A German research consortium has published the pretraining report for Soofi S 30B-A3B. It is an open base model for German and English. Training ran end to end on Deutsche Telekom’s Industrial AI Cloud in Munich. Preview weights are on Hugging Face. It is worth noting that among some of the fully open base models tested, Soofi S records the highest English and German aggregate scores.
What is Soofi S 30B-A3B?
Soofi S is a Mixture-of-Experts (MoE) hybrid Mamba Transformer foundation model. It totals ~31.6B parameters and activates ~3.2B per token. As a base model, it has no instruction tuning, alignment, or safety tuning. The KI Bundesverband coordinates the consortium, funded by the German Federal Ministry for Economic Affairs and Energy. Participants include Fraunhofer IAIS, DFKI, TU Darmstadt, ellamind, and Merantix Momentum.
How the architecture works?
The efficiency claim starts with the layer stack. The network holds 52 layers. That is 23 Mamba-2 sequence-mixing layers, 23 granular MoE layers, and 6 Grouped-Query Attention (GQA) layers. Only those 6 GQA layers maintain a KV cache. Each MoE layer holds 128 routed experts, activates 6 per token, and adds 2 shared experts. Other details: model dimension 2688, squared ReLU, RMSNorm, and no positional embeddings.
Soofi S adopts the Nemotron 3 Nano reference design without modification. The research team gives three reasons for that choice. Those are deployability on stacks such as vLLM, serving efficiency, and scientific control. Because the backbone is fixed, Nemotron 3 Nano becomes an architecture-identical baseline. The data recipe is the only moving part.
