FlashMLA operates by leveraging advanced techniques in multi-head latent attention to optimize the decoding process for large language models. At its core, it utilizes a kernel-based architecture that is specifically tailored for NVIDIA's Hopper GPUs, which are designed to handle complex computations efficiently.

The architecture of FlashMLA is built around the concept of multi-head attention, which allows the model to focus on different parts of the input sequence simultaneously. This is crucial for understanding context in natural language processing tasks, where the relationships between words can vary significantly depending on their positions in a sentence.