Multimodal AI Advances Applications, but Faces 94% Energy Penalty from Inflation

Multimodal large language models are rapidly expanding the capabilities of artificial intelligence, yet their increased complexity introduces significant energy demands that remain largely unaddressed. Mona Moghadampanah, Adib Rezaei Shahmirzadi, Farhana Amin, and Dimitrios S. Nikolopoulos, all from Virginia Tech, investigate this issue through a detailed analysis of ‘modality inflation’, the increased computational workload resulting from processing multiple types of data, such as text and images. Their work represents the first stage-level examination of energy consumption during multimodal inference, revealing substantial overheads ranging from 17% to 94% compared to text-only models. The team demonstrates that these inefficiencies stem from both the initial processing of visual information and the handling of expanded data sequences, and importantly, identifies opportunities for optimisation through dynamic adjustments to processing speed and voltage, paving the way for more sustainable and efficient multimodal AI systems.

LLM Inference, Energy Efficiency and Scaling

Research into large language models (LLMs) and multimodal models is increasingly focused on energy efficiency and scalability, crucial for practical deployment. A key area of investigation involves dynamic frequency scaling, where GPU frequency is adjusted based on workload to optimize performance and energy use. Understanding how LLMs are used, including prompt types and token lengths, is also vital for effective optimization strategies. Techniques like quantization, which reduces the precision of model weights, lower memory footprint and computational cost, while careful monitoring and control of GPU resources using tools like NVIDIA Management Library are also being explored.

The development of efficient architectures for multimodal models, which combine vision and language, is another significant focus. Researchers are investigating methods to distribute workloads across multiple GPUs or devices through parallelism and disaggregation, and adapting image resolution to reduce processing time. Reducing the number of tokens processed, a major bottleneck in LLM performance, is a recurring theme. Advancements in visual question answering and multimodal reasoning are also driving progress, with new benchmarks and techniques for aligning visual and language features. Building scalable serving infrastructure, optimizing resource allocation, and improving model pre-training and fine-tuning are all essential for successful LLM deployment.

A notable trend is the shift towards efficiency, addressing the substantial energy consumption, memory usage, and inference speed of LLMs. The growing importance of multimodality, combining vision and language for more complex applications, is also apparent. Overcoming the challenges of productionization, moving LLMs from research to real-world use, requires building scalable and reliable serving infrastructure, often guided by Service Level Objectives to ensure performance and minimize cost. Many researchers are leveraging open-source models and frameworks like Transformers, PyTorch, and vLLM to accelerate progress.

Multimodal LLM Inference Energy Breakdown Revealed

A pioneering study has provided a detailed, stage-level analysis of energy consumption during multimodal large language model (MLLLM) inference, an area previously overlooked in favor of text-only models. Researchers developed a methodology to dissect the MLLM pipeline into vision encoding, prefill, and decoding stages, enabling precise energy measurements at each step. Experiments using four representative MLLMs on A100 GPUs quantified the energy overhead introduced by multimodal inputs compared to text-only baselines, revealing overheads ranging from 17% to 94%. The team measured energy usage and generated energy per request heatmaps, revealing substantial GPU underutilization during multimodal execution and the impact of input complexity on energy scaling.

Results demonstrate that a single GPU frequency does not minimize energy consumption, with intermediate frequencies often proving most efficient. The prefill stage, processing expanded visual token sequences, often accounts for the majority of the multimodal energy overhead, particularly in models like InternVL3. Stage-wise dynamic voltage and frequency scaling (DVFS) was developed and tested, demonstrating its effectiveness in reducing energy consumption with minimal performance impact, paving the way for more energy-efficient MLLM serving systems.

Multimodal Models Show Significant Energy Inefficiency

Scientists have conducted a detailed analysis of energy consumption in multimodal large language models (MLLMs), revealing significant inefficiencies compared to text-only models. This research represents the first stage-level breakdown of energy use during MLLM inference, dissecting the process into vision encoding, prefill, and decoding stages. Experiments using four representative MLLMs on A100 GPUs demonstrated that incorporating additional modalities increases energy demands, with overheads ranging from 17% to 94% for identical inputs. The study meticulously quantified energy usage across each stage, identifying that energy bottlenecks vary depending on the model’s architecture.

Some models experience high energy consumption due to computationally intensive vision encoders, while others are burdened by the large number of visual tokens generated during the prefill stage. Analysis of GPU power traces revealed substantial underutilization during multimodal execution, indicating potential for optimization. Measurements confirm that stage-wise dynamic voltage and frequency scaling (DVFS) is an effective optimization technique, delivering energy savings with only a modest impact on performance. The research highlights “modality inflation,” where multimodal inputs increase workloads through extra encoding and expanded token sequences, providing practical insights for designing more energy-efficient MLLM serving systems.

Visual Input Dramatically Alters Energy Use

Research has characterized energy consumption during inference with vision-language large language models, focusing on the impact of incorporating visual data. Scientists discovered that adding visual inputs leads to significant variations in energy use, with overheads ranging from 17% to 94% across different model architectures when processing the same inputs. The team’s stage-level analysis identified that energy demands can be dominated by the vision encoding process in some models, with certain encoders consuming over six times more energy than more balanced designs. Furthermore, the study reveals that expanding the number of tokens used to represent visual information during the prefill stage can substantially increase both energy consumption and processing time.

Analysis of multi-image workloads demonstrated differing scalability behaviors across models, indicating that the energy cost per additional image varies considerably. These findings suggest potential for improving the energy efficiency of multimodal large language model serving systems through careful workload and stage-specific system design, including the use of dynamic voltage and frequency scaling. Future research should extend this characterization to other modalities and explore energy efficiency when different inference stages are processed by specialized hardware.