Multimodal Large Language Models (MLLMs) are enhancing AI capabilities but demand significant energy due to “modality inflation,” the increased workload from processing different data types like text and images. Virginia Tech researchers, including Mona Moghadampanah and Dimitrios S. Nikolopoulos, conducted a detailed analysis of energy consumption during MLLM inference, revealing overheads of 17% to 94% compared to text-only models. Their findings emphasize inefficiencies in processing visual data and expanded sequences. Strategies such as dynamic voltage and frequency scaling (DVFS) can optimize energy use with minimal performance loss. Key trends focus on developing efficient architectures, adjusting GPU workloads, and employing techniques like quantization to lower computational costs. The research underscores the need for scalable infrastructures for deploying MLLMs effectively, targeting improved energy efficiency, and operational reliability. Understanding model behavior is crucial in crafting effective optimization strategies, ensuring a sustainable future for multimodal AI applications. For more insights, refer to the detailed study on energy characterization and optimization in MLLM inference.
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