BharatCompute for AI ascendancy

Artificial Intelligence (AI) capability rests on four key pillars: Algorithms, data, talent, and AI compute, with AI compute often considered the most crucial.

Graphical processing units (GPUs) have become the standard measure of AI compute, spurring a global race to build massive GPU capacities.

In this context, the government's March decision to deploy over 10,000 GPUs as part of the India AI Mission is a significant step forward. This initiative, to be achieved through public-private partnership (PPP), reflects India's willingness to leverage the strengths of the private sector, a departure from past practices of confining such capital-intensive national capacities to the public sector.

India's AI compute capacities are modest compared to leaders like the US and China. The global GPU market was valued at $23 billion in 2022, with an estimated 1 to 2 billion GPUs worldwide, predominantly in the US and China. Both countries have aggressively acquired GPUs over the past decade and have bigger plans to advance their AI compute capabilities. The US launched the National AI Research Resource programme in January, while China aims to increase its aggregate compute power by over 50 per cent, including 10 exascale systems, by 2025. India cannot and does not need to mimic their strategy. Instead, it should adopt a smart fast-follower's approach, which I term BharatCompute.

Why is AI compute so important? In AI, "compute❞ refers to the computational power needed to perform complex operations, like training and running AI models. It is crucial for optimising deep learning models, which often have millions or billions of parameters involving large datasets requiring extensive computation. Additionally, compute power is needed for real-time inference, enabling predictions from new data.