India’s AI Ambition: Crafting a Sovereign Large Language Model

India stands at a pivotal moment in its technological evolution, aiming to carve out a significant presence in the global artificial intelligence (AI) landscape. The nation’s ambition to develop its own sovereign large language models (LLMs) is a strategic move to assert digital independence, preserve cultural identity, and address unique socio-economic challenges.

This article, exclusive to TheBrink, delves into India’s high-stakes endeavor to build its equivalent of a GPT-class model, exploring the motivations, challenges, key players, and the nuanced strategies that set this journey apart.

The Strategic Imperative

India’s push for sovereign LLMs is driven by a confluence of geopolitical, economic, and cultural factors. With over 1.4 billion people and 22 official languages, India’s linguistic and cultural diversity demands AI models that can understand and generate content in local languages, from Hindi to Tamil to Assamese. Global models like ChatGPT, while powerful, lack the depth to handle India’s regional dialects, idioms, or context-specific nuances. For instance, a query about “snacks” isn’t just about snacks, it’s about a cultural ritual embedded in daily life.

Moreover, reliance on foreign AI systems raises concerns about data privacy and national security. Indian data processed on overseas servers could be subject to foreign regulations, potentially compromising sensitive information. A sovereign LLM would enable India to retain control over its data, aligning with the government’s “Digital India” and “Atmanirbhar Bharat” (Self-Reliant India) initiatives.

Economically, the Indian AI market is projected to reach $17 billion by 2030, with applications spanning healthcare, agriculture, education, and governance. A homegrown LLM could catalyze innovation, reduce dependency on costly foreign tech, and create jobs in the AI ecosystem.

The global AI race is dominated by tech giants like OpenAI and Google, making India’s endeavor a David-versus-Goliath challenge.

The Government’s Role: The IndiaAI Mission

At the heart of India’s AI strategy is the IndiaAI Mission, a government-led initiative to pump AI innovation. Launched with a focus on building foundational LLMs, the mission has approved proposals from four AI startups, Sarvam AI, Soket AI, Gnani.ai, and Gan.AI, while 500 others await approval. The government’s role is multifaceted, providing computational resources, funding, and policy support to bridge the gap between ambition and execution.

One critical bottleneck is the availability of graphics processing units (GPUs), the computational workhorses for training LLMs. As of May 2025, India’s compute capacity stood at 34,000 GPUs, with 15,916 added recently. However, this is low in comparison to the millions of GPUs deployed by global leaders. To address this, the government has floated tenders for GPU provisioning and is exploring public-private partnerships to scale infrastructure.

The IndiaAI Mission also encourages collaboration rather than competition among startups. By pooling resources, such as datasets and compute power, these companies aim to create models that are competitive and tailored to India’s needs. This collaborative approach is a departure from the siloed strategies of Western tech giants, reflecting a uniquely Indian ethos of collective progress.

The Startup Vanguard

The four startups leading India’s LLM charge, bring diverse expertise to the table. Each has a distinct focus, and they share a common goal: building AI that resonates with India’s multifaceted identity.

• Sarvam AI: Known for its work on multilingual models, Sarvam AI is developing LLMs that prioritize Indic languages. Its models aim to support applications in education and healthcare, where language barriers often hinder access. Sarvam advocates for “frugal innovation,” leveraging efficient training techniques to maximize impact with limited resources.

• Soket AI: Focused on enterprise solutions, Soket AI is building LLMs for sectors like finance and customer service. Its models emphasize privacy and on-device processing, reducing reliance on cloud infrastructure, a critical feature for India’s connectivity-challenged regions.

• Gnani Ai: Specializing in voice-based AI, Gnani ai is creating LLMs that excel in speech recognition and generation across Indian languages, highlighting the importance of voice interfaces in a country where literacy rates vary, and many prefer oral communication.

• Gan AI: Targeting content creation, Gan AI develops LLMs for generating personalized video and audio content. Its technology has potential applications in entertainment, marketing, and e-learning, tapping into India’s vibrant digital content market.

These startups face daunting challenges, including limited funding, talent shortages, and the high cost of training LLMs. However, their agility and deep understanding of India’s context give them an edge in crafting solutions that global models cannot replicate.

The Case for Smaller, Vertical Models

Unlike the West’s obsession with massive, general-purpose LLMs, India is betting on smaller, vertical models tailored to specific domains. Competing head-on with models like Llama or GPT-4 would be prohibitively expensive, training a single large model can cost upwards of $100's of million. Instead, India’s strategy focuses on efficiency and specialization.

Smaller models, or small language models (SLMs), require less compute and data, making them feasible for India’s resource-constrained environment. For example, BharatGPT Mini, launched in June 2025, is a 534-million-parameter SLM designed to operate offline and support 14 Indic languages. Its compact size enables deployment on low-end devices, democratizing AI access in rural areas with limited internet connectivity.

Vertical LLMs, trained on domain-specific datasets, offer precision over generality. A healthcare-focused LLM could assist doctors in diagnosing diseases using regional medical terminology, while an agricultural model could provide farmers with localized crop advice. This approach aligns with India’s diverse needs, where one-size-fits-all solutions fails.

Sarvam AI’s Raghavan, “With new training techniques, you can now train state-of-the-art models with a tenth of the compute needed two years ago. We don’t need more compute; we need better infrastructure, fast data pipelines, low-level GPU access, and flexible training configurations.” This mindset underscores India’s focus on working smarter, not just harder.

Challenges and Roadblocks

India’s AI journey is fraught with obstacles, both technical and systemic. The GPU shortage remains a critical hurdle, with startups competing with limited resources. While the government is addressing this, scaling compute infrastructure to global levels will take years. In the interim, startups rely on cloud providers like AWS or Azure, which increases costs and introduces latency issues.

Data is another challenge. Training LLMs requires vast, high-quality datasets, but India’s digital data is mostly fragmented, unstructured, or unavailable in local languages. Initiatives like the National Language Translation Mission (NLTM) aim to create open-source datasets, but progress is slow. Startups must also navigate ethical concerns around data privacy and bias, ensuring their models don’t perpetuate social inequalities.

Talent scarcity is another persistent issue. India produces thousands of engineers annually, but few have expertise in cutting-edge AI research. Many top researchers migrate to the U.S. or Europe, lured by better opportunities. Retaining and nurturing talent requires investment in education and research institutions, a long-term endeavor.

Finally, regulatory uncertainty dominates. India’s AI policy framework is still evolving, with debates over data localization, intellectual property, and AI governance. A balanced approach that leads to innovation while safeguarding public interest is crucial, but achieving consensus is complex in a diverse democracy.

The Road Ahead: Opportunities and Innovations

Despite these challenges, India’s AI ecosystem is loaded with potential. The country’s startup culture, known for its resilience and ingenuity, is well-suited to tackle these hurdles. Innovations like BharatGPT Mini and Hanooman GPT showcase India’s ability to create impactful AI with limited resources.

Public-private partnerships will be key to success. The government’s collaboration with startups, academia, and industry can accelerate progress, as seen in initiatives like the AI for All program. International partnerships, can provide access to advanced tech and expertise, though India must guard against becoming a mere data provider for foreign firms.

India’s focus on open-source AI is another differentiator. By releasing models and datasets publicly, India can foster a global community of developers, reducing costs and accelerating innovation. This aligns with the ethos of projects like Mozilla’s Common Voice, which crowdsources multilingual speech data.

The potential applications of sovereign LLMs are huge. In education, AI could personalize learning for millions of students, bridging gaps in teacher availability. In healthcare, it could enable telemedicine in remote areas. In governance, it could streamline public services, from issuing licenses to processing grievances. These use cases, grounded in India’s realities, underscore the transformative power of homegrown AI.

A Global Perspective

India’s AI ambitions resonate with global trends, as nations like China, Russia, and the EU pursue sovereign AI to counter U.S. dominance. However, India’s approach is distinct, emphasizing inclusivity, affordability, and cultural relevance. While China focuses on state-controlled AI and the EU prioritizes regulation, India’s model blends government support with entrepreneurial dynamism.

The global AI race is not zero-sum, and India has an opportunity to lead in niche areas like multilingual AI and frugal innovation. By addressing challenges like GPU access and data quality, India could become a hub for AI solutions tailored to the Global South, where similar linguistic and economic constraints exist.

India’s quest to build a sovereign LLM is a bold gamble, blending ambition with pragmatism. It’s a story of a nation striving to harness AI to empower its people, preserve its heritage, and shape its future. The road is long, and the challenges are steep, but India’s unique strengths, its diversity, its startup ecosystem, and its collective spirit, offer hope.

For The Brink readers, this is a glimpse into India’s evolving identity in a digital world. As the nation takes its first steps toward an AI-powered future, the world watches.

-Chetan Desai (chedesai@gmail.com)