Infosys chairman and Aadhaar architect Nandan Nilekani on August 13 said that he is bullish on Artificial intelligence (AI) startup Sarvam AI, as it has put the world’s apprehensions and built a small language model (SLM) with two billion parameters.
“There was a big buzz about whether India can build models. But Sarvam has shown that it is possible, to build models, smaller models, 2 billion parameters. And they have shown that it is very very use-case first,” Nilekani said at the launch of Sarvam AI’s full-stack Generative AI (Gen AI) platform.
Sarvam AI in partnership with AI infrastructure provider Yotta on August 13 launched a suite of generative AI tools and models for both enterprises and developers.
The offerings also include Sarvam Agents, Sarvam Models, and an API for developers, alongside open-source solutions. Founded in 2023 by Vivek Raghavan and Pratyush Kumar, Sarvam AI is a homegrown generative AI startup that has raised $41 million in its Series A funding round, led by Lightspeed Ventures with participation from Peak XV Partners and Khosla Ventures.
“We will be unlikely running on large frontier models, but we will be using much smaller, targeted data models,” Nilekani said, advocating that Sarvam AI’s frugal engineering has shown that AI can be cost-effective for India.
Nilekani also reiterated that India will be the AI use-case capital of the world. “We think that for India it is all about applying AI technologies in every day usecases,” he added. The country is now putting together infrastructure and tools using AI, he said.
On May 8, the cofounder-cum-chairman of Infosys, said that India is not in the arms race to build LLMs and, instead, will focus on building AI use-cases that will reach every citizen.
Nilekani added that model building will ultimately get commoditised, and it's important to build it in a way that the engineering and capability can be used to deliver high-performance applications.
“I’m very bullish on what Sarvam has built. And they have shown that India can build models that are population scale and effective,” Nilekani said.
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