Optimize OpenShift workloads with software-defined memory

The blog post discusses optimizing workloads on OpenShift through a memory-first architecture using Kove:SDM, which disaggregates memory from compute resources. This setup allows organizations to efficiently handle AI and analytics workloads without needing to adjust application code or purchase additional hardware. The validation tests confirm that this approach can significantly increase workload density, processing more concurrent classifiers while maintaining stability and performance, thus overcoming the traditional memory limits of a server.