SIS-LLM: Evaluating the Sustainability of Large Language Model Inference in GPU Deployments Using the SIS Framework

SIS-LLM is an evaluation system based on the SIS framework, designed to quantify the environmental sustainability and carbon footprint of large language model (LLM) inference in GPU deployments. This tool fills the gap of specialized tools in the field of AI sustainability assessment, helping researchers and engineers measure energy consumption characteristics, calculate carbon footprints, compare optimization strategies, and drive the AI industry from purely pursuing performance to responsible technological development.

With the widespread application of LLMs in various fields, the energy consumption and carbon emission issues of GPU inference have become increasingly prominent. Traditional performance metrics (latency, throughput) can no longer meet the needs; environmental sustainability must be incorporated into the evaluation framework to enable developers to make more responsible deployment decisions.

The SIS (Sustainability in Software) framework is a standardized methodology for evaluating the environmental sustainability of software systems. Its core values include: 1. Standardized measurement to support comparison between different systems; 2. A full lifecycle perspective covering development, deployment, and other stages; 3. Operability, providing optimization recommendations.

SIS-LLM applies the SIS framework to LLM GPU inference scenarios. Its goals are to help users measure energy consumption characteristics, calculate carbon footprints, compare the sustainability performance of models and optimization strategies, identify energy consumption bottlenecks, and provide recommendations—filling the gap where general tools lack refined assessment of LLM inference.

GPU Energy Consumption Monitoring

Collect GPU core power consumption, memory power consumption, temperature, fan speed, and power consumption curves via the NVIDIA NVML interface.

Inference Load Characteristic Analysis

Record the relationships between input sequence length, number of output tokens, batch size, model layers, and energy consumption.

Carbon Footprint Calculation Model

Convert energy consumption into carbon footprint by combining regional grid carbon emission factors, data center geographic location, operating time (fluctuations in renewable energy proportion), and PUE (Power Usage Effectiveness) coefficient.

Model Selection Decision-Making

Treat sustainability as the third dimension to help select models with higher energy efficiency.

Deployment Optimization Guidance

Targeted adjustments to batch processing strategies, quantization schemes (INT8/INT4), memory access patterns, and dynamic batch processing to reduce energy consumption.

Compliance and Reporting

Provide data to support ESG (Environmental, Social, and Governance) reports and carbon audits, meeting carbon emission regulatory requirements.

SIS-LLM marks a shift in the AI industry's values towards emphasizing sustainability. Future outlooks include: 1. Becoming an industry standard where model releases must be accompanied by sustainability reports; 2. Spurring green AI competitions for efficient model architectures; 3. Policy-driven adoption of assessment tools by enterprises; 4. Collaborative optimization of low-power inference architectures between hardware and software.

SIS-LLM reminds us that technological progress should not come at the expense of the environment, providing AI practitioners with a method to quantify environmental costs. Through continuous measurement and optimization, we can reduce resource consumption while maintaining AI capabilities, achieving harmonious coexistence between technology and nature.