en.Wedoany.com Reported - On June 1, Micron Technology showcased an AI-optimized end-to-end memory and storage product portfolio at COMPUTEX 2026, covering applications from data centers to intelligent edge. The portfolio includes HBM4, SOCAMM2, DDR5 RDIMM, data center SSDs, and storage solutions for AI PCs, automotive, and embedded systems, primarily targeting computing demands shifting from model training to large-scale inference, long-context processing, and agent workloads.
The core message of Micron's announcement is that the bottleneck in AI infrastructure is shifting from pure computing power expansion to the synergy of memory bandwidth, capacity, power consumption, and storage hierarchy. The company disclosed that HBM4 36GB 12H can achieve a 2.6x improvement in large language model inference throughput while doubling bandwidth; the 256GB SOCAMM2, targeting low-power data center memory applications, offers 256GB capacity with approximately one-third the power consumption and one-third the footprint of standard RDIMMs; the 256GB DDR5 RDIMM based on 1γ technology has begun sampling, reaching speeds up to 9200MT/s—40% faster than current production modules—and reducing operating power consumption by over 40% compared to a combination of two 128GB modules.
Data center SSDs are also a key part of this portfolio. Micron stated that the Micron 9650 SSD is the world's first commercial PCIe Gen6 SSD, designed for AI inference and training workloads; the Micron 6600 ION now offers up to 245TB capacity, reducing rack space by 82% and cutting power consumption in half compared to hard-drive-based deployments. For AI data centers, the storage layer is no longer just a static repository for models and data, but an active working layer closely tied to KV caching, data lakes, training data preparation, and continuous inference service operations.
This product lineup indicates that AI infrastructure construction is entering a phase of "full-stack memory and storage restructuring." Early AI development focused more on GPU count and peak computing power, but as inference workloads scale, enterprises need to handle longer contexts, higher concurrency requests, more complex agent tasks, and greater data access pressure. Insufficient memory bandwidth limits model response speed, inadequate capacity affects long-context and multi-task scheduling, and poor storage performance slows data loading, cache calls, and inference pipelines. By showcasing HBM, LPDDR, DDR, SOCAMM, and SSDs within the same AI infrastructure tier, Micron reflects that memory manufacturers are transitioning from single-component suppliers to key participants in AI system performance optimization.
Changes are also occurring at the edge. Micron noted that as AI inference expands from data centers to PCs, smartphones, automotive systems, and embedded devices, local devices require higher-density DRAM to keep models and agents resident and operational, along with faster, more reliable storage to support local model caching, real-time in-vehicle sensor fusion, and edge-side responses. For the information and communications technology industry, this means AI infrastructure will extend simultaneously to the cloud and the edge, with memory and storage choices directly impacting AI application response speed, energy consumption levels, and deployment costs.
Micron will host an invitation-only product showcase at its Taipei TFC Plaza office from June 2 to 4. Subsequent variables focus on customer adoption progress, production scale, and platform compatibility with GPUs and servers for HBM4, SOCAMM2, and high-capacity SSDs, as well as whether data center customers can improve overall token output efficiency through memory and storage upgrades. As AI applications move from training clusters to continuous inference services, memory and storage are becoming another key competitive frontier alongside data center networking, computing power, and energy.
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