🔍 Read the full analysis: 2.8 Trillion Tokens And The Push To Scale Indic Pretraining on ThorstenMeyerAI.com
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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 produce it. No release date, complete technical specification, or independent evidence of its effect on model performance has been provided.
NVIDIA and AI4Bharat at IIT Madras say they plan to release Nemotron-pretraining-Indic-v1, a corpus containing approximately 2.8 trillion retained tokens across 22 Indian languages, alongside the GPU-accelerated pipeline detailed in the original analysis. The resource is intended for teams training language models in Indic languages, but it has not yet been released and the announcement does not give a publication date.
The project reports 2,601.0 billion native-script tokens and 224.6 billion romanized tokens, counted using the Gemma-4 tokenizer. Native-script text covers all 22 languages; romanized material—Indic-language text written in Latin letters—is included for 12. The project describes these as 34 language-script configurations. It cautions that the totals include translations and script variants of shared source documents, not a count of unique English documents.
The described workflow begins with English documents from 15 Nemotron-CC-v2 subsets, including web text, question-and-answer material, mathematics and code. Using NVIDIA’s NeMo Data Designer framework, it translates documents, assesses translation quality, repairs outputs that score poorly and converts some accepted translations into Latin script. The project says records retain source identifiers and that checks compare outputs with inputs, including checks intended to preserve equations, tables and code.
According to the announcement, developers will be able to use the corpus directly or adapt the prompts, workflows and quality checks for other source material. The teams also report that continued pretraining for Nemotron 3.5 Super VL used 10 billion Indic tokens from a 12-language subset alongside naturally authored Indic text. That figure describes a reported training use, not the size of the planned corpus or proof of a performance gain.
A Larger Data Source for Indic Models
A large corpus could provide model developers with more training material for languages that have often had fewer openly available resources than English. The announced mix includes technical content, web text and question-and-answer data, which may be useful for systems designed to answer questions or explain technical subjects in Indic languages. The project, however, has not supplied independent evaluations showing that this corpus improves model quality, coverage or reliability.
Publishing the generation pipeline as well as the text could let researchers inspect how translations were produced and adapt the process. Source identifiers and input-output checks may support traceability, while reusable workflows could help teams create data from their own sources. Those benefits remain prospective until the code, documentation, licenses and quality results can be reviewed.
The corpus is based on translations from English and is not presented as a substitute for naturally authored Indic material. Translation can carry across the topics and framing of the source documents without capturing the full range of local knowledge, expression and cultural context. The project says naturally authored Indic data remains essential; the eventual value of the corpus will depend partly on how it is combined with such material.
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Building on Earlier Indic Data Work
AI4Bharat is an Indian-language research lab at IIT Madras that develops open datasets and models. The announcement cites Sangraha, its open pretraining corpus for 22 Indian languages, as part of the foundation for open Indic-language model research. Nemotron-pretraining-Indic-v1 adds a substantial collection of translated material and a stated process for generating further data from selected English sources.
The project says its 22 languages are those listed in India’s Constitution and that they are the first languages of 97% of Indians, or nearly 1.2 billion people, citing the 2011 Census. That population figure is tied to that census and should not be read as a current population estimate. The reported corpus inventory is also specific to tokenization: its totals use the Gemma-4 tokenizer and include retained translations and script variants.
The reported use of 10 billion Indic tokens in continued pretraining for Nemotron 3.5 Super VL is a separate figure from the approximately 2.8 trillion-token inventory. The announcement says that training also included naturally authored Indic text. It does not say that the full planned corpus was used in the training run or provide a comparison showing the effect of the data.
“We do not have enough data for Indian languages.”
— Mitesh Khapra, head of AI4Bharat
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Release Terms and Quality Still Pending
The announcement does not specify when or where the corpus and pipeline will be published, or the exact terms for using, adapting or redistributing them. It also provides no complete account of filtering rules, quality-acceptance thresholds, per-language token totals or detailed translation-quality scores. These details will affect how researchers assess coverage and decide whether the data fits their training needs.
There is no independent evaluation in the source material measuring the corpus’s effect on downstream model performance or translation fidelity. The reported total is a tokenizer-based count, not a measure of unique source documents, and it does not establish that models have trained on the entire inventory. The eventual balance between synthetic translations and naturally authored Indic text in future training mixtures is also unspecified.
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Publication and Independent Evaluation
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, documentation, code and licensing terms, and determine whether the reported quality checks are sufficiently detailed for their use.
Further assessments could examine translation fidelity, coverage across languages and subject areas, and how models perform when trained with the corpus alongside naturally authored Indic text. Until the release and such evaluations are available, the scale and described workflow are confirmed as project claims, while the data’s practical impact remains unsettled.
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Key Questions
What is Nemotron-pretraining-Indic-v1?
It is a planned corpus of approximately 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.
Has the corpus been released?
No release date is specified in the announcement. The corpus and pipeline are described as planned releases, not resources already available.
How were the tokens produced?
The described workflow translates selected English documents from 15 Nemotron-CC-v2 subsets using NVIDIA’s NeMo Data Designer framework. It assesses translation quality, repairs low-scoring outputs and converts some accepted translations into Latin script.
Does the token count represent unique documents?
No. The project says the total includes translations and script variants of shared source documents. It is a token count calculated with the Gemma-4 tokenizer, not a count of unique documents.
Has the project shown that the corpus improves model performance?
The source material does not provide independent performance evaluations. It reports that a 12-language subset contributed 10 billion Indic tokens to continued pretraining for Nemotron 3.5 Super VL, but does not establish the corpus’s effect on model results.
Primary source: Hugging Face · via ThorstenMeyerAI.com
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