In my many conversations with Equinix customers, a common theme has emerged: Many of them understand the potential of AI but don’t feel they’re truly ready to capitalize on it. They’re often struggling to get the powerful hardware they need to support AI workloads in all the right places. Or, they may need help making sure they’re getting the full power of that hardware.
Organizations around the globe are facing these same challenges, but there are certain factors unique to Europe that could make AI adoption especially difficult. Let’s consider a few of these and what you can do to overcome them, whether you’re a European business or a multinational planning to expand to the continent.
Managing AI regulatory complexity
With the launch of the European Union Artificial Intelligence Act, Europe has emerged as a global leader in regulating AI risk. Now, many businesses in Europe must simultaneously build their AI strategy while also ensuring that strategy is compliant. The Act includes requirements such as:
- Data governance: Businesses know they need to scale up massive AI datasets for their model training. New data governance requirements could make that more difficult. AI data and model marketplaces can help. They allow partners to exchange data inside a neutral environment. Thus, partners can benefit from one another’s AI data without sacrificing control over their own data or losing track of data lineage.
- AI resiliency: The Act also includes requirements to ensure that AI systems are robust and highly available. Businesses can achieve this by deploying geo-redundant infrastructure and working with a vendor-neutral partner that makes it easy to switch platforms when needed to avoid downtime.
- Model and data privacy: Finally, the Act emphasizes cybersecurity for high-risk AI use cases, particularly when it comes to protecting against data and model poisoning. One way that businesses can address this is with a private AI approach, where they host proprietary models on private infrastructure. This allows them to feed sensitive data into their AI models while keeping that data within their security domain.
Addressing energy limitations
There’s no way around it: AI workloads require a lot of data, and processing that data requires a lot of energy. Businesses in Europe often feel that finding reliable, scalable power makes it more difficult for them to pursue AI to its full potential. It’s a legitimate problem to which there are no easy answers. However, there are certain steps that businesses can take to work around these limitations.
AI workloads are distributed by nature. Businesses can use this fact to their advantage by strategically positioning their AI workloads in certain markets that are best suited to meet their energy needs. For instance, they might want to position their compute-intensive training workloads in parts of Europe with cooler climates that could help enable more energy-efficient cooling. Also, markets with land available to support new large-scale renewable energy projects could be better positioned to scale energy generation in the future.
It may also be helpful to work with a provider that prioritizes energy-efficient data centers and investing in renewable energy projects. This helps address the challenge from two different angles: using less energy to run the same workloads, while also helping to replenish the energy that is used.
Equinix is an industry leader on both accounts. Our average annual power usage effectiveness (PUE)—a key metric for data center efficiency—was 1.42 in 2023, down about 22% from our 2019 baseline. We’ll continue to implement further efficiency improvements as we pursue our target average annual PUE threshold of 1.3 or better across our global data center portfolio.
In addition, we prioritize power purchase agreements (PPAs) to help provide additionality to local energy grids. Within Europe, we’ve signed PPAs that support renewable energy projects in Finland, France, Portugal, Spain and Sweden. In 2023, we reached 96% renewables coverage globally, including 100% coverage in EMEA. Learn more about our renewable energy strategy.
Meeting AI-ready infrastructure requirements
Refitting a legacy private data center to support AI-ready digital infrastructure would be extremely difficult. For one thing, these data centers weren’t built with the capacity requirements of modern AI workloads in mind. And it’s not just a matter of deploying the right AI hardware, either: Businesses would also need to implement advanced cooling capabilities to support that hardware. This is just one example of why it’s helpful to work with a leading colocation provider like Equinix: We’ve done the hard work of investing in AI-ready infrastructure so that our customers don’t have to.
AI workloads require more compute capacity to process ever-larger datasets, causing the servers to create more heat. Implementing liquid cooling can help address this challenge. Liquid is significantly more efficient at transporting heat than air is, meaning that liquid-cooled servers enable much higher data center density than air-cooled servers. Denser data centers are better able to support compute-intensive workloads like AI.
It’s also important to remember that one AI-ready data center typically isn’t enough. To support AI, different organizations need different types of data centers in different locations. For instance, global service providers may turn to high-capacity hyperscale data centers to train their large language models (LLMs), while enterprises training smaller models for their own private use may prefer traditional colocation data centers instead. For AI inference workloads, which are very sensitive to latency, businesses will need data centers at the edge to ensure proximity to their data sources.
On top of all this, AI success depends on reliable, high-performance network infrastructure. Businesses need to move data quickly between their own AI infrastructure in different locations, and also connect with business partners and service providers in their AI ecosystem. For instance, GPUs may be underutilized if they’re processing data faster than new datasets can reach them.
To enable AI adoption in Europe, businesses should work with a provider that’s consistently invested in all the different elements of AI-ready infrastructure: advanced cooling capabilities, a comprehensive platform of both colocation and hyperscale data centers in many European markets, and virtual networking capabilities to create dedicated connections on demand. This is exactly what Platform Equinix® offers.
The right AI infrastructure partner for businesses in Europe
Equinix is present in more than 70 global markets, including many in Europe. Our data center portfolio includes both Equinix IBX® colocation data centers and Equinix xScale® hyperscale data centers, enabling customers to meet the diverse needs of their different AI workloads. We’ve also announced that we’re expanding support for advanced liquid cooling technologies to more than 100 of our data centers.
Equinix Fabric®, our virtual networking solution, helps customers get the dedicated high-performance connections they need to keep AI datasets moving without delay.
Finally, our partner ecosystem includes leading AI hardware providers, making it easier for our customers to get the right AI infrastructure in the right locations. In particular, we can help businesses pursue private AI by deploying proprietary models on infrastructure that they control. This puts them in a better position to protect their AI data and meet regulatory requirements.
Learn more about impactful private AI use cases: Read our joint e-book with NVIDIA.
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