India’s First LLM Model

A Bangalore-based startup Sarvam has been chosen by the government to build India’s first LLM after a scrutiny of the 67 applications. The government will provide it compute resources. It will get incentivized by Rs. 10000 crore IndiaAI Mission. Sarvam model will be having reasoning capability, will respond to voice, and will be fluent in Indian languages. It will get access to 4000 GPUs for six months for the company to build and train its model.

It will not be an open-source model but could be fine-tuned to Indian languages. It will have 70 billion parameters, and many innovative features in engineering and programming. It will be in a position to compete with some of the best models in the world.

There will be three variants– Sarvam Large for advanced reasoning and generation, Sarvam Small for real time interactive applications and Sarvam-Edge for on-device tasks.

It will be optimized in India using local infrastructure and talent.

Sarvam’s goal is to build multi-modal, multi-scale foundational models from scratch.

The development occurs after the arrival of the Chinese DeepSeek model.

Those Indian companies that provide GPU support are Jio, Hiranandani-backed Yotta, Tata Communications, E2E Networks, NxtGen Datacentre, CMS Computers, Ctrls Datacenters, Locuz Enterprise Solutions, Orient Technologies and Vensysco Technologies.

There are certain challenges. India has to market and monetize a closed-source model in competitive global space. Sarvam’s proprietary approach aims at strategic autonomy and enterprise appeal. There is monetizing potential through subscriptions or application programming interface (API) access. However, the global market is very competitive. OpenAI was losing money on ChatGPT Pro subscription due to unexpectedly high usage outpacing the $200-per-month pricing that was set. This underscores the challenge Sarvam faces.

Indian users of OpenAI are the second-largest bas. They overwhelmingly prefer foreign models. There are issues of transparency, biases and data privacy, especially in sensitive sections like healthcare and finance.