Storage takes the helm of computing power: AI agents push storage into the frontline battlefield
AI agents will place storage at the forefront of operations, and storage capacity has become a system bottleneck. As generative AI evolves into agents, increased computing power leads to insufficient capacity in KV caches, causing waste. Multiple concurrent scenarios lead to uncontrolled delays. As the core component connecting the medium and the system, the architecture of SSD control chips determines their actual performance under AI loads. The industry consensus is that storage is transforming from a “data carrier” into an “AI full-link infrastructure,” and the demand for training will double compared to推理. To address the issue of KV cache expansion, Nvidia proposed the CMX architecture, but its implementation still relies on hierarchical SSD flash memory and QoS guarantees. Agent-based AI brings about qualitative changes; agents need to maintain context, retrieve knowledge, and continuously interact, generating new types of loads such as Context Memory and vector data. In the face of mixed multi-agent loads, predictability of performance becomes a key indicator instead of peak performance. Winbond Technology shapes performance at the control chip level, mapping different agents into independent priority queues, enabling multi-tenant concurrent scenarios…