The government has opened talks with leading Indian-origin artificial intelligence scholars and researchers at top global firms to persuade them to return to India and help in the development of the country's generative AI model and applications, sources have told Moneycontrol.
The move to reverse the brain drain comes at a time when a little-known Chinese company, DeepSeek, has stormed the AI world with its low-cost AI assistant, threatening the dominance of American companies which have been pouring billions of dollars into the new tech frontier.
“Once we create an environment for innovation, Indian-origin developers may be encouraged to return and contribute to building something significant in India,” a government official told Moneycontrol.
“We are already in talks with prominent AI researchers, including Niki Parmar, who has made major contributions to machine learning and deep learning.”
The government wants these AI experts to mentor Indian startups and young researchers. These innovators could leverage the IndiaAI Mission’s GPU computing facility to develop AI models tailored to India's needs, the official said on condition of anonymity.
Some leading Indian-origin AI experts have expressed interest in returning to India to help establish a strong foundation for the country's AI journey.
“This is the perfect opportunity for Indian-origin professionals working at OpenAI, Google DeepMind, Mistral AI, and Perplexity AI to return and contribute. If we are to build an alternative to China’s DeepSeek, we need their expertise,” the official said.
The official said that the government will encourage top academic minds to leverage the computing infrastructure.
“We will provide them with mentoring and other support to form their company to scale. We also want top India-origin folks to mentor them in developing AI applications and use cases,” the official said.
“We want to nurture youngsters, and we need to find them. If you look at DeepSeek, it has all youngsters and people who have worked with companies like Google DeepMind.”
Joining the AI race
With this initiative, India aims to develop its Large Language Model (LLMs), joining global AI leaders such as OpenAI’s ChatGPT and China’s DeepSeek.
The India AI Compute Facility will drive this effort, having secured 18,693 GPUs to power LLM development.
Initially, the government planned to acquire 10,000 GPUs but secured nearly 19,000 GPUs through competitive bidding.
The facility will include 12,896 Nvidia H100 GPUs, 1,480 Nvidia H200 GPUs and other high-performance AI chips from AMD, Intel and AWS.
As many as 10,000 GPUs are ready for immediate use, while the rest will be deployed gradually.
Starting January 3, the official said startups and researchers can apply for GPU access through a government portal.
GPUs will be provided by L1 and L2 bidders. L1 bidders include E2E Networks, Jio Platforms, Ctrls Datacenters, CMS Computers, Locuz Enterprise Solutions, NxtGen Datacenter & Cloud Technologies. L2 bidders include Orient Technologies, Tata Communications, Vensysco Technologies, and Yotta Data Services.
The government has introduced a tiered approval mechanism for AI projects requesting GPU resources.
For large-scale projects, higher-level approvals from officials such as the principal scientific adviser, the minister, and the cabinet will be required.
“If subsidies worth crores of rupees are involved, government compliance rules will be followed. The system is designed to ensure quick online approvals. Approval committees will include government representatives, industry bodies like NASSCOM, academicians, researchers, and AI experts,” a second government official said.
The committees will evaluate the purpose and the applicants to prevent misuse of GPU resources. The subsidy will be provided directly to GPU resource providers to support AI development.
DeepSeek’s success has sparked a debate on whether India should build use cases on top of existing Large Language Models (LLM) or build foundational models. As reported by Moneycontrol, some experts have warned that India’s hesitation in building a foundational LLM could be a strategic blunder.
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