By Sridhar Pinnapureddy
Generative AI has undeniably shifted from a promising research frontier to a transformative force, reshaping industries from rapid code development and product design to legal drafting and creative content generation. While the public imagination often fixates on the astonishing capabilities of large language models (LLMs), the true, often unseen, enabler of this unprecedented scale and sophistication is the modern data centre. Witnessing the growth of space over years, I see firsthand how our infrastructure isn’t just supporting innovation; it’s actively architecting the future of intelligence.
Training a frontier-scale LLM is an undertaking of immense resource-intensity. Models with hundreds of billions of parameters demand thousands of high-performance GPUs or custom AI accelerators. These powerful units must operate in tightly orchestrated parallel clusters, communicating seamlessly and at lightning speed.
Datacentres must provide a meticulously engineered environment for this scale, integrating cutting-edge high-speed interconnects and advanced orchestration systems to ensure data flows between nodes in milliseconds. These ensure that vast datasets and computational results flow between nodes in milliseconds, preventing bottlenecks that could cripple training efficiency.
Goldman Sachs projects that AI will drive a 165% increase in data centre power demand by 2030. This isn’t just a statistic; it’s a clear mandate for massive infrastructure expansion and modernisation, requiring strategic foresight and significant investment.
At its core, data is the foundation, the very lifeline of generative AI. Vast, diverse and meticulously curated datasets – encompassing text, images, audio, and video, must be stored, processed, and delivered to training systems at high speeds that were unimaginable just a few years ago. This necessitates robust, high-throughput storage solutions and intelligent data management platforms.
Nasscom’s recent analysis indicates that India’s data centre capacity has expanded more than fourfold in the past seven years. AI-ready facilities are not just growing in isolation; they are catalysing demand for allied technology services such as cloud migration, cybersecurity, and AI-driven automation. This reflects a broader global trend where data centre growth isn’t merely about physical expansion, but about fostering an entire ecosystem of specialised technology services essential for deployment.
In the realm of AI, the efficiency of communication between compute nodes can be as decisive as raw processing power. The constant exchange of gradients during training, or the distribution of inference requests, demands ultra-low latency and immense bandwidth. Networking represents the largest single infrastructure expenditure for generative AI training. This significant investment supports high-bandwidth, low-latency architectures optimised for GPU-to-GPU communication, rapid storage access, and complex distributed AI tasks.
Furthermore, as AI models move into production, inference workloads are increasingly being deployed at the edge, closer to data sources and end-users, to reduce latency, optimise resource utilisation and ensure real-time responsiveness for critical applications.
The sheer energy consumption of state-of-the-art LLMs is a central consideration, often consuming megawatt-hours of electricity for a single training run. This isn’t just an environmental concern; it’s a critical business imperative. Data centre operators are responding with relentless innovations in cooling technologies, from advanced air-cooling techniques to sophisticated liquid cooling solutions like direct-to-chip and immersion cooling, which can significantly improve power usage effectiveness (PUE).
Beyond cooling, we are seeing increased adoption of heat reuse strategies, transforming waste heat into usable energy, and aggressive integration of renewable energy sources. Nasscom highlights sustainability consulting and energy efficiency solutions as emerging high-growth service areas, with green-certified capacity in India’s leading cities growing at a 15% CAGR.
The imperative to upgrade and modernise infrastructure is no longer solely an IT department concern; it has finally moved to become the boardroom agenda. Organisations recognise that their ability to leverage AI for competitive advantage, or even to simply keep pace, hinges on robust, scalable, and future-proof data centre capabilities. This has led to engaging integrated service providers capable of delivering end-to-end data centre planning, deployment, optimisation, and lifecycle refresh. These strategic partnerships ensure that infrastructure investments are aligned with long-term AI strategies, mitigating risk and maximising return.
Once trained, LLMs must serve millions, and in some cases billions of queries daily across diverse geographies and applications. Datacentres enable this global delivery through sophisticated techniques like model sharding, distributed inference, and autoscaling clusters that dynamically adjust capacity to meet fluctuating demand. This distributed architecture ensures redundancy, resilience, and critically, seamless, low-latency AI experiences for users worldwide. From a chatbot assisting a customer in Tokyo to a design tool used by an engineer in New York, the underlying datacenter network ensures consistent, high-performance access.
In the future, strategic alliances between data centre operators, cloud hyperscalers, and AI solution providers, both in India and globally, will be pivotal in developing the next generation of AI-ready infrastructure. As power requirements escalate, network complexity increases, and sustainability pressures intensify, competitive advantage will accrue to those who can combine scale with efficiency, and innovation with operational resilience.
Generative AI may be the visible, awe-inspiring face of this technological transformation, but data centres remain its strategic backbone. Their continued evolution, driven by visionary leadership and continuous innovation, will define the trajectory of AI’s global impact for decades to come.
The writer is founder and CEO, CtrlS Datacenters
