Building India's Own LLMs

 

Building India's Own LLMs: The Strategic Push for Indigenous AI Foundation Models

The world is undergoing a profound transformation driven by Artificial Intelligence (AI), particularly through the advent of Large Language Models (LLMs) and foundational AI systems. Models like OpenAI's GPT series, Google's Gemini, and Anthropic's Claude have captured global imagination, demonstrating remarkable capabilities in understanding and generating human-like text, translating languages, writing different kinds of creative content, and answering questions in an informative way. These powerful tools, often termed 'foundation models' due to their broad applicability across various tasks, are rapidly becoming critical infrastructure for innovation, economic growth, and societal change.

Amidst this global AI race, India, a nation with immense digital ambition and a burgeoning tech ecosystem, is embarking on a crucial mission: to build its own indigenous LLMs and foundation models. This isn't merely about replicating Western advancements; it's a strategic imperative rooted in the unique socio-economic, linguistic, and cultural fabric of the country. 

The vision, often encapsulated by the government's mantra of 'AI for All,' aims to harness the power of AI not just for a select few, but to empower every citizen, bridge linguistic divides, address uniquely Indian challenges, and secure the nation's digital future. The push for sovereign AI capabilities represents a confluence of national aspiration, economic necessity, and the pursuit of technological self-reliance in an increasingly AI-driven world.

The Compelling Case: Why India Needs Sovereign AI

The drive to develop homegrown LLMs stems from several interconnected factors, each highlighting the limitations of relying solely on globally dominant models and the opportunities presented by building indigenous alternatives:

1. Data Sovereignty and Security: Foundation models are trained on colossal datasets. When Indian users, businesses, and government entities rely heavily on foreign platforms, sensitive data potentially flows across borders, raising concerns about privacy, security, and misuse. Building indigenous models allows India to maintain greater control over its data, ensuring it is handled according to national laws and priorities.
2. Embracing Linguistic Diversity: India is home to an unparalleled linguistic landscape, with 22 constitutionally recognized languages and hundreds of dialects spoken across its vast territory. Global LLMs, primarily trained on English-centric web data, often struggle to capture the nuances, complexities, and cultural context of Indian languages. Many Indian languages are 'low-resource' in the digital realm, meaning there's a scarcity of high-quality training data. Indigenous LLMs are essential to bridge this gap, providing AI services that understand and interact effectively in Hindi, Tamil, Bengali, Marathi, Telugu, Kannada, and numerous other languages, thereby democratizing access to information and digital services.
3. Economic Engine and Innovation Hub: Developing a domestic AI foundation model ecosystem is a significant economic opportunity. It can spur innovation, create high-value jobs for AI researchers, engineers, and data scientists, and nurture a vibrant startup landscape. Relying solely on licensing expensive foreign models can drain foreign exchange and stifle local innovation. Building indigenous capacity allows India to capture a greater share of the rapidly growing global AI market and develop solutions tailored specifically for Indian industries.
4. Strategic Autonomy and Reduced Dependency: Over-reliance on technology controlled by a few global corporations or nations introduces strategic vulnerabilities. Access, pricing, and model behaviour can be subject to external corporate policies or geopolitical considerations. Developing sovereign AI capabilities enhances India's strategic autonomy, ensuring that its AI trajectory aligns with national interests, ethical frameworks, and developmental goals, free from undue external influence.
5. Solving India-Specific Challenges: From optimizing agricultural practices for diverse agro-climatic zones and providing personalized healthcare advice in regional languages to developing tailored educational content and improving citizen service delivery through GovTech platforms, India faces unique challenges that require context-aware AI solutions. Indigenous LLMs, trained on relevant Indian datasets and attuned to local needs, are far better positioned to tackle these specific problems effectively and inclusively.

The Architects of India's AI Future: Key Players and Initiatives

Realizing the vision of indigenous AI requires a concerted effort from various stakeholders. India is witnessing the emergence of a multi-pronged approach involving the government, academia, research institutions, established corporations, and dynamic startups.

• Government Leadership: The IndiaAI Mission: The Indian government has recognized the strategic importance of AI and is playing a pivotal role in orchestrating the national effort. The landmark IndiaAI Mission, approved by the Union Cabinet in March 2024 with a significant outlay of ₹10,372 crore (approximately US$1.25 billion), provides a comprehensive framework. This mission is built on seven key pillars:

• IndiaAI Compute: Addressing the critical need for high-performance computing infrastructure by setting up dedicated AI compute facilities and potentially subsidizing access for researchers and startups. The recent launch of the IndiaAI compute portal (March 2025), offering subsidized access (up to 40%) to compute resources, networking, storage, and cloud services, is a major step in this direction.
• IndiaAI Datasets Platform: Facilitating the availability of high-quality, diverse datasets, especially for Indian languages, is crucial for training robust models. This involves creating mechanisms for data collection, curation, and sharing.
• IndiaAI FutureSkills: Focusing on skilling and upskilling the workforce with necessary AI expertise.
• IndiaAI Startup Financing: Providing financial support and fostering an environment conducive to AI startups.
• IndiaAI Innovation Centres (IAIC): Establishing centres of excellence to drive cutting-edge research and development. A call for proposals launched in January 2025 under IAIC invited collaboration on building state-of-the-art foundational models trained on Indian datasets, receiving significant interest (67 proposals by mid-February 2025, including 22 for LLMs/LMMs).
• IndiaAI Applications Development Initiative: Promoting the creation of AI applications relevant to India's societal needs.
• Safe & Trusted AI: Developing ethical guidelines, frameworks, and standards for responsible AI deployment. Complementing the IndiaAI mission is the Digital India Bhashini initiative under the Ministry of Electronics & IT (MeitY), which focuses specifically on building datasets and AI models for Indian languages to break down communication barriers.                                                       
  

• Academic Prowess and Research Labs: The Foundation Builders: India's premier academic institutions, particularly the Indian Institutes of Technology (IITs) and International Institutes of Information Technology (IIITs), are crucial hubs for fundamental research and talent development. AI4Bharat, an initiative incubated at IIT Madras, stands out as a cornerstone of India's indigenous LLM efforts. It has made significant open-source contributions, focusing explicitly on building datasets and models for the 22 constitutionally recognized Indian languages. Their work includes: 
• Curating massive datasets through large-scale crawling, synthetic data creation, and crowdsourcing (e.g., IndicVoices speech dataset). Their pretraining corpus boasts 251 billion tokens across 22 languages, with 74.7 million prompt-response pairs for 20 languages.
• Developing open-source Indic language models like IndicBERT, IndicBART, and Airavata.
• Creating benchmarks (IndicGLUE, IndicNLG, IndicXTREME) for evaluating Indian language models.
• Launching ambitious projects like the "Ten Trillion Token" initiative to gather vast amounts of diverse linguistic data, aiming to build truly native Indic AI models. These open-source contributions are vital, enabling startups and other researchers to build upon existing work, accelerating the ecosystem's growth.

• Industry and Startups: Driving Innovation and Application: While established IT giants like TCS, Infosys, and Wipro contribute significantly through AI services and R&D, a vibrant startup ecosystem is spearheading the development of specific Indian LLMs:

  • Krutrim AI: Founded by Ola Cabs co-founder Bhavish Aggarwal, Krutrim generated significant buzz as one of the first Indian companies to announce its ambition to build a full-stack AI ecosystem, including its own LLM trained on Indian data, supporting multiple Indian languages.
  • Sarvam AI: Co-founded by veterans from AI4Bharat, Sarvam AI launched OpenHathi, an open-source Hindi LLM series, and later Sarvam 2B, an open-source multilingual model supporting 10 Indian languages, developed in collaboration with NVIDIA. It aims to make LLMs accessible for Indian businesses and developers.
  • Soket AI (formerly Karya): This startup focuses on ethical data sourcing, particularly for low-resource languages, employing rural workers to generate high-quality datasets vital for training less biased and more representative models.
  • Gyan AI: Developed Paramanu, a family of lightweight AI models optimized for Indian languages (Assamese, Bangla, Hindi, Tamil), designed to be computationally efficient and cost-effective for specific applications.
  • Tech Mahindra: Launched Project Indus, an open-source effort focused on Hindi dialects, aiming to improve enterprise AI solutions.
  • Hanooman: A multilingual LLM being developed by a consortium led by Seetha Mahalaxmi Healthcare (SML) in partnership with IIT Bombay and supported by Reliance Jio, aiming to support a wide range of Indian languages.
  • Enterprise-focused players: Companies like Yellow.ai (with its YellowG model) and Uniphore leverage conversational AI and LLM technology primarily for enterprise automation, customer service, and contact centre optimization, often supporting multiple Indian languages.

The Essential Ingredients: Tackling the Data, Compute, and Talent Triangle

Building powerful foundation models is a resource-intensive endeavor, hinging critically on three pillars: data, computing power, and talent. India faces unique challenges and opportunities in each area.

1. Data: The Fuel for AI: While India generates vast amounts of data daily, converting this into high-quality, labelled, and diverse training data suitable for LLMs – especially for less-resourced Indian languages – remains a significant hurdle. Most existing global datasets are heavily skewed towards English and Western contexts.

   • Challenge: Scarcity of large-scale, clean, digitized text and speech corpora for many Indian languages. Ensuring data quality, mitigating inherent biases present in raw data, and addressing privacy concerns are critical.
   • Opportunity: Initiatives like Digital India Bhashini's Bhasha Daan (language donation), AI4Bharat's extensive data collection efforts, and parallel initiatives like People+ai (supported by Nandan Nilekani) assembling language tokens from government documents, aim to bridge this gap through crowdsourcing, public data creation, and partnerships. Ethical data sourcing models like Soket AI are also emerging.

2. Compute: The Engine of AI: Training state-of-the-art LLMs demands massive computational power, primarily provided by specialized Graphics Processing Units (GPUs) like those from NVIDIA (e.g., H100, L40S) or AMD (MI Instinct series).

   • Challenge: Access to sufficient, affordable High-Performance Computing (HPC) infrastructure is a major bottleneck globally, and particularly acute for Indian startups and researchers competing with heavily funded global labs. The cost of acquiring and maintaining large GPU clusters is prohibitive for many. Potential US restrictions on GPU exports could add another layer of complexity.
   • Opportunity: The IndiaAI Mission's focus on creating national AI compute infrastructure and the launch of the subsidized IndiaAI Compute Portal are direct responses to this challenge. Partnerships, like Reliance Industries' collaboration with Nvidia to build AI infrastructure and develop LLMs, are also vital. Research into more compute-efficient model architectures and training techniques is equally important.

3. Talent: The Brains of AI: India possesses a vast pool of IT and engineering talent. However, building cutting-edge foundation models requires specialized expertise in deep learning, natural language processing, distributed systems, and AI ethics.

   • Challenge: Nurturing and retaining top-tier AI research talent capable of pushing the frontiers of LLM development. Bridging the gap between academic research and industry application.
   • Opportunity: India's strong educational base provides fertile ground. Initiatives like the IndiaAI FutureSkills pillar, specialized AI Master's and PhD programs at leading institutions, and industry-academia collaborations aim to build the necessary specialized workforce.

Navigating the Roadblocks: Challenges on the Path to Sovereign AI

Despite the momentum, the path towards robust indigenous AI foundation models is fraught with challenges:

• Scale of Investment: Competing with the billions of dollars poured into leading AI labs like OpenAI, Google DeepMind, and Anthropic requires sustained, large-scale investment from both public and private sectors in India.
• Multilingual Complexity: Building a single model that performs well across dozens of languages with different scripts, grammatical structures, and cultural contexts is technically demanding.
• Bias and Ethical Considerations: Ensuring models are fair, equitable, and free from harmful biases reflecting societal prejudices requires careful dataset curation, algorithmic auditing, and the development of robust ethical guidelines tailored to the Indian context.
• Ecosystem Coordination: Effective collaboration between government agencies, research labs, universities, large corporations, and startups is crucial to avoid duplication of effort and build a cohesive national AI ecosystem.

The Vision: An AI-Empowered, Inclusive India

The successful development and deployment of indigenous LLMs hold transformative potential for India. Imagine AI tutors interacting with students in their native languages, healthcare assistants providing preliminary diagnoses in remote areas, farmers receiving hyper-local agricultural advice via voice commands, government services becoming seamlessly accessible across linguistic barriers, and businesses innovating faster with AI tools understanding the Indian market.

This journey is about more than just technological prowess; it's about building AI that serves India's unique needs, reflects its diversity, and empowers its billion-plus citizens. It's about ensuring that the benefits of the AI revolution are shared broadly, fostering inclusive growth, and strengthening India's position as a responsible and innovative leader on the global technology stage.

Conclusion: Laying the Foundation for India's AI Decade

India stands at a pivotal moment in the AI revolution. The concerted push towards building indigenous Large Language Models and foundation models, spearheaded by the IndiaAI Mission and fueled by the combined energies of academia, industry, and a dynamic startup scene, signifies a bold national ambition. While significant challenges related to data, compute, funding, and linguistic complexity remain, the strategic imperative is clear, and the foundational steps are being laid. The success of this mission will not only determine India's technological competitiveness but will also shape its ability to harness AI for achieving national development goals and realizing the vision of an inclusive, empowered, and self-reliant digital future. The journey is complex, but the potential rewards – an AI ecosystem truly built by India, for India – are immense.