The article explores the architecture and functionality of NVIDIA GPUs, detailing their compute cores, memory hierarchy, and comparison with TPUs. It emphasizes the importance of Tensor Cores for matrix multiplication in modern machine learning tasks and outlines the evolution of GPU specifications across generations. The content builds on previous chapters, providing a comprehensive understanding of GPU capabilities in the context of large language models.