Viktor Haag | Searcharxiv

arXiv2026

From Data to Device: ELMOD An Efficient German-First 2.7B Language Model for Mobile Inference

We present ELMOD - Efficient Language Model for On-Device Deployment - a compact (2.7B) German language model designed for efficient inference on resource-constrained hardware. ELMOD was trained on a limited computational budget (55k H100 GPU hours) using exclusively publicly available data. We developed a suite of German-specific data pre-processing, which differ from English-oriented counterparts in their handling of morphological variation, compounding, and orthographic conventions. Furthermore, we introduced a quality filtering and rephrasing step, which increased the instructional quality of the data, improved performance during the annealing phase, and reduced overall compute requirements. Thanks to our architectural model and data choices, including prefiltering, our educational-quality filtering and rephrasal to raise the educational-quality, ELMOD is the strongest performer in its size class (<3B), matching the performance of 7B-parameter models in German.

cs.CL↗

arXiv2017

Lattice Boltzmann Benchmark Kernels as a Testbed for Performance Analysis

Lattice Boltzmann methods (LBM) are an important part of current computational fluid dynamics (CFD). They allow easy implementations and boundary handling. However, competitive time to solution not only depends on the choice of a reasonable method, but also on an efficient implementation on modern hardware. Hence, performance optimization has a long history in the lattice Boltzmann community. A variety of options exists regarding the implementation with direct impact on the solver performance. Experimenting and evaluating each option often is hard as the kernel itself is typically embedded in a larger code base. With our suite of lattice Boltzmann kernels we provide the infrastructure for such endeavors. Already included are several kernels ranging from simple to fully optimized implementations. Although these kernels are not fully functional CFD solvers, they are equipped with a solid verification method. The kernels may act as an reference for performance comparisons and as a blue print for optimization strategies. In this paper we give an overview of already available kernels, establish a performance model for each kernel, and show a comparison of implementations and recent architectures.

cs.PF↗