Artificial intelligence (AI) learning is moving from research into mainstream business use. Many AI/machine learning workloads require storage solutions, which have been optimized both for working on very large data sets and for very high IOPS and/or throughput and low-latency performance. The expectation is that AI compute will come to resemble high-performance computing (HPC) in that not only will servers scale up, that is, adding more GPUs per server, but also scale out, that is, using a distributed clustered server environment. This will require the use of shared storage file systems to avoid storage bottlenecks. Download to read more.