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A primary challenge of using deep learning models is the requirement for high computational power. Deep learning algorithms operate on large amounts of data and often involve complex architectures, such as deep neural networks with many layers, which necessitate substantial processing capabilities. This need for high computational resources arises from the volume of calculations involved in training these models, including matrix operations and gradient computations, especially when handling large datasets.

Moreover, the training process for deep learning can be resource-intensive, often requiring specialized hardware like GPUs (Graphics Processing Units) or TPUs (Tensor Processing Units) to process the data more efficiently. This requirement can present a barrier to entry for organizations with limited computational infrastructure, as they may need to invest heavily in hardware or cloud computing resources to effectively run deep learning workflows.

This challenge stands out as the most significant compared to the other considerations. While excessive training time can impact practical usability, it is often a consequence of the high computational power needed, rather than an independent issue. Inability to scale with data would typically be more relevant to traditional machine learning models, which are not as flexible or capable when faced with large datasets. Similarly, a lack of accuracy in deep learning is generally not a primary concern for the model's architecture itself, as deep learning models