Recent advancements in transformer model efficiency have been highlighted through various innovations in attention mechanisms. Fine-tuning router networks allows transformers to bypass unnecessary computation steps, enhancing processing efficiency. Notably, the introduction of FlashAttention and its derivatives, including FlashAttention2, FA2, and FA3, are being explored for their production applications. A new method called Sage Attention has emerged, offering 4/8-bit quantization designed to accelerate the attention mechanism with a drop-in replacement API for Flash Attention, achieving a threefold speedup while maintaining 99% accuracy. Additionally, quantized attention techniques have demonstrated speed improvements of 2.1x and 2.7x compared to FlashAttention2 and xformers, respectively. These developments indicate a significant shift towards optimizing computational resources in transformer models, as demonstrated by Chaim Rand's work on attention layer optimization.