PyTorch Distributed Checkpointing (DCP) offers a customizable solution for managing model checkpoints in distributed training, allowing significant reductions in storage size through compression techniques. By implementing the zstd compression algorithm, the team achieved a 22% decrease in checkpoint sizes while optimizing performance with multi-threading. The article details the customization process and encourages developers to explore DCP's extensibility for improved efficiency in their workflows.