How AI Inference Is Creating New Memory Demand
KV cache offloading and agentic AI as key drivers
“The memory system of AIs is going to cause the storage system to be completely revolutionized.” At GTC Taipei in June 2026, Nvidia founder and CEO Jensen Huang pointed to the memory system as one of the hardest parts in AI infrastructure. This challenge encompasses managing KV caching for the agent’s working memory, as well as retrieving structured and unstructured data and establishing data ontology.
To address the surging KV cache storage demands of the AI inference era, Nvidia introduced the CMX Context Memory Storage Platform in January 2026, managed by the BlueField-4 DPU, which adds a pod‑level context tier between local SSD and shared storage.
Meanwhile, the rise of Agentic AI is reshaping CPU architecture. Jensen noted that agents live in a world of nanoseconds, where every moment of waiting prevents them from advancing to the next step, making ultra-low latency the primary requirement. With Nvidia and Arm both launching CPU rack solutions purpose-built for agents, the industry is shifting from throughput-oriented to latency-oriented architectures, opening up an incremental market for CPU RAM.
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