From Data to Device: ELMOD An Efficient German-First 2.7B Language Model for Mobile Inference
This work addresses the challenges of deploying large language models for German on resource-constrained mobile devices, where the language’s complex morphology, compound words, and orthographic rules pose significant hurdles. To this end, the authors propose ELMOD, a 2.7B-parameter German language model specifically optimized for on-device inference. By integrating tailored data preprocessing, education-quality–oriented filtering, and an automatic rewriting mechanism—combined with a compact Transformer architecture and efficient inference optimizations—ELMOD achieves state-of-the-art performance among sub-3B-parameter models on German-language tasks, rivaling that of 7B-scale models. The entire training process requires only 55k H100 GPU hours, demonstrating a highly efficient use of computational resources while maintaining strong linguistic competence.