这篇论文告诉你，把数据画成图再给模型看，能省电还更准。电信场景实测能耗降2.5倍，精度翻倍，值得做时序分析的人看看。
论文提出用视觉语言模型处理数值时间序列，将KPI窗口编码为2D图，在Llama-3.2-90B、Qwen2.5-VL-72B和Pixtral-12B上实现3.6-10.4倍输入token缩减。实测推理能耗降低1.8-2.5倍，在电信边缘部署中每天节省约7.2MJ。微调的Llama-3.2-90B-Vision异常检测精度比文本版高220.7%，比LSTM和ARIMA高144%。Pixtral-12B在公共基准上J/F1提升20.6倍，平均F1为0.82。
A Picture is Worth a Thousand Tokens: How Vision Language Models Cut AI Energy Costs While Improving Accuracy
LLM inference accounts for over 90% of AI operational energy, scaling directly with input token count---a critical inefficiency for telecom network analytics and numerical time-series data analysis (NTSDA), where raw multivariate KPI windows from 4G/5G cell sites expand into thousands of floating-point tokens. Vision-Language Models (VLMs) eliminate this mismatch by encoding time-series as 2D plots, achieving 3.6-10.4x input token reduction across Llama-3.2-90B, Qwen2.5-VL-72B, and Pixtral-12B architectures. This translates to 1.8-2.5x measured inference energy reduction, saving approximately 7.2 MJ/day at telecom edge deployments and CloudRAN that monitor 200 cells per 15-minute interval. Critically, efficiency gains do not sacrifice accuracy: a fine-tuned Llama-3.2-90B-Vision VLM achieves 220.7% higher precision than its text-only counterpart and outperforms LSTM and ARIMA baselines by over 144% on telecom anomaly detection. On public benchmarks, Pixtral-12B achieves a 20.6x improvement in J/F1 score at mean F1 = 0.82. At 24 KPIs, text representations exceed the 128K context window of most production LLMs, rendering text-only processing infeasible without truncation, while visual representations remain within standard limits. These results establish VLMs as an energy-efficient and accuracy-superior modality for numerical time-series workloads, providing empirical grounding for AI inference systems that treat energy consumption as a first-class engineering constraint.