Most translation models are primarily trained for high-resource Asian and European languages. Most African languages, spoken by hundreds of millions of people, are relatively neglected, compared to their high-resource counterparts.
Although AI underinvestment across the African continent has created a significant barrier to adoption among citizens, AI could generate $1.2 trillion for Africa’s economy by 2030, equivalent to 6% of its GDP, according to a UNESCO report.
Existing open-source LLMs underperform on African machine translation, and the shortage of large-scale, high-quality, open-source parallel data has constrained the development of competitive small language models in this space.
Tether’s AI Research group has developed TranslatePsy-AfriSLM to narrow this digital divide and lower the barrier to entry for AI adoption across the continent. TranslatePsy-AfriSLM is a collection of open-source machine translation models that outperform bigger systems like Google’s TranslateGemma-27B and Alibaba’s Qwen3.5-122B-A10B.
Africa’s linguistic diversity, paired with the world’s fastest-growing youth population – 70% of sub-Saharan Africa under thirty– is a key indicator of the potential for high-impact AI.
But while AI tools evolve and proliferate across high-income countries, only one African country (South Africa) scores higher than 50 out of 100 in AI infrastructure on the 2025 Government AI Readiness Index by Oxford Insights.
Several recent AI initiatives have promised to tackle the AI disparity in Africa; for example, Google’s AI policy blueprint for Africa, which lays out how African nations can harness AI for economic growth. Nonetheless, when it comes to specific, foundational, and immediately transformational tools, rather than long-term policy promises, open-source AI is most useful, particularly when it comes to African languages.
Unfortunately, most frontier open-source models underperform on languages other than English. Most frontier AI models work well in a handful of languages, and poorly in the rest. Smaller, more efficient models are useful to tackle the widening skills gap.
Healthcare is one of the highest-impact applications. This is due to the variety of many different local languages and the fact that connectivity can be unreliable in the communities that need information most.
Combined with Tether QVAC MedPsy, a small foundation model for medical and healthcare applications, TranslatePsy-AfriSLM creates a potential pathway to deliver medical knowledge and health education in the local languages of hundreds of millions of people.
The potential for agriculture is also huge. Local translation could allow farmers to get agricultural information in their own language. In humanitarian and disaster-response settings (which often lack reliable connectivity), offline translation can support coordination on the ground without needing a network connection.
Tether’s solar-powered kiosks across Sub-Saharan Africa let residents charge a phone, swap a battery, and access digital financial services where the grid and the banking system don’t reach.
For NGOs and field organizations, local-language translation would allow field workers to communicate across multiple communities without having to carry separate translation systems. Tether’s researchers were able to achieve stronger translation performance with significantly smaller models, all without cloud dependence.
Tether’s multilingual models are fully open source. Any developer can download them directly from Hugging Face and integrate on-device translation into their own applications, rather than relying on cloud APIs or requiring users to switch to a standalone translation app.
As translation takes place directly on the user’s hardware, the models can be integrated into applications without relying on proprietary cloud APIs or transmitting sensitive text to external providers.
The smallest TranslatePsy-AfriSLM model has just 800 million parameters, yet it outperformed Qwen3.5-122B-A10B, TranslateGemma-27B, and NLLB-3.3B across three separate benchmarks.
TranslatePsy-AfriSLM covers 19 Sub-Saharan African languages including Hausa, Amharic, Yoruba, Lingala, Swahili, Igbo, Zulu, Somali, Oromo, Malagasy, Kinyarwanda, Xhosa, Afrikaans, Wolof, Luganda, Nyanja, Shona, Tswana, and Southern Sotho.
TranslatePsy-AfriSLM is being released alongside Tether’s European language models, TranslatePsy-EuroNano. These models are small enough to run efficiently on edge devices while supporting nine European languages from a single multilingual deployment, making multilingual experiences practical for a much wider range of software.
At its smallest tier, Tether’s deployment is 17.6 times smaller while maintaining comparable translation quality.
Alongside its research into open-source language models that provide access to frontier AI capacity, Tether is building on its mission to ensure that data stays with the user with QVAC, a local AI that keeps your data on your device. QVAC is also free to run with no per-token or per-use cost.
Africa’s AI economy reaching $1.2 trillion by 2030 depends on how readily available access is to tools that people need, in languages they actually speak. Open-source models that run on people’s phones and laptops help narrow this gap by providing infrastructure that developers can build on, and technology that people can use.
TranslatePsy-EuroNano and TranslatePsy-AfriSLM are available through QVAC SDK for integration across Android, iOS, Linux, macOS, and Windows, and for download on Hugging Face at this link.