14:40 GMT
The headline number is notable, but the real contribution here is the open pipeline rather than the token count. For languages with thin web corpora, synthetic data generation is fast becoming the only viable path to competitive pretraining, and releasing the recipe openly lets smaller teams replicate it instead of waiting for Western labs to get around to Indic languages. That said, 2.8 trillion tokens is a means, not an end — filtering quality and downstream benchmark results on Indic tasks will decide whether this actually moves the needle. It also fits neatly into India's broader AI sovereignty push, where local-language capability is increasingly treated as strategic infrastructure. On our view, this is an unglamorous but potentially high-leverage contribution, the kind that matters more over time than flashier model launches.
Scaling Indic Pretraining: 2.8 Trillion Tokens and an Open Data Generation Pipeline