High-end Hitachi Vantara arrays and Nvidia AI support - Blocks & Files

This article explores Hitachi Vantara's stance on integrating NVIDIA AI technologies, particularly the STX architecture, into its high-end storage arrays. While NVIDIA's STX and CMX technologies are d... Read original →

Hitachi Vantara’s reluctance to embed NVIDIA’s STX reflects a strategic bifurcation between AI runtime infrastructure and enterprise storage. Technically, STX’s reliance on GPU Direct and BlueField-4 DPUs optimizes ephemeral data paths, clashing with traditional arrays’ focus on durability and ACID compliance—creating incompatible memory semantics. This divergence forces DPU vendors to rethink offload logic and compels AI frameworks to handle heterogeneous storage tiers. Geopolitically, tightening U.S.-EU export controls on AI accelerators will inflate TCO as firms deploy hybrid on-prem stacks. Competitors like Dell and Lenovo may push converged mid-range offerings, while Infinidat could double down on NVMe-oF plus KV Cache to counter CMX. Within 18 months, standardized interfaces for ‘hot/cold data separation’ will emerge, shifting AI infrastructure from forced integration toward intelligent decoupling—where enterprise storage enables AI via metadata orchestration, not native execution.