Tether AI Research has released multiple sets of open-source translation models that can run offline on smartphones, laptops, and other edge devices without uploading data to third-party clouds. The series belongs to QVAC and includes TranslatePsy-AfriSLM, AfriNano, and EuroNano.

AfriSLM covers 19 languages including Hausa, Amharic, Yoruba, Lingala, Swahili, Igbo, Zulu, Somali, Oromo, Malagasy, Kinyarwanda, Xhosa, Afrikaans, Wolof, Luganda, Nyanga, Shona, Tswana, and Southern Sotho, with a combined user population of about half of Africa. AfriNano covers 8 African languages, while EuroNano covers 9 European languages with 90 translation directions.

AfriSLM offers models with 800 million, 2 billion, and 4 billion parameters, as well as quantized versions. The company claims that the smallest 800 million parameter model outperforms Qwen3.5-122B-A10B, TranslateGemma-27B, and NLLB-3.3B on FLORES-200, BOUQuET, and SMOL. EuroNano's minimum deployment is about 36MB, which is approximately 94% smaller than similar Firefox offline solutions; the high-quality English translation direction retains about 98.4% quality compared to Meta NLLB-200.

The training introduced quality estimation filtering, using scores from MetricX, AfriCOMET, SSA-COMET, etc., to eliminate up to 96% of low-quality open-source corpora. The accompanying synthetic parallel corpus contains about 216 million bidirectional samples and approximately 3.237 billion training tokens. The paper states that filtered synthetic data dominates the quality-efficiency frontier. Weights and datasets have been uploaded to Hugging Face's qvac account, and the paper has been accepted by EMNLP 2026, authored by Milan Gritta, Patrik Lambert, Jihye Back, and Amril Nazir.

CEO Paolo Ardoino stated that language should not determine who can use AI, pointing out that about 4 billion people have been excluded first by traditional finance and then by advanced tools. The company frames the release as "stable intelligence after stable currency, stable energy, and stable communication," emphasizing bringing AI into the users' world rather than sending people into the cloud.

In market mechanisms, buyers are end users lacking cloud services and bandwidth, as well as localization projects in education and healthcare, while sellers are publishers investing stablecoin profits into edge models. The event is driven by the simultaneous release of open-source weights and conference papers, with funds flowing from USDT reserve earnings into data cleaning and small model fine-tuning, rather than renting large cloud inference. Beneficiaries are developers of low-resource languages and applications that can be deployed offline, while those under pressure are general large model service providers that rely on cloud APIs, charge per call, and perform poorly on African languages.

Source: Public Information