Gogia said predictive memory tiering addresses inefficiencies that often leave expensive DRAM underutilized, but cautioned that optimization should complement, rather than replace, sound infrastructure design.
“Predictive tiering attacks the waste inside that reflex,” he said, referring to the tendency to address performance challenges by purchasing more memory instead of improving utilization.
Rawat said organizations that optimize compute, memory, storage, and software together are likely to scale AI deployments faster, lower operating costs, and generate stronger returns on AI investments than those relying primarily on increasing hardware capacity.
