Energy Analysis and Optimization for Multimodal LLM Inference
Research Paper#Multimodal Large Language Models (MLLMs), Energy Efficiency, Inference Optimization🔬 Research|Analyzed: Jan 3, 2026 16:22•
Published: Dec 27, 2025 19:49
•1 min read
•ArXivAnalysis
This paper addresses the critical issue of energy inefficiency in Multimodal Large Language Model (MLLM) inference, a problem often overlooked in favor of text-only LLM research. It provides a detailed, stage-level energy consumption analysis, identifying 'modality inflation' as a key source of inefficiency. The study's value lies in its empirical approach, using power traces and evaluating multiple MLLMs to quantify energy overheads and pinpoint architectural bottlenecks. The paper's contribution is significant because it offers practical insights and a concrete optimization strategy (DVFS) for designing more energy-efficient MLLM serving systems, which is crucial for the widespread adoption of these models.
Key Takeaways
- •Multimodal inputs significantly increase energy consumption in MLLM inference due to 'modality inflation'.
- •Energy bottlenecks vary across MLLM architectures, stemming from vision encoders or large visual token sequences.
- •GPU underutilization is observed during multimodal execution.
- •Stage-wise DVFS is an effective optimization strategy for energy savings with minimal performance impact.
Reference / Citation
View Original"The paper quantifies energy overheads ranging from 17% to 94% across different MLLMs for identical inputs, highlighting the variability in energy consumption."