Due to large neural network size and high redundancy, there is a growing interest in various techniques to reduce the number of weights and accelerate training and inference. An active area of research in this field is sparsity - encouraging zero values in parameters that can then be discarded from storage or computations. The recently introduced NVIDIA Ampere accelerator architecture supports 2:4 sparsity pattern, i.e., halves a model’s parameter count, requiring that every group of consecutive four values contains at least two zeros. This leads to twice the math throughput of dense matrix units. In general, we consider N:M sparsity. In this work, your will explore how to efficiently build, zip and update N:M-sparse networks. As a starting point, we encourage you to read the following papers: (1), (2), (3). You will be provided an algorithm helpful in this setting. Your ideas are welcome