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

TL;DR - An arXiv study shows that rendering numerical time-series as 2D plots for Vision-Language Models cuts input tokens 3.6–10.4x versus text tokenization, reducing measured inference energy 1.8–2.5x while improving accuracy on telecom KPI anomaly detection. It reframes energy as a first-class design constraint by choosing modality rather than compressing the model.

• Evaluated across Llama-3.2-90B-Vision, Qwen2.5-VL-72B, and Pixtral-12B; estimated savings of ~7.2 MJ/day at telecom edge/CloudRAN deployments monitoring 200 cells per 15-minute interval.
• Accuracy improved rather than degraded: fine-tuned Llama-3.2-90B-Vision reported 220.7% higher precision than its text-only counterpart and beat LSTM/ARIMA baselines by over 144% on telecom anomaly detection.
• On public benchmarks, Pixtral-12B showed a 20.6x improvement in J/F1 (energy-per-quality) at mean F1 = 0.82.
• Scaling argument: at 24 KPIs, text representations blow past the 128K context window of most production LLMs, making text-only processing infeasible without truncation, while plot-based visual inputs stay within limits.