Google's Transformer model relies heavily on self-attention mechanisms to process input data in parallel, rather than sequentially as in traditional recurrent neural networks (RNNs). The self-attention mechanism computes attention weights based on the input data itself, allowing the model to capture long-range dependencies and contextual information more effectively. This has led to state-of-the-art performance in various NLP tasks, including machine translation and text summarization, demonstrating the potential of attention mechanisms in real-world applications.