Why TCO and efficiency are reshaping data centre decisions in India

By Chetan Hingu, Country Head – GCC & Value Business, Alliances, AMD India

For many infrastructure teams today, the challenge is not finding more compute. It is finding ways to support increasing workloads without seeing power, cooling, and operating costs rise at the same rate. As capacity expands to support AI, cloud, and digital services, infrastructure decisions are increasingly being shaped by economics. Total cost of ownership (TCO) and performance per watt have become important measures because they help organisations understand not just what a system can do today, but what it will cost to run and scale over time. 

Infrastructure decisions are now economics decisions

In India, one of the key challenges is how to efficiently deliver compute within the limits of power, space, and cost.

TCO is now a primary lens for infrastructure planning. Beyond hardware, organisations are accounting for power consumption, cooling, software licensing, rack density, and ongoing operational overheads. In India, where power availability and cost remain critical considerations, these factors often have a greater impact on long-term value than upfront systems’ costs.

At the same time, performance per watt is becoming a practical requirement, not just a technical metric. Operators are increasingly working within fixed power envelopes, especially in colocation and hyperscale environments, while supporting both traditional workloads and growing AI demand.

As a result, the focus has shifted to the amount of consistent, usable output delivered per watt and per rupee overtime.

The evolving role of CPUs in AI and data centre economics

To talk about data centre TCO and efficiency, we must talk about CPUs’ increasingly critical role in modern AI infrastructure, not just as supporting components, but as the backbone of efficient, scalable systems.

As AI evolves from large-scale training to more dynamic, agentic and inference-driven workloads, the importance of CPUs is growing. These workloads demand continuous data processing, orchestration, and real-time decision making in areas where high-performance CPUs are essential. They manage data pipelines, coordinate tasks across distributed systems, and help ensure GPUs are fully utilised rather than sitting idle.

In GPU-accelerated environments, the right CPU can have a measurable impact on overall system efficiency. Optimised CPU-GPU balance can improve accelerator utilisation, reduce bottlenecks, and increase throughput – helping lower the cost per workload.

At the same time, a significant portion of enterprise and AI inference workloads can run efficiently on CPUs alone. Modern high-core-count processors can handle many smaller models, data-intensive applications, and mixed workloads without requiring dedicated accelerators, offering a highly cost-effective and flexible deployment model.

This is where metrics like core density, memory bandwidth, and I/O performance become critical. By enabling workload consolidation and higher utilisation per server, CPUs can play a central role in improving performance per rack and reducing overall infrastructure costs.

Recent advances in processor and accelerator architectures have demonstrated significant gains in energy efficiency for AI training and high-performance computing workloads, enabling substantial reductions in energy consumption for comparable performance over previous-generation systems.

As AI becomes more distributed and continuous, CPUs are not just enabling the system, they are becoming a key driver of efficiency, scalability, and economics in modern data centres.

Beyond TCO and efficiency: Building future-ready infrastructure with an open ecosystem

As India’s data centre and cloud ecosystem expand, the focus is shifting from simply adding capacity, to scaling infrastructure more intelligently.

We are already seeing this play out in real deployments, where organisations are consolidating workloads onto fewer, more powerful systems, or increasing performance within existing infrastructure footprints to improve utilisation and reduce operational overhead Enterprises across industries are increasingly leveraging high-performance cloud and on-premise infrastructure to accelerate digital transformation initiatives, highlighting a wider shift toward IT environments built for stronger performance, better utilisation, and tighter control over infrastructure costs. By increasing compute density, businesses may be able to lower rack footprint, reduce software licensing costs, and simplify infrastructure management – delivering immediate gains in both cost efficiency and scalability.
This approach is particularly important in India, where data centre operators must balance rapid growth with constraints around power, space, and cost. Consolidation is no longer just an optimisation, it’s becoming a necessity for sustainable scaling.

At the same time, future-ready infrastructure is equally critical to achieving long-term TCO and efficiency. AI workloads are evolving rapidly, and organisations need the flexibility to adapt without constantly rearchitecting their environments. This is where open ecosystems and standards play a key role.

By enabling interoperability across hardware, software, and deployment environments, open platforms allow enterprises to scale across cloud, on-premises, and edge with great flexibility – while enabling vendor freedom-of-choice. This helps ensure that infrastructure investments made today can continue to deliver value as workloads, models, and technologies evolve.

Ultimately, the opportunity is not just to build faster systems, but to build infrastructure that is efficient today and adaptable for the future.