India Large Language Model Market Research Report, 2030

Large Language Models (LLMs) are the foundation for Generative AI. While general purpose global LLMs enable several use cases and have wider applicability, there are several reasons and challenges for building local language models. The rapid advancements in Large Language Models (LLMs) like OpenAI (ChatGPT) and Llama have sparked a global AI race, with countries vying to develop their own domestic AI capabilities. A country like India, with unique linguistic diversity and growing digital economy, is no exception. India has 22 scheduled languages and countless dialects. An India-specific LLM could better capture the nuances of Indian languages, culture, and context compared to globally focused models, which tend to capture more western sentiments and contexts. While there are several considerations of developing an LLM, including those beyond the scope of this article, developments are already underway in India, and many are reaching reasonable maturity. For example, BharatGPT is an indigenous large language model (LLM) being developed by corover.ai, which is an Indian conversational AI company, and other collaborators. BharatGPT is being designed with a focus on Indian languages and use cases. At the time of writing, it supports several over 12 Indian languages for text and 14 languages for voice interactions. It also supports over 120 languages globally. There are also other companies such as Tech Mahindra, Reliance Industries, and Ola who have announced their plans to develop India specific LLMs and specialized AI applications. According to the research report, “India Large Language Model Market Research Report, 2030” published by Actual Market Research, the India market is projected to grow with 36.73% CAGR with 2025-30. India is home to great writing systems such as the Br?hm? and the Kharosth? that date back centuries. It’s also a land where languages such as Sanskrit, Tamil and Kannada have etched an oral history of more than 1,000 years. Today, modern India boasts 22 official languages, numerous dialects and a heterogenous terrain where tone, accent and vocabulary can vary every few kilometres. This cultural diversity lends itself to the development of local large language models (LLMs) that better suit the needs of a multilingual country. Most global LLMs are trained on data in English, sourced from Reddit and the internet, which is not representative of India, said Ankush Sabharwal, CEO of CoRover, the conversational artificial intelligence (AI) startup behind BharatGPT, India’s homegrown generative AI (GenAI) initiative. Large Language Models (LLMs) have emerged as a cornerstone of the Artificial Intelligence (AI) revolution, driving transformative changes across industries. These models, powered by deep learning algorithms and vast datasets, are capable of understanding, generating, and manipulating human language with unprecedented accuracy. As AI continues to shape the future, LLMs are becoming integral to innovation, particularly in regions that are rapidly advancing their technological infrastructure. India, a global leader in the IT services industry, is making significant strides in AI and Machine Learning (ML), particularly in the development and deployment of LLMs. Over the past decade, the country has transitioned from being a leader in traditional IT services to a formidable force in cutting-edge technologies, including AI and ML. This evolution is a result of technological adoption and reflects a broader strategic vision embraced by both the public and private sectors.

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The government’s ambitious initiatives, such as Digital India and the National AI Strategy, have laid the groundwork for a robust digital ecosystem. These policies aim to harness the power of AI to drive economic growth, improve public services, and enhance India’s global competitiveness. India is extensively leading the LLM research and development to build AI capabilities, building LLMs that can understand and process multiple Indian languages, and creating AI-driven solutions tailored to the local market. Enterprises have launched several initiatives to integrate LLMs into their services, enhancing everything from customer support to enterprise automation. Strategic partnerships with global AI leaders are also playing a crucial role in advancing India’s LLM capabilities. Collaborations are enabling Indian firms to access cutting-edge AI technologies and expertise, helping them to stay competitive in the global market. These partnerships are also fostering innovation in areas like healthcare, where AI-powered diagnostics and personalized treatment plans are becoming increasingly prevalent. Public-private partnerships are pivotal in India’s LLM journey, with the government and industry working together to develop and deploy AI technologies for public services. For instance, the Indian government’s collaboration to develop AI solutions for agriculture is a prime example of how LLMs can be leveraged to address critical societal challenges. These AI models are helping farmers optimize crop yields, manage resources efficiently, and access real-time information, thereby improving agricultural productivity and sustainability. Content generation and curation are rapidly gaining momentum in India’s large language model (LLM) industry, driven by the country’s expansive digital ecosystem, diverse linguistic landscape, and booming content consumption across multiple sectors. India’s population communicates in hundreds of languages and dialects, creating a unique demand for AI systems that can generate, understand, and curate content that is culturally relevant, contextually accurate, and linguistically diverse. The rise of internet penetration, affordable smartphones, and digital platforms such as social media, e-commerce, online education, and entertainment has fueled an unprecedented explosion in content creation and consumption. Large language models enable Indian businesses, media companies, and educational institutions to automate the production of vast volumes of text from news articles and marketing materials to localized social media posts and instructional content while ensuring quality, consistency, and engagement with varied audiences. Furthermore, content curation powered by LLMs helps users navigate India’s immense information space by filtering, personalizing, and recommending content tailored to regional preferences, languages, and interests, thereby enhancing user experience and retention. The growing emphasis on vernacular content, coupled with government initiatives promoting digital literacy and inclusion, further accelerates the adoption of AI-driven content solutions that bridge communication gaps and democratize access to information. Indian enterprises also benefit from LLM-enabled content generation and curation in sectors like healthcare, finance, and customer support, where automated yet nuanced communication enhances service delivery and operational efficiency. Additionally, the relatively lower costs of cloud computing and increasing availability of regional datasets make fine-tuning and deploying LLMs more feasible, encouraging innovation in content-centric applications. Ethical AI practices and data privacy considerations are becoming integral to development, reflecting India’s evolving regulatory environment. The vibrant startup ecosystem and collaborations with global AI research hubs also contribute to the rapid advancement of content generation and curation technologies. The growth of large language models with above 500 billion parameters in India’s LLM industry is propelled by the country’s ambition to develop highly capable AI systems that can effectively address its complex linguistic diversity, vast user base, and multifaceted digital economy. India’s linguistic landscape encompasses hundreds of languages and dialects, each with unique syntactic and semantic structures, requiring exceptionally large models to capture nuanced language patterns and contextual variations accurately. Models with more than 500 billion parameters possess the capacity to process and understand this diversity at scale, enabling more precise language generation, translation, and comprehension across regional languages, which is critical for bridging communication gaps and promoting digital inclusion. Additionally, India’s rapidly expanding sectors such as e-commerce, healthcare, education, and finance increasingly demand advanced AI solutions capable of handling large-scale, real-world tasks that involve complex reasoning, multilingual support, and domain-specific knowledge capabilities enhanced by ultra-large models. The availability of massive datasets generated by India’s extensive digital footprint, combined with improved computational infrastructure through cloud services and emerging AI hardware, facilitates the training and deployment of such expansive models domestically and in hybrid cloud environments. Furthermore, the aspiration to reduce dependency on foreign AI technologies and to develop indigenous AI capabilities motivates investments in scaling model sizes to compete globally and customize AI solutions for local needs. Large-scale models also enable breakthroughs in multilingual natural language processing, low-resource language support, and cross-modal AI integration, which are crucial for India’s heterogeneous market and innovation landscape. Despite challenges such as high computational costs and energy consumption, ongoing research into model efficiency and optimization techniques is helping Indian AI developers leverage these ultra-large models more sustainably. Government policies promoting AI research, innovation grants, and collaborations between academia and industry further accelerate growth in this segment.

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Nikita Jabrela

Business Development Manager

Considered in this report • Historic Year: 2019 • Base year: 2024 • Estimated year: 2025 • Forecast year: 2030 Aspects covered in this report • Large Language Model Market with its value and forecast along with its segments • Various drivers and challenges • On-going trends and developments • Top profiled companies • Strategic recommendation By Service • Consulting • LLM Development • Integration • LLM Fine-Tuning • LLM-backed App Development • Prompt Engineering • Support & Maintenance

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By Model Size • Below 1 Billion Parameters • 1B to 10B Parameters • 10B to 50B Parameters • 50B to 100B Parameters • 100B to 200B Parameters • 200B to 500B Parameters • Above 500B Parameters By Type • General Purpose LLMs • Domain-Specific LLMs • Multilingual LLMs • Task-Specific LLMs • Others(open source, low source LLMs) By Modality • Text • Code • Image • Video • Others (Audio, 3D, Multimodal Combinations) The approach of the report: This report consists of a combined approach of primary as well as secondary research. Initially, secondary research was used to get an understanding of the market and listing out the companies that are present in the market. The secondary research consists of third-party sources such as press releases, annual report of companies, analyzing the government generated reports and databases. After gathering the data from secondary sources primary research was conducted by making telephonic interviews with the leading players about how the market is functioning and then conducted trade calls with dealers and distributors of the market. Post this we have started doing primary calls to consumers by equally segmenting consumers in regional aspects, tier aspects, age group, and gender. Once we have primary data with us we have started verifying the details obtained from secondary sources. Intended audience This report can be useful to industry consultants, manufacturers, suppliers, associations & organizations related to this industry, government bodies and other stakeholders to align their market-centric strategies. In addition to marketing & presentations, it will also increase competitive knowledge about the industry.