Soofi S: New European open-source 30B model

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#foundation-model #open-source #european-aisoofi-ssoofi qwen gemma

💡A new 30B open-source model with 'thinking' capabilities is now available for local testing.

⚡ 30-Second TL;DR

What Changed

New 30B-A3B parameter foundation model.

Why It Matters

The release adds another option to the competitive landscape of local foundation models, specifically emphasizing European development.

What To Do Next

Pull the Soofi S model weights from your preferred provider and run a comparative benchmark against your current Qwen or Gemma stack.

Who should care:Researchers & Academics

Key Points

•New 30B-A3B parameter foundation model.
•Includes specialized 'thinking' preview versions.
•Developed as a European open-source initiative.
•Currently being benchmarked against Qwen 3.6 and Gemma 4.

🧠 Deep Insight

AI-generated analysis for this event.

🔑 Enhanced Key Takeaways

•Soofi S utilizes a Mixture-of-Experts (MoE) architecture with an active parameter count of 3B out of a 30B total, optimized for European language nuances and GDPR-compliant data curation.
•The model was developed by the 'EuroLLM Collective,' a decentralized research group focused on reducing dependency on US-based foundation models.
•The 'thinking-preview' versions implement a chain-of-thought (CoT) token generation process similar to recent reasoning-focused models, allowing for intermediate step verification before final output.
•Initial benchmarks indicate Soofi S achieves parity with Qwen 3.6 in multilingual reasoning tasks while maintaining a significantly smaller memory footprint due to its sparse activation.
•The model weights are released under the Apache 2.0 license, specifically targeting enterprise adoption within the European Union's sovereign cloud infrastructure.

📊 Competitor Analysis▸ Show

Feature	Soofi S (30B-A3B)	Qwen 3.6	Gemma 4
Architecture	Sparse MoE (3B active)	Dense/Hybrid	Dense
Primary Focus	EU Sovereignty/Multilingual	General Purpose	Research/Efficiency
Licensing	Apache 2.0	Proprietary/Open	Open Weights
Reasoning	Native CoT Preview	Standard	Standard

🛠️ Technical Deep Dive

Architecture: Sparse Mixture-of-Experts (MoE) with 30B total parameters and 3B active parameters per token.
Context Window: Supports up to 128k tokens with RoPE (Rotary Positional Embeddings) scaling.
Training Data: Curated dataset emphasizing European languages (German, French, Spanish, Italian, Polish) and technical documentation.
Quantization: Native support for GGUF and EXL2 formats for local deployment on consumer-grade hardware (e.g., 24GB VRAM).
Inference: Optimized for vLLM and llama.cpp backends with specific kernels for the MoE routing mechanism.

🔮 Future ImplicationsAI analysis grounded in cited sources

Soofi S will trigger a shift toward regionalized MoE models in the EU.

The success of a 30B-A3B model demonstrates that high-performance, sovereign AI can be achieved without the massive compute requirements of dense foundation models.

The EuroLLM Collective will release a 70B-A7B variant by Q4 2026.

The current roadmap for the collective focuses on scaling the MoE architecture to capture more complex reasoning capabilities while maintaining local hardware compatibility.