Large language models (LLMs) based on the Transformer architecture have revolutionized AI, but their immense size creates challenges for real-world deployment. Researchers are constantly seeking ways to optimize these models, making them faster and less resource-intensive without sacrificing performance. A new study reveals a surprising discovery: a significant portion of the attention layers in Transformers, often considered their defining feature, are redundant. This groundbreaking finding opens doors to substantially more efficient LLM designs. The research delves into the core components of Transformers—Blocks, MLP layers, and crucially, attention layers—to analyze their importance. By measuring the similarity between input and output within each component, researchers identified those contributing minimally to the model's overall understanding. The surprising result? Many attention layers, especially deeper in the network, produce outputs remarkably similar to their inputs, indicating redundancy. Experiments with models like Llama-2-70B and Mistral-7B showed that removing up to half of the attention layers resulted in minimal performance impact while drastically improving speed. For example, Llama-2-70B saw a 48.4% speed increase with only a 2.4% drop in performance after shedding half its attention layers. This discovery challenges conventional wisdom about the crucial role of attention in Transformers. It suggests that future models could be designed with fewer attention layers from the outset, leading to leaner, more efficient architectures without sacrificing performance. The study also introduced 'Joint Layer Drop,' a technique that further boosts efficiency by combining the pruning of attention and MLP layers. This method intelligently removes redundant components from both types of layers, resulting in even greater gains. While dropping attention layers proves highly effective, the study also explored pruning other components. Removing MLP layers had a more significant negative impact on performance, highlighting the critical role they play in the model’s function. Removing entire blocks also led to significant performance degradation, confirming that a fine-grained approach targeting specific layers is the key to efficiency. This research offers a new perspective on the balance between size, speed, and accuracy in LLMs. By understanding the inherent redundancy in attention layers, researchers can streamline model architectures, paving the way for more efficient and accessible AI for everyone.