German AI consortium releases Soofi S, an open 30B model that tops benchmarks in both English and German
A German research consortium launched Soofi S 30B-A3B, an open 30-billion-parameter language model trained on Deutsche Telekom’s cloud infrastructure. It outperforms all fully open competitors on both English and German benchmarks.

- Soofi S 30B-A3B is an open 31.6B-parameter model trained on Deutsche Telekom’s cloud infrastructure in Munich.
- It outperforms all fully open competitors on both English and German benchmarks due to a German-weighted training dataset.
- The model uses a hybrid architecture that activates only a fraction of parameters per token, improving efficiency for long contexts.
- This release marks a notable advancement for European AI, emphasizing open-source accessibility and regional language optimization.
A German AI research consortium has unveiled Soofi S 30B-A3B, an open-source language model with 31.6 billion parameters, trained entirely on Deutsche Telekom’s cloud infrastructure in Munich. The model introduces a hybrid architecture designed to activate only a fraction of its parameters per token, enabling stable throughput even with very long contexts. Unlike many multilingual models that prioritize English, Soofi S was deliberately trained with a German-weighted dataset, allowing it to surpass all fully open competitors on both German and English benchmark tests.
The consortium behind Soofi S includes academic and industry partners, positioning the model as a significant step for European AI development. Its open release aligns with growing demand for high-performance, regionally optimized language models that balance efficiency with multilingual capability. The hybrid architecture also suggests potential applications in enterprise settings where long-context processing is critical, such as document analysis or conversational AI systems.
Provides a high-performance, open multilingual model optimized for German and English, with efficient long-context processing.
Offers a European-based alternative for AI applications requiring strong multilingual support and cloud-native deployment.
Highlights Europe’s growing role in open AI development with regionally tailored models.
- hybrid architecture
- A model design that activates only a subset of parameters per input token, reducing computational load while maintaining performance.
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