Large language models (LLMs) are revolutionizing how we interact with technology, but their immense size presents challenges for efficient deployment. One of the core components of LLMs, the attention mechanism, requires significant computational resources. Researchers have been working on optimizing attention, with techniques like FlashAttention significantly speeding up processing and reducing memory usage. Now, a team has introduced INT-FlashAttention, a groundbreaking approach that leverages INT8 quantization to further boost performance, especially on widely-used Ampere GPUs. The key idea behind INT-FlashAttention is to use a lower precision integer format (INT8) for representing and processing data within the attention mechanism. This not only shrinks the memory footprint but also allows for faster computations on hardware optimized for INT8 operations. While previous attempts have been made to quantize attention, INT-FlashAttention distinguishes itself by being the first to fully integrate INT8 into FlashAttention's workflow, making it remarkably faster than the original FlashAttention using FP16 (half-precision floating point). The experimental results are promising, demonstrating a substantial 72% increase in inference speed compared to FP16 and even better accuracy than FlashAttention with FP8 (an alternative lower precision format). While this advancement offers a potent way to accelerate LLM inference, it's important to acknowledge current limitations. At present, the implementation of INT-FlashAttention uses a simplified approach to quantizing one part of the attention mechanism. The next step for researchers is exploring finer-grained quantization methods to further optimize performance and potentially improve accuracy. Nevertheless, INT-FlashAttention stands as a significant step toward more efficient LLMs, potentially bringing the power of these models to a broader range of devices and applications.