Anubis-Energy: An Analysis Tool for Energy Consumption and Thermal Management in LLM Inference

Anubis-Energy is a professional analysis tool for the trade-off between energy consumption and thermal management during LLM inference. It focuses on the impact of static and dynamic performance analyzers on the energy efficiency of model inference, providing data support for green AI deployment. This article will discuss from dimensions such as background, core issues, technical methods, research findings, application value, and future directions.

In recent years, the scale of AI models has grown exponentially, from millions to hundreds of billions of parameters, leading to a sharp increase in energy consumption (e.g., the training energy consumption of GPT-4 is equivalent to the annual electricity consumption of thousands of households). Energy efficiency optimization has become a key issue in AI engineering, but there is a lack of specialized tools to quantify the trade-offs between performance, latency, energy consumption, and heat dissipation. Anubis-Energy fills the gap in the field of LLM inference energy efficiency analysis.

The project focuses on the energy consumption and thermal impact of the performance analyzers themselves. Static analyzers analyze code at compile time, while dynamic analyzers collect data at runtime but consume resources. The research needs to quantify the overhead of these analyzers and their degree of interference with the thermal state of the system under test.

Measurement dimensions include: energy consumption (real-time power consumption via hardware interface), thermal state (chip temperature/thermal throttling events), inference performance (tokens per second, latency), and analyzer overhead. The experiment uses a control design: first measure the baseline without analyzers, then test the impact of static analyzers, gradually enable dynamic analyzers to measure cumulative overhead, and finally map the three-dimensional trade-off space of performance-energy consumption-information quantity.

Dynamic analyzers significantly increase energy consumption (high-frequency sampling can increase power consumption by 10-30%); thermal throttling can mask real performance (frequency reduction due to high temperature is easily misjudged as an algorithm problem); static analyzers have little impact but limited information; there exists an optimal sampling frequency that balances information acquisition and system interference.

It has guiding significance for multiple scenarios: data centers (balancing SLA and power costs), edge devices (extending battery life), performance debugging (selecting appropriate analysis tools), and hardware selection (optimal energy efficiency configuration).

Current challenges: hardware differences make cross-platform comparison difficult, insufficient precision of OS energy consumption APIs, and virtualization environments increase measurement complexity. Future plans: support more hardware such as ARM/AI accelerators, develop real-time energy consumption visualization dashboards, establish an energy consumption benchmark database, integrate with CI/CD to implement energy efficiency regression testing, and explore the impact of model compression on energy efficiency.