A Picture is Worth a Thousand Tokens: How Vision Language Models Cut AI Energy Costs While Improving Accuracy

LLM inference dominates AI's operational energy use, and numerical time-series data—like telecom network metrics—creates especially inefficient token-heavy inputs when represented as text. This paper shows that converting time-series data into 2D visual plots and processing them with vision-language models dramatically reduces token counts and energy consumption while improving accuracy. Applications include telecom network monitoring, anomaly detection in 4G/5G infrastructure, and broader numerical time-series analysis tasks constrained by context window limits. This approach offers a practical path toward more sustainable, accurate AI systems for industries handling large volumes of sensor or KPI data. Paper: https://arxiv.org/abs/2608.07427