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“This approach allows each model to develop stronger linguistic grounding and cultural nuance, creating systems that feel more natural and reliable for the communities they are meant to serve. At the same time, all Tiny Aya models retain broad multilingual coverage, making them flexible starting points for further adaptation and research,” the company said in a statement.<\/p>\n\n
Cohere noted that these models, which were trained on a single cluster of 64 H100 GPUs (a type of high-powered chip by Nvidia) using relatively modest computing sources, are ideal for researchers and developers building apps for audiences that speak native languages. The models are capable of running directly on devices, so developers can use them to power offline translation. The company noted that it built its underlying software to suit on-device usage, requiring less computing power than most comparable models.<\/p>\n\n In linguistically diverse countries like India, this kind of offline-friendly capability can open up a diverse set of applications and use cases without the need for constant internet access.<\/p>\n\n The models are available on HuggingFace, the popular platform for sharing and testing AI models, and the Cohere Platform. Developers can download them on HuggingFace, Kaggle, and Ollama for local deployment. The company is also releasing training and evaluation datasets on HuggingFace and plans to release a technical report detailing its training methodology.<\/p>\n![\"\"](\"https:\/\/techcrunch.com\/wp-content\/uploads\/2026\/02\/Screenshot-2026-02-17-at-9.45.42-AM.jpg\")