NEW DELHI, Nov 10: India is stepping up efforts to establish a stronger domestic artificial intelligence ecosystem through its Rs 10,372 crore IndiaAI Mission, with the introduction of three indigenous AI foundation models and the expansion of computing infrastructure marking important developments in the country’s technology ambitions.
The initiative is aimed at strengthening India’s capabilities in artificial intelligence, reducing dependence on foreign-controlled technologies and providing domestic researchers, startups, educational institutions and businesses with greater access to advanced computing resources.
The government has been pursuing a multi pronged strategy covering AI research, computing infrastructure, semiconductor manufacturing, data availability, startup financing and the development of skilled professionals.
According to information released on November 10, three indigenous foundation models were introduced under the mission, while more than 45,000 graphics processing units (GPUs) had been onboarded for the country’s common computing facility by June 2026.
The developments represent a significant step in India’s attempt to build technological capabilities across different stages of the artificial intelligence value chain.
Three indigenous AI models introduced
The Ministry of Electronics and Information Technology recently unveiled three homegrown foundation models named EKA-1, Dhīr 2B and Dhīr 5B.
The models were developed by Bengaluru-based Socket AI and ZenteiQ under the Foundation Model initiative of the IndiaAI Mission.
EKA-1 is a 26-billion-parameter foundation model designed to support mathematics, programming and advanced reasoning tasks. It has reportedly been trained on 7.5 trillion tokens and supports more than 100 Indian and international languages, in addition to programming languages.
Its capabilities are intended to cover complex tasks involving multiple stages of reasoning, code execution and specialised computational requirements.
The model has also been designed for deployment across different computing environments, including on-premises infrastructure, private cloud systems and air-gapped networks.
Air-gapped environments are computer systems that remain physically or logically isolated from external networks. Such arrangements can be particularly useful for organisations handling confidential information or operating sensitive infrastructure.
The other two models, Dhīr 2B and Dhīr 5B, were developed by ZenteiQ using a mixture-of-experts architecture and trained from scratch with Google’s Cloud Tensor Processing Units.
These models are expected to support specialised applications and provide additional options for organisations seeking AI solutions suited to particular operational requirements.
MeitY Secretary S. Krishnan highlighted the potential of these models for enterprise applications, particularly in environments where organisations require greater control over their data and computing systems.
He also pointed towards the benefits of smaller and specialised models, which can improve productivity and operational efficiency without necessarily requiring the deployment of extremely large general-purpose AI systems.
The government has indicated that the models will remain accessible through AIKosh, allowing researchers, students and innovators to explore their capabilities and develop applications.
IndiaAI Mission receives Rs 10,371.92 crore allocation
The IndiaAI Mission was approved by the Union government in March 2024 with a total financial outlay of Rs 10,371.92 crore over five years.
Its primary objective is to develop an inclusive artificial intelligence ecosystem that supports innovation, research, entrepreneurship and responsible technology deployment.
The mission has been divided into seven major components, each addressing a different requirement of the domestic AI industry.
A substantial portion of the funding has been allocated to computing capacity, which is essential for training, testing and operating advanced artificial intelligence models.
The allocation includes approximately Rs 4,563.36 crore for IndiaAI computing infrastructure and Rs 1,971.37 crore for foundation models.
Other components cover datasets, application development, future skills, startup financing and safe and trusted artificial intelligence.
The government has also earmarked funding for improving access to data and encouraging the development of AI applications across different sectors.
The programme follows a public-private partnership approach, with the government working alongside private technology providers rather than attempting to independently develop every component of the ecosystem.
This strategy is intended to make computing resources available to a wider range of users while encouraging private-sector participation.
Expanding access to GPUs and computing resources
Graphics processing units have become a critical component of the artificial intelligence industry because they can perform large numbers of calculations simultaneously.
Unlike conventional processors that are commonly designed for general computing tasks, GPUs are particularly effective at handling the parallel computations required for training and running AI models.
Their availability has consequently become an important factor determining the ability of countries and companies to participate in advanced AI development.
India has been working to expand access to these resources through its common computing infrastructure.
More than 45,000 GPUs had been onboarded by June 2026, while 237 projects had accessed subsidised AI computing facilities by August.
The government has also supported the development of 62 AI prototypes and the deployment of 20 AI-based solutions across public-sector institutions.
These initiatives are intended to encourage practical applications of artificial intelligence rather than limiting the programme to research and experimental projects.
The IndiaAI Compute Portal provides computing resources through a shared service model, allowing startups, universities and public institutions to access infrastructure without having to independently purchase expensive hardware.
The government has previously stated that subsidised computing access would help reduce the financial burden on smaller organisations and research groups.
This is particularly relevant for startups, which often face difficulties obtaining the computing capacity required to develop advanced AI products.
Semiconductor manufacturing remains a key priority
India’s artificial intelligence ambitions are closely connected with its broader semiconductor manufacturing strategy.
The country continues to depend heavily on imported advanced processors and other critical hardware used in high-performance computing systems.
To address this challenge, the government has been encouraging domestic semiconductor manufacturing through financial incentives and industrial partnerships.
By 2026, 12 semiconductor projects had been approved across six states, involving investment commitments exceeding Rs 1.64 lakh crore.
These projects are expected to strengthen India’s electronics manufacturing capabilities and gradually expand its role in the international semiconductor supply chain.
Semiconductors are essential not only for artificial intelligence but also for telecommunications, automobiles, consumer electronics, industrial automation and defence technologies.
Developing domestic manufacturing capabilities could therefore provide wider economic benefits beyond the AI sector.
However, establishing a competitive semiconductor industry requires considerable investment, advanced manufacturing technology, reliable electricity and water supplies, specialised equipment and highly trained professionals.
India’s progress will depend on its ability to develop these supporting capabilities alongside the construction of manufacturing facilities.
Reducing dependence on foreign technology
Despite the progress made under the IndiaAI Mission, India continues to rely substantially on foreign technology providers for advanced AI infrastructure.
Many widely used foundation models are developed by international companies, while a large share of high-end computing hardware is imported.
Cloud infrastructure is also dominated by global providers, including Amazon Web Services, Microsoft Azure and Google Cloud.
This dependence creates challenges for domestic businesses and institutions seeking greater control over their technological systems.
Developing indigenous foundation models is one way to address this issue, but technological independence requires more than creating locally developed software.
It also involves access to computing hardware, semiconductor manufacturing, research facilities, specialised talent, reliable datasets and commercially viable applications.
The government’s strategy therefore aims to strengthen multiple parts of the technology ecosystem simultaneously.
According to the Stanford Global AI Vibrancy Report published in November 2025, India ranked third globally in AI competitiveness and ecosystem vibrancy.
The country is also a major contributor to AI-related projects on GitHub, reflecting the growing participation of Indian developers in the international technology community.
These indicators highlight India’s existing strengths in software development and technical talent, although converting them into globally competitive AI products remains a major task.
Funding and utilisation challenges remain
While the government has announced significant investments in AI infrastructure, questions remain about the pace of implementation and actual utilisation of available resources.
Budget documents indicate that expenditure under the IndiaAI Mission has not consistently matched the original allocations.
For the financial year 2025-26, the initial allocation was Rs 2,000 crore, but the revised estimate was reduced to Rs 800 crore.
The Union Budget for 2026-27 allocated Rs 1,000 crore to the mission.
The utilisation of available computing infrastructure has also emerged as an important concern.
A report published by the Rhodium Group in October 2026 suggested that nearly 80 per cent of available GPUs were sitting unused despite subsidised access.
The report highlighted that a significant portion of domestic AI workloads continues to be handled through international cloud platforms.
Several factors contribute to this situation, including the limited number of Indian startups training large foundation models, the preference of established companies for international cloud services and the shortage of domestic AI products capable of generating substantial computing demand.
Funding constraints also affect the ability of smaller companies to develop advanced technologies.
Although India has a large pool of software professionals and technology entrepreneurs, building globally competitive AI systems requires sustained financial support and access to specialised infrastructure.
Opportunities across different sectors
The expansion of domestic AI capabilities could create opportunities across several sectors of the Indian economy.
In healthcare, artificial intelligence can assist with medical image analysis, administrative operations and research support.
In agriculture, AI-based systems can help analyse weather information, monitor crop conditions and provide farmers with data-driven recommendations.
Educational institutions could use specialised models to develop learning tools, support research and improve access to digital educational resources.
Government departments may also benefit from AI applications designed to process documents, analyse large datasets and improve the delivery of public services.
The availability of indigenous models could be particularly useful for applications involving Indian languages and local requirements.
However, the successful adoption of these technologies will depend on their accuracy, affordability, security and ability to function reliably in real-world conditions.
Building a sustainable domestic AI ecosystem
India’s latest developments indicate that the country is moving beyond simply adopting artificial intelligence products developed elsewhere.
The introduction of indigenous foundation models, expansion of GPU infrastructure and approval of semiconductor projects represent different elements of a broader strategy to strengthen domestic technological capabilities.
The next major challenge will be ensuring that these initiatives translate into commercially successful products, meaningful research outcomes and wider adoption across industries.
The government will also need to address infrastructure utilisation, investment requirements, talent development and the availability of reliable domestic hardware.
The success of the IndiaAI Mission will ultimately depend on whether Indian startups, universities and established businesses can use the available resources to develop competitive technologies.
As global competition in artificial intelligence intensifies, the ability to develop, deploy and manage advanced AI systems is becoming an increasingly important measure of technological strength.
India’s Rs 10,371.92 crore programme provides a foundation for pursuing that objective, but sustained implementation and stronger participation from the private sector will be essential to achieving long-term results.