Optimizing Computing for India's AI Capabilities

Published Date : 31/05/2025 

As AI compute demand skyrockets, India is exploring diverse approaches to bolster its national AI capabilities. From centralized data centers to distributed computing and edge computing, the country is positioning itself for an AI-driven future. 

The demand for Artificial Intelligence (AI) compute has exploded over the past decade. Since 2011, the amount of compute used to train AI models has grown by a factor of 350 million. Since 2010, as deep learning gained traction, the compute requirements of AI models began doubling every 5.7 months. In 2015, large-scale machine learning (ML) models emerged with 10- to 100-fold higher training compute requirements. Unlike data and models, compute is a scarce resource.

National innovation capabilities in AI depend on a country’s common infrastructure. Developing these foundational capabilities is imperative for economic development and security. The rise in computing demand has coincided with growing demand for data centres. According to a mid-range scenario by McKinsey, global data centre capacity could triple by 2030, with about 70 percent of such demand driven by AI. However, data centres require a combination of scarce resources, such as real estate and power (AI is projected to drive a 165 percent increase in data centre power demand by 2030), alongside high-end strategic and technological inputs such as advanced semiconductors. This demand is also fuelled by the need for cloud service providers that cater to AI workloads. As AI technologies and computing demand continue to evolve, national computing strategies must span a diversity of approaches, including centralised data centre infrastructure as well as distributed computing capabilities.

As India stands on the cusp of AI-driven transformation, it becomes imperative to articulate the components of a forward-looking compute strategy. India’s compute landscape is marked by robust data centre growth (a Compound Annual Growth Rate or CAGR of 24 percent since 2019); public provisioning of Graphics Processing Units (GPUs) through the IndiaAI Mission’s compute portal, which aims to offer access to GPUs at reduced rates; and proposals for a decentralised network of micro data centres that optimise space, energy, and cost. In addition, there is supercomputing availability under the National Supercomputing Mission for academic research and development, along with innovations such as the Ziroh Lab and the Indian Institute of Technology (IIT)-Madras’ Kompact AI, which enable AI models to run on Central Processing Units (CPUs) instead of GPUs. These developments highlight the need to align this momentum with the evolving nature of compute demand—from training to inference—and to recognise the emerging importance of edge computing (running AI models on devices and near data sources) in critical sectors. Policy direction in this domain must be shaped by technological, economic, and geopolitical realities.

National innovation capabilities in AI depend on a country’s compute infrastructure as one of the foundational capabilities. Developing these core capabilities is vital for economic development and security. Given the strategic importance of AI as an arbiter of global dominance, and the unequal distribution of computing resources, compute has emerged as the fulcrum of geopolitical posturing—with export controls, retaliatory measures, and national AI innovation policies determining a country’s AI innovation trajectory.

Currently, start-ups tend to veer towards application-focused innovation to ensure quick returns on investment, however, given the emerging importance of deep tech, it is important to understand the overall nature of compute demand that might unlock broader innovation potential. As countries seek to bolster domestic innovation capacities, large-scale infrastructural investments have become paramount. The concept of ‘AI factories’ is gaining traction as a model for optimised computing infrastructures capable of handling AI workloads and life-cycle management. Specialised data centres for AI are becoming a central infrastructural trend, attracting substantial private investment. Industry leaders such as NVIDIA are collaborating with firms such as Foxconn, Dell, and ASUS to build dedicated facilities for generative AI (Gen AI) workloads. NVIDIA is also investing in the development of AI supercomputers in the United States (US) that will power the AI factories of the future. The US hosts several of the largest technology firms in the AI value chain, including developers such as OpenAI, cloud service providers like Amazon Web Services, Microsoft, and Google, as well as leading chip designers and manufacturers such as NVIDIA, AMD, and Intel, and semiconductor equipment producers such as KLA, LAM, and Applied Materials. This environment supports a private sector-driven approach to AI infrastructure. Private investments in the US are buoyed by its current policy direction to develop strategic technological capabilities in the country, aiming to strengthen its AI infrastructure and tech manufacturing.

China’s version of AI factories, known as ‘intelligent computing centres’, has expanded at an accelerated pace in recent years, driven by a state-led approach with support from private players including Alibaba, Tencent, and Baidu. China has also developed an underwater computing centre, such as the one in Hainan, to boost AI development and its national AI computing network. This cluster supports high-end AI applications, reportedly including DeepSeek. Underwater centres are more energy efficient and help overcome real estate constraints, while potentially delivering higher computational efficiency than land-based models. However, China’s aggressive buildout resulted in below-standard facilities and oversupply. Many data centres were designed primarily for pretraining workloads, rather than inference tasks, more suited to training rather than running models. With the advent and adoption of DeepSeek, the overall industry demand has shifted more towards inference, which requires a different hardware profile. 

Frequently Asked Questions (FAQS): 

Q: What is the significance of compute in AI innovation?

A: Compute is a critical resource for AI innovation, as it enables the training and inference of AI models. The exponential growth in compute demand reflects the increasing complexity and scale of AI applications.

Q: How is India addressing the growing need for AI compute?

A: India is addressing the growing need for AI compute through robust data centre growth, public provisioning of GPUs, proposals for micro data centres, and innovations like Ziroh Lab and Kompact AI, which enable AI models to run on CPUs.

Q: What are AI factories, and how do they contribute to AI innovation?

A: AI factories are specialised data centres designed to handle AI workloads and lifecycle management. They contribute to AI innovation by providing optimised computing infrastructures and attracting substantial private investment.

Q: What are the challenges in building AI infrastructure in India?

A: Challenges in building AI infrastructure in India include the need for high-end strategic and technological inputs, such as advanced semiconductors, and the requirement for energy-efficient and cost-effective solutions.

Q: How does China's approach to AI infrastructure differ from India's?

A: China's approach to AI infrastructure is more state-led, with a focus on building large-scale intelligent computing centres and innovative solutions like underwater data centres. India's approach is more diverse, combining public and private efforts with a focus on decentralised and edge computing. 

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