Developers and researchers working on German‑English language applications often struggle with the trade‑off between model quality and deployment cost. Large dense models deliver strong results but require massive GPU memory and high per‑token compute, making real‑time or low‑latency services expensive and difficult to scale. Open base models that are not instruction‑tuned force teams to invest extra effort in alignment and safety tuning before they can be used in production. Moreover, many open models lack transparent architecture details, which hinders reproducibility and limits the ability to adapt the model to specific hardware constraints.
Soofi S 30B‑A3B addresses these pain points directly. As a Mixture‑of‑Experts hybrid Mamba‑Transformer with 31.6 B total parameters but only ~3.2 B activated per token, it delivers the highest aggregate English and German scores among fully open base models while keeping inference compute low. The architecture fixes 52 layers—23 Mamba‑2, 23 MoE, and only 6 Grouped‑Query Attention layers that maintain a KV cache—so memory usage grows slowly with context length. This design enables near‑flat decode throughput from 4K to 256K tokens and yields an 8‑9× increase in tokens‑per‑second‑per‑GPU compared with dense 14‑24B models at 40K context.
Because the model follows the Nemotron 3 Nano reference design without modification, it can be dropped into existing serving stacks such as vLLM or TensorRT‑LLM with minimal code changes. The pretraining recipe is publicly available, allowing teams to reproduce results or continue training on domain‑specific data. The model is released as open weights on Hugging Face, providing a ready‑to‑use foundation that can be fine‑tuned for instruction following, safety, or specialized tasks without starting from scratch.
In practice, teams can load Soofi S, activate only the needed experts per token, benefit from the reduced KV cache footprint, and deploy high‑quality German‑English language services at a fraction of the cost of comparable dense models. This efficiency opens up new possibilities for real‑time chatbots, translation tools, and multilingual content generation in resource‑constrained environments.
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