Hard disk drives (HDDs) utilize spinning platters and magnetic heads to read and write data. While HDDs are relatively inexpensive and offer high capacities—with high-end models holding up to $16$ TB—they suffer from typically catastrophic failure modes and high read latencies. Because physical platters must rotate into position, accessing data can take over $8$ ms, limiting HDDs to approximately $100$ input/output operations per second (IOPs). Spin speeds are physically limited by centrifugal force; spinning much faster than their typical $7,200$ RPM would shatter the platters. Furthermore, increasing bandwidth is difficult because the bit rate only scales with the square root of the information density, keeping bandwidth restricted to roughly $100$–$200$ MB/s. Due to these performance bottlenecks, HDDs are primarily relegated to archival purposes and low-grade storage for massive datasets in deep learning.