Researchers have developed TokenPowerSandbox, an evidence-gated workflow for energy-aware large language model serving. The system uses a CPU-resident projector and short GPU probes to estimate energy consumption without exhaustive profiling. In experiments with a 7B parameter model, the approach achieved low mean absolute percentage error (MAPE) in predicting energy usage, but also highlighted limitations in certifying latency based on energy accuracy alone. --- Why it matters: This matters for AI engineers because it provides a more efficient and accurate way to estimate energy consumption of large language models, which is crucial for deploying them in real-world applications. The approach can help reduce the environmental impact of these models while maintaining their performance. Source: https://arxiv.org/abs/2608.18149