New Token-Energy Law quantifies LLM decoding energy use

Researchers have introduced the Token-Energy Law, a model that quantizes the energy consumption of large language models (LLMs) during decoding. This law relates energy per token to "active bytes" (logical byte demand) and "realized decode time" (elapsed time per token). The model aims to provide an interpretable framework for understanding how factors like model architecture, hardware, and serving configurations influence energy efficiency. Experiments using this law on NVIDIA Hopper and Blackwell GPUs demonstrated high accuracy in predicting energy consumption. AI

IMPACT Provides a framework for optimizing LLM deployment for energy efficiency, potentially reducing operational costs and environmental impact.

RANK_REASON Academic paper introducing a new theoretical framework for LLM energy efficiency. [lever_c_demoted from research: ic=1 ai=1.0]

Read on arXiv cs.LG →

• arXiv
• graphics processing unit
• Hugging Face
• large language models
• NVIDIA Blackwell
• NVIDIA Hopper
• Token-Energy Law

• paper
• infra

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