The Price of Prompting: Profiling Energy Use in Large Language Models Inference (2407.16893v1)

Abstract: In the rapidly evolving realm of artificial intelligence, deploying LLMs poses increasingly pressing computational and environmental challenges. This paper introduces MELODI - Monitoring Energy Levels and Optimization for Data-driven Inference - a multifaceted framework crafted to monitor and analyze the energy consumed during LLM inference processes. MELODI enables detailed observations of power consumption dynamics and facilitates the creation of a comprehensive dataset reflective of energy efficiency across varied deployment scenarios. The dataset, generated using MELODI, encompasses a broad spectrum of LLM deployment frameworks, multiple LLMs, and extensive prompt datasets, enabling a comparative analysis of energy use. Using the dataset, we investigate how prompt attributes, including length and complexity, correlate with energy expenditure. Our findings indicate substantial disparities in energy efficiency, suggesting ample scope for optimization and adoption of sustainable measures in LLM deployment. Our contribution lies not only in the MELODI framework but also in the novel dataset, a resource that can be expanded by other researchers. Thus, MELODI is a foundational tool and dataset for advancing research into energy-conscious LLM deployment, steering the field toward a more sustainable future.

The Price of Prompting: Profiling Energy Use in LLMs Inference

The paper "The Price of Prompting: Profiling Energy Use in LLMs Inference" presents an in-depth exploration into the energy consumption associated with the inference phase of LLMs. This topic is particularly pertinent given the rapid advancement and widespread deployment of LLMs across various applications, necessitating a closer examination of their sustainability impacts.

Introduction to MELODI

Central to the paper is the introduction of MELODI (Monitoring Energy Levels and Optimization for Data-driven Inference), a framework specifically designed for monitoring and analyzing the energy consumed during LLM inference processes. Unlike previous efforts focused mainly on the training phase, MELODI provides a comprehensive approach to monitor real-time energy usage using tools like Scaphandre and Nvidia-smi, enabling detailed tracking of CPU and GPU power consumption respectively.

Methodology and Dataset

The paper outlines the methodology for deploying the MELODI framework, emphasizing its application across a diverse range of models and hardware. Notably, the dataset compiled using MELODI encompasses a variety of prompt datasets, ensuring a robust platform for comparative analysis. The dataset itself is pivotal, providing granular data on energy consumption trends, including prompt complexities and model responses.

Key Findings

1. Energy Consumption Variability: The research establishes that there is significant disparity in energy usage across different model sizes, with larger models consuming substantially more energy. For instance, models like codellama-70b show energy usage about 100 times higher than their smaller counterparts.
2. Impact of Hardware: The paper highlights the inefficiencies in CPU-based processing, evidenced by higher energy consumption on laptops compared to workstations, particularly when using only CPU resources.
3. Response and Energy Correlation: A strong correlation was found between energy consumption and response characteristics (e.g., token length), rather than prompt complexity. This discovery underscores the potential for energy savings through managing response generation.
4. Predictive Modeling: The development of predictive models using response characteristics reveals high accuracy in forecasting energy consumption, showcasing the potential for optimizing response processes to minimize energy usage.

Implications and Future Directions

The implications of this research are multifaceted. Practically, the findings advocate for strategic management of response lengths and the exploration of power-efficient hardware configurations to curb energy demands. Theoretically, the insights inform the development of more energy-conscious LLM architectures.

Future studies could delve into enhancing the accuracy of CPU power monitoring tools and broadening the scope of tested hardware configurations to generalize these findings further. Additionally, investigating the trade-off between response quality and energy consumption could yield actionable strategies for balancing performance with sustainability.

Conclusion

In summary, "The Price of Prompting" offers a critical lens on the sustainability challenges associated with LLM deployment. Through comprehensive analysis and the development of the MELODI framework, this work provides a pivotal resource for advancing energy-efficient AI technologies, setting a foundation for further exploration into the complexities of energy use in AI systems.