Opacus has enhanced its capabilities for private training of large-scale models by introducing Fully Sharded Data Parallelism (FSDP) along with Fast Gradient Clipping (FGC) and Ghost Clipping (GC). These advancements improve memory efficiency and scalability for training large models, allowing for greater batch sizes and reduced memory consumption compared to previous methods like Differentially Private Distributed Data Parallel (DPDDP). The article details the implementation of FSDP with Opacus and provides insights on memory and latency performance.