Can Sarvam AI overcome challenges to build India's first indigenous LLM under the INDIAai Mission?

Last week, Bengaluru-based
Sarvam AI
became the first startup chosen by
GoI
to build an indigenous foundational LLM under
INDIAai Mission
, reportedly from among over 400 applicants. GoI will allocate the company 4,096
Nvidia H100 GPUs
for 6 months, obtained from empanelled firms like Jio, CtrlS, Yotta and
Tata Communications
.
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In March 2024, GoI had announced its ₹10,372 cr (about $1.25 bn) INDIAai Mission to democratise AI innovation and computing access, enhance data quality, and nudge India to become an AI powerhouse. The government started investing to create a high-end, commonly accessible computing facility equipped with 18,693 GPUs.
This is especially important considering '2022 Unesco State of the Education Report' stated that India has the highest relative AI skill penetration rate in the world. '2024 Stanford AI Index Report' places India among the top countries in the AI advancement world. Sarvam is the first-off-the-block contender in INDIAai that hopes to actualise these expectations.
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GoI reckons that this sovereign model, with 70 bn parameters, will compete with the best in the world. Sarvam has affirmed that its model, capable of sophisticated reasoning and voice-first interactions, with fluency across 22 Indian languages, including English, will be secure and fit for deployment at population scale in 6 months. But there is no gainsaying the reality that training and developing this LLM entirely in India is likely to encounter its fair share of hurdles.
Speech babel:
Obtaining and curating large datasets representing India's linguistic diversity, including dialects, is hard to find and complex to deal with. Non-English Indian languages have their own intricate grammars, structures and syntaxes. Building contextual models that recognise all of this, and are still fluent, isn't easy, especially when benchmarking against global LLMs. Add to that the need to recognise and eliminate gender, religion, caste and other biases within the context of the country's complex societal norms.
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Content & its discontents:
Data cleansing will be time-consuming, and copyright and licensing could be arduous.
Gift of the gab:
India has a large workforce. But building LLMs requires specialist researchers, engineers and linguists with higher-order skills in natural language processing, machine learning and complex architectures. Such talent is hard to come by, and equally hard to retain, in a global marketplace that knows their value. Eventual success hinges on igniting interest of researchers, industry experts and developers to build applications and services, on top of Sarvam's models that will encourage extensive adoption.
Networking:
Interoperability with diverse devices, applications and platforms will be a formidable challenge, too. As will be adapting to evolving technologies, architectures and optimisation techniques.
Infra dig:
Sarvam has to create magic, despite India contributing less than 1.5% to global AI research, India having insufficiently mature high-performance concentrated computing assets, cloud infrastructure that is just becoming robust, academia that's catching up, and a talent pool that is currently in flight.
Data deficit:
GoI has stepped up to spur AI funding. It has to now step up to bridge the country's data disadvantage. Because data adequacy is at the heart of AI development. INDIAai's datasets platform, AIKosh just went live. But a lone government-managed platform can only be a start.
India has to access vast amounts of multimodal data that repositories of entities like Jio, Airtel, MakeMyTrip, Zomato and PhonePe have, within the ambit of privacy and rules-based norms. And the vast amounts of data that lie locked within government departments like health, education, agriculture, finance, railways and aviation.
Tie-ups along with startups:
Formal government-to-government exchange programmes, collaborations with renowned global research universities and other bilateral and multilateral arrangements can help India narrow the lag from not being first-mover. According to consultancy firm Zinnov, MNCs have set up around 2,975 GCCs in India. These are full-fledged innovation and R&D centres employing 1.9 mn professionals, generating revenues of $65 bn in 2024 - more than any other country in the world by a distance. It's a pointer to what can be achieved.

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