This research investigates the energy efficiency trade-offs between different text generation methods in large language models, providing insights for sustainable AI deployment.

• Different decoding methods (like beam search, top-k, nucleous sampling) have varying energy footprints
• Researchers benchmarked energy consumption across diverse NLP tasks and generation configurations
• Findings reveal opportunities to optimize LLM deployments for both quality output and energy efficiency
• Results enable engineers to make informed decisions balancing generation quality and computational resources

This work matters for engineering AI systems that are not only powerful but also environmentally sustainable and cost-effective in production environments.

Energy-Conscious LLM Decoding: Impact of Text Generation Strategies on GPU Energy Consumption