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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, alongside the GPU-accelerated pipeline used to generate it. The corpus is largely translated from English sources, and a release date, licensing details and full quality results have not been provided.
NVIDIA and AI4Bharat at IIT Madras plan to release Nemotron-pretraining-Indic-v1, a corpus reported to contain about 2.8 trillion retained tokens across 22 Indian languages, together with the GPU-accelerated pipeline used to produce it, as described in the original analysis. The project is aimed at teams training language models in Indic languages, but the teams have not specified when the data and pipeline will become available.
The announced inventory comprises 2,601.0 billion native-script tokens and 224.6 billion romanized tokens, counted using the Gemma-4 tokenizer. Native-script material covers all 22 languages; romanized text—Indic-language content written in Latin letters—is included for 12. The project describes these as 34 language-script configurations. It cautions that records can include translations and script variants of shared source documents, so the token total is not a count of unique documents.
The workflow begins with English documents selected from 15 Nemotron-CC-v2 subsets, covering material such as web text, question-and-answer content, mathematics and code. Using NVIDIA’s NeMo Data Designer framework, the pipeline translates documents, evaluates translation quality, repairs outputs that score poorly and converts some accepted translations into Latin script. The announcement says records retain source identifiers and that checks compare outputs with inputs, including checks intended to preserve equations, tables and code.
The project says developers will be able to use the corpus or adapt the prompts, workflows and quality checks for other source material. It also reports 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 training figure is separate from the full corpus inventory: it does not mean the model was trained on all 2.8 trillion reported tokens.
A Larger Data Source for Indic Models
A large corpus could give model developers more training material across Indian languages, which the announcement identifies as an area where data availability remains a constraint. The source mix includes technical subjects and question-and-answer material, potentially useful for models intended to explain technical topics or respond to users in Indic languages. The announcement, however, provides no independent evaluation showing whether the corpus improves model quality or by how much.
Publishing the generation pipeline alongside the data could let researchers inspect or adapt how the translations were made, rather than relying only on a finished text collection. Source identifiers and input-output checks may support traceability. Their usefulness will depend on what is included in the eventual release: the code, documentation, licenses, quality measurements and filtering rules have not yet been detailed in the source material.
The corpus is not described as a substitute for naturally authored Indic writing. Since much of it is translated from English, it may reproduce the topics and framing of those source documents without capturing the full range of local knowledge and cultural context. The project says naturally authored Indic data remains essential.
GPU-accelerated data pipeline for machine learning
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From Sangraha to Translated Data
AI4Bharat, a research lab at IIT Madras, develops open datasets and models for Indian languages. 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 large body of translated material and a described process for producing further data from curated English sources.
The project says its 22 languages are those listed in India’s Constitution. It also cites the 2011 Census, saying speakers of those languages represented 97% of India’s population, or nearly 1.2 billion people. That population figure is tied to the 2011 Census and should not be treated as a current estimate. The reported token totals, meanwhile, are counts under the Gemma-4 tokenizer and include accepted translations and script variants.
“We do not have enough data for Indian languages.”
— Mitesh Khapra, head of AI4Bharat
Indic language NLP training datasets
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Release Terms and Quality Still Pending
The announcement does not give a release date or hosting location, nor does it specify the precise licenses governing use, adaptation or redistribution. It also omits full filtering and acceptance criteria, per-language token breakdowns, detailed quality scores and independent evaluations of translation fidelity.
It is not yet clear how the project will document the relative contributions of translated material and naturally authored Indic text in future training mixtures. The reported inventory is a corpus count, not a measure of unique source documents or proof that a model used the entire collection. Without performance comparisons, the effect of the data on downstream models remains unestablished.
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Publication and Independent Evaluation
The next milestone is the planned publication of the corpus and its generation pipeline. Once they are available, developers and researchers will be able to examine the data, documentation, licensing and quality checks directly. The source material does not state when that publication will happen.
Evaluation after release can test translation quality across languages and subject areas, examine how well the workflow preserves technical content, and compare model results with and without the corpus. Until those details and results are available, the project is a planned resource with reported scale and workflow—not a demonstrated improvement in model performance.
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Key Questions
What is Nemotron-pretraining-Indic-v1?
It is a planned corpus for language-model pretraining, reported to contain about 2.8 trillion retained tokens across 22 Indian languages. NVIDIA and AI4Bharat also plan to release the GPU-accelerated generation pipeline.
How was the corpus produced?
The described workflow translates English documents drawn from 15 Nemotron-CC-v2 subsets, evaluates translation quality, repairs some low-scoring outputs and romanizes a portion of accepted material. The project says it retains source identifiers and checks outputs against inputs.
When will the corpus be available?
No release date has been specified in the source material. It also does not name a hosting location or provide detailed licensing terms.
Does the 2.8 trillion-token figure mean 2.8 trillion unique documents?
No. It is a token count measured with the Gemma-4 tokenizer. The project says the inventory can contain translations and script variants of shared source documents, so it is not a count of unique documents.
Has the project shown that the corpus improves 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 Indic text. It provides no performance comparison establishing the corpus’s effect on model quality.
Primary source: Hugging Face · via ThorstenMeyerAI.com
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