Cohere Unveils Multilingual AI Models at India AI Summit

In a significant announcement at the ongoing India AI Summit, the enterprise AI company Cohere launched a groundbreaking family of multilingual AI models named Tiny Aya. This collection is designed to bridge language barriers, supporting over 70 languages and allowing seamless operations on everyday devices without requiring a constant internet connection.

Open-Weight Models for Greater Accessibility

The Tiny Aya models are characterized as open-weight, meaning their underlying code is publicly accessible, permitting modifications and usage by developers and researchers alike. This commitment to open-source principles aligns with the increasing demand for versatile AI solutions that can adapt to various linguistic and cultural contexts.

Supporting Diverse South Asian Languages

Developed by Cohere Labs, the research division of Cohere, Tiny Aya holds a substantial focus on South Asian languages. It supports languages such as Bengali, Hindi, Punjabi, Urdu, Gujarati, Tamil, Telugu, and Marathi, which are essential for engaging diverse communities in this linguistically rich region.

The base model of Tiny Aya consists of an impressive 3.35 billion parameters, indicating its considerable size and complexity. To cater to different user needs, Cohere has also introduced Tiny Aya-Global, a variant fine-tuned for improved responsiveness to user commands, making it ideal for applications requiring extensive language support. Additionally, the Tiny Aya family includes regional variants: Tiny Aya-Earth for African languages, Tiny Aya-Fire targeting South Asian languages, and Tiny Aya-Water for the Asia Pacific, West Asia, and Europe.

Enhancing Linguistic and Cultural Context

Cohere’s strategic approach aims to enhance linguistic grounding and cultural nuances within the models, ensuring they resonate authentically with the communities they are designed to serve. According to a company statement, this methodology not only enriches the user experience but also creates flexible starting points for further adaptation and research.

Technical Framework and Offline Capabilities

Cohere emphasizes that these models have been trained using a single cluster of 64 GPUs, specifically Nvidia’s powerful H100 chips, under relatively modest computing conditions. This enables researchers and developers to build applications targeting audiences proficient in native languages. One of the standout features of the Tiny Aya models is their ability to operate directly on devices, allowing for offline translation capabilities that are particularly advantageous in linguistically diverse countries like India.

Such offline functionality can unlock numerous applications and use cases, especially in areas where internet accessibility is limited or inconsistent. By reducing dependency on online resources, Tiny Aya empowers users with immediate language support, fostering better communication and understanding.

Where to Access the Models

Developers and researchers interested in exploring these multilingual capabilities can find the Tiny Aya models available on HuggingFace, a prominent platform for sharing and experimenting with AI models, as well as on the Cohere Platform. They are also accessible for local deployment through other platforms such as Kaggle and Ollama. To further support the AI community, Cohere plans to release training and evaluation datasets on HuggingFace and will issue a detailed technical report outlining the methodologies employed in their training processes.

Future Plans and Market Positioning

Cohere’s advancements come at a time when demand for AI solutions catering to diverse linguistic environments is growing exponentially. The startup’s CEO, Aidan Gomez, hinted last year about plans for an upcoming public offering. The company’s growth trajectory appears promising, as reported by CNBC, with a remarkable annual recurring revenue of $240 million for 2025 and a consistent quarter-over-quarter growth rate of 50%.

Conclusion

Cohere’s launch of the Tiny Aya multilingual model family marks an important milestone in the evolution of enterprise AI, especially in addressing language diversity. By combining open-weight accessibility and advanced linguistic support, these models not only facilitate communication but also empower developers to create adaptive, offline-capable applications tailored to diverse communities. As AI continues to shape various industries, Cohere’s innovative approaches set a promising precedent for future developments in multilingual AI technologies.

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Emmanuel Kesse

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