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NVIDIA has described Nemotron-Pretraining-Indic-v1, an approximately 2.83 trillion-token corpus covering 22 Indian languages and 34 language-script configurations. Developed with IIT Madras and its AI4Bharat lab, the collection is designed for continued pretraining of multilingual models and is paired with a data-generation pipeline that translates, scores and rewrites source material. NVIDIA says the corpus and pipelines will be released, but a definitive schedule and complete artifact-by-artifact license terms have not yet been published.
What NVIDIA announced
Nemotron-Pretraining-Indic-v1 is an Indic-language pretraining resource associated with NVIDIA’s Nemotron 3.5 Super VL model work. NVIDIA’s 2026 technical documentation puts its size at about 2.83T tokens, spanning 22 Indian languages in 34 language-script configurations.
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The scale is notable because much existing open Indic material is far smaller. However, the headline total should not be read as 2.83T tokens of independently collected native-language writing. NVIDIA says most of the corpus was translated from English-source documents, primarily from Nemotron-CC, then passed through model-based quality controls.
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How the open data pipeline works
Twelve priority languages
About 2.50T tokens are allocated to 12 priority languages in native-script and romanized forms. For documents shorter than 4,096 tokens, the workflow translates English material with Gemma-4-31B-it, assigns a quality score from 0 to 100, rewrites documents scoring below 75, and scores the rewritten versions again. This staged process is part of a NeMo Data Designer workflow intended to make large-scale synthetic translation more consistent.
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Ten lower-resource languages
The remaining 10 languages contribute about 327B native-script tokens. NVIDIA says Bodhan-MT is used for translation in this group, while Gemma-4-31B-it performs quality checks. The two portions together account for roughly 2.83T tokens, although the figures are rounded.
What “synthetic” means here
The pipeline starts with English documents rather than assembling an equivalent volume of naturally written Indic text. Translation, scoring and rewriting are model-assisted steps, so the resulting data can expand coverage quickly but may also reproduce translation artifacts, stylistic uniformity or errors that automated checks fail to detect. Human verification is therefore important for judging factuality, cultural coverage and language naturalness.
Which languages are included?
NVIDIA identifies 22 Indian languages and 34 language-script configurations, including native and romanized variants in the priority-language portion. The announcement materials supplied for this release do not provide a definitive language-by-language list, so individual language names should not be inferred from the count alone.
Does continued pretraining improve results?
NVIDIA reports results before and after Indic continued pretraining. The training stage used a 10B-token Indic mixture—much smaller than the full 2.83T-token corpus—so the benchmark changes are not a direct test of training on every released token.
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| Benchmark | Coverage | Before | After | Change |
|---|---|---|---|---|
| MILU | 10 languages | 72.48 | 77.90 | +5.42 |
| Indic MMLU-Pro | 12 languages | 51.83 | 56.81 | +4.98 |
| Indic ARC-C | 12 languages | 84.17 | 92.83 | +8.66 |
| Indic ARC-E | 12 languages | 90.73 | 96.77 | +6.04 |
| Indic GSM8K strict | 11 languages | 72.67 | 82.49 | +9.82 |
These are NVIDIA-reported measurements, not an independent replication. They indicate gains across knowledge, reasoning and grade-school mathematics tests, but they do not by themselves establish equal improvements for every language, domain or model size.
Translation quality results
NVIDIA also reports chrF++ improvements after the Indic work. English-to-Indic scores rose from 35.43 to 47.47, while Indic-to-English scores increased from 58.75 to 64.30. chrF++ is a character n-gram translation metric; the figures show better overlap with reference translations but do not replace human judgments of meaning, fluency or regional usage.
Is the corpus actually open?
NVIDIA says it plans to release the full corpus, the generation pipelines and, with AI4Bharat, a human-verified Indic evaluation suite. At the time of the announcement, no definitive release schedule or complete license terms for every artifact were provided. Until download locations and licenses are published, “open” describes a stated release plan rather than a fully verified, immediately usable package.
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- Confirm that the corpus files are downloadable rather than limited to documentation or model checkpoints.
- Check whether data, code, model outputs and evaluation sets have separate licenses.
- Review language and script coverage at the document level, including the balance of native and romanized text.
- Inspect deduplication, filtering and provenance records before using the data for commercial or high-stakes systems.
How this relates to AI4Bharat’s IndicLLMSuite
AI4Bharat’s IndicLLMSuite is related context, not the same dataset. Its repository documentation describes Sangraha, a cleaned Indic pretraining collection totaling 251B tokens across 22 languages, alongside IndicAlign datasets and the Setu cleaning, filtering and deduplication pipeline. AI4Bharat’s organization site also describes curation and synthetic-generation work and a 2.2-million-pair parallel corpus across 22 languages.
| Resource | Reported scale | Languages | Data and tooling emphasis |
|---|---|---|---|
| Nemotron-Pretraining-Indic-v1 | About 2.83T tokens | 22 languages; 34 language-script configurations | Large translated corpus, model scoring and rewriting; planned corpus and pipeline release |
| Sangraha in IndicLLMSuite | 251B tokens | 22 languages | Cleaned pretraining collection with Setu processing and IndicAlign datasets |
| AI4Bharat parallel-corpus effort | 2.2 million translation pairs | 22 languages | Parallel data and synthetic-generation infrastructure; broader release details vary by artifact |
The two efforts can therefore be compared on volume, provenance and release completeness, but their token counts are not interchangeable: Nemotron-Pretraining-Indic-v1 is much larger and heavily translation-derived, while Sangraha is a separate cleaned collection.
What the release could change
A corpus at this scale could lower the cost of adapting open models to Indian languages, especially where native training text is scarce. Romanized data may also help systems handle the way users type Indic languages on standard keyboards. The main technical questions will be whether synthetic translations preserve local terminology and dialect variation, how much duplication remains, and whether human-verified evaluations confirm the reported gains across all 22 languages.
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Until the files, scripts, evaluation suite and licenses are available for inspection, the strongest established claim is narrower: NVIDIA has documented a 2.83T-token Indic corpus and an open-release plan, and it reports substantial benchmark improvements from a smaller 10B-token continued-pretraining mixture.
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