# Optimizing Large Language Models: Metrics, Energy Efficiency, and Case Study Insights

arXiv: https://arxiv.org/abs/2504.06307 · HTML: https://mlsanity.com/paper/2504.06307/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- Builds on 15 of its 28 references that are in the map.
- First posted on arXiv 7 Apr 2025.

## Builds on (most cited first, 15 of 15)

- [Improved Baselines with Visual Instruction Tuning](https://mlsanity.com/paper/2310.03744/) (2023)
- [Deep Compression: Compressing Deep Neural Networks with Pruning, Trained Quantization and Huffman Coding](https://mlsanity.com/paper/1510.00149/) (2015)
- [GPTQ: Accurate Post-Training Quantization for Generative Pre-trained Transformers](https://mlsanity.com/paper/2210.17323/) (2022)
- [Phi-3 Technical Report: A Highly Capable Language Model Locally on Your Phone](https://mlsanity.com/paper/2404.14219/) (2024)
- [FrugalGPT: How to Use Large Language Models While Reducing Cost and Improving Performance](https://mlsanity.com/paper/2305.05176/) (2023)
- [LLM-QAT: Data-Free Quantization Aware Training for Large Language Models](https://mlsanity.com/paper/2305.17888/) (2023)
- [Good Debt or Bad Debt: Detecting Semantic Orientations in Economic Texts](https://mlsanity.com/paper/1307.5336/) (2013)
- [Estimating the Carbon Footprint of BLOOM, a 176B Parameter Language Model](https://mlsanity.com/paper/2211.02001/) (2022)
- [A Systematic Review of Green AI](https://mlsanity.com/paper/2301.11047/) (2023)
- [FrugalML: How to Use ML Prediction APIs More Accurately and Cheaply](https://mlsanity.com/paper/2006.07512/) (2020)
- [Towards Optimizing the Costs of LLM Usage](https://mlsanity.com/paper/2402.01742/) (2024)
- [Greening Large Language Models of Code](https://mlsanity.com/paper/2309.04076/) (2023)
- [Efficient and Green Large Language Models for Software Engineering: Literature Review, Vision, and the Road Ahead](https://mlsanity.com/paper/2404.04566/) (2024)
- [Green AI: Exploring Carbon Footprints, Mitigation Strategies, and Trade Offs in Large Language Model Training](https://mlsanity.com/paper/2404.01157/) (2024)
- [Towards Green AI in Fine-tuning Large Language Models via Adaptive Backpropagation](https://mlsanity.com/paper/2309.13192/) (2023)

## Built on by (most cited first, 3 of 3)

- [Quantifying the Climate Risk of Generative AI: Region-Aware Carbon Accounting with G-TRACE and the AI Sustainability Pyramid](https://mlsanity.com/paper/2511.04776/) (2025)
- [EnviroLLM: Resource Tracking and Optimization for Local AI](https://mlsanity.com/paper/2512.12004/) (2025)
- [The Battery Price of edge AI: A study of the Environmental Impact of LLM Inference on Mobile Devices](https://mlsanity.com/paper/2609.11940/) (2026)

## Abstract (arXiv)

The rapid adoption of large language models (LLMs) has led to significant energy consumption and carbon emissions, posing a critical challenge to the sustainability of generative AI technologies. This paper explores the integration of energy-efficient optimization techniques in the deployment of LLMs to address these environmental concerns. We present a case study and framework that demonstrate how strategic quantization and local inference techniques can substantially lower the carbon footprints of LLMs without compromising their operational effectiveness. Experimental results reveal that these methods can reduce energy consumption and carbon emissions by up to 45% post quantization, making them particularly suitable for resource-constrained environments. The findings provide actionable insights for achieving sustainability in AI while maintaining high levels of accuracy and responsiveness.