads.
In our analysis of the global user–provider landscape shaping the AI compute ecosystem, we find that leading AI labs – such as OpenAI, xAI and Google – constitute the only user group capable of generating the high AI workloads that the AIGFs aim to attract. At the moment, Europe currently hosts only one such lab, Mistral, which makes the conventional ‘anchor customer’ model – where a single leading AI lab ensures utilisation – highly improbable.
Instead, a multi-client model that aggregates demand from a diverse set of users, including companies, startups, and academia, is the better suited option for Europe. Individually, these users generate low-to-moderate AI workloads for commercial applications that existing AI factories cannot adequately supply. In this scenario, AIGFs would need to offer more than raw compute to remain competitive with private providers, such as neoclouds. By providing value-added services – including structured onboarding, curated software stacks, and ongoing support – AIGFs could foster dynamic AI ecosystems for SMEs, startups, and companies.
In conclusion, policymakers should focus on three priorities when reviewing upcoming AIGF proposals and their stated goals:
-
Demand quantification: Include projected AI workloads and user commitments.
-
Realistic objectives: Align buildouts with EU AI industry dynamics and strategy.
-
Clear value proposition: Differentiate AIGFs from hyperscalers and neoclouds through services and ecosystem offerings.
These takeaways offer guidance for policymakers in Europe and its Member States to come up with feasible and effective solutions to strengthen Europe’s AI ecosystem in the long-term.
Glossary
Introduction
2025 marked a clear shift in the European Commission’s approach to artificial intelligence (AI) – away from a primary focus on AI regulation and towards competitiveness and industrial capacity. As President Ursula von der Leyen said at the AI Action Summit in France, the aim is to secure Europe’s ‘specific place in the global race for AI’. 1 Her speech struck a newly assertive tone, underscoring a newfound self-confidence: ‘Too often, I hear that Europe is late to the race – while the US and China have already gotten ahead. I disagree because the AI race is far from over. Truth is, we are only at the beginning. The frontier is constantly moving, and global leadership is still up for grabs’. 2
Two months later, on 9 April 2025, the European Commission published the AI Continent Action Plan, setting out how the EU aims to become a ‘global leader in Artificial Intelligence, a leading AI continent’. 3 At its core is a major expansion of computing infrastructure, pursued through two tracks:
First, a network of 19 AI factories 4 (each comprising up to 25,000 H100 GPU equivalents) across Europe will provide small and medium enterprises (SMEs), researchers and startups with access to AI compute to develop and test AI systems. 5
Second, the European Commission proposed five AIGFs designed for training and deploying frontier AI models. 6 Each AIGF would host at least 100,000 H100 GPU equivalents 7 – roughly four times the capacity of an AI factory. A dedicated €20 billion fund under InvestAI – a €200 billion initiative announced by von der Leyen at the AI Action Summit – aims to cover about one-third of each site’s capital expenditures.
On paper, all these initiatives appear to be a coherent response to Europe’s past struggles in the global AI race. However, the proposed solution mainly focuses on the underlying assumption that the lack of compute capacity – or insufficient supply – is the main reason why Europe has not been able to fully realise its AI potential. This perspective misses the other side – demand, or more precisely, the interconnected network of users and providers that will determine whether the AIGFs actually are utilised and whether their establishment ultimately proves to be a meaningful investment with regard to the envisioned objective. In this policy brief, we focus only on AIGFs and argue that AIGFs’ goal – to train and deploy frontier AI models – can be achieved only if sufficient demand is factored in from the beginning.
The politically coined AIGFs are basically what the industry calls ‘GPU clusters’. 8 Their business model generally is rooted in two distinct strategies: (1) securing an anchor customer, often a frontier AI lab, to ensure stable utilisation, and (2) offering on-demand capacity to a diverse set of clients with a variety of compute needs.
The current definition of and vision for AIGFs are aligned closely with the first model – focussing on providing large compute capacities for users with demand for high AI workloads to train and deploy frontier AI models. While this strategy works in the United States (US) – where most frontier AI labs are based and, as we demonstrate in the empirical part of this policy brief, drive AI data centre buildouts – Europe’s demand is far less certain because there are too few frontier labs to meet demand. Thus, policymakers must be realistic about the feasibility of the envisioned goal – training and deploying frontier AI models. Given that Europe currently has only one leading AI lab, Mistral, the likelihood of pursuing Scenario One, i.e., building AIGFs to train and deploy frontier AI models by securing an anchor customer with exceptionally high demand, appears limited. We argue that Scenario Two – aggregating demand by engaging a diverse set of stakeholders – is far more feasible in the European context, taking into account the EU AI ecosystem’s characteristics and the debate around expanding AI adoption across industry segments. Pursuing this path would require adjusting AIGFs’ objectives to (1) articulate a clear value proposition that differentiates them from other compute providers, particularly neoclouds, and (2) evidence sufficient aggregated demand to avoid idle capacity.
Training and Deploying Frontier AI Models
The European Commission’s AI Continent Action Plan places particular emphasis on frontier AI models, stating that the proposed AIGFs are supposed to train and deploy frontier AI models with billions of parameters. 9 10 This focus reflects the commission’s view that Europe currently is lagging behind in frontier AI development. To put these goals and observations into context, the following section briefly maps AI labs that have brought such models to market and describes Europe’s position within this landscape.
Mapping Frontier AI Models
Only a small number of AI labs are developing today’s frontier models, and measuring their performance is difficult. One practical approximation is to examine training compute. This does not imply causality, but in practice, larger models trained with more compute often achieve better performance. However, measuring AI models’ performance this way is difficult due to a lack of publicly available data on training compute. One of the best estimations comes from Epoch AI, which estimates training compute for leading models and defines ‘large models’ here as those trained with more than 10²⁵ floating-point operations (FLOP). 11 FLOPs are the standard measure for compute, quantifying the number of calculations a processor can perform, usually at a per-second rate. It is a key performance indicator for AI processors. The first model trained at this scale was GPT-4, released by OpenAI in March 2023. By October 2025, 36 AI models had crossed the 10²⁵-FLOP threshold (see Chart 1).
Chart 1: Global Landscape of Frontier AI Models by Developer and Region
Source: Epoch AI and own research
As Chart 1 indicates, leadership in frontier AI is highly concentrated: 25 of 36 models come from just five labs, with OpenAI alone responsible for nine. The United States (US; highlighted in light purple) dominates this landscape, as US-based labs trained 28 of the models. This is a reflection of deep capital pools, great access to AI talent and strong academia–industry linkages. The talent concentration is particularly strong: According to the Global AI Talent Tracker in 2022, 12 57% of the world’s top AI researchers worked in the US, compared with 16% in the United Kingdom (UK), France and Germany combined.
At first glance, China (highlighted in yellow), appears to be underrepresented, with only six models (GLM-4, GLM-4-Plus, GLM-4.6, Doubao-pro and Pangu Ultra), but this should not be read as a lack of technical capacity to develop models. Two factors explain the gap: First, many Chinese labs do not disclose compute details. Second – and more importantly – China faces severe compute constraints due to export controls that limit access to the most advanced chips. Despite these compute constraints, China has managed to produce very capable models by contributing significantly to development of algorithmic innovations for more efficient training. This is made possible by the strong talent base China can draw on: In 2022, 13 47% of all top-tier AI researchers measured in the Global AI Talent Tracker originated from China, while 28% still worked in the country.
Europe (highlighted in dark blue) appears only via France’s Mistral, which trained two models of that size. This underrepresentation has three main causes. First, Europe lacks sufficient AI talent and continues to lose both homegrown and internationally trained graduates, particularly to the US. 14 Second, data access remains a constraint: Legal frameworks in the US and China allow for large-scale training on web-crawled data at speeds and scales Europe has yet to match. 15 16 Third – and in the view of the European Commission, the decisive factor – is compute. The background is that compute needs for training are rising sharply: Since 2010, the FLOPs required for state-of-the-art models have grown by 4.4x 17 per year, while training expenditures have increased by 2.4x annually, 18 driven largely by US frontier labs. Against this backdrop, the commission conceived the AIGF initiative.
Phases of Generative AI Training and Deployment
The European Commission’s stated aim with the AIGFs is to train and deploy frontier AI models. In this context, knowing that compute demand varies widely – depending on the model pipeline’s stage – is important. Development and use of generative AI can be divided roughly into three phases: pre-training, post-training and inference. 19 20 Not all stages are equally compute-intensive.
-
Pre-training: During this phase, models learn general patterns from very large datasets using self-supervised learning. This happens usually only once per base model and is a very large investment because the model processes vast datasets over many training steps: Training can run for weeks on thousands of specialised chips (see ‘Case Study ChatGPT Training Clusters’ info box)
-
Post-training: The pre-trained model then is refined to improve performance for specific goals. Two main approaches are used that can range from moderate to heavy compute requirements: 21
-
Fine-tuning: training the model on domain-specific data (e.g., medical literature) to improve accuracy in that field. This is usually only moderately compute-intensive (smaller, targeted datasets normally are used).
-
Reinforcement learning (RL): adjusting the model’s behaviour based on human or automated feedback, so that it better aligns with desired objectives and values. This can be very compute-heavy due to larger and more complex datasets being processed. 22
-
-
Inference: Finally, the model is deployed in user-facing applications, such as chatbots or search engines. At this stage, additional computational resources – known as inference compute – are required each time the model generates an output. Inference costs are recurring and can become the dominant expense at scale. They grow mainly with the number of tokens processed per request: Longer prompts and outputs require more computations. This effect is particularly strong for ‘reasoning models’ that produce step-by-step explanations (often called chain-of-thought), which generate many intermediate tokens. 23
What does this mean for AIGFs? To train and deploy frontier models, the most compute-heavy parts are (1) pre-training, (2) post-training’s reinforcement learning phases and (3) deployment (inference) of reasoning models. For pre-training, AIGFs must provide very large, well-connected GPU clusters – usually on one site. In contrast, reinforcement learning within post-training and inference of reasoning models likely can be equally compute-intensive, but can be distributed across several sites. However, this is only a snapshot of today’s practice: The field is moving at an extraordinary pace, and the compute demand across these stages may look very different in the coming years.
User and Provider Landscape
In the AI Continent Action Plan, the discussion of frontier AI model development and AIGF deployment clearly establishes that the European Commission is beginning to assume an active role within a distinct network of users and providers shaping the global AI ecosystem centred around GPU clusters. The following section introduces these actors and their strategies, providing essential context for understanding how the AI industry typically designs, builds and operates AI compute infrastructure.
Chart 2: AI Compute Infrastructure Ecosystem
Over the past six years, the industry share of global AI compute has risen sharply, from 40% in 2019 to 80% in 2025. 24 According to Hawkins et al. (2025), 95% of this commercially available AI compute infrastructure is operated 25 by companies headquartered in the US or China. This shift from public to private sector ownership reflects changes in workloads that supercomputers are expected to handle. While government supercomputers are designed for a broad range of scientific tasks and to support foundational research, the demand today can be traced back to large-scale AI workloads.
Users
Users of AI compute infrastructure can be separated into two groups: first, a small number of AI labs that have developed large models, such as Open AI, xAI, Meta, Google, Anthropic, Mistral or DeepSeek. 26 They comprise a mix of newly established labs often backed by hyperscalers (e.g., Anthropic-Amazon, OpenAI-Microsoft) and labs operated by hyperscalers themselves (e.g., Google, Meta). They lead advanced AI model development and pursue multiple strategies to meet their high demand for AI compute for both training and inference.
The second user group entails all other clients with low-to-moderate demand and diverse AI workloads – such as companies, startups, SMEs and academia – in current discourse, often termed ‘industrial AI’. This group is broad and heterogeneous, spanning, for example, healthcare startups training diagnostic tools on sensitive patient data, SMEs in automotive developing predictive maintenance systems, financial firms experimenting with fraud detection or creative industries adopting generative AI for content production. 27 28 However, their demand for compute is difficult to assess – varying widely by sector and evolving rapidly with technological progress – and AI’s disruptive potential has not been confirmed yet for many industries. This group represents the innovation ecosystem’s broad base, in which applied AI solutions are developed, commercialised and embedded across industries. These actors require affordable, flexible and variable compute resources, currently through cloud-based services, AI factories and private investments in smaller-scale compute infrastructures.
Providers
Providers of AI compute infrastructure also can be separated into two groups: First, hyperscalers typically offer (general) cloud capacity via multi-service platforms for a variety of businesses and individual customers across multiple continents. They either build AI clusters themselves or lease them from third parties that design, own and operate the facilities. These arrangements allow hyperscalers to deploy their own IT equipment, and it is common to pre-lease space in unfinished facilities to confirm demand. 29 Their infrastructure does not focus merely on AI workloads, even though new buildouts increasingly are focusing on customised AI infrastructure. Some hyperscalers, such as Google and Meta, occupy a dual role in the ecosystem: as operators of AI infrastructure and as users through their own AI labs. This dual role gives them a clear advantage and flexibility towards other AI labs, enabling them to use GPU clusters for their own purposes or rent capacity to external clients.
However, neoclouds are a new class of cloud providers specialising in providing GPU-heavy compute capacity for AI. Many evolved from crypto mining operators and repurposed these facilities – already provisioned for high power and cooling – into GPU clusters. Unlike hyperscalers that typically pursue multi-year, region-wide buildouts, neoclouds focus on a small set of AI labs and startups with very large near-term needs using flexible contracts and rapid deployments. As evidenced by the compute mapping table in annex 1 and their previous activities in crypto mining, neoclouds tend to scale capacity more quickly and ambitiously, often paired with comparatively low prices. 30
Mapping Existing and Announced GPU Clusters
The next section will focus on mapping existing and announced GPU clusters – which display a similar infrastructure setup as described by the political term ‘AIGF’ – in the US and Europe. This analysis provides the foundation for identifying the different strategies that users may pursue to meet their demand for AI compute according to their specific needs. To set the stage, the following section introduces the key terms and metrics commonly used to describe AI compute.
There is no single agreed-upon definition of large-scale AI compute infrastructure: terms such as ‘AI data centre’, ‘AI supercomputer’, 31 ‘AI cluster’, ‘mega cluster’ and ‘GPU cluster’ are used interchangeably. The latter makes a direct connection to what lies at the heart of such a cluster: graphics processing units (GPUs). While traditional high-performance computing (HPC) mainly operates on central processing units (CPUs), which are designed for versatility and handle many different tasks sequentially, GPUs are built for parallel processing, allowing them to perform thousands of calculations at once. This makes GPUs particularly effective for handling the enormous computational demand for training and deploying AI models. 32
Measuring the Size of AI/GPU Clusters
An AI/GPU cluster’s capacity typically is characterised using three metrics
-
floating point operations per second (FLOPS)
-
chip count (often in H100 equivalents)
-
power capacity (in megawatts (MW) and gigawatts (GW))
FLOPs are the standard measure for compute quantifying the number of calculations a processor can perform, usually at a per-second rate. For example, Nvidia’s H100 can deliver up to 4 quadrillion FLOPs (4,000 TeraFLOPs). 40 However, in practice, comparing raw FLOPs across GPU generations is difficult, as many additional technical details must be factored in. 41 Furthermore, it quickly aggregates to ‘trillions’ or ‘quadrillions’ of operations per second – a very abstract metric. Another metric for comparison and to normalise performance is translating GPU generations in H100 equivalents, factoring in their respective performance in FLOPs. 42 43
Many AI supercomputer announcements also refer to power capacity (in MW and GW) as a reference point. Power capacity represents the maximum electrical load a site can support to run – and cool – chips at scale. It is introduced increasingly as the standard metric by setting the upper bound on the size of AI compute infrastructure that a facility can host, independent of the GPU type and generation used. On top of this, it allows for clear differentiation between what is available and when during a phased rollout, which is connected directly to questions about grid connection or permitting. Ideally, all three metrics are publicly available to paint the full picture.
Strategies to Meet Growing AI Compute Demand
The large table that maps AI compute in the US and EU in the annex presents a consolidated overview of all initiatives identified through in-depth desk research, complemented by the detailed dataset provided by EpochAI. 50 Mapping AI compute capacity above 30,000 GPUs 51 that already exists or is planned in the US and Europe highlights the massive scale of current expansion efforts. However, caution is warranted, as many of these initiatives remain at the announcement stage. The table presents publicly available information on each initiative’s location, name, starting year, providers, users and capacity measured in H100 equivalents and power capacity. Public data are particularly scarce for the last two columns, as reflected by the numerous question marks.
The following section breaks down the large table that can be found in Annex 1 into subsets of initiatives grouped according to three user strategies for meeting AI compute needs: (1) AI labs building out their own infrastructure, (2) AI labs acting as anchor customers and (3) all user groups (AI labs, companies, etc.) renting cloud infrastructure.
Strategy 1: Own Infrastructure Built by AI Labs
Table 1 depicts all publicly available information on GPU clusters that display AI labs building their own infrastructure.
Table 1: AI labs Building Their Own GPU Clusters
A distinction is made between GPU clusters that are already operational or under construction (shown in purple) and those that have only been announced (not shown).
|
Country |
Location |
Name |
Available Since/ |
Provider |
User |
H100 equivalents |
Power Capacity |
Links |
|---|---|---|---|---|---|---|---|---|
|
US |
Omaha, Nebraska |
Papillion Campus |
2021 |
|
|
? |
? |
|
|
US |
Omaha, Nebraska |
Google Omaha AI Data Center |
2024 |
|
|
? |
150MW |
|
|
US |
Austin, Texas |
Tesla Cortex Phase 1 |
2024 |
Tesla |
Tesla |
50,000 |
130MW |
|
|
US |
? |
Meta AI Research Supercluster (RSC) |
2024 |
Meta |
Meta |
2x 24,000 |
? |
|
|
US |
Memphis, Tennessee |
Colossus Phase 1 (xAI) |
2024 |
xAI |
xAI |
100,000 |
150MW |
|
|
US |
Lancaster, Ohio |
Lancaster Data Center Campus |
2024 |
|
|
? |
? |
|
|
US |
Council Bluffs, Iowa |
Google Council Bluffs |
2025 |
|
|
? |
? |
|
|
US |
Austin, Texas |
Tesla Cortex Phase 2 |
2025 |
Tesla |
Tesla |
? |
? |
|
|
US |
Memphis, Tennesee |
Colossus Phase 2 (xAI) |
2025 |
xAI |
xAI |
230,000 |
200MW |
|
|
US |
New Albany, Ohio |
New Albany Cluster |
2025 |
|
|
? |
? |
|
|
US |
Lincoln, Nebraska |
Lincoln Data Center |
2025 |
|
|
? |
? |
|
|
US |
Austin, Texas |
Tesla Cortex Phase 3 |
2026 |
Tesla |
Tesla |
100,000 |
? |
|
|
US |
Toledo, Ohio |
Meta Prometheus |
2026 |
Meta |
Meta |
? |
1 GW |
|
|
US |
Richland Parish, Louisiana |
Louisiana Meta AI cluster Hyperion |
2027 |
Meta |
Meta |
? |
1.5GW |
|
|
US |
Memphis, Tennesee |
xAI Colossus Phase 3 (also called Colossus 2) |
2029 |
xAI |
xAI |
1,000,000 |
1-1.5GW |
|
As evidenced in Table 1, AI labs such as Google, Meta and xAI 52 play pivotal roles in the AI ecosystem, each building out their own massive compute infrastructures to meet growing training and deployment needs. As previously mentioned, Google and Meta are both AI labs and hyperscalers.
Google has deployed several data centre clusters in Nebraska (Papillion Campus, Omaha AI Data Center) and in Ohio (New Albany Cluster, Lancaster Data Center Campus) within close proximity. These are serving Google’s needs for both HPC and AI workloads. However, publicly available information does not provide many details on each site’s capacity and hardware setups. Market analysts have suggested that all four clusters combined will comprise a GW-scale cluster by 2026. 53 Furthermore, Google currently is building a cluster in Columbus, Ohio.
Meta announced two new GPU clusters in the GW range in Louisiana (Prometheus by 2026 and Hyperion by 2027). Furthermore, xAI is expanding its Colossus GPU cluster in Memphis massively, with the ambitious goal to install a million H100 equivalents by 2029. 54
Strategy 2: AI Labs Acting as Anchor Customers
Table 2 below provides all publicly available information on GPU clusters in which AI labs act as anchor customers.
Table 2: AI Labs as Anchor Customers
A distinction is made between GPU clusters that are already operational or under construction (shown in purple) and those that have only been announced (not shown).
|
Country |
Location |
Name |
Available Since/ |
Provider |
User |
H100 equivalents |
Power Capacity |
Links |
|---|---|---|---|---|---|---|---|---|
|
US |
Abilene, Texas |
OpenAI Stargate Abilene Oracle OCI Supercluster Phase 1 |
2025 |
Oracle |
OpenAI |
? |
200MW |
|
|
US |
St. Joseph County, Indiana |
Project Rainier (AWS) |
2025 |
AWS |
Anthropic |
? |
455MW |
|
|
US |
Phoenix, Arizona |
OpenAI/ Microsoft Goodyear |
2025 |
Microsoft Azure |
OpenAI |
100,000 |
? |
|
|
Norway |
Kvandal |
Stargate Norway |
2026 |
Nscale |
OpenAI |
100,000 |
230MW |
|
|
US |
Muskogee, Oklahoma |
CoreWeave Muskogee |
2026 |
CoreWeave |
Undisclosed client |
? |
100MW |
|
|
US |
Mount Pleasant, Wisconsin |
OpenAI/Microsoft Mt Pleasant, Wisconsin Phase 1 |
2026 |
Microsoft Azure |
OpenAI |
? |
300MW |
|
|
US |
Atlanta, Georgia |
OpenAI/Microsoft Atlanta |
2026 |
Microsoft Azure |
OpenAI |
? |
324MW |
|
|
US |
Abilene, Texas |
OpenAI Stargate Abilene Oracle OCI Supercluster Phase 2 |
2026 |
Oracle |
OpenAI |
? |
1.2GW |
|
|
France |
Bruyères-le-Châtel, Essonne |
Fluidstack France Gigawatt Campus |
2026 |
Fluidstack |
Mistral |
500,000 |
? |
|
|
US |
Denton, Texas |
CoreWeave Cluster (OpenAI/Microsoft) |
2027 |
CoreWeave |
OpenAI |
? |
297MW |
|
|
France |
Bruyères-le-Châtel, Essonne |
Fluidstack France Gigawatt Campus |
2028 |
Fluidstack |
Mistral |
? |
1GW |
|
Table 2 indicates that AI labs often act as anchor customers, thereby confirming the use of the total or majority of new AI compute capacities due to their high compute demand for AI training and deployment. OpenAI’s cooperation with hyperscalers is an interesting case study. ChatGPT’s success in 2022 fuelled OpenAI’s massive infrastructure buildout. 55 Initially, OpenAI partnered exclusively with Microsoft Azure, which built a dedicated AI training cluster called Goodyear in Arizona. 56 Another OpenAI/Microsoft cluster (Mount Pleasant) is planned for 2026, with a power capacity of 300MW. 57 However, OpenAI ended its exclusive partnership with Microsoft in January 2025 and started diversifying partnerships with other hyperscalers and neoclouds as a strategic customer. Since then, OpenAI has announced several new deals, such as the partnership with the neocloud provider Nscale in Norway (Stargate Norway), 58 the neocloud CoreWeave in Denton (CoreWeave cluster) 59 and the Oracle/Crusoe Abilene deal (Stargate Abiliene Oracle OCI Supercluster Phase 1/Phase 2). 60 Anthropic is pursuing a similiar approach, aiming for a large GPU cluster with a power capacity of 455MW by the end of 2025, provided by the hyperscaler AWS. 61 The European AI lab Mistral also announced a strategic partnership recently with neocloud provider Fluidstack, ultimately aiming for a GW-scale GPU cluster in France by 2028. 62
Strategy 3: On Demand
Table 3 below depicts publicly available information on GPU clusters that display on-demand strategies.
Table 3: On Demand
A distinction is made between GPU clusters that are already operational or under construction (shown in purple) and those that have only been announced (not shown).
|
Country |
Location |
Name |
Available Since /From |
Provider |
User |
H100 equivalents |
Power Capacity |
Links |
|---|---|---|---|---|---|---|---|---|
|
US |
Mountain View, California |
Lambda Labs Cluster |
2023 |
Lambda |
multi-tenant |
? |
21MW |
|
|
US |
Lawrence, Kansas |
Lawrence Livermore NL El Capitan Phase 2 |
2024 |
US Department of Energy |
multi-tenant |
? |
35MW |
|
|
Finland |
Mäntsälä |
Nebius Finland |
2025 |
Nebius |
multi-tenant |
60,000 |
75MW |
|
|
US |
? |
Oracle OCI Supercluster |
2025 |
Oracle |
multi-tenant |
? |
? |
|
|
US |
? |
Together AI cluster |
2025 |
Hypertec |
multi-tenant |
90,955 |
? |
|
|
France |
Paris |
Nebius Paris cluster |
2024 |
Nebius |
multi-tenant |
? |
? |
|
|
US |
? |
Oracle OCI Supercluster B200s |
2025 |
Oracle |
multi-tenant |
? |
? |
|
|
US |
? |
Project Ceiba (AWS) |
2025 |
AWS |
multi-tenant |
53,000 |
? |
|
|
US |
Ellendale, Delaware |
Applied Digital CoreWeave Ellendale Phase 1 |
2025 |
CoreWeave |
multi-tenant |
? |
100MW |
|
|
US |
Kansas City, Missouri |
Nebius Kansas City Cluster |
2025 |
Nebius |
multi-tenant |
35,000 |
40MV |
|
|
France |
Valence Romans Agglo |
Sesterce Valence |
2026 |
Sesterce |
multi-tenant |
40,000 |
? |
|
|
UK |
Loughton, Essex |
Nscale Loughton |
2026 |
Nscale |
multi-tenant |
45,000
|
90MW |
|
|
US |
Ellendale, Delaware |
Applied Digital CoreWeave Ellen dale Phase 2 |
2026 |
CoreWeave |
multi-tenant |
? |
250MW |
|
|
US |
Vineland, New Jersey |
Nebius New Jersey |
2026 |
Nebius |
multi-tenant |
? |
300MW |
|
|
France |
Southern France |
Sesterce Southern France 250MW |
2027 |
Sesterce |
multi-tenant |
200,000 |
250MW |
|
|
US |
Ellendale, Delaware |
Applied Digital Ellendale Phase 3 |
2027 |
CoreWeave |
multi-tenant |
? |
400MW |
|
|
France |
Grande Est |
Sesterce Grand Est France A |
2028 |
Sesterce |
multi-tenant |
250,000 |
300MW |
|
|
France |
Grande Est |
Sesterce Grand Est France B |
2028 |
Sesterce |
multi-tenant |
250,000 |
300MW |
|
|
EU |
Colocation |
Hypertec Cluster |
2029 |
Hypertec |
multi-tenant |
100,000 |
2GW |
|
|
France |
Grande Est |
Sesterce Grand Est France A |
2030 |
Sesterce |
multi-tenant |
500,000 |
600MW |
|
|
France |
Grande Est |
Sesterce Grand Est France B |
2030 |
Sesterce |
multi-tenant |
500,000 |
600MW |
|
Table 3 highlights the current popularity of the third strategy pursued by all user groups: to rent cloud infrastructure from hyperscalers – such as Microsoft, Amazon (AWS) or Oracle – or from neocloud providers such as CoreWeave, Nebius or Nscale. This works for all types of AI workloads (small to high, training or inference) and provides a high flexibility level. While this can be categorised as an additional strategy for AI labs, the second group of users described above – those with low-to-moderate demand – typically rents AI compute capacity in the cloud. Depending on workload profile, development phase (training stages or inference) and budget, they choose between a hyperscaler or neocloud. Neoclouds have been announcing massive new AI infrastructure deals in particular. Examples include the deal between Applied Digital and CoreWeave in Ellendale, 63 64 and the two large-scale AI infrastructure investments by Sesterce in France. 65
Takeaways for AIGFs – Recommendation Based on Two Scenarios
The analysis indicates that different stakeholder groups – including AI labs, companies, startups, SMEs and researchers – pursue distinct approaches to meet their demand for AI compute. Furthermore, users and providers’ strategies are linked closely. We are talking about billions in hardware investments for large-scale GPU clusters alone, so providers must avoid idle capacity at all costs. Thus, hyperscalers or neoclouds generally follow one of two strategies: securing an anchor customer, often a frontier AI lab to ensure stable utilisation, or offering on-demand capacity for a diverse set of clients with a variety of compute needs.
Chart 3: AI Gigafactories: Strategic Choices and Demand Scenarios
Anchor Customers with High AI Compute Demand
To achieve AIGFs’ stated goal to train and deploy frontier AI models, securing an anchor customer or few strategic customers with high AI compute demand is key.
In the US, recent AI cluster buildouts have been driven either by labs operating their own infrastructure (e.g., xAI) or labs acting as anchor customers (e.g., OpenAI). Although we did not analyse Chinese labs’ strategies, this pattern notably also can be observed in China: Some labs (e.g., DeepSeek) train on self-built clusters. 66 For example, Huawei’s Pangu trains on in‑house designed chips (Ascend NPUs). 67 Other companies with AI labs – such as ByteDance, a major GPU customer of Oracle 68 – reportedly train models on that capacity. In Europe, Mistral – in teaming up with Fluidstack – already has applied this strategy. 69 Accordingly, when evaluating gigafactory bids, the European Commission should place explicit weight on the named customer, their demand projections’ robustness and their commitments’ reliability. However, because Europe currently has only one leading AI lab – Mistral – the prospects of securing European anchor customers with high demand are very limited in the near future.
Multi-Client User Setup (Low-to-Moderate Demand)
If securing a strategic customer proves unattainable, a different outcome could be to aggregate sufficient demand from clients with low-to-moderate AI workloads. Importantly, in this multi-client scenario, AIGFs likely no longer would be built to train and deploy frontier AI models. Instead, they would cater to a diverse set of users, effectively ‘competing’ with AI factories – the 13 smaller-scale facilities established all over Europe – in terms of client portfolios, and with neoclouds in terms of scale.
Competing with neoclouds is risky, as they are often cheaper due to economies of scale and readily available access. As evidenced in our table 3, neoclouds are betting on extraordinarily rising demand for AI compute, leading to very large data-centre expansion in GPU cluster plans. If AI adoption slows, and demand does not grow as projected, excess capacity could lead to price competition that might leave AIGFs struggling to compete. Moreover, many leading neoclouds – such as Nebius or Nscale – are European companies, thereby reducing AIGFs’ sovereignty advantage that might otherwise encourage companies to switch to AIGFs.
Therefore, AIGFs should be positioned between AI factories and neoclouds, and compete on services and offerings, not simply on compute prices/GPU-hour prices: Serving a heterogeneous set of users will require more than raw compute – it demands a broader range of services very similar to the definition of ‘AI factories’ as ‘dynamic ecosystems that foster innovation, collaboration and development in the field of AI’. 70 In this scenario, AIGF should adopt elements from the AI factory model and act as a one-stop shop – a single storefront that bundles the ingredients needed to train and deploy all kinds of AI applications, such as structured onboarding, maturity diagnostics, access to compute resources, curated software stacks and ongoing support. Simultaneously, they could focus on larger, high-priority commercial needs that current AI factories either cannot meet or are too slow to provide.
Practical options include dedicating compute to Public AI models developed by European AI labs or pursuing sectorial approaches that aggregate enough stable demand – e.g., automotive in Germany or health and media in Spain. In this way, AIGFs could become more than infrastructure and foster an AI ecosystem that links SMEs, start-ups and academia.
In conclusion, we are proposing that policymakers should keep three things in mind when drafting criteria for AIGFs, as well as reviewing AIGF proposals:
(1) Demand quantification: Demand estimates must be part of each submission. Reliable public data are scarce, so it is even more important that submissions include demand estimates of potential AI workloads being served and evidence of commitments, e.g., in the form of letters of intent.
(2) A realistic goal: Strengthening the European AI ecosystem effectively requires a clear strategy and feasible objective behind the buildout of AI compute, one that is connected to industry dynamics in play.
(3) A clear value proposition: AIGFs’ success is tied strongly to a clear positioning to and differentiation from other types of providers – namely hyperscalers and neoclouds.
Annex 1
Table US/EU AI Compute Mapping
A distinction is made between GPU clusters that are already operational or under construction (shown in purple) and those that have only been announced (not shown).
|
Country |
Location |
Name |
Available Since/ |
Provider |
User |
H100 equivalents |
Power Capacity |
Sources |
|---|---|---|---|---|---|---|---|---|
|
US |
Omaha, Nebraska |
Papillion Campus |
2021 |
|
|
? |
? |
|
|
US |
Omaha, Nebraska |
Google Omaha AI Datacenter |
2024 |
|
|
? |
150MW |
|
|
US |
Mountain View, California |
Lambda Labs Cluster |
2023 |
Lambda |
multi-tenant |
? |
21MW |
|
|
US |
Lawrence, Kansas |
Lawrence Livermore NL El Capitan Phase 2 |
2024 |
US Department of Energy |
multi-tenant |
? |
35MW |
|
|
US |
Austin, Texas |
Tesla Cortex Phase 1 |
2024 |
Tesla |
Tesla |
50 000 |
130MW |
|
|
US |
Undisclosed Location |
Meta AI Research Supercluster (RSC) |
2024 |
Meta |
Meta |
2x 24 000 |
? |
|
|
France |
Paris |
Nebius Paris cluster |
2024 |
Nebius |
multi-tenant |
? |
? |
|
|
US |
Memphis, Tennesee |
Colossus Phase 1 (xAI) |
2024 |
xAI |
xAI |
100 000 |
150MW |
|
|
US |
Council Bluffs, Iowa |
Google Council Bluffs |
2025 |
|
|
? |
? |
|
|
US |
Lancaster, Ohio |
Lancaster Data Center Campus |
2025 |
|
|
? |
? |
|
|
US |
Abilene, Texas |
OpenAI Stargate Abilene Oracle OCI Supercluster Phase 1 |
2025 |
Oracle |
OpenAI |
? |
200MW |
|
|
US |
? |
Oracle OCI Supercluster B200s |
2025 |
Oracle |
multi-tenant |
? |
? |
|
|
Finland |
Mäntsälä |
Nebius Finland |
2025 |
Nebius |
multi-tenant |
60 000 |
75MW |
|
|
US |
? |
Oracle OCI Supercluster |
2025 |
Oracle |
multi-tenant |
? |
? |
|
|
US |
? |
together AI cluster |
2025 |
Hypertec |
multi-tenant |
90 955 |
? |
|
|
US |
Kansas City, Missouri |
Nebius Kansas City Cluster |
2025 |
Nebius |
multi-tenant |
35 000 |
40MW |
|
|
US |
Austin, Texas |
Tesla Cortex Phase 2 |
2025 |
Tesla |
Tesla |
? |
? |
|
|
US |
? |
Project Ceiba (AWS) |
2025 |
AWS |
multi-tenant |
53 000 |
? |
|
|
US |
Phoenix, Arizona |
OpenAI/Microsoft Goodyear |
2025 |
Microsoft Azure |
OpenAI |
100 000 |
? |
|
|
US |
Ellendale, Delaware |
Applied Digital CoreWeave Ellendale Phase 1 |
2025 |
CoreWeave |
multi-tenant |
? |
100MW |
|
|
US |
St. Joseph County, Indiana |
Project Rainier (AWS) |
2025 |
AWS |
Anthropic |
? |
455MW |
|
|
US |
Memphis, Tennesee |
Colossus Phase 2 (xAI) |
2025 |
xAI |
xAI |
230 000 |
200MW |
|
|
US |
New Albany, Ohio |
New Albany Cluster |
2025 |
|
|
? |
? |
|
|
US |
Lincoln, Nebraska |
Lincoln Datacenter Campus |
2025 |
|
|
? |
? |
|
|
US |
Austin, Texas |
Tesla Cortex Phase 3 |
2026 |
Tesla |
Tesla |
100 000 |
? |
|
|
Norway |
Kvandal |
Stargate Norway |
2026 |
Nscale |
OpenAI |
100 000 |
230MW |
|
|
US |
Muskogee, Oklahoma |
CoreWeave Muskogee |
2026 |
CoreWeave |
undisclosed client |
? |
100MW |
|
|
France |
Valence Romans Agglo |
Sesterce Valence |
2026 |
Sesterce |
multi-tenant |
40 000 |
? |
|
|
UK |
Loughton, Essex |
Nscale Loughton |
2026 |
Nscale |
multi-tenant |
45,000 |
90MW |
|
|
US |
Ellendale, Delaware |
Applied Digital CoreWeave Ellen dale Phase 2 |
2026 |
CoreWeave |
multi-tenant |
? |
250MW |
|
|
US |
Mount Pleasant, Wisconsin |
OpenAI/Microsoft Mt Pleasant, Wisconsin Phase 1 |
2026 |
Microsoft Azure |
OpenAI |
? |
300MW |
|
|
US |
Vineland, New Jersey |
Nebius New Jersey |
2026 |
Nebius |
multi-tenant |
? |
300MW |
|
|
US |
Atlanta, Georgia |
OpenAI/Microsoft Atlanta |
2026 |
Microsoft Azure |
OpenAI |
? |
324MW |
|
|
US |
Abilene, Texas |
OpenAI Stargate Abilene Oracle OCI Supercluster Phase 2 |
2026 |
Oracle |
OpenAI |
? |
1.2GW |
|
|
France |
Bruyères-le-Châtel, Essonne |
Fluidstack France Gigawatt Campus |
2026 |
Fluidstack |
Mistral |
500 000 |
? |
|
|
US |
Toledo, Ohio |
Meta Prometheus |
2026 |
Meta |
Meta |
? |
1 GW |
|
|
US |
Memphis, Tennesee |
xAI Colossus Phase 3 (also called Colossus 2) |
2026 |
xAI |
xAI |
1 000 000 |
1-1.5GW |
|
|
France |
Southern France |
Sesterce Southern France 250MW |
2027 |
Sesterce |
multi-tenant |
200 000 |
250MW |
|
|
US |
Denton, Texas |
CoreWeave Cluster (OpenAI/Microsoft) |
2027 |
CoreWeave |
OpenAI |
? |
297MW |
|
|
US |
Ellendale, Delaware |
Applied Digital Ellendale Phase 3 |
2027 |
CoreWeave |
multi-tenant |
? |
400MW |
|
|
US |
Richland Parish, Louisiana |
Louisiana Meta AI cluster Hyperion |
2027 |
Meta |
Meta |
? |
1.5GW |
|
|
France |
Grande Est |
Sesterce Grand Est France A |
2028 |
Sesterce |
multi-tenant |
250 000 |
300MW |
|
|
France |
Grande Est |
Sesterce Grand Est France B |
2028 |
Sesterce |
multi-tenant |
250 000 |
300MW |
|
|
France |
Bruyères-le-Châtel, Essonne |
Fluidstack France Gigawatt Campus |
2028 |
Fluidstack |
Mistral |
? |
1GW |
|
|
EU |
Colocation |
Hypertec Cluster |
2029 |
Hypertec |
multi-tenant |
? |
2GW |
|
|
France |
Grande Est |
Sesterce Grand Est France A |
2030 |
Sesterce |
multi-tenant |
500 000 |
600MW |
|
|
France |
Grande Est |
Sesterce Grand Est France B |
2030 |
Sesterce |
multi-tenant |
500 000 |
600MW |
Acknowledgements
We would like to thank Jan-Peter Kleinhans, Arno Amabile, Albert Cañigueral Bagó, Nicolas Flores-Herr, Jenia Jitsev, Christian Temath, Sarah Budai, Nicole Lemke, Catherine Schneider and Maria Nowicka for their constructive feedback and support during the research and writing process; Maximilian Gottwald and Jack Walmsley for their help in research and text edits; Alina Siebert for designing the charts and the publication layout; Luisa Seeling for her support in editing the text and Sebastian Rieger and Iana Pervazova for helping us spread the word about this publication.
Table of Contents
1 European Commission (2025). Speech by President von der Leyen at the Artificial Intelligence Action Summit. https://ec.europa.eu/commission/presscorner/detail/en/speech_25_471.
2 European Commission (2025). Speech by President von der Leyen at the Artificial Intelligence Action Summit. https://ec.europa.eu/commission/presscorner/detail/en/speech_25_471.
3 European Commission (2025). The AI Continent Action Plan. https://digital-strategy.ec.europa.eu/en/library/ai-continent-action-plan.
4 Jakob Steinschaden (2025). 6 neue AI Factories in Tschechien, Litauen, den Niederlanden, Rumänien, Spanien und Polen. https://www.trendingtopics.eu/6-neue-ai-factories/.
5 European Commission (2025). AI Factories. https://digital-strategy.ec.europa.eu/en/policies/ai-factories.
6 European Commission (2025). The AI Continent Action Plan. https://digital-strategy.ec.europa.eu/en/library/ai-continent-action-plan.
7 Commission (2025). Call for expression of interest in AI gigafactories (AIGFs). https://www.eurohpc-ju.europa.eu/document/AIGIGAFACTORIESCONSULTATION.pdf.
8 There is no single agreed-upon definition of large-scale AI compute infrastructure: Terms such as ‘AI data centre’, ‘AI supercomputer’, ‘AI cluster, ‘mega cluster’ and ‘GPU cluster’ are used interchangeably. In this paper, we used the latter, as it makes a direct connection with what lies at the heart of such a cluster: graphics processing units (GPUs). While traditional high-performance computing (HPC) mainly operates on central processing units (CPUs), which are designed for versatility and handle many different tasks sequentially, GPUs are built for parallel processing, allowing them to perform thousands of calculations at once. This makes GPUs particularly effective for handling the enormous computational demand for training and deploying AI models.
9 European Commission (2025). The AI Continent Action Plan. https://digital-strategy.ec.europa.eu/en/library/ai-continent-action-plan.
10 The European Commission’s Continent Action Plan states that AI gigafactories’ objective is to ‘develop and train’ frontier AI models. In the subsequent call for expressions of interest, the Commission added that its purpose also includes deploying very large AI models. Accordingly, we define AIGFs’ goal as to train and deploy frontier AI models.
11 Epoch AI (2025). Over 30 AI models have been trained at the scale of GPT-4. https://epoch.ai/data-insights/models-over-1e25-flop.
12 Macro Polo (2025). The Global AI Talent Tracker 2.0. https://archivemacropolo.org/interactive/digital-projects/the-global-ai-talent-tracker/. The AI Global Talent Tracker measures AI talent by sampling authors of accepted papers at the Neural Information Processing Systems (NeurIPS) conference – widely regarded as the top AI conference – along with all oral presentation authors. It then hand-codes each author’s education and current affiliation using public sources.
13 Macro Polo (2025). The Global AI Talent Tracker 2.0. https://archivemacropolo.org/interactive/digital-projects/the-global-ai-talent-tracker/.
14 Siddhi Pal (2024). Where is Europe's AI workforce coming from? https://www.interface-eu.org/where-is-europes-ai-workforce-coming-from - conclusion. For further information on AI talent distribution: Siddhi Pal, Catherine Schneider & Ruggero Marino Lazzaroni (2025). Technical Tiers: A New Classification Framework for Global AI Workforce Analysis https://www.interface-eu.org/technical-tiers-in-ai-talent, and Siddhi Pal, Catherine Schneider & Laura Nurski (2025). Solving Europe's AI talent equation: Supply, demand and missing pieces. https://www.interface-eu.org/solving-europes-ai-talent-equation - ai-talent-in-europe.
15 Joshua Love et al. (2023) Entertainment and Media Guide to AI: Geopolitics of AI Text and data mining around the globe. https://www.lexology.com/library/detail.aspx?g=bb8a1903-83b4-48df-9c10-c00484e30848.
16 Blake Brittain (2025). Anthropic wins key US ruling on AI training in authors’ copyright lawsuit. https://www.reuters.com/legal/litigation/anthropic-wins-key-ruling-ai-authors-copyright-lawsuit-2025-06-24/?utm_source=chatgpt.com.
17 Jaime Sevilla & Edu Roldán (2024). Training compute of frontier AI models grows by 4-5x per year. https://epoch.ai/blog/training-compute-of-frontier-ai-models-grows-by-4-5x-per-year.
18 Ben Cottier, Robi Rahman, Loredana Fattorini, Nestor Maslej & David Owen (2025). How much does it cost to train frontier AI models? https://epoch.ai/blog/how-much-does-it-cost-to-train-frontier-ai-models.
19 Felix Sieker, Alex Tarkowski, Lea Gimpel & Cailean Osborne (2025). Public AI – White Paper. https://www.bertelsmann-stiftung.de/de/publikationen/publikation/did/public-ai-white-paper-a-public-alternative-to-private-ai-dominance.
20 To give an example on how the phases of generative AI training and deployment look in practice: A public service portal might launch a ‘citizen services copilot’. The base model – already pre-trained at scale – is licensed from a provider. The agency then fine-tunes it on local regulations and FAQs. Once the system goes live, computational resources are needed primarily to handle the thousands of daily queries from citizens interacting with the model.
21 For this policy brief, we define low–to–moderate compute demand as workloads that can be executed on a small cluster (single‑ to low‑double‑digit modern GPUs) for hours to a few days. In contrast, heavy workloads typically need hundreds to thousands of GPUs over weeks and specialised infrastructure. See: KI Bundesverband (2025). KI-Infrastruktur für das Training großer Modelle in Deutschland. https://ki-verband.de/wp-content/uploads/2025/08/VER1_KI-Rechenzentren-1-1.pdf.
22 In reinforcement learning, a model generates answers to given prompts, which often will be ‘judged’ by a separate model. This step is very compute-heavy, as today, for reinforcement learning, a substantial amount of synthetic data is created to generate prompts and a second model usually is trained and deployed to provide feedback (judge) on answers to prompts from the model being trained.
23 Felix Sieker, Alex Tarkowski, Lea Gimpel & Cailean Osborne (2025). Public AI – White Paper. https://www.bertelsmann-stiftung.de/de/publikationen/publikation/did/public-ai-white-paper-a-public-alternative-to-private-ai-dominance.
24 Konstantin F. Pilz, James Sanders, Robi Rahman & Lennart Heim (2025). Trends in AI Supercomputers. https://arxiv.org/abs/2504.16026.
25 Zoe Hawkins, Vili Lehdonvirta & Boxi Wu (2025). AI Compute Sovereignty: Infrastructure Control Across Territories, Cloud Providers and Accelerators. https://papers.ssrn.com/sol3/papers.cfm?abstract_id=5312977.
27 McKinsey & Company (2025): What is generative AI? https://www.mckinsey.com/featured-insights/mckinsey-explainers/what-is-generative-ai.
28 European Commission (2025): AI in automotive: applications, opportunities and barriers. https://digital-strategy.ec.europa.eu/en/events/ai-automotive-applications-opportunities-and-barriers.
29 Dylan Patel, Jeremie Eliahou Ontiveros & Maya Barkin (2025). Microsoft’s Data Center Freeze – 1.5GW Self-Build Slowdown & Lease Cancellation Misconceptions. https://newsl etter.semianalysis.com/p/microsofts-datacenter-freeze.
30 Dylan Patel (2024). Inference Math, Simulation and AI Megaclusters - Stanford CS 229S - Autumn 2024. https://www. youtube.com/watch?v=hobvps-H38o.
31 For simplification and standardisation purposes, the term ‘AI supercomputer’ will be used throughout the paper to refer to AI computing infrastructure.
32 Felix Sieker, Alex Tarkowski, Lea Gimpel & Cailean Osborne (2025). Public AI – White Paper. https://www.bertelsmann-stiftung.de/de/publikationen/publikation/did/public-ai-white-paper-a-public-alternative-to-private-ai-dominance.
33 Nvidia (2025). Nvidia H100 Tensor-Core-GPU. https://www.nvidia.com/de-de/data-center/h100/.
34 Konstantin F. Pilz, James Sanders, Robi Rahman & Lennart Heim (2025). Trends in AI Supercomputers. https://arxiv.org/abs/2504.16026.
35 Advanced Micro Devices Inc. (2025). AMD Instinct™ MI300 Series Accelerators. https://www.amd.com/en/products/accelerators/instinct/mi300.html.
36 Google Cloud. AI-Entwicklung mit Google Cloud TPUs beschleunigen. https://cloud.google.com/tpu?hl=de.
37 Eran Tal, Nicolaas Viljoen, Joel Coburn & Roman Levenstein (2024). Our Next-generation Meta Training and Inference Accelerator. https://ai.meta.com/blog/next-generation-meta-training-inference-accelerator-AI-MTIA/.
38 Amazon Web Services (2025). AWS Trainium. https://aws.amazon.com/de/ai/machine-learning/trainium/.
39 Dylan Patel (2024). Inference Math, Simulation and AI Megaclusters - Stanford CS 229S - Autumn 2024. https://www.youtube.com/watch?v=hobvps-H38o.
40 Nvidia (2025). Nvidia H100 Tensor Core GPU. https://www.nvidia.com/en-us/data-center/h100/.
41 Philip Kiely (2025). Comparing GPUs across architectures and tiers. https://www.baseten.co/blog/comparing-gpus-across-architectures-and-tiers/.
42 Konstantin F. Pilz, James Sanders, Robi Rahman & Lennart Heim (2025). Trends in AI Supercomputers. https://arxiv.org/abs/2504.16026.
43 CharlesD (2024). Estimates of GPU or equivalent resources of large AI players for 2024/5. https://www.lesswrong.com/posts/bdQhzQsHjNrQp7cNS/estimates-of-gpu-or-equivalent-resources-of-large-ai-players.
44 r/singularity (2023). Jensen Huang just gave us some numbers for the training of GPT4 that are useful to predict GPT5. https://www.reddit.com/r/singularity/comments/1bi8rme/jensen_huang_just_gave_us_some_numbers_for_the/.
45 Dylan Patel (2024). Inference Math, Simulation and AI Megaclusters - Stanford CS 229S - Autumn 2024. https://www.youtube.com/watch?v=hobvps-H38o.
46 Dylan Patel (2024). Inference Math, Simulation and AI Megaclusters - Stanford CS 229S - Autumn 2024. https://www.youtube.com/watch?v=hobvps-H38o
47 Dylan Patel (2024). Inference Math, Simulation and AI Megaclusters - Stanford CS 229S - Autumn 2024. https://www.youtube.com/watch?v=hobvps-H38o.
48 Dylan Patel & Daniel Nishball (2024). 100,000 H100 Clusters: Power, Network Topology, Ethernet vs. InfiniBand, Reliability, Failures, Checkpointing. https://newsletter.semianalysis.com/p/100000-h100-clusters-power-network.
49 Matthew Griffin (2025). OpenAI GPT-5 is costing $500 million per training run and still failing. https://www.fanaticalfuturist.com/2025/05/openai-gpt-5-is-costing-500-million-per-training-run-and-still-failing/.
50 Konstantin Pilz, Robi Rahman, James Sanders & Lennart Heim (2025). GPU Clusters. https://epoch.ai/data/gpu-clusters.
51 We chose to map GPU clusters with more than 30,000 GPUs to ensure that they do not overlap with the AI factories, which are defined as having up to 25,000 GPUs.
52 Tesla plays a special role; it is not an AI lab itself, but operates several compute-heavy business lines, including its Full-Self-Driving (FSD) System or its general-purpose robotic humanoid, Optimus.
53 Dylan Patel, Daniel Nishball & Jeremie Eliahou Ontiveros (2024). Multi-Data Center Training: OpenAI’s Ambitious Plan To Beat Google’s Infrastructure. https://newsletter.semianalysis.com/p/multi-datacenter-training-openais.
54 Artisan Baumeister (2025). Colossus 2 enthüllt: Warum XAI das größte Datencenter-Spektakel der Gegenwart wagt. https://www.techzeitgeist.de/colossus-2-enthuellt-warum-xai-das-groesste-datencenter-spektakel-der-gegenwart-wagt/.
55 Dylan Patel, Jeremie Eliahou Ontiveros & Maya Barkin (2025). Microsoft’s Data Center Freeze – 1.5GW Self-Build Slowdown & Lease Cancellation Misconceptions. https://newsletter.semianalysis.com/p/microsofts-datacenter-freeze.
56 Karen Hao (2024). AI Is Taking Water From the Desert. https://www.theatlantic.com/technology/archive/2024/03/ai-water-climate-microsoft/677602/.
57 Dylan Patel, Daniel Nishball & Jeremie Eliahou Ontiveros (2024). Multi-Data Center Training: OpenAI’s Ambitious Plan To Beat Google’s Infrastructure. https://newsletter.semianalysis.com/p/multi-datacenter-training-openais.
58 Quickchannel (2025). Announcement: Stargate Norway. https://qcnl.tv/p/ibfrFnPcQraFxwbZSpXzVQ.
59 CoreWave (2025). CoreWeave Announces Agreement With OpenAI to Deliver AI Infrastructure. https://www.coreweave.com/news/coreweave-announces-agreement-with-openai-to-deliver-ai-infrastructure.
60 Dylan Patel, Jeremie Eliahou Ontiveros & Maya Barkin (2025). Microsoft’s Data Center Freeze – 1.5GW Self-Build Slowdown & Lease Cancellation Misconceptions. https://newsletter.semianalysis.com/p/microsofts-datacenter-freeze.
61 Jeremie Eliahou Ontiveros, Dylan Patel, AJ Kourabi & Myron Xie (2025). Amazon’s AI Resurgence: AWS & Anthropic’s Multi-Gigawatt Trainium Expansion. https://newsletter.semianalysis.com/p/amazons-ai-resurgence-aws-anthropics-multi-gigawatt-trainium-expansion.
62 Fluidstack (2025). Fluidstack to Build 1 GW AI Supercomputer in France. https://www.fluidstack.io/resources/blog/fluidstack-to-build-1-gw-ai-supercomputer-in-france.
63 Applied Digital (2025). Applied Digital Announces 250MW AI Data Center Lease With CoreWeave in North Dakota. https://ir.applieddigital.com/applied-digital-announces-250mw-ai-data-center-lease-with.
64 Jeremie Eliahou Ontiveros, Dylan Patel & Daniel Nishball (2025). How Oracle Is Winning the AI Compute Market. https://newsletter.semianalysis.com/p/how-oracle-is-winning-the-ai-compute-market.
65 Georgia Butler (2025). Sesterce invests €450m in AI data center in Valence, France. https://www.datacenterdynamics.com/en/news/sesterce-invests-450m-in-ai-data-center-in-valence-france/.
66 Dylan Patel, AJ Kourabi, Doug Olaughlin & Reyk Knuhtsen (2025). DeepSeek Debates: Chinese Leadership On Cost, True Training Cost, Closed Model Margin Impacts. https://newsletter.semianalysis.com/p/deepseek-debates.
67 ArXiv In-depth Analysis (2025). Pangu Ultra: Can Dense LLMs Compete at Scale? Huawei’s 135B Parameter Bet on Ascend NPUs. https://arxiv.org/abs/2504.07866.
68 Jeremie Eliahou Ontiveros, Dylan Patel & Daniel Nishball (2025). How Oracle Is Winning the AI Compute Market. https://newsletter.semianalysis.com/p/how-oracle-is-winning-the-ai-compute-market.
69 Fluidstack (2025). Fluidstack to Build 1 GW AI Supercomputer in France. https://www.fluidstack.io/resources/blog/fluidstack-to-build-1-gw-ai-supercomputer-in-france.
70 European Commission (2025). The AI Continent Action Plan. https://digital-strategy.ec.europa.eu/en/library/ai-continent-action-plan.
Authors
Julia Christina Hess
Senior Policy Researcher Global Chip Dynamics
