Nonetheless, a small but determined group of Indian builders is starting to shape the country’s AI future. For example, Sarvam AI has created OpenHathi-Hi-v0.1, an open-source Hindi language model that shows the Indian AI field’s growing ability to address the country’s vast linguistic diversity. The model, built on Meta’s Llama 2 architecture, was trained on 40 billion tokens of Hindi and related Indian-language content, making it one of the largest open-source Hindi models available to date. Pragna-1B, the multilingual model from Upperwal, is more evidence that India could solve for its own linguistic complexity. Trained on 300 billion tokens for just $250,000, it introduced a technique called “balanced tokenization” to address a unique challenge in Indian AI, enabling a 1.25-billion-parameter model to behave like a much larger one. The issue is that Indian languages use complex scripts and agglutinative grammar, where words are formed by stringing together many smaller units of meaning using prefixes and suffixes. Unlike English, which separates words with spaces and follows relatively simple structures, Indian languages like Hindi, Tamil, and Kannada often lack clear word boundaries and pack a lot of information into single words. Standard tokenizers struggle with such inputs. They end up breaking Indian words into too many tokens, which bloats the input and makes it harder for models to understand the meaning efficiently or respond accurately. With the new technique, however, “a billion-parameter model was equivalent to a 7 billion one like Llama 2,” Upperwal says. This performance was particularly marked in Hindi and Gujarati, where global models often underperform because of limited multilingual training data. It was a reminder that with smart engineering, small teams could still push boundaries. Upperwal eventually repurposed his core tech to build speech APIs for 22 Indian languages, a more immediate solution better suited to rural users who are often left out of English-first AI experiences. “If the path to AGI is a hundred-step process, training a language model is just step one,” he says. At the other end of the spectrum are startups with more audacious aims. Krutrim-2, for instance, is a 12-billion-parameter multilingual language model optimized for English and 22 Indian languages. Krutrim-2 is attempting to solve India’s specific problems of linguistic diversity, low-quality data, and cost constraints. The team built a custom Indic tokenizer, optimized training infrastructure, and designed models for multimodal and voice-first use cases from the start, crucial in a country where text interfaces can be a problem.