SPDL (Scalable and Performant Data Loading) is a new data loading solution developed by Meta Reality Labs, designed to enhance the efficiency of AI model training. Leveraging thread-based parallel processing, SPDL achieves high throughput in standard Python interpreters with lower resource consumption compared to traditional process-based solutions. It is compatible with Free-Threaded Python and offers higher throughput without GIL compared to FT Python implementations with GIL. Key advantages of SPDL include high throughput, comprehensible performance, no encapsulation of preprocessing operations, no introduction of domain-specific languages (DSL), seamless integration of asynchronous tools, flexibility, simplicity, and fault tolerance. The background highlights that as model sizes increase, so do computational demands for data; SPDL accelerates model training by maximizing GPU utilization.