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    Home - AI - Inside India’s scramble for AI independence
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    Inside India’s scramble for AI independence

    TechurzBy TechurzJuly 4, 2025Updated:May 10, 2026No Comments2 Mins Read
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    Inside India’s scramble for AI independence
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    Under its compute program, the government is deploying more than 18,000 GPUs, including nearly 13,000 high-end H100 chips, to a select group of Indian startups that currently includes Sarvam, Upperwal’s Soket Labs, Gnani AI, and Gan AI. 

    The mission also includes plans to launch a national multilingual data set repository, establish AI labs in smaller cities, and fund deep-tech R&D. The broader goal is to equip Indian developers with the infrastructure needed to build globally competitive AI and ensure that the results are grounded in the linguistic and cultural realities of India and the Global South.

    According to Abhishek Singh, CEO of IndiaAI and an officer with MeitY, India’s broader push into deep tech is expected to raise around $12 billion in research and development investment over the next five years. 

    This includes approximately $162 million through the IndiaAI Mission, with about $32 million earmarked for direct startup funding. The National Quantum Mission is contributing another $730 million to support India’s ambitions in quantum research. In addition to this, the national budget document for 2025-26 announced a $1.2 billion Deep Tech Fund of Funds aimed at catalyzing early-stage innovation in the private sector.

    The rest, nearly $9.9 billion, is expected to come from private and international sources including corporate R&D, venture capital firms, high-net-worth individuals, philanthropists, and global technology leaders such as Microsoft. 

    IndiaAI has now received more than 500 applications from startups proposing use cases in sectors like health, governance, and agriculture. 

    “We’ve already announced support for Sarvam, and 10 to 12 more startups will be funded solely for foundational models,” says Singh. Selection criteria include access to training data, talent depth, sector fit, and scalability.

    Open or closed?

    The IndiaAI program, however, is not without controversy. Sarvam is being built as a closed model, not open-source, despite its public tech roots. That has sparked debate about the proper balance between private enterprise and the public good. 

    “True sovereignty should be rooted in openness and transparency,” says Amlan Mohanty, an AI policy specialist. He points to DeepSeek-R1, which despite its 236-billion parameter size was made freely available for commercial use. 

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