NUMA (Non-Uniform Memory Access) awareness is crucial for optimizing high-performance deep learning applications, as it impacts memory access patterns and overall system efficiency. By understanding NUMA architecture and implementing strategies that leverage it, developers can significantly enhance the performance of deep learning models on multi-core systems.

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.