NVIDIA Hopper Tensor Core brings groundbreaking performance, requiring the utilization of new hardware features like TMA, Warpgroup level MMA, asynchronous barriers (mbarriers), Thread Block Cluster, and more. Despite having a compiler with these features, crafting a fast GEMM kernel remains challenging. In this talk, we will initially discuss the NVGPU and NVVM dialects, where the Hopper features have been implemented. Following that, we will delve into the implementation of multistage GEMM and warp-specialized GEMM, as used by libraries like Cutlass. Here, we will leverage MLIR's Python bindings to meta-program the IR.