NVIDIA Unveils 2.8T-Token Indic Pretraining Corpus and Open Pipeline

What happened

NVIDIA, working with IIT Madras and its AI4Bharat lab, on October 16, 2026 introduced Nemotron-pretraining-Indic-v1, a synthetic pretraining corpus of approximately 2.8 trillion tokens across 22 Indian languages that is releasing soon, together with the open, GPU (a chip built for many calculations at once, used for graphics and AI)-accelerated data generation pipeline used to create it. The pipeline captures the full generation recipe, including prompts, LLM (the kind of AI system trained on text to produce text) workflows, and quality checks, so developers can adapt it to their own source material. NVIDIA is a semiconductor company based in Santa Clara, and its products and services include graphics processing units.

The 22 languages are those listed in India’s Constitution, which the announcement describes as the first languages for 97% of Indians, nearly 1.2 billion citizens at the 2011 Census. Authors on the post span AI4Bharat researchers Mohammed Safi Ur Rahman Khan, Mitesh Khapra, and Anoop Kunchukuttan and NVIDIA’s Niket Agarwal, Raviraj Joshi, Anusha Kamath, Markus Kliegl, Yev Meyer, Sriharsha Niverty, Kiran Praveen, and Utkarsh Vaidya.

NVIDIA said every record traces back to a curated source document and each output is checked against its input. Corpus Composition and Language Coverage The corpus contains 2,601.0 billion native-script tokens and 224.6 billion romanized tokens, counted with the Gemma-4 tokenizer. Across 34 language-script configurations, the corpus provides native-script data for all 22 languages and romanized text for twelve.

Key facts

• NVIDIA, working with IIT Madras and its AI4Bharat lab, on October 16, 2026 — introduced: Nemotron-pretraining-Indic-v1, a synthetic pretraining corpus of approximately 2.8 trillion tokens across 22 Indian languages that is releasing soon, together with the open, GPU-accelerated data generation pipeline used to create it
• Corpus Composition and Language Coverage The corpus — includes: 2,601.0 billion native-script tokens and 224.6 billion romanized tokens, counted with the Gemma-4 tokenizer
• Across 34 language-script configurations, the corpus — provides: native-script data for all 22 languages and romanized text for twelve