Government Shares Vision on Making AI Infrastructure in India More Accessible

The Principal Scientific Adviser shared a white paper on access to AI infrastructure, detailing data, compute and DPI roles.

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Written by Akash Dutta, Edited by Ketan Pratap | Updated: 30 December 2025 12:59 IST
Highlights
  • The white paper recommends treating AI systems as digital public goods
  • India is constructing a GPU cluster with 3,000 next-gen GPUs
  • The government suggests taking a digital public infrastructure approach

The white paper also recommends developing resource-efficient AI systems

Photo Credit: Pexels/Brett Sayles

The Office of the Principal Scientific Adviser (PSA) to the Government of India has published a white paper titled “Democratising Access to AI Infrastructure,” outlining key aspects of expanding access to artificial intelligence (AI) infrastructure across the country. The PSA states that the document was prepared with input and feedback from domain experts, stakeholders, and colleagues, and was released on Monday. It examines physical and digital AI infrastructure, current capacities in India, and considerations for broader and more equitable access to compute, datasets and model ecosystems.

India's Vision to Create Democratic Access to AI Infrastructure

In a post on X (formerly known as Twitter), the official handle of the Office of the Principal Scientific Adviser shared the white paper, stating, “For India, democratising access means treating AI infrastructure as a shared national resource, empowering innovators across regions to build local-language tools, adapt assistive technologies, and create solutions aligned with India's diverse needs.”

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The white paper defines democratising access to AI infrastructure as making essential elements of AI, such as computing power, datasets, and model toolchains, available and affordable, so that a wider set of users can participate in developing and deploying AI technologies. It suggests that access to these building blocks should not be limited to a small group of global players and urban hubs, but should instead be treated as shared resources that support innovation across institutions and regions.

In its assessment of AI infrastructure, the paper distinguishes between physical infrastructure and digital infrastructure. Physical infrastructure includes data centres, Graphics Processing Units (GPUs), Tensor Processing Units (TPUs) and other specialised processors that support training and deploying large AI models. The report points out that while India hosts nearly 20 percent global data, its share of global data centre capacity is just three percent. It also highlights the planned expansion of compute capacity through initiatives under the IndiaAI Mission, including a secure GPU cluster with 30,000 next-generation units for sovereign and strategic applications.

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PSA's white paper notes that data centres in India are geographically concentrated, with Mumbai and Navi Mumbai holding the largest share, followed by hubs in Chennai, Bengaluru, Hyderabad, the Delhi NCR region, Pune and Kolkata. Alongside physical compute capacity, the document discusses how high-quality datasets and foundational model ecosystems are critical for AI development and highlights the need for these resources to be more widely accessible.

The core of vision highlighted in the white paper is the “digital public infrastructure (DPI)” approach. As per the PSA, the approach includes treating AI systems as digital public goods, which will enable stakeholders to utilise data, compute, and the ecosystem of models and algorithms without needing to be in physical proximity. However, instead of using a single platform or a monolithic system, the PSA advises using a set of modular public-good enablers to address the gaps in the AI ecosystem.

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In the initial phase, the focus is recommended to be on lighter-weight elements, such as directories, metadata standards, access protocols, or registries. After developing capacity, the focus should be on data access systems, consent-based data flows, and a “coordinated computer-exchange mechanism,” commonly known as compute.

The white paper does not propose formal policy changes. Instead, it can be understood as a broad vision outlined by the government to shape the country's AI infrastructure at the infant stage, so that when the technology is scaled, there is no need to make large changes to the infrastructure to expand access to non-urban centres and individual stakeholders.

 

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