Tech Explained: Here’s a simplified explanation of the latest technology update around Tech Explained: Nvidia embeds itself at the heart of India’s sovereign AI push in Simple Termsand what it means for users..
Once known primarily as a graphics chip maker, Nvidia has transformed into the backbone of the global artificial intelligence boom and India is rapidly becoming a key frontier in that expansion.
As New Delhi accelerates its IndiaAI Mission and seeks to build sovereign AI capacity at scale, the US-based company is embedding itself across the country’s entire AI stack, from domestic compute infrastructure and homegrown large language models (LLMs) to enterprise deployments led by IT services giants.
Globally, Nvidia’s chips and software platforms have become the default choice for hyperscalers, startups and governments racing to develop AI capabilities. A similar pattern is now emerging in India as public and private players invest heavily in domestic AI capacity.
“AI is essential infrastructure, just as electricity or the internet was in previous generations. Think of AI as a five-layer cake. It starts at the bottom with energy, then the chips. We build infrastructure, we create models, and finally, applications. Each of these layers has its own diverse ecosystem, and we are working with India’s technology leaders at every single level of this stack,” said Vishal Dhupar, Managing Director, South Asia at Nvidia.
Reducing reliance on overseas hyperscalers
A major focus of India’s AI strategy is to reduce dependence on foreign cloud providers for critical workloads. Nvidia is positioning itself as the technological backbone of that effort through partnerships with domestic cloud operators.
The company is working with Indian providers including Yotta, Larsen & Toubro (L&T) and E2E Networks to build large-scale sovereign compute capacity within the country.
Yotta’s Shakti Cloud is being expanded with more than 20,000 Nvidia Blackwell Ultra GPUs, creating one of the largest domestic AI compute platforms in India.
Meanwhile, E2E Networks is developing a Blackwell-based GPU cluster hosted at L&T’s Vyoma Data Center in Chennai. The platform will include Nvidia HGX B200 systems, Nvidia AI Enterprise software and access to the company’s Nemotron open models.
Together, these projects aim to enable advanced AI training and inference workloads to be run domestically rather than on overseas hyperscaler infrastructure — a key requirement for data sovereignty and national security use cases.
Powering India’s homegrown AI models
Nvidia’s influence extends far beyond hardware. Its competitive advantage increasingly lies in a full-stack ecosystem that spans chips, software frameworks, datasets and models, allowing developers to scale from infrastructure to applications within a single platform.
As model development becomes a critical layer of sovereign AI capability, Indian startups and government-backed initiatives are adopting Nvidia’s open Nemotron models and NeMo tools.
Sarvam.ai, which is building a full-stack generative AI platform for India, is using NeMo Curator to assemble high-quality multilingual datasets along with select Nemotron resources.
According to Nvidia, these foundational models have been pre-trained from scratch across parameter sizes ranging from 3 billion to 100 billion using the NeMo framework and Megatron-LM, then post-trained with NeMo RL on H100 GPUs through cloud partners including Yotta.
Government-backed consortium BharatGen has also developed a 17-billion-parameter mixture-of-experts model using NeMo for pre-training and NeMo RL for post-training, while AI systems firm Chariot is building an 8-billion-parameter real-time text-to-speech model tailored to India’s linguistic diversity.
“We recently released Nvidia Nemotron-3 Nano, a highly efficient language model, and I’m very pleased to share that the larger versions Super and Ultra will be coming soon,” Dhupar added.
IT services take Nvidia-powered AI global
India’s global system integrators, long the backbone of the country’s technology exports, are also leveraging Nvidia’s enterprise software, datasets and models to deliver AI systems worldwide.
“India plays a critical role in how enterprise AI gets built and scaled globally. Many of the world’s largest AI system are developed and deployed by global system integrators headquartered out of India, serving international companies at massive scale,” Dhupar said.
Companies including Infosys, Tech Mahindra, Persistent Systems and Wipro are using Nvidia AI Enterprise to deploy AI agents across sectors such as finance, telecom, healthcare and drug discovery.
Infosys, for instance, has built a 2.5-billion-parameter coding model using the NeMo framework and integrated it into its Topaz platform. The model supports agent development, code generation, refactoring and end-to-end software engineering workflows, trained on curated code datasets, synthetic data and mathematical reasoning inputs.
More than a chip supplier
Nvidia’s deepening presence across infrastructure, model development and enterprise deployment suggests the company is no longer just supplying hardware to India’s AI ecosystem.
Instead, it is becoming a foundational layer shaping how artificial intelligence is built, trained and deployed, both for domestic priorities and global markets.