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TL;DR
NVIDIA and AI4Bharat at IIT Madras say they plan to release Nemotron-pretraining-Indic-v1, a corpus with about 2.8 trillion retained tokens across 22 Indian languages, plus the GPU-accelerated pipeline used to create it. The release date, licensing terms and detailed quality results have not been provided, and the token total includes translated and script-variant material rather than unique source documents.
NVIDIA and AI4Bharat at IIT Madras say they plan to release Nemotron-pretraining-Indic-v1, a corpus containing about 2.8 trillion retained tokens across 22 Indian languages, along with the GPU-accelerated pipeline used to produce it. The resource is intended to give language-model developers more training material, but the teams have not specified when it will be available or published independent evaluations of its effect on model performance.
The announced inventory includes 2,601.0 billion native-script tokens spanning all 22 languages and 224.6 billion romanized tokens covering 12. Romanized text represents Indic-language content written using Latin letters. The project says the two formats amount to 34 language-script configurations. Counts use the Gemma-4 tokenizer.
The figures describe retained text, not a collection of unique source documents. The announcement says records may include translations and script variants derived from shared source material. The corpus begins with English documents from 15 Nemotron-CC-v2 subsets, including web text, question-and-answer content, mathematics and code.
According to the project, the workflow uses NVIDIA’s NeMo Data Designer to translate source documents, assess translation quality, repair outputs with low scores and convert some accepted translations into Latin script. Records retain source identifiers, and checks are designed to compare outputs with inputs and preserve elements such as equations, tables and code. The teams describe the release as planned, not already available.
A Larger Data Source for Indic Models
A large, accessible corpus could give developers a broader starting point for training models in languages that the announcement says face a shortage of available data. Its source mix includes technical and question-and-answer material, potentially useful for systems intended to explain or discuss subjects such as programming and mathematics in Indic languages. That is a possible use, not a demonstrated result: the announcement offers no independent benchmark showing how much the corpus improves a model.
The planned release includes the generation workflow as well as the text. If the code, documentation and quality checks are published in usable form, researchers may be able to inspect how translated material was produced and adapt the process to other source collections. Source identifiers and input-output checks could support traceability, though their practical effectiveness cannot be judged from the information released so far.
The corpus is not described as a substitute for naturally authored Indic-language writing. Translation from English can provide scale and subject coverage, but it may carry over the original material’s framing and cannot by itself represent the range of local knowledge and expression in writing produced directly in those languages. The project says naturally authored Indic data remains essential.
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From Sangraha to Translated Data
AI4Bharat, an Indian-language research lab at IIT Madras, develops open datasets and models. The announcement identifies Sangraha, its open pretraining corpus for 22 Indian languages, as part of the foundation for Indic-language model research. Nemotron-pretraining-Indic-v1 adds translated material and a described workflow for generating further data from curated English sources.
The project says the 22 languages are those listed in India’s Constitution and that, according to the 2011 Census, they are the first languages of 97% of Indians, or nearly 1.2 billion people. That population figure comes from the cited census and should not be read as a current population estimate.
The announcement also says continued pretraining for Nemotron 3.5 Super VL used 10 billion Indic tokens from a 12-language subset of the corpus, together with naturally authored Indic text. That reported training use is separate from the full corpus inventory: a token count depends on the tokenizer, and the corpus total does not mean a model trained on all of it.
““We do not have enough data for Indian languages.””
— Mitesh Khapra, head of AI4Bharat
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Release Terms and Quality Evidence
The teams have not provided a release date, hosting location or full redistribution and adaptation terms. The announcement also leaves out per-language token counts, detailed filtering rules, translation-quality results and the thresholds used to accept or repair outputs.
There is no independent assessment of translation fidelity or evidence that the corpus improves downstream model performance. The reported total includes accepted rewrites and script variants, so it is not a count of unique documents. The announcement also does not specify how much translated material versus naturally authored Indic text will be used in future training mixtures. These details will affect how developers and researchers interpret and use the resource.
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Publication and Independent Review
The next milestone is the planned publication of the corpus and its generation pipeline. Once released, developers and researchers will be able to inspect the data and workflow, review the accompanying documentation and terms, and assess whether the quality controls suit their use cases. The project has not stated when that review can begin.
Further evaluation will be needed to establish translation quality across languages and subject areas, the balance of data types, and whether training on the corpus produces measurable gains. Until those details are available, the announced token count and workflow describe the resource’s intended scale and construction, not proof of its effects.
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Key Questions
What is Nemotron-pretraining-Indic-v1?
It is a planned corpus of about 2.8 trillion retained tokens across 22 Indian languages, which NVIDIA and AI4Bharat at IIT Madras say they intend to release with its data-generation pipeline.
How are the tokens divided?
The announced inventory lists 2,601.0 billion native-script tokens across 22 languages and 224.6 billion romanized tokens across 12. The project says the counts use the Gemma-4 tokenizer.
Are the tokens all unique, naturally written documents?
No. The project says records may include translations and script variants based on shared source documents. Much of the described workflow translates English material, and the total is not a count of unique documents.
When will the corpus be available?
The teams describe the release as planned, but have not announced a date or specified where the corpus and pipeline will be hosted.
Has the corpus been shown to improve model performance?
The announcement reports use of 10 billion Indic tokens from a 12-language subset in continued pretraining for Nemotron 3.5 Super VL, alongside naturally authored text. It does not provide independent performance comparisons or establish the corpus’s impact on model quality.
Primary source: Hugging Face · via ThorstenMeyerAI.com
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