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HomeHeadlinesViksit Bharat PanelsSovereign AI Infrastructure: Domestic Compute Clusters & Indic Foundation Models
SAMPLE DATAIllustrative conference panel agenda, video recordings, chapters, and sample transcripts. The real Viksit Bharat conference panels will sync with live video feeds as sessions are held.
Session HeldTechnology and AIInnovation and Technology

Sovereign AI Infrastructure: Domestic Compute Clusters & Indic Foundation Models

EventViksit Bharat Conferences — Delhi, November 2026
Venue & SlotDelhi · Hall A — Tech Theatre
Date2026-11-09
Duration61:15
Official Event Information
Interactive Chapters (4)Click a chapter to jump
Available in Session: Full Video Transcript Policy Recommendations Key Discussion Points

Executive Summary

Leading AI researchers and cloud infrastructure builders examine the national rollout of 10,000+ GPU sovereign compute capacity and the training of multi-modal models across all 22 official scheduled Indian languages.

Key Discussion Points

  • 1National AI compute cluster tenders have awarded 12,000 GPUs for subsidized access by domestic startups and research institutions.
  • 2Indic benchmark datasets curated under Bhashini are closing the accuracy gap for complex judicial, medical, and agricultural dialogues.
  • 3Data sovereignty regulations ensure citizen health and biometric records remain within domestic jurisdictional boundaries.

Main Policy & Industry Recommendations

Expand power grid wheeling access for AI data centres operating on dedicated green captive solar and nuclear plants.

Mandate open weights publication for foundation models developed under public-funded AI research grants.

Strategic Industry Implications

Democratises enterprise and SME access to frontier intelligence tools without recurring dollar licensing costs.

Session Transcript

Timestamped record
[00:45]Dr. Arvind Swaminathan:

AI cannot be a consumer-only technology for a nation of 1.4 billion people. To power governance, commerce, and science in 2047, India must own the model weights, the training corpora, and the compute stacks.

[14:30]Pooja Singhania:

English-centric tokenisers are deeply inefficient for Indian languages like Hindi, Tamil, or Odia. By building native Indic tokenisers, we reduce compute inference costs by up to 60 percent for public service chatbots.

[32:10]Harish Nambiar:

We have energized tier-4 data halls in Greater Noida and Navi Mumbai specifically for liquid-cooled high-density accelerator racks. Access is now available on a per-token sovereign pricing model.

Session ModeratorVerified

Dr. Arvind Swaminathan

Chief Scientist, Artificial Intelligence

Centre for Development of Advanced Computing (C-DAC)

Expertise: High-Performance Computing & Indic NLP
Distinguished Panelists (2)
Pooja Singhania
Verified

Co-Founder & CEO

Vani AI Labs

Multilingual Voice LLMs

Harish Nambiar
Verified

Chief Technology Officer

Yotta Data Services

Hyperscale AI Cloud Infrastructure

Related Policy Frameworks
  • ›IndiaAI Mission Compute Capacity Allocation Guidelines.
  • ›Digital Personal Data Protection (DPDP) Operational Rules.
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