- Tether’s African model supports 19 languages while running directly on local devices
- Tether removed 96% of low-quality training material before building AfriSLM
- EuroNano supports 90 translation directions while occupying only 36MB
Tether AI Research has unveiled a new set of translation systems that work entirely offline, requiring no connection to any network.
The <a href="https://tether.io/news/tether-releases-open-source-ai-translation-models-for-african-and-european-languages/” rel=”nofollow noopener” target=”_blank”>release includes QVAC TranslatePsy-AfriSLM, built for 19 African languages, alongside QVAC TranslatePsy-EuroNano, built for nine European languages.
Each model processes translations directly on the device, keeping personal data local rather than sending it to remote cloud servers.
Translation designed for devices with limited resources
The African release includes Hausa, Amharic, Yoruba, Lingala, Swahili, Igbo, Zulu, Somali, Oromo, Malagasy, Kinyarwanda, Xhosa and Afrikaans.
It also handles Wolof, Luganda, Nyanja, Shona, Tswana and Southern Sotho, covering languages used across several regions of Africa.
Tether says those 19 languages collectively account for about 50% of the continent’s population — the figure represents language reach rather than individual users.
AfriSLM contains 800 million parameters, yet Tether says the LLM surpassed Qwen3.5-122B-A10B, TranslateGemma-27B and NLLB-3.3B.
The comparisons were measured using FLORES-200, BOUQuET and SMOL, although benchmark performance does not establish identical results across every practical translation situation.
Tether attributes the results partly to a data-screening system that discarded as much as 96% of material judged unsuitable.
The company also released smaller and larger versions, including 0.8B, 2B and 4B parameter configurations for different hardware requirements.
EuroNano takes another approach, using English as an intermediary language while connecting European languages through 90 possible translation routes.
That package occupies only 36MB, which Tether says represents about 94% less storage than a comparable Firefox offline arrangement.
The compact footprint means translation can remain available after connectivity disappears, without requiring separate downloads for every language combination.
That distinction matters for field workers, travellers and local applications where storage capacity and network availability can both impose practical constraints.
Why Tether is starting with African languages
Tether’s African focus also connects with the physical infrastructure it has developed across parts of Sub-Saharan Africa.
The company has installed solar-powered kiosks that provide phone charging, battery exchanges and access to digital financial services in communities.
Those locations could eventually provide another route for distributing educational material, agricultural information and other locally translated content without depending on continuous connectivity.
“Four billion people were left behind by the traditional financial system, and the most powerful technology of our age has repeated that failure,” said Paolo Ardoino, CEO of Tether.
“Language should not determine who can benefit from artificial intelligence. Open translation models like these are a step toward a future where education and AI tools reach hundreds of millions of people who have neither reliable connectivity nor access to expensive systems.”
The same technology could also operate alongside QVAC MedPsy, Tether’s smaller model intended for healthcare-related applications on local devices.
“A mother could get real medical information she understands, instead of guessing. A child could learn in their own language. That is the future we are building through QVAC,” Ardoino added.
AfriSLM is currently available through Hugging Face, and the associated research has also been accepted for presentation at EMNLP 2026.
