Scaling Indic Pretraining: 2.8 Trillion Tokens and an Open Data Generation Pipeline

Native-script data for 22 languages, romanized data for twelve, and a reusable GPU-accelerated pipeline for translation, quality assessment, and repair.

Authors: AI4Bharat — Mohammed Safi Ur Rahman Khan, Mitesh Khapra, Anoop Kunchukuttan. NVIDIA — Niket Agarwal, Raviraj Joshi, Anusha Kamath, Markus Kliegl, Yev Meyer, Sriharsha Niverty, Kiran Praveen, Utkarsh Vaidya.

We're excited to introduce Nemotron-pretraining-Indic-v1 approximately 2.8 trillion tokens across 22 Indian languages , releasing soon alongside the open, GPU-accelerated data generation pipeline used to create it. These are the 22 languages listed in India's Constitution: the first languages for 97% of Indians, or nearly 1.2 billion citizens at the 2011 Census. Developed by NVIDIA with IIT Madras , India's premier engineering institute, and its AI4Bharat lab, this release gives model builders a substantial new resource for Indic pretraining. The pipeline captures the full generation recipe, including prompts, LLM workflows, and quality checks, so developers can adapt it to their own source material and generate more data for their needs.

We're sharing the open generation recipe so the community can not just reproduce the data but take this work further. Teams can use the corpus directly, adapt the prompts and quality checks, or bring their own source documents to the workflow. By opening up both the data and the process, we aim to make large-scale Indic data generation more accessible and enable more developers to contribute to stronger models across Indian languages.

To build this corpus, we start with documents from 15 Nemotron-CC-v2 subsets, covering web text, question-answer material, mathematics, code, and other specialized content. We use NeMo Data Designer , NVIDIA's open-source framework for generating synthetic data, to run translation, quality checks, and targeted repair. Throughout the workflow, we retain each source identifier so developers can trace an output back to its original document. Clear provenance like this is part of what makes open data practical to audit and build on. Figure 2 shows the workflow for twelve languages.

Figure 2. Four-stage Indic synthetic-data pipeline for twelve languages, implemented using NeMo Data Designer and Gemma-4-31B-it. We translate English documents, assess translation quality, repair low-scoring outputs, and convert a subset of accepted translations into Latin script. The other ten languages use the separate Indic Translate workflow described below.

These gains support the combined synthetic-and-natural Indic CPT recipe, while leaving the choice of mixture for other models and languages to further study.

Start with the languages and scripts your model needs, inspect a sample, and review the dataset card and license before training. Configurations use language, script, and region tags: for example, hi-Deva-IN denotes Hindi in Devanagari, while hi-Latn-IN denotes romanized Hindi.

AI4Bharat, IIT Madras' Indian language research lab, builds open-source datasets and models for Indian languages. Its resources, such as Sangraha , an open pretraining corpus for 22 Indian languages, have helped lay the foundation for open Indic language-model research. Our joint open-source release represents a substantial expansion of the open synthetic Indic training data that will be available to the community. It brings a broad mix of curated English material, including mathematics, code, and question-answer content, into Indian languages, giving teams more training material for building models that can explain technical concepts, work through problems, and answer questions in those languages. Every record traces back to a curated source document, and each output is checked against its input, so the synthetic text stays grounded in real information.

Mitesh Khapra, who heads AI4Bharat and made the 2025 TIME100 AI list , has named the core problem: Indian language technology trails English because "we do not have enough data for Indian languages." We're answering that call with more data, plus an open recipe for generating more.

The corpus also accounts for how people write. It includes native-script data for all 22 languages and romanized text for twelve, spanning 34 language-script configurations. Our workflows combine document-level translation, quality assessment, and targeted repair, with checks to preserve content such as equations, tables, and code. This data complements naturally authored Indic text, which remains essential for local knowledge and cultural context.