Sarvam AI Launches Indic Language Model 'Sarvam-1'

Sarvam AI has launched Sarvam-1, a 2 Bn parameter large language model built specifically for Indian languages.

In a blogpost, the startup said that the model is optimised for 10 Indian languages, including Hindi, Bengali, Tamil, and Telugu, besides English.

The model aims to tackle two key challenges – token inefficiency and poor data quality for Indic languages.

Token inefficiency refers to the number of pieces (tokens) a language model needs to break a word into in order to process it. For instance, in English, a word like “apple” might be processed as one token. But in some Indian languages, the same word might get split into 4-8 tokens. This makes processing slower and less efficient.

Sarvam-1 claims to have achieved a token efficiency rate of 1.4-2.1 tokens per word (vs. 4-8 in existing models). It said that the LLM is trained on Sarvam-2T, a 2-trillion-token dataset curated specifically for Indian languages. This ensures better performance in areas like cross-lingual translation and question-answering.

Despite being smaller than models like Meta’s Llama-3.2-3B, Sarvam-1 claims to have outperformed them in several industry benchmarks.

Sarvam-1 is now available for download on Hugging Face.

Earlier on Thursday (October 25), chip giant Nvidia’s CEO Jensen Huang said that the .

Meanwhile, Sarvam AI also announced its partnership with Yotta Data Services. The Sarvam-1 model has been trained on Yotta’s Shakti Cloud infrastructure, the startup said.

Earlier this year, the startup launched its comprising multiple products — Sarvam Agents, Sarvam 2B, Shuka 1.0, Sarvam Models, and A1.

The in its Series A funding round led by Lightspeed Venture Partners, in participation with Peak XV Partners and Khosla Ventures, in December last year.

At the heart of all these is the , which is expected to clock a CAGR of 48% between 2023 and 2030 to become an over $17 Bn opportunity.

The post appeared first on .