In recent years, I/O requirements have significantly increased in applications in the knowledge areas of big data and artificial intelligence. Therefore, there is a solid motivation to improve this I/O to avoid bottlenecks in data access. For this purpose, the Expand Ad-Hoc parallel file system is being designed and developed. Since the I/O workload can change throughout the application’s execution, the file system used for those applications must be malleable enough to adjust accordingly. It may need to allocate additional resources or release existing ones to accommodate the workload necessities and not waste them. This work introduces the Expand Ad-Hoc parallel file system design to support malleability and the initial evaluation performed on the HPC4AI Laboratory supercomputer in Torino. The results show that the malleability operations scaled well when the resources allocated to the file system were modified.