Inside the $2 Billion Supercluster

The Yotta-Nvidia supercluster will be deployed at Yotta’s 60-megawatt D2 hyperscale data centre at its Greater Noida campus in Uttar Pradesh, with the facility scalable to 250 megawatts. Additional capacity will be supported by Yotta’s Navi Mumbai campus, which is scalable to a massive 2 gigawatts. The deployment involves Nvidia’s latest Blackwell Ultra architecture — the B300 series GPUs — which represent the cutting edge of AI training and inference hardware, offering significant performance improvements over the previous Hopper generation.

The 20,736 GPUs will form an HGX B300 Blackwell Ultra supercluster with liquid cooling, optimised for large language model training, scientific computing, drug discovery simulations, and inference workloads. Each B300 GPU offers approximately 2.5 times the training performance of the H100 for large language models, meaning the combined cluster will deliver computing power equivalent to several hundred thousand older-generation GPUs. This positions India to train frontier AI models domestically rather than relying on cloud computing resources hosted overseas.

A $1 Billion DGX Cloud Deal: Nvidia’s Largest in Asia-Pacific

Alongside the infrastructure deployment, Yotta has signed a four-year commercial engagement worth over $1 billion with Nvidia to establish one of the Asia-Pacific region’s largest Nvidia DGX Cloud clusters within the supercluster. Nvidia’s DGX Cloud service has been utilising Yotta’s GPU infrastructure over the past year, and the expanded deployment extends that relationship significantly.

The DGX Cloud model allows organisations — from startups to large enterprises — to access Nvidia’s most advanced AI computing resources on demand without purchasing hardware. For India’s rapidly growing AI ecosystem, this means that startups like those recently raising significant capital can access world-class computing power locally. The billion-dollar funding rounds flowing into Indian AI companies require corresponding compute infrastructure, and the Yotta-Nvidia partnership aims to meet this demand domestically.

10,000 GPUs Committed to IndiaAI Mission

Perhaps the most significant aspect of the announcement is the commitment of over 10,000 Nvidia B300 GPUs from the supercluster to the government’s IndiaAI Mission. This initiative, launched with a budget of Rs 10,372 crore, aims to build sovereign AI infrastructure that allows Indian researchers, startups, and government agencies to train and deploy AI models on Indian soil, ensuring data sovereignty and reducing latency for domestic applications.

The IndiaAI Mission has been building a network of AI computing resources across the country, but the Yotta contribution represents the single largest private-sector commitment to the programme. The GPUs will be available to approved researchers and organisations through the IndiaAI Compute Portal, dramatically expanding the amount of compute available for government-backed AI research in areas such as agriculture, healthcare, language translation, and national security.

Nvidia’s Broader India Strategy: L&T, Reliance, and Sovereign AI

The Yotta partnership is part of a much larger Nvidia engagement with India. During the India AI Impact Summit, Nvidia CEO Jensen Huang unveiled plans to deploy tens of thousands of Blackwell GPUs across the country through multiple partnerships. Engineering giant Larsen and Toubro is working with Nvidia to build a gigawatt-scale network of AI data centres, designed to keep critical data and model training within India’s borders — a key requirement for government and regulated industries.

Reliance Industries, through its Jio Platforms subsidiary, has also been expanding its AI infrastructure partnership with Nvidia. The broader picture is one of India rapidly building the physical infrastructure needed to participate in the global AI race at the frontier level. As Huang stated during the summit, artificial intelligence is driving the largest infrastructure buildout in human history, and every country will need to build sovereign AI capabilities. India, with its 1.4 billion population, deep engineering talent, and growing digital economy, is among the most important markets for Nvidia globally.

The deployment also has implications for India’s broader technology and science ecosystem. AI computing infrastructure supports not just commercial applications but also scientific research — from protein folding and drug discovery to climate modelling and materials science. With 20,736 Blackwell Ultra GPUs available in India, researchers who previously had to queue for overseas cloud computing resources or work with limited local hardware will have access to genuinely world-class compute.

The Road Ahead for AI Infrastructure in India

India’s total AI computing capacity is set to increase dramatically in 2026 and 2027. Between Yotta, Jio, Adani, and several other data centre operators, the country is on track to have over 100,000 advanced GPUs operational by 2027. Whether this is enough to match the compute needs of a country with India’s ambitions remains to be seen — China and the United States each have millions of high-end GPUs in operation. But the trajectory is clear, and the Yotta-Nvidia supercluster represents the most tangible proof yet that India is serious about becoming an AI power in its own right.

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The combined momentum of private capital, government policy and global tech investment is positioning India as one of the world’s most active AI markets, with total venture funding for the country’s top AI companies now exceeding $2.9 billion.

Sarvam AI Targets Unicorn Status With Massive Raise

Sarvam AI’s funding round represents one of the largest raises by an Indian AI startup to date. The company, which has built large language models specifically optimised for Indian languages, launched a 105-billion-parameter model in February 2026 that reportedly outperforms global competitors on mixed Hindi-English text, a capability known as Indic code-switching. The model handles the natural way millions of Indians communicate, seamlessly blending languages within single conversations.

At the India AI Impact Summit held in New Delhi in February, Sarvam unveiled several products including its Indus chatbot, which competes with OpenAI’s ChatGPT in the Indian market, and the Sarvam Kaze smart glasses, the company’s first hardware product. Prime Minister Narendra Modi tested the Kaze glasses at the expo, giving the device significant public visibility.

Sarvam has also announced partnerships with Qualcomm, HMD and Bosch to deploy its AI models on devices including smartphones, feature phones, cars, laptops and smart glasses. This on-device strategy differentiates the company from cloud-only approaches and addresses India’s data residency requirements while reducing latency for end users.

Krutrim, backed by Ola founder Bhavish Aggarwal, has already achieved unicorn status with $50 million in equity plus $230 million in committed financing, making the Indian AI startup space increasingly competitive. Global AI labs including Anthropic, which opened its first India office in Bengaluru, are also racing to capture the market.

Government Backs 12 Sovereign AI Labs Under IndiaAI Mission

The Indian government’s direct funding of 12 organisations to build sovereign AI foundation models marks a significant policy shift towards ensuring that India develops its own AI capabilities rather than relying entirely on foreign models. The selected organisations include Sarvam AI, Gnani AI, Fractal Analytics, Tech Mahindra’s Maker’s Lab and the IIT Bombay-led BharatGen consortium.

The BharatGen project received the largest allocation, with funding of approximately Rs 1,000 crore, roughly four times the next-highest funded project. The consortium released its Param 2 model at the AI Impact Summit, a 17-billion-parameter model supporting all 22 official Indian languages with multimodal capabilities including text, speech and images.

The sovereign AI strategy is driven by several considerations. India’s 1.4 billion population generates vast amounts of data in dozens of languages, and the government has determined that relying on foreign models trained primarily on English-language data is insufficient for governance, education and healthcare applications. By building indigenous models trained on Indian datasets, the country aims to ensure that AI tools work effectively for all citizens, not just English-speaking urban populations.

Under the IndiaAI Mission, approved in March 2024 with a budget of Rs 10,371.92 crore over five years, the government is also expanding its GPU compute infrastructure. Plans call for adding more than 20,000 GPUs to India’s existing base of 38,000 under the IndiaAI Compute Portal, with a separate secure cluster of 3,000 next-generation GPUs being built for sovereign and strategic applications.

Microsoft, Google and Adani Drive Infrastructure Build-Out

Microsoft’s $17.5 billion India commitment, its largest ever in Asia, is now moving from announcement to execution. The flagship hyperscale data centre in Hyderabad is scheduled to go live in mid-2026 with three availability zones. Microsoft is also doubling its pledge to train 20 million Indians in AI skills by 2030, addressing the talent pipeline that will be essential for the country’s AI ambitions.

At the AI Impact Summit, Google highlighted climate modelling, extreme weather prediction, language tools and an AI cricket coach tailored for Indian users. Amazon Web Services, Nvidia and Qualcomm also showcased platforms designed for scale in India, with a focus on cost efficiency and regulatory compliance.

India’s data centre market, valued at approximately $10 billion in 2025, is expected to more than double to $22 billion by 2030, according to a report released on 12 April. The Adani Group has announced plans to allocate $100 billion for AI data centres using renewable energy in India by 2035, with an additional $150 billion expected in supporting infrastructure including server manufacturing and sovereign cloud platforms.

The Indian IT industry, projected to grow 6.1 per cent to $315 billion in FY26, is also being reshaped by AI adoption. Companies including Tata Consultancy Services, Infosys and HCLTech are deploying AI tools across client engagements, with Anthropic partnering with Infosys to deploy Claude models in the telecommunications sector through a dedicated Centre of Excellence.

India’s Second Position in Global AI Adoption

India’s growing importance in the global AI ecosystem is reflected in several metrics. OpenAI CEO Sam Altman revealed at the summit that India accounts for more than 100 million weekly active ChatGPT users, second only to the United States. Anthropic confirmed India as the second-largest user of Claude after the US. These figures underscore both the demand for AI services and the opportunity for Indian companies to build products tailored to local needs.

On GitHub, India emerged as the second-largest contributor worldwide to public generative AI projects in 2024, reflecting the depth of the country’s developer talent pool. The government’s AI FutureSkills programme is supporting 500 PhD scholars, 5,000 postgraduates and 8,000 undergraduates in AI research and training, while 570 AI Data Labs across the country are building grassroots AI skills in Tier-2 and Tier-3 cities.

The convergence of government funding, private investment, global tech commitments and a massive user base is creating an AI ecosystem in India that few predicted. The broader technology sector is being reshaped at every level, from semiconductor design to cloud services, even two years ago. The challenge ahead lies in translating this momentum into tangible outcomes for citizens, from improved government services and healthcare diagnostics to agricultural productivity and financial inclusion. If the current trajectory holds, India’s sovereign AI push could reshape not just the country’s technology landscape but its economic future.

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India took a decisive step in the global artificial intelligence race on 24 March 2026 with the launch of BharatGPT-2, the country’s most advanced sovereign large language model. Developed by a consortium led by IIT Bombay, IIT Madras and Reliance Jio’s AI division under the government’s IndiaAI Mission, the model supports all 22 scheduled languages of the Indian Constitution and has been benchmarked as competitive with GPT-4-class models on multilingual tasks.

The launch, held at Vigyan Bhawan in New Delhi with IT Minister Ashwini Vaishnaw presiding, positions India alongside the United States, China, France and the UAE as nations investing in sovereign AI capabilities — systems trained on domestic data, hosted on domestic infrastructure and designed to address local needs.

Technical Architecture: Built for Bharat

BharatGPT-2 is a 340-billion-parameter model trained on a curated dataset of 12 trillion tokens spanning English, Hindi, Tamil, Bengali, Marathi, Telugu, Gujarati and 16 other Indian languages. Unlike its predecessor, BharatGPT-1, which was primarily an academic research model, BharatGPT-2 has been optimised for commercial deployment with an enterprise API already available to government agencies and approved startups.

The model’s training infrastructure was hosted on the AIRAWAT supercomputing cluster at C-DAC Pune, supplemented by 4,000 NVIDIA H100 GPUs provided through the IndiaAI Compute Mission. Total training costs are estimated at Rs 800 crore, funded through a combination of government grants, Reliance’s investment and academic research budgets — a fraction of what OpenAI and Google spend on comparable models.

“The key innovation is not just model size but data curation,” explained Professor Pushpak Bhattacharyya of IIT Bombay, who led the linguistics team. “We built specialised tokenizers for each language family — Indo-Aryan, Dravidian, Tibeto-Burman — rather than treating all languages as English with different scripts.” This approach to fintech innovations transforming India and other sectors enables more natural language understanding in regional contexts.

Real-World Applications Already in Deployment

BharatGPT-2 is not just a research showcase. Several government departments have already begun integrating the model into citizen-facing services. The Unified Payments Interface helpline now uses a BharatGPT-2-powered chatbot that can resolve common transaction disputes in Hindi, Tamil and Bengali without human intervention, reducing average resolution time from 48 hours to under 10 minutes.

The Ayushman Bharat Digital Health Mission has deployed a BharatGPT-2 module that translates medical reports between languages, enabling doctors in Kerala to read diagnostic notes written by colleagues in Rajasthan. The agriculture ministry is piloting a voice-based crop advisory service that allows farmers to ask questions in their local dialect and receive science-backed recommendations drawn from ICAR research databases.

In the private sector, Infosys and Wipro have signed early-access agreements to integrate BharatGPT-2 into their client service platforms, potentially reducing their dependence on OpenAI and Anthropic APIs. Flipkart is testing the model for customer support in regional languages, reporting a 30 per cent improvement in first-contact resolution rates compared to English-only models.

The Startup Ecosystem’s AI Surge

India’s AI startup ecosystem, already vibrant, has received a significant boost from the government’s commitment to sovereign AI. According to data from Tracxn, Indian AI startups raised USD 2.3 billion in the first quarter of 2026, a 78 per cent increase over Q1 2025. Bengaluru-based Krutrim, founded by Ola CEO Bhavish Aggarwal, secured a USD 500 million Series B in February, making it India’s first AI unicorn valued at over USD 3 billion.

Sarvam AI, a Bengaluru startup focused on building open-source Indian language models, has attracted attention from Y Combinator and Andreessen Horowitz for its Indic voice AI platform that powers call centre operations in 11 languages. Meanwhile, Hyderabad-based AI Health Highway is using custom fine-tuned models to screen chest X-rays at primary health centres in Telangana, processing over 5,000 scans daily.

The growth extends across India’s telecom and 5G expansion, where AI is being deployed for network optimisation, predictive maintenance and personalised content delivery at scale.

Geopolitical Dimensions: Data Sovereignty and AI Arms Race

The push for sovereign AI is as much about geopolitics as it is about technology. India’s Digital Personal Data Protection Act, fully enforceable from mid-2026, requires that sensitive personal data processed by government AI systems be stored and processed within India. BharatGPT-2’s entirely domestic hosting infrastructure — spread across data centres in Pune, Hyderabad and Chennai — satisfies this requirement.

The model also addresses concerns about cultural bias in Western AI systems. Testing by IIT Delhi’s AI fairness lab found that GPT-4 and Claude struggled with culturally specific queries — misidentifying Indian festivals, providing incorrect legal information about Indian laws and defaulting to Western dietary recommendations. BharatGPT-2, trained on curated Indian datasets, scored 40 per cent higher on cultural accuracy benchmarks.

India’s approach has drawn interest from other developing nations. Nigeria, Indonesia and Brazil have all expressed interest in the IndiaAI Mission’s methodology, viewing it as a template for building sovereign AI capabilities without the USD 10-billion-plus budgets of American tech giants. The record research output from Indian institutions is providing the academic foundation for this global leadership role.

Challenges: Compute, Talent and Adoption

Despite the momentum, significant challenges remain. India’s total AI compute capacity, while growing, is still roughly one-tenth of what the United States possesses. The IndiaAI Compute Mission aims to bridge this gap by deploying 10,000 GPUs for public research by 2027, but the global GPU shortage and export controls on advanced chips create supply uncertainties.

Talent retention is another concern. India produces over 500,000 AI and machine learning graduates annually, but a significant proportion — estimated at 30 per cent of top-tier talent — migrate to the US, UK or Singapore for higher salaries and more advanced research environments. The BharatGPT-2 project has helped retain some researchers by offering competitive compensation and the chance to work on a nationally significant project, but the brain drain remains structural.

Adoption among India’s 63 million SMEs is the final hurdle. While large enterprises can integrate BharatGPT-2 through APIs and dedicated teams, small businesses need plug-and-play AI tools with minimal technical expertise. The Indian D2C brands leveraging technology offer a model for how startups can democratise AI access for smaller players.

The Road Ahead for Indian AI

BharatGPT-2 is not an endpoint but a beginning. ISRO has already been tapped to develop BharatGPT-3, a multimodal model capable of processing text, images, audio and video in Indian languages, with a target completion date of 2028. The government’s total AI investment under the IndiaAI Mission stands at Rs 10,372 crore through 2030.

For now, BharatGPT-2 represents something that India’s AI community has long aspired to: a world-class AI system that speaks the country’s languages, respects its data sovereignty and serves its unique developmental needs. In the global AI race, India has announced that it intends to be a creator, not just a consumer.

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From Demos to Deployment: AI Impact Summit 2026 India Showcases Real-World Applications

The most striking feature of the summit was the absence of vaporware. Autonomous drones demonstrated live crop-disease scanning above a simulated agricultural plot inside the venue. Wearable devices designed for frontline health workers showed real-time vital monitoring and AI-assisted diagnosis. Industrial robots performed predictive maintenance routines on mock assembly lines, illustrating how manufacturers are already reducing downtime by 30 per cent.

Language technology drew particular attention. Multiple exhibitors displayed tools that translate court judgments, government circulars, and educational content into Indian languages with accuracy rates exceeding 95 per cent. For a country with 22 scheduled languages and hundreds of dialects, this capability addresses a fundamental accessibility gap.

India’s sovereign AI ecosystem, which includes startups such as Krutrim and Sarvam AI, featured prominently. These companies demonstrated large language models trained specifically on Indian datasets, offering domain expertise in agriculture, legal services, and healthcare that global models often lack.

Global Tech Giants Stake Their Claim in India’s AI Future

Google operated one of the largest pavilions at the summit, highlighting climate modelling, extreme weather prediction, regional language tools, and an AI cricket coach tailored for Indian users. The cricket coach, which analyses batting technique through smartphone video, generated long queues and social media buzz throughout the event.

Microsoft showcased secure AI platforms designed for governance and enterprise adoption. Its demonstration of AI-powered land records management attracted interest from state government officials seeking to digitise property registration systems. Nvidia drew crowds with live GPU-powered robotics demonstrations and industrial AI systems that process sensor data in real time.

Other global players, including OpenAI, Qualcomm, Amazon Web Services (AWS), and Schneider Electric, positioned their platforms for scale in India. The common theme was cost efficiency and regulatory compliance, two factors that will determine which platforms win enterprise contracts in a price-sensitive market.

Indian Conglomerates Showcase AI at Scale

Domestic industry matched the international presence. Tata Group demonstrated AI applications across manufacturing and mobility, including a predictive quality control system deployed at Tata Steel plants. HCLTech focused on enterprise AI and secure data systems, presenting case studies from banking and insurance clients who have integrated AI into customer service workflows.

Reliance Industries highlighted AI use cases in telecom, retail, and cloud infrastructure. Its Jio Platforms division revealed that AI-driven network optimisation has reduced dropped calls by 18 per cent across its 5G network in the past six months. The latest gadget innovations in India increasingly rely on on-device AI processing, a trend that Reliance is positioning to exploit through its affordable smartphone partnerships.

Country pavilions from Japan, the United Kingdom, and Germany added an international dimension, hosting bilateral discussions on AI standards, data-sharing frameworks, and joint research initiatives. Japan’s pavilion focused on robotics for elderly care, a technology area where India sees growing demand as its population ages.

The Rs 10,000 Crore IndiaAI Mission Takes Shape

Government officials used the summit to provide an update on the Rs 10,000 crore IndiaAI Mission, announced in the 2024 Union Budget. The mission aims to build sovereign AI infrastructure, including compute capacity, datasets, and a national AI marketplace. As of February 2026, the government has deployed 10,000 GPUs across four data centres and onboarded 150 startups onto its compute subsidy programme.

IT Minister Ashwini Vaishnaw stated at the summit that “India will not be a consumer of AI built elsewhere. We will build, train, and deploy AI that understands Indian contexts.” The minister announced a new round of grants targeting AI applications in agriculture, healthcare, and education, with a combined allocation of Rs 2,500 crore for the 2026-27 fiscal year.

The emphasis on sovereignty reflects geopolitical realities. With the United States, China, and the European Union pursuing distinct regulatory approaches, India is carving its own path. The country’s draft AI governance framework, expected to be finalised by June 2026, will mandate transparency in algorithmic decision-making and require impact assessments for AI systems used in public services.

Challenges Remain Despite the Optimism

For all the excitement, the summit also surfaced persistent challenges. India’s AI talent pipeline remains narrow: according to a NASSCOM report released at the event, only 12 per cent of Indian engineering graduates have skills directly applicable to AI and machine learning roles. The gap between research output and commercial deployment also persists, with many university projects failing to transition into scalable products.

Data quality is another concern. Several panellists noted that India’s public datasets are fragmented, inconsistently formatted, and often outdated. Without reliable training data, even the most sophisticated models underperform. The government’s push to create open datasets through the India Datasets Platform is a positive step, but progress has been slower than anticipated.

Energy consumption is an emerging issue as well. Training large AI models requires enormous computational power, and India’s data centres are not yet fully powered by renewable sources. Environmental advocates at the summit called for a green AI mandate that ties compute subsidies to renewable energy commitments.

What Comes Next for India’s AI Ambitions

The AI Impact Summit 2026 demonstrated that India has moved decisively from aspiration to action. The combination of government investment, corporate commitment, and startup innovation creates a foundation that few countries can match at India’s scale. The real test will come in the next 12 months as pilot projects move into production and the regulatory framework takes shape.

For investors, the signal is clear: AI in India is no longer a future bet but a present reality. For citizens, the promise is equally tangible — from faster government services to better healthcare in rural areas. Whether India can execute at the speed its ambitions demand will define the next chapter of its technology story.

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