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German Consortium Releases Soofi S, a 30B Open-Source AI Model

🔄 Updated 74d ago — new reporting from Hacker News Front Page
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Key points

  • Soofi S is a 30B parameter open-source model by a German AI consortium.
  • Trained on Deutsche Telekom's AI cloud, focusing on English, German.
  • Uses 3.2 of its 31.6B parameters per token for efficient processing.
  • Critics question its adherence to Chinchilla scaling laws.
  • Designed for European industrial applications.

Overview

The German AI consortium has announced Soofi S, an open-source language model containing 30 billion parameters. Soofi S is geared towards industrial AI applications across Europe, integrating a Mixture-of-Experts architecture for efficiency and transparency in model deployment.

Technical Features

The model activates only 3.2 of its 31.6 billion parameters per token, leading to consistent processing speeds, particularly for long inputs. Soofi S's training primarily covered German and English datasets, using Deutsche Telekom's AI cloud infrastructure.

Benchmark Performance

Soofi S outperformed other fully open-source models, such as Olmo 3 32B and Apertus 70B, in benchmarks, excelling in English, German, and coding tasks, despite criticisms over its token-to-parameter ratio surpassing Chinchilla scaling laws.

Applications and Availability

Initially aimed at technical and regulatory documentation and code-generation applications, Soofi S is poised to be foundational for further specialized models within the Soofi family. It is currently undergoing industry trials with plans for broader availability pending results.

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How outlets covered it

The Soofi Consortium has unveiled Soofi S, an open-source model tailored for industrial AI applications in Europe. This 30B Mixture-of-Experts model aims to enhance control, transparency, and efficiency in areas such as regulatory documentation and coding.

The German AI consortium has introduced Soofi S, an open-source language model with 30 billion parameters. It outperforms existing models in English and German benchmarks, emphasizing efficient resource use by activating only 3.2 of its total parameters per token, which ensures constant processing speed.