Artificial intelligence is pushing many areas of our digital lives into a new era, and, from an engineering point of view, data centers are where this new era begins.
Enterprises moving beyond pilot projects now face the challenge of supporting dense GPU clusters, infinitely rising power requirements, and far tighter integration demands with cloud and network ecosystems. Traditional colocation footprints were never designed for these conditions, which is why many organizations are turning to AI-ready colocation as a more practical path to scalable infrastructure. These facilities deliver higher power density, advanced cooling solutions, and predictable, low-latency interconnects – just to name a few – offering enterprises a solid base that aligns with long-term growth.
For many organizations, AI-ready colocation options now offer a more realistic way to expand compute capacity without incurring the costs, risks, or long timelines associated with building new data centers. Let’s take a closer look at how.
The urgency of rethinking colocation for enterprise AI needs
Enterprises adopting advanced AI models are running into physical limitations inside traditional colocation footprints. GPU clusters draw much more power, produce much more heat, and require far deeper integration with cloud platforms and network fabrics than legacy architectures ever anticipated.
As these pressures are starting to contour, enterprises find themselves having to reassess how their data center strategy aligns with their long-term AI roadmaps. Many organizations discover that only AI-ready colocation solutions provide the combination of density, consistency, and operational control needed for modern workloads. The shift in many cases is no longer optional, but a structural requirement for any enterprise planning for sustained AI expansion.
Next-level power density demand
AI platforms operate well beyond conventional density ranges. Instead of the old, moderate rack loads, enterprises today are planning for higher densities, and are increasingly interested in whether a facility can support sustained 40-80 kW per rack without electrical instability.
This requires a different approach to data center planning, with upgraded substations, resilient medium-voltage pathways, and distribution systems built for predictable GPU load behavior. Providers capable of delivering this level of density at scale offer their tenants the reliable foundation for workloads that can’t afford power-related interruptions.
New hardware, new cooling needs
GPUs and AI accelerators come with the challenge of dealing with increased heat, and the thermal problem is becoming more pressing with new generations. These hardware produce a lot of heat, and air cooling alone simply can’t keep up, which pushes facilities toward liquid-based technologies specifically designed for continuous high-TDP operation. Direct-to-chip loops and immersion systems are becoming the standard in environments meant for long-term AI use. Facilities that treat these methods as baseline engineering (and not custom add-ons) allow enterprises to deploy hardware with fewer constraints.
Low-latency interconnection
AI workflows are dependent on rapid access to data, cloud services, and adjacent compute clusters. Those facilities that are positioned within rich carrier and cloud ecosystems reduce latency and improve overall performance. Enterprises seeking deterministic connectivity increasingly rely on AI-ready colocation to keep their AI pipelines stable, scalable, and more seamlessly connected with external platforms.
The benefits of AI-ready colocation for the enterprise
Hyperscalers are easily building their massive GPU campuses; however, most enterprises and mid-market organizations struggle to expand AI infrastructure on their own. Setting up private, AI-ready data centers is expensive, very complex, and time-consuming. Colocation provides a compelling, more practical path forward.
Getting to value faster
With AI-optimized colocation, enterprises can set up GPU clusters in a matter of weeks instead of years, because colocation partners already deliver everything that’s needed: the required power, cooling, and connectivity infrastructure as well.
Cost-efficiency
Building and maintaining an AI-ready data center demands substantial capital outlay. Colocation converts this into a pay-as-you-go model, making it possible for enterprises to expand GPU resources without having to invest in the underlying facility.
Scalability
AI growth rarely follows a steady path. An enterprise may suddenly need twice the compute power to support new models or rising demand. AI-oriented colocation gives businesses the ability to expand capacity quickly, adapting their footprint as needs evolve.
Abundant access to interconnection
Many colocation facilities anchor rich connectivity hubs with cloud-on ramps, abundant carrier options, and partnering opportunities. This interconnected environment helps enterprises combine colocation with hybrid cloud strategies, selecting the best location for each workload.
How colocation providers design AI-ready colocation facilities
AI workloads are reshaping the way data centers are engineered and operated. Colocation providers are adjusting power distribution models and rethinking thermal strategies to support the sustained demands of GPU-driven environments. As these pressures grow, AI-ready colocation can offer a practical solution to high-density enterprise workloads and save them the lengthy commitment and higher cost of build cycles.
Evolving power architecture for AI workloads
To keep up with AI demand, providers are securing large utility power commitments that can scale into the hundreds of megawatts. New facility designs use high-capacity substations and medium-voltage distribution to create a more stable electrical base for GPU-heavy environments. Redundant utility feeds and layered UPS systems help maintain steady and reliable power conditions, which make it smoother to operate during sudden load changes. This lowers the risk of performance issues, as well as the unnecessary stress on hardware.
Next-generation cooling for high-density compute
Cooling approaches are shifting quickly as GPU densities rise. Direct-to-chip liquid loops nd immersion systems are now built into standard design packages because air cooling cannot support the heat output of high-TDP processors. Many facilities now use liquid distribution manifolds to make it easier for customers to deploy liquid-cooled racks and to improve how efficiently heat is removed throughout the hall.
AI-optimized modular suites
The industry is noticeably moving away from generic space toward data halls that are designed and built specifically for AI clusters.
These often come with thermal zones designed for predictable hot-aisle behavior and branch-circuit capacity suited for sustained GPU draw. Reinforced flooring and refined containment layouts are part of the design to help stabilize environmental conditions, and modular construction shortens the time required to bring GPU racks online.
Sustainability strategies for high-power AI environments
Rising power consumption from AI clusters makes sustainability an increasingly important concern – and not just from a design point of view.
Colocation providers are increasingly adopting renewable PPAs and water-side economization to decrease their overall environmental impact. Many facilities today also deploy granular efficiency metrics, like segmented PUE, or live thermal mapping to offer enterprises better visibility into their operational performance. This level of transparency strengthens how AI-ready colocation aligns with internal ESG commitments.
How AI-ready colocation looks like
Evaluating AI-ready colocation requires a closer look at how well a facility can support high-density compute. Enterprises should assess whether the provider can sustain 40-80 kW per rack, and whether the cooling strategy includes proven liquid technologies.
Network capabilities matter as well, especially low-latency paths to cloud platforms and adjacent GPU clusters. Growth potential is key, since many AI teams need room to scale within the same campus. Sustainability practices, including the use of renewable energy and efficiency reporting, are becoming important factors in how enterprises choose providers. And last, but not least, location also plays a role: placing clusters near users or complementary workloads reduces latency and improves overall performance.
Why wholesale colocation is powering enterprise AI
Wholesale colocation is becoming a cornerstone of large-scale AI deployments. Retail space still plays a role for early testing and small GPU clusters, but enterprises moving into full production are leaning more and more towards data centers offering wholesale environments, because these facilities provide entire halls and dedicated suites where power, cooling, and density requirements are already in place for deploying high-performance workloads.
Providers that offer this capacity in a pre-built format can offer tenants the benefit of moving in quickly, without having to wait through long construction timelines. Many wholesale agreements now reach hundreds of megawatts and are driving the next wave of data center expansion. As demand grows, enterprises are turning to AI-ready colocation for constant scale and predictable operations. Wholesale spaces offer the capacity, control, and stability needed to support dense GPU farms over longer periods of time amking it the preferred option for enterprises planning long-term.
Future prospects
AI is reshaping the expectations placed on data centers, and the industry is only at the beginning of that curve. As enterprises move deeper into accelerated computing, the environments supporting these workloads will need to evolve at the same pace.
Providers investing in building AI-ready colocation are already laying the groundwork. The organizations that thrive will be those able to adapt with confidence, and AI-ready colocation is present as one of the few models capable of keeping up with AI’s accelerating trajectory.
Michael Zrihen is Senior Director of Marketing & Internal Operations Manager at Volico Data Centers.
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