Weekly recap cover, week ending July 24, 2026A 3D NAND word line staircase with channel holes through it, the lead topic of the week ending July 24, 2026; below it, one drawing for each topic the week covered: NAND flash (1), DRAM and HBM (3), Market and earnings (2), Research papers (3).THE WEEK IN MEMORY AND STORAGEWEEK ENDINGJuly 242026LEAD · NAND FLASH1323

Week endingFriday, July 24, 2026Covers July 18 to July 24, 2026 · 9 items

AMD Launches HBM4 Accelerators, TrendForce Sees NAND Easing, and Korea Teases Memory Deals

HBM4 moved from announcement to product this week. AMD launched its Instinct MI400 series with HBM4 and put its Helios rack into production, with Samsung reported as a key memory supplier, and Nvidia described how Rubin's HBM4 is meant to keep more of a model resident on the GPU.

On the market side, TrendForce said NAND should rebalance in the second half of next year, Samsung disputed a report about V10 NAND shipments to Nvidia, and South Korea's presidential office said Samsung and SK hynix will announce large deals with US firms. Three papers looked at CXL memory racks, in-NAND vector search and 3D memory for mixture of experts models.

Top stories

The rest of the week

DRAM and HBM

Market and earnings

Research papers

  1. Paper builds a CXL memory rack backed by SSDs to reuse KV cache in multi-turn serving (opens in a new tab)

    HyMCache combines a little in-device DRAM with large SSD-backed capacity behind CXL, and uses prefetching and write buffering to keep reads fast. The authors test it on a prototype and report big DRAM savings against a distributed DRAM design; it is a preprint.

    arXiv ·

  2. Paper runs vector similarity search inside the 3D NAND array for retrieval workloads (opens in a new tab)

    D-NOVA embeds a retrieval pipeline directly in NAND strings using a new distance metric tailored to the string, plus an adapter that maps embeddings into a form the array handles well. Results are from the authors' evaluation and the work is accepted at MICRO.

    arXiv ·

  3. Paper pairs FeFET and DRAM chiplets in a 3D accelerator for mixture of experts models (opens in a new tab)

    ThAME stacks non-volatile FeFET memory and DRAM chiplets and maps attention and expert layers to the memory that suits each, with a network designed for irregular token routing. The speedups are simulated, and the paper is accepted at ESWEEK.

    arXiv ·