The source reports that 10 billion Indic tokens from a mixture of synthetic and natural text were used in a continued-pretraining stage for Nemotron 3.5 Super VL, with scores rising on five reported Indic benchmarks.
Thesis
The announcement makes the generation process part of the data release: its value is not only a large Indic corpus, but a reproducible workflow for extending and auditing it.
Observed facts
The source describes approximately 2.8 trillion tokens across 22 Indian languages, including 2,601.0 billion native-script tokens and 224.6 billion romanized tokens.
1The source says records preserve source identifiers and describes translation, quality assessment, and targeted repair in the data-generation workflow.
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Mechanism
That workflow turns curated English source material into synthetic Indic text while retaining a traceable link to the source and adding quality gates.
Why now
The publication pairs a sizeable corpus with an open recipe that teams can adapt to their own source material, broadening the practical unit of contribution from a fixed dataset to a repeatable data-generation process.
Implications
Model builders can evaluate the corpus as one input to Indic pretraining and adapt the pipeline, rather than treating the published token count as a ready-made training result.
Evidence boundary
Those results compare checkpoints across a stage combining synthetic and natural Indic data; they do not isolate the released corpus's causal contribution, and the token counts depend on the stated tokenizer.
Watch conditions
Watch for controlled evaluations that separate corpus effects from natural-data contributions and test the full released dataset across languages and scripts.