Last week, Germany's KI Bundesverband, together with Fraunhofer, DFKI, TU Darmstadt and other institutions, officially open-sourced Soofi S 30B-A3B. This isn't another "fully open-source LLM" press release — it uses a 253,000 GPU-hour pretraining report to pull the European sovereign-AI narrative down to the engineering layer. The model is a 31.6B-parameter sparse MoE, activating only 3.2B per token — inference cost is closer to 3B than to a traditional 30B. The architecture reuses NVIDIA Nemotron 3 Nano's hybrid scheme: Mamba-2 and attention layers are interleaved, and only 6 of 52 layers maintain a KV cache. The result: at 40K context with 32 concurrent users, the per-second-per-GPU generation token count is about 8× a same-size dense model, and throughput from 4K to 256K context is nearly a flat line. The data ratio is the key move this time. In 27T tokens of training, the German proportion is pulled from 7.2% in the first phase to 15.3% in the second phase — far above the 5% non-English total in Nemotron's original recipe. HumanEval 73.8, MBPP-DE 84.2, INCLUDE-DE 61.2 — taking the fully-open-source crown on eight combined German-English benchmarks, leaving Apertus 70B, OLMo 3 32B, Alia 40B, and EuroLLM 22B in its wake. Training was completed on Deutsche Telekom's Industrial AI Cloud in Munich: 512 B200s, canal-water cooling, local renewable energy, waste heat sent to Tucherpark for heating. The researchers open-source weights, training and evaluation code, and the complete data card, formally aligned with the OSI 1.0 Open Source AI Definition. My take: what really bottlenecks sovereign AI is the data + compute + licensing trinity. Soofi S uses "heavy German data + end-to-end reconstructable training set + keeping compute in Europe" to submit all three at once — this kind of "full transparency" sample is more of a reference than a single leaderboard score. But MoE's factual recall is still a short board — RULER retrieval drops to 3% above 32K, and finding specific words in long documents still needs a dense model as a fallback.