Research demonstrating how strategic optimization techniques can substantially reduce the environmental impact of LLM deployments while maintaining performance.

• Quantization methods significantly lower energy consumption during inference
• Local inference approaches reduce cloud computing dependencies and associated emissions
• Framework provides measurable metrics for evaluating LLM sustainability
• Case study demonstrates real-world application of these optimization techniques

This engineering research offers practical solutions for organizations seeking to balance AI capabilities with environmental responsibility and cost efficiency.

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