Originally published in ET.com

It’s 2025, and AI has joined the workforce—a bold shift that is reshaping businesses and industries while driving an urgent need for better computational infrastructure. Hyperscale data centers are at the core of this transformation, offering the power, cooling, and networking capabilities required to sustain AI’s exponential growth.

Why Are Hyperscale Data Centers Essential for AI?

AI workloads demand high-performance computing, scalable infrastructure, and energy efficiency. With AI expected to drive 70% of global data center demand by 2030 (McKinsey), hyperscale data centers must evolve rapidly to support AI training, inference, and deployment.

However, meeting these requirements is not without challenges:

Power-intensive AI models require high-density servers and optimized energy management.
Traditional cooling techniques are often insufficient for AI workloads.
Older data center architectures struggle to support AI’s need for high-bandwidth, low-latency networking.

To stay ahead, hyperscale data centers must focus on five key areas:

1. High-Density Servers for AI Workloads

The computational demands of AI models are unprecedented, requiring servers with significantly higher power capacities than traditional enterprise computing. According to Goldman Sachs, a single query on ChatGPT can consume 10 times more power than a Google search. AI models, especially large language models (LLMs) and generative AI systems, rely on GPUs and TPUs, which consume considerably more energy than conventional CPUs.

As a result, data centers must transition from standard server configurations to high-density architectures designed to handle these massive workloads efficiently.

To accommodate this shift, data centers are exploring new power distribution strategies, such as on-premises high-voltage substations, direct current (DC) power distribution, and intelligent power management systems. Additionally, innovations like dynamic workload allocation and AI-driven power optimization help reduce wastage and ensure maximum efficiency.

2. Advanced Cooling Solutions

AI servers generate extreme amounts of heat, making traditional air-cooling solutions insufficient. Records show that the energy consumption of an average rack has doubled to 17 kW. It is further expected to reach 30 kW by 2027 and may reach 50-70 kW per rack in the next few years. As rack densities increase, liquid-based cooling methods are becoming a necessity rather than an option. There are three primary types of advanced cooling solutions that data centers are integrating to sustain AI workloads:

Rear-Door Heat Exchangers: These units absorb and dissipate heat at the rack level, significantly reducing the burden on facility-wide cooling systems.
Direct-to-Chip Cooling: By using liquid-cooled cold plates in direct contact with the CPUs and GPUs, this method ensures optimal temperature control for AI processing units.
Liquid Immersion Cooling: AI servers are submerged in non-conductive cooling fluids, providing superior thermal management and enabling ultra-high-density deployments exceeding 200 kW per rack.

The transition to liquid cooling also presents sustainability benefits, as these solutions reduce overall energy consumption and water usage in comparison to traditional air cooling methods.

3. AI-Optimized Racks & Hardware

AI-optimized racks go beyond traditional designs to accommodate the increased power draw, weight, and thermal requirements of modern AI workloads. These racks must integrate not just high-power-density components but also advanced cooling mechanisms to prevent overheating.

A key consideration is the structural integrity of racks. AI-specific hardware, such as NVIDIA DGX systems and custom-built AI accelerators, often requires enhanced rack designs with increased load-bearing capacity. Additionally, high-speed interconnects must be incorporated to allow seamless data transfer between AI training clusters.

Furthermore, rack-level redundancy and modularity are critical for scalability. AI workloads require dynamically scalable infrastructure, meaning that power, cooling, and networking resources must be easily expandable as demand grows.

4. Digital Infrastructure for AI Scalability

AI models operate on vast amounts of data, requiring low-latency, high-bandwidth digital infrastructure to ensure uninterrupted processing. Older data center architectures, designed for standard enterprise applications, often fail to meet the speed and efficiency required by AI workloads.

To address these challenges, hyperscale data centers are investing in multi-tier network architectures that provide ultra-fast data transfer speeds and seamless cross-connectivity. Some of the key advancements in AI-centric digital infrastructure include:

Carrier-neutral data center designs: These allow businesses to connect with multiple network providers, ensuring flexibility and eliminating vendor lock-in.
High-speed fiber networks: Data centers are deploying 400G and terabit-scale network architectures to handle AI-driven data exchange efficiently.
Edge computing integration: By processing AI workloads closer to the source of data generation, edge computing reduces latency and enhances real-time AI inference capabilities.

5. Sustainability and ESG Commitments

The rapid expansion of AI workloads is driving an unprecedented increase in power consumption, making sustainability a core priority for hyperscale data centers. AI models, particularly those used in deep learning and neural networks, require massive amounts of energy, with some training cycles consuming as much electricity as small cities.

To address this, leading data centers are implementing aggressive sustainability measures, including:

Renewable energy adoption: Many hyperscale facilities are shifting towards solar, wind, and hydro-powered electricity to reduce carbon footprints.
AI-driven energy efficiency optimizations: AI models can be used to predict and optimize energy consumption, reducing waste and improving operational efficiency.
Sustainable cooling solutions: By leveraging liquid cooling and other low-impact technologies, data centers can drastically cut down on both power and water usage.

Sustainability is no longer an afterthought but a fundamental pillar of future-ready hyperscale infrastructure.

The Road Ahead for Hyperscale Data Centers

In an AI-driven world, hyperscale data centers must continuously innovate to remain efficient, scalable, and sustainable. By addressing power, cooling, networking, and environmental concerns, these facilities will be well-equipped to support the next generation of AI applications.

Sify is making bold strides in AI-driven infrastructure, scaling its data center footprint, integrating AIOps, and acquiring high-performance GPUs. As India’s first NVIDIA certified DGX-ready data center service provider for air and liquid cooling, it can deploy solution specifications to support 130 kW/rack capacity. On its commitment to sustainability, Sify has already contracted for 231 MW renewable energy, reinforcing its leadership to sustainable AI-driven infrastructure.

Disclaimer: Content Produced by ET Edge

Originally published in ET.com