As enterprises look to capitalize on AI, there are many infrastructure challenges they’ll need to address. They’ll need to store and manage large volumes of data. They’ll need scalable compute to process all that data. And they’ll need high-performance networks to move that data between processing locations.
As AI workloads continue to drive greater processing requirements, many enterprises are looking to deploy powerful GPU hardware to keep up with those requirements. In turn, they’ll need to deploy those GPUs inside data centers that can support higher rack density and provide advanced cooling technology. These high-density data centers will have higher energy demands than the conventional data centers of the past.
Enterprises need to think about how to enable their AI strategies while staying on track with their sustainability goals. It’s a complex question with no easy answers. However, choosing the right IT infrastructure partners can simplify things and meet sustainability goals.
Optimizing for data center efficiency
While running AI workloads on efficient hardware and optimizing data centers for energy efficiency are critical components of a sustainable AI strategy, enterprises must also consider a broader range of factors. These include the lifecycle impact of hardware production, the sourcing of clean, low-carbon energy, the efficiency of algorithms, and overall data management practices. Additionally, organizations often face challenges such as high initial costs, the need for specialized skills, and the integration of sustainable practices into existing workflows. A comprehensive approach that addresses all these challenges is essential for truly sustainable AI deployment.
With the advent of ever-more powerful GPUs, AI hardware is growing faster all the time—and thus, more efficient. GPUs process data much quicker than CPUs, which means they’re often much better suited to support large AI datasets in an energy-efficient manner. This is one reason why it would be so difficult to enable a sustainable approach to AI from inside a legacy on-premises data center. Upgrading traditional data centers to support higher rack densities and GPUs—including implementing advanced cooling capabilities—is time-consuming and expensive.
Deploying inside high-performance data centers from a leading colocation provider offers a better way, allowing organizations to leverage economies of scale and specialized design, operation and management. At Equinix, we’re always investing in our data centers to ensure our customers can deploy the latest AI-ready hardware and run that hardware efficiently. Our customers can benefit from these investments from the moment they deploy with us.
In addition to helping customers pursue a sustainable approach to AI, we’re using AI as a tool to enable sustainability in our own operations. For instance, we’re using AI to optimize our data center cooling systems. This includes building a digital twin of one of our data centers and pairing it with predictive models that incorporate climate data. Our data center operators can tweak settings to determine how cooling systems would react in a particular scenario before they apply those changes in the physical facility. This is just one small example of how Equinix is working to optimize the efficiency of our data centers.
Enabling shared resource consumption
While it’s true that training AI models consumes a lot of energy, this doesn’t necessarily mean it wastes a lot of energy. In fact, we can think of a trained model as stored energy that can be used and reused in the future. The energy to train the model only gets consumed once, but the possibilities for the model once it’s trained are endless.
Different organizations have the potential to share AI models, which in turn means they’ll share the energy burden of training those models and optimize the energy required for training. The challenge is that organizations must find a way to share models while protecting any underlying proprietary data. In short, they need private AI.
As an example, consider airline codeshare partners. Since they tend to serve different customers in different parts of the world, they may not have the data required to train a comprehensive model. What they can do instead is federated model training: Each airline trains their own small model and then shares it with the group. This lowers the total amount of data center capacity and energy used for model training across all the partners.
Federated training requires a neutral platform for all partners to connect with one another while protecting their data. Our portfolio of vendor-neutral data centers in strategic markets worldwide makes Equinix uniquely suited to provide that platform. This is just one example of how Equinix ecosystems enable sustainability. Our customers can connect with thousands of partners and service providers that share their commitment to prioritizing sustainability and efficiency.
Designing for greater density
While GPUs offer enhanced processing efficiency, their adoption brings new challenges, particularly related to heat management. GPUs have a higher compute density than traditional hardware, meaning they can perform more calculations in the same physical space. This increased compute capacity generates more heat, which can lead to overheating if not properly managed. To fully leverage the performance and efficiency of GPUs, enterprises must implement advanced cooling solutions. For instance, many of our data centers at Equinix have adopted liquid cooling systems, which are essential for effectively dissipating the heat generated by densely packed GPU hardware.
The flip side of investing in data center efficiency—and minimizing the energy consumed by the data center—is ensuring that the energy you do use comes from renewable or low-carbon sources whenever possible. Again, partnering with a leading colocation provider like Equinix can help. We’ve pursued a renewable energy strategy that emphasizes power purchase agreements (PPAs) to support new wind and solar energy projects. These PPAs provide additionality, meaning that they directly result in adding new renewable energy capacity to local grids. Across our global data center portfolio, we’ve achieved 96% renewable energy coverage, including 100% coverage in 235+ facilities.
Investing in renewable energy is certainly important, but it won’t be enough to meet the growing demand for power on its own. All low-carbon fuel sources must be considered. In the future, we may see a shift toward large data center campuses that are colocated with their energy sources, in what’s known as a “behind-the-meter” approach. Using on-site power generation methods such as nuclear small modular reactors (SMRs) and other fuel-cell technology could help data centers contribute to the stability of local grids, since they would use those grids as a backup power source only.
Managing the changing hardware lifecycle
Finally, enterprises must consider how growing power density might impact the hardware lifecycle. This includes thinking about the embodied carbon and raw materials that each existing server represents. If widespread AI adoption leads to quicker hardware refresh cycles, then organizations will need to be proactive about addressing the problem of e-waste. They’ll need to integrate circularity into their practices, giving new life to existing assets and materials whenever possible.
A responsible asset disposal strategy must consider each asset’s next best use after it’s retired from a data center. This could mean refurbishing or repurposing an asset, or recycling it to harvest critical components. The landfill should only be used as a last resort for assets that truly can’t be reused or recycled.
Equinix has implemented our own enterprise-scale responsible electronics disposal program. In 2023, we were able to give a second useful life to 37% of Equinix-owned servers leaving our business.
Access a global platform of data centers designed for efficiency and sustainability
Only Equinix offers a global platform with all the different kinds of data centers needed to support various distributed AI workloads, including hyperscale, colocation and edge data centers. You can deploy your AI workloads in the locations that best meet the different needs of those workloads, all while getting access to our efficiency investments, renewable energy coverage and ecosystem of partners to support your sustainability goals.
Also, interconnection at Equinix is among the most efficient ways to move AI data between processing locations. Equinix Fabric®, our software-defined interconnection solution, can be particularly helpful: As a virtual solution, it only requires a single physical connection between you and Equinix, instead of a dedicated physical link to every point you need to interconnect with.
Equinix is also committed to transparency in our sustainability efforts, as demonstrated by these recent recognitions:
- We were named to the CDP Climate Change A List for the third consecutive year. This indicates that we have consistently been among the global leaders in sustainability reporting transparency.[1]
- We were named to the S&P Global Sustainability Yearbook for 2025, indicating that our Corporate Sustainability Assessment score was among the top 15% of companies in our industry.[2]
- We were named to the 2025 Corporate Knights Global 100, a list that ranks the most sustainable corporations in the world based on 25 key performance indicators.[3]
To learn more about what makes a data center AI-ready, check out our Tech Talk, Why You Need a High-Performance Data Center that’s AI-Ready.
To learn more about Equinix’s commitment to sustainability, access our Future First sustainability report.
[2] Sustainability Yearbook 2025, S&P Global
[3] Shawn McCarthy, 2025 Global 100 list: World’s most sustainable companies are still betting on a greener world, Corporate Knights, January 22, 2025.
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