The pace of change within the HPC-AI market continues accelerating across all fronts, including the storage system. Traditional workloads such as seismic processing, life sciences, and weather analysis typically relied on checkpoint/restart mechanisms to periodically capture the state of modeling/simulation onto scratch storage to protect against system failure. Users were forgiving and could support the time to rerun failed simulations. Fast forward to today, AI training and inferencing has become prominent for recent generative AI applications as well as for the augmentation of traditional HPC modeling and simulation. Users have become much less forgiving as the cost of rerunning a training job could run into the millions of dollars and the value of the data itself is substantial, delivering unprecedented scientific and business value to researchers and companies in both traditional HPC and commercial enterprise markets. To Learn more download the whitepaper.