Founder, CMD & CEO,
ESDS Software Solution Ltd
The infrastructure requirements of modern AI applications are fundamentally reshaping the data-center landscape today. With businesses adopting increasingly complex workloads and the training and deployment of these in demand, high performance compute, dense GPU racks and ultra-efficient and ‘green’ cooling systems are re-imagining colocation. Mckinsey reported demand for AI-ready data center capacity will increase 33% per year on average from 2023-2030, approximately 70% of all data center capacity.
The Rise of AI Workloads and Infrastructure Challenges
AI workloads are intensely computationally expensive, and demand not just large amounts of time but also specialized hardware such as NVIDIA 100s, H100s, and similar high-wattage GPUs. According to Grand View Research, the worldwide data center GPU market hit $14.9 billion in 2023 and will rise to $8.7 billion by 2030, growing at a CAGR (compounded annual growth rate) of 28.5% This strong growth wave is swamping legacy IT environments and is driving enterprises toward GPU-enabled colocation.
AI will continue to build momentum in 2025
The rapid development of artificial intelligence is poised to usher in a revolutionary age for the data center sector. In addition to changing the digital infrastructure environment, this technological revolution is radically altering it.
AI applications are growing in almost every industry. The demand for more potent and effective data center infrastructure is being driven by the billions of dollars that have been invested in AI in recent years. As a result, the construction of data centers worldwide is currently at an all-time high. According to all indications, demand for AI will only increase in 2025.
The accelerated development of semi-conductor technology is at the heart of the AI revolution. With the latest GPU technology, a computation process that once took 32 hours to complete may now be completed in just one second. AI programs may now train on ever-larger data sets due to the growing processing rates. The entire AI ecosystem is becoming more useful as a result of the increasing speed at which AI models can be trained, iterated, and improved. With every new GPU generation, the rate of advancement in AI will continue to pick up speed.
Why Traditional Colocation is Evolving?
Traditional colocation sites were orientated on hosting servers with average CPU design parameters and cooling with medium power densities in-between. However, AI workloads require much more: power densities of 30 kW to 100 kW per rack, sophisticated cooling such as immersion or liquid- to-chip, and high-speed networking to enable distributed compute. The result is that colo providers are repositioning their facilities as specialized AI centers tailored for compute-hungry use cases.
Key Characteristics of AI-Optimized Data Centers
These next-gen facilities are purpose-built to support AI at scale. Their defining features include:
- Power densities over 30–50 kW per rack
- Liquid or direct-to-chip cooling technologies
- High speed interconnect such as InfiniBand and 100G+ Ethernet
- Dual power & connectivity for uptime guarantee
- Compliance capabilities for regulated industries
This culminates to provide performance, reliability, and scalability for AI workloads.
Hybrid Cloud and Colocation: A Powerful Partnership
GPU-dense colocation is not a replacement for cloud — it complements cloud. In hybrid environments, organisations place sensitive or performance-sensitive workloads in colocation for control and compliance, then leverage cloud for elasticity and burst compute.
Advantages of this model include:
- Reduced cloud egress fees
- Custom GPU configurations
- Avoidance of vendor lock-in
- Optimized TCO
In addition, cloud GPU market revenue is forecast to hit $45.5B in 2030 according to Grand View
research – up from $8.9B in 2024.
Colocation Providers Step up: Innovations and Examples
To keep pace in the AI era, colocation companies are modernizing dated facilities and constructing
new ones that incorporate:
- Liquid-cooled and high-density rack systems
- Pods or pre-fabricated AI containers, supporting acceleration for interpretation modeling across utility model strengths.
- Remote assistance in the management of AI clusters.
- Next-gen hardware integration such as NVIDIA DGX and AMD MI300
This makes colocation providers strategic partners, rather than merely places to house hardware.
What’s Next: AI Infrastructure in 2025 and Beyond
With the rise of GenAI, large language models (LLMs), and real-time inference, dense and intelligent compute more per infrastructure is a critical requirement. Colocation providers are deploying smart energy management, containerized AI modules and AI-based monitoring applications to be able to run future-proof operations. In addition to this, data localization laws and green mandates are making GPU-optimized colo even more appealing to enterprises—particularly in areas where commercial cloud giants have regulatory issues.
Conclusion
As more and more enterprises scale AI adoption, infrastructure decisions will help dictate their competitive position. Colocation isn’t your old legacy option; it’s the underpinning of innovation —and the best way to get the GPU density, operational efficiency, and strategic flexibility your organization demands.
In the age of artificial intelligence, selecting the right colocation site is not just a matter of square footage or megawatt capacity. It’s not about AI for AI’s sake; It’s about the intelligent systems that deliver results.
-Author is Piyush Somani, the Founder, CMD & CEO at ESDS Software Solution Ltd
About The Author
