
Nvidia will keep pushing Storage-Next partners toward hardware and software support for GPU-initiated storage, with the clearest near-term signal coming from flash and array vendors adapting to SCADA-style workloads.
This is a roadmap-setting move for the AI infrastructure stack. If vendors align behind Nvidia’s storage model, the next wave of AI spending could shift from raw GPU counts toward storage systems optimized for inference latency and 512-byte IOPS.
AI reasoning
This is a roadmap-setting move for the AI infrastructure stack. If vendors align behind Nvidia’s storage model, the next wave of AI spending could shift from raw GPU counts toward storage systems optimized for inference latency and 512-byte IOPS.
Curated summary
Nvidia is arguing that AI performance now depends as much on storage as on compute. The company has open-sourced its cuFile APIs and storage stack, launched Storage-Next with more than 40 flash and storage vendors, and introduced SCADA, a framework that lets GPUs initiate storage requests directly. The pitch is aimed at the small-read, KV-cache-heavy workloads that dominate modern inference.
Supporting evidence
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Source news
Nvidia says AI’s next bottleneck is storage, not just silicon
Nvidia has open-sourced cuFile, launched Storage-Next with 40-plus vendors and put GPU-initiated storage at the center of AI inference economics.


















