Author: GIN Technology Desk Published: May 14, 2026
India’s Union Cabinet has approved the IndiaAI Mission with a total budget outlay of ₹10,371.92 crore — and the money is already moving. What started as a policy document is rapidly becoming physical infrastructure, funded research, and working AI products. The goal, as Prime Minister Narendra Modi’s government has framed it, is straightforward but historically ambitious: make AI in India, and make AI work for India.
At the heart of the mission is the question of compute — the raw processing power on which all artificial intelligence is built. The IndiaAI Compute Portal now provides access to over 18,693 GPUs, supplied by approved vendors including Jio Platforms, Tata Communications, E2E Networks, Yotta Infrastructure, and CtrlS, among others. The portal gives startups, universities, researchers, and government bodies affordable access to high-end processors including Nvidia H100, H200, AMD MI300x, and Intel Gaudi 2 chips — hardware that until recently was available only to well-capitalised global technology companies.
The pricing has been deliberately kept accessible. Accessing standard GPUs through the portal costs ₹115.85 per hour, while more powerful chips are priced at ₹150 per hour — a fraction of what comparable compute costs on international cloud platforms. For a PhD student in Bhopal or a two-person AI startup in Indore, this is transformative.
One of the mission’s most consequential decisions was the selection of twelve AI startups — including Sarvam AI, Soket AI, Gnani AI, and the IIT Bombay-led BharatGen consortium — to develop India’s first large multimodal foundation models using Indian languages and data. In April 2025, Sarvam AI was separately selected to build India’s first sovereign large language model, with capabilities spanning reasoning, voice interaction, and multilingual understanding across India’s 22 scheduled languages.
The mission is structured around seven pillars covering AI compute infrastructure, datasets, foundation models, application development, future skills, startup financing, and safe and trusted AI governance. On the skills side, the IndiaAI FutureSkills programme supports 500 PhD fellows, 5,000 postgraduate students, and 8,000 undergraduate students, with 31 Data and AI Labs already launched in Tier-2 and Tier-3 cities in association with NIELIT and industry partners.
The geopolitical logic behind the mission is as important as the technical ambition. India imports the vast majority of its AI infrastructure — chips, models, cloud services — from the United States and China. The IndiaAI Mission is an explicit attempt to reduce that dependency, building sovereign capability that cannot be switched off by export controls or geopolitical friction. The focus is on creating AI technologies that are multilingual, context-aware, and tailored to India’s unique social and economic landscape — not simply adapting Western models for Indian users, but building from the ground up with Indian data, Indian languages, and Indian problems in mind.
India is not the first country to attempt national AI sovereignty. But with 1.4 billion citizens, one of the world’s largest pools of STEM graduates, and a government willing to put over a billion dollars behind the mission, it may be the most consequential attempt yet.