The second part of a series on profiling in PyTorch focuses on optimizing multi-layer perceptron (MLP) models. The article discusses the benefits and challenges of fusing multiple linear layers into a single operation, reducing memory access latency and improving overall performance. This approach is particularly useful for large-scale models that require significant computational resources. --- Why it matters: Optimizing MLPs matters to AI engineers because it can significantly improve the performance of deep learning models in applications such as natural language processing, computer vision, and speech recognition. Source: https://huggingface.co/blog/torch-mlp-fusion