Nvidia’s Five-Layer AI Stack Signals Big Opportunity for Indian Start-Ups

At the India AI Impact Summit 2026, Nvidia outlined what it calls a practical roadmap for scaling artificial intelligence in India — a five-layer “industrial stack” that places start-ups at the core of the country’s sovereign AI ambitions.

In a pre-summit briefing on Tuesday, Vishal Dhupar, Managing Director of Nvidia South Asia, said AI should be viewed not as a standalone model or chatbot, but as a full industrial system spanning energy, chips, infrastructure, foundational models and end-user applications.

“We see AI as an entire industrial system,” Dhupar said, adding that real scale will come when Indian companies build across all five layers rather than focusing only on applications.

Developers and Start-Ups Form the Base

According to Nvidia, India today has nearly 8,00,000 developers working on its AI platforms. More than 4,000 start-ups are part of the company’s Inception programme, which provides early-stage firms access to tools, GPU credits, technical support and go-to-market guidance.

These companies are building across sectors including healthcare diagnostics, fintech automation, retail analytics, logistics optimisation and industrial AI.

Dhupar said the shift underway is significant. “They are not just users of AI, they are model builders,” he noted, referring to Indian firms developing their own foundational and domain-specific models.

He cited companies such as Sarvam AI, BharatGen and Chariot as examples of local teams creating language and industry-focused AI models tailored to Indian use cases.

For early-stage founders, this marks a shift from building AI-enabled apps to owning intellectual property in foundational technology, potentially improving margins and long-term defensibility.

Infrastructure as New Start-Up Enabler

A major barrier for AI start-ups has been access to large-scale computing power. Nvidia’s five-layer stack addresses this through partnerships with Indian infrastructure players building what Dhupar called “AI factories.”

The company is working with Yotta Data Services, Larsen & Toubro and E2E Networks to deploy high-density GPU clusters in India, it said.

Referring to Yotta’s Shakti Cloud expansion, powered by over 20,000 Nvidia Blackwell Ultra GPUs, Dhupar said the aim is to make sovereign AI infrastructure accessible to Indian start-ups without heavy upfront capital investment. He added that easier access to high-performance compute would significantly lower barriers for founders training large language, speech and computer vision models.

Open Models and Local Language Advantage

On the software layer, Nvidia said it is pushing “open models” and its Nemotron toolkit. According to Dhupar, Nemotron is not simply a pre-trained model but a development framework that includes curated datasets, training recipes and optimisation libraries.

He said, this was particularly relevant in India, where linguistic diversity creates both a challenge and an opportunity. Start-ups can use these building blocks to fine-tune models for regional languages, enterprise workflows or sector-specific use cases. Dhupar highlighted companies such as Gnani.ai, which builds speech AI systems, and Commotion, which focuses on AI-driven enterprise automation, as examples of firms integrating Nvidia’s stack into commercial products.

Beyond Consumption to Creation

The broader message from Nvidia’s briefing was that India’s AI growth should extend beyond adopting global platforms. The company said it sees India developing capabilities across the full AI value chain — from energy and chips to infrastructure, foundational models and industry applications.

“It’s not an AI product launch, it’s building a foundational AI system,” Dhupar said, describing Nvidia’s role as a technology partner supporting India’s sovereign AI efforts.

For Indian start-ups, the five-layer framework outlines potential areas of participation across infrastructure services, model development and application-layer innovation. The structure suggests opportunities not only in deploying AI solutions, but also in building core capabilities within the ecosystem.