Quantisation Costs More in Greek than in English, Cybersecurity Knowledge of Small Local Language Models

Small open language models that run on an ordinary office computer are an attractive basis for a security awareness assistant, and platforms such as SentinelSphere ship one in a compressed quantised form, that stores the numbers inside the model with fewer bits. This study asks how much cybersecurity knowledge this compression costs, and whether the cost is larger in Greek than in English. A sample of questions from the CyberMetric benchmark was translated into Greek with an open translation model that was not itself tested, the translation was checked by translating it back, and the same questions were put in both languages to Phi-4-mini at three levels of compression, to the general model Qwen2.5-3B and to the Greek adapted model Krikri-8B, all on a two core processor. Phi-4-mini answers Greek questions clearly worse than English ones even with light compression. The four bit compression that platforms ship costs almost nothing in English but a clear share of accuracy in Greek, and the strongest compression brings Greek close to guessing while English stays usable. The general model is the most accurate in English but among the weakest in Greek, where it needs about four times as many text pieces per question, while the Greek adapted model keeps the gap smallest at the price of a larger and slower file. Files made without the calibration step that relies on mostly English text, including the file the platform publishes, lose the same Greek accuracy at four bits. A compression level found acceptable in English should therefore not be assumed acceptable in Greek.