This blog post is part of a series designed to help developers learn NVIDIA CUDA Tile programming for building high-performance GPU kernels, using matrix multiplication as a core example. In this post, you’ll learn: How to implement high-performance matrix multiplication using NVIDIA cuTile : Understand the flow of Tile loading, computation, and storage. About the block-level parallel programming mindset : Shift from thread-level thinking to block-level thinking. Best practices for Tile programming : Learn performance optimization strategies from the code. Before you begin, be sure your environment meets the following requirements (see the quickstart for more information): Environment requirements: CUDA 13.1 or higher GPU architecture NVIDIA Blackwell (e.g., NVIDIA RTX 50 series) Python : 3.10 or higher Install cuTile Python: Note: cuTile is the next-generation GPU programming framework for NVIDIA.…