Infosys unveils Small Language Models built on NVIDIA AI stack

Small language models (SLMs) are algorithms based on machine learning that are trained on a more specific dataset than large language models (LLMs).

Trade

• Watchlist
• Portfolio
• Message
• Set Alert

 

 

live

• bselive
• nselive

Volume

Todays L/H

More

×

Coinciding with Jensen Huang's India visit, Infosys on October 24 announced the launch of two small language models built using the NVIDIA AI stack, for banking and IT-specific applications.

The announcement further deepens Infosys' enterprise AI journey with NVIDIA, and the small language models are utilizing industry-specific data, enhanced by NVIDIA and in collaboration with Sarvam AI.

Small language models (SLMs) are algorithms based on machine learning that are trained on a more specific dataset than large language models (LLMs).

The IT major said it plans to make foundational small language models available as a service so businesses could build on it.

On October 17, Infosys Chief Executive Officer Salil Parekh said the company is developing its SLM using open-source components and its proprietary data sets. Strong focus on Generative AI (Gen AI) has prompted the IT major to work on its SLM, which is being designed for specific industry applications, Parekh said.

"By integrating the NVIDIA AI stack with Infosys Topaz, we are taking advantage of very advanced enterprise AI capabilities to tackle unique business challenges, enhance operational efficiency, and deliver bespoke solutions that drive business value for our clients," Balakrishna DR, Executive Vice President, Global Services Head, AI and Industry Verticals, Infosys said.

Infosys is also working with NVIDIA to streamline AI application development and various use cases for its clients. Its center of excellence also showcased an exclusive AI Experience Zone featuring latest capabilities from NVIDIA AI and Infosys Topaz suite.

The recent advancements in agentic and physical AI are ushering innovation and productivity for enterprises.

Agentic AI systems consume data from multiple sources to independently analyze and develop strategies, and execute tasks such as supply chain optimization, cybersecurity risk analysis and helping medical professionals.