FlashMLA is a high-efficiency MLA decoding kernel optimized for Hopper GPUs, specifically designed for variable-length sequence services. Developed using CUDA 12.3 and above, it supports PyTorch 2.0 and above. FlashMLA's primary advantages lie in its efficient memory access and computational performance, achieving up to 3000 GB/s memory bandwidth and 580 TFLOPS computational performance on H800 SXM5. This technology is significant for deep learning tasks requiring large-scale parallel computing and efficient memory management, especially in natural language processing and computer vision. Inspired by FlashAttention 2&3 and the cutlass project, FlashMLA aims to provide researchers and developers with a highly efficient computational tool.