Sarvam to Yotta: NVIDIA shows India AI ecosystem scale

Jensen Huang not attending the India AI Impact Summit 2026 might have grabbed all the early headlines, but that was until everyone realised just how much of India’s burgeoning AI ambitions rest squarely on NVIDIA’s chip to models to leading edge applications stack. It will make your head spin!

“We build infrastructure, we create models, and finally applications,” outlined Vishal Dhupar, Managing Director, Asia-South, NVIDIA, during a briefing call. “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.”

Dhupar wasn’t exaggerating one bit, “Indeed, NVIDIA’s ecosystem in India is thriving and is growing fast.”

Let’s start with models, shall we? In the past, I’ve written about NVIDIA’s push to release their family of Nemotron open-source models, trained on uniquely Indian data sets for local relevance.

According to Dhupar and Jay Puri, Executive VP at NVIDIA (who attended the India AI Impact Summit 2026), these open models, datasets, tools and libraries are “enabling organizations to build frontier speech, language and multimodal models at scale and across languages for government, consumer and enterprise applications.”

India-first AI models built using Nemotron

India’s AI-native startups and enterprise innovators are already putting Nemotron to work in ways that feel both ambitious and deeply local. Sarvam.ai, for instance, is open-sourcing its Sarvam-3 series of text and multimodal LLMs trained for 22 Indic languages, with model variants spanning 3B, 30B and 100B parameters. 

Built using NVIDIA’s NeMo framework and Megatron-LM, and trained on NVIDIA H100 GPUs via cloud partners including Yotta, these sovereign models now power Sarvam’s Pravah platform for production-grade inference across government and enterprise use cases.

Another example NVIDIA highlights is BharatGen, which is backed by the Government of India. Using the NVIDIA NeMo framework and RL libraries, the initiative has built a 17-billion-parameter mixture-of-experts model designed for public services, agriculture, security and cultural preservation. 

Meanwhile, Gnani.ai is building a 14-billion-parameter speech-to-speech model on NVIDIA Nemotron Speech datasets and libraries. By fine-tuning for Indic languages, Gnani claims a 15x reduction in inference costs while scaling to more than 10 million calls per day across telecom, banking and hospitality customers.

CoRover.ai is deploying Nemotron Speech and NVIDIA Riva libraries for ultralow-latency multilingual speech AI powering Indian Railways’ customer service stack, supporting around 10,000 concurrent users and over 5,000 ticket bookings daily. 

Tech Mahindra, Zoho and NPCI are also building domain-specific models – from an 8B-parameter classroom-ready Indic model to financial service assistants for UPI – demonstrating just how rapidly sovereign AI models are moving from experimentation to deployment at population scale.

NVIDIA GPUs powering AI datacentres

Of course, none of this model ambition exists without the brute-force compute humming behind the scenes. Under the IndiaAI Mission’s compute push, NVIDIA is working with next-generation cloud providers to build AI factories that can train and serve these models domestically. 

Yotta’s Shakti Cloud is emerging as one of the centrepieces – a hyperscale sovereign AI cloud powered by over 20,000 NVIDIA Blackwell Ultra GPUs across its Navi Mumbai and Greater Noida campuses. The GPU-dense infrastructure is designed for high-bandwidth training and inference, offered on a pay-per-use model to make advanced AI compute accessible to enterprises and public-sector customers alike.

E2E Networks is building a complementary NVIDIA Blackwell-based GPU cluster on its TIR platform hosted at the L&T Vyoma datacentre in Chennai. Featuring NVIDIA HGX B200 systems, enterprise software and Nemotron models, the platform is designed to power sovereign development across agentic AI, healthcare, finance, manufacturing and agriculture. 

Together, these AI cloud factories are not just hosting workloads – they’re manufacturing intelligence at scale for India’s next wave of model builders and startups.

Even the hardware ecosystem is localising fast. Netweb Technologies is launching its Tyrone Camarero AI supercomputing systems built on NVIDIA Grace Blackwell architecture and manufactured in India, featuring GB200 NVL4 platforms with four Blackwell GPUs and two Grace CPUs. The message is unmistakable: AI infrastructure is no longer something India merely consumes – it is increasingly something India builds.

Enabling academic and government research in AI

Beyond startups and hyperscale datacentres, NVIDIA is also embedding itself into India’s research and public-sector AI backbone. Through a collaboration with the Anusandhan National Research Foundation (ANRF). The partnership supports ANRF’s AI for Science and Engineering programs and aims to seed the next generation of Indian AI breakthroughs.

Venture support is also scaling in parallel. NVIDIA is working with firms like Peak XV, Nexus Venture Partners, Accel and Elevation Capital to identify and fund promising AI startups, while more than 4,000 Indian startups are already part of the NVIDIA Inception program. 

Take a step back and the pattern becomes impossible to ignore. From Sarvam’s sovereign LLMs to Yotta’s GPU megaclusters, from railway chatbots to research labs, NVIDIA’s end-to-end stack – infrastructure, models and applications – is quietly becoming the scaffolding on which “Made in India” AI is being built.