G42, an AI & cloud computing company based in Abu Dhabi announced that it will soon launch NANDA – a cutting-edge Hindi Large Language Model. NANDA is a 13-billion parameter model trained on approximately 2.13 trillion tokens of language datasets, including Hindi.
With a name inspired by one of India’s highest peaks, NANDA is the result of a collaboration between Inception (a G42 company), Mohamed bin Zayed University of Artificial Intelligence – the world’s first graduate research university dedicated to AI – and Cerebras Systems. The model was trained on Condor Galaxy, one of the world’s most powerful AI supercomputers for training and inferencing built by G42 and Cerebras.
NANDA’s release will mark a significant milestone in the realm of AI for India, offering over half a billion Hindi language speakers the opportunity to harness the potential of generative AI.
“India has solidified its position as a global technology leader, driven by transformative initiatives like Digital India and Startup India under Prime Minister Narendra Modi’s leadership. As the country stands on the brink of AI-powered growth, G42 is proud to contribute to this journey with the launch of NANDA in support of India’s AI ambitions,” says Manu Jain, CEO – G42 India.
“G42 has a strong track record in the development of language and domain-specific LLMs. With NANDA, we are heralding a new era of AI inclusivity, ensuring that the rich heritage and depth of Hindi language is represented in the digital and AI landscape. NANDA exemplifies G42’s unwavering commitment to excellence and fostering equitable AI,” said Dr. Andrew Jackson, Acting CEO of Inception, a G42 company.
In August 2023, G42 launched JAIS, the world’s first open-source Arabic LLM. JAIS transformed Arabic Natural Language Processing (NLP), unlocking access to native language generative AI capabilities for over 400 million Arabic speakers globally. With models ranging from 590 million to 70 billion parameters, JAIS set a new standard for linguistic AI which G42 now seeks to replicate for other regions whose languages are still underrepresented.
