Memory

HBM supply remains tight — demand exceeding available capacity through FY26. All HBM3E production sold out; HBM4 qualification underway at key AI chip customers.

Cloud Memory +307% Y/Y as hyperscaler AI infrastructure buildout accelerates. Server DRAM content per unit rising with larger LLM model footprints.

Enterprise SSD demand accelerating as hyperscalers expand AI training infrastructure. Each LLM training run requires petabytes of high-throughput storage — enterprise NAND is the primary medium. QLC SSDs gaining share at cloud scale due to lower $/GB vs. TLC.

LLM inference is breaking standard server memory limits. A standard 2-socket server supports 8 DDR5 DIMM slots — max ~4TB of DRAM. Running a 70B parameter model at full precision requires ~140GB; a 405B model requires ~800GB. CXL memory expansion modules allow a single server to reach 4–8TB of total memory, enabling on-server inference at scales previously requiring memory-disaggregated clusters.