How can the AI ecosystem work around memory bottlenecks?

We expect the memory shortage to be a constraint on AI builds through the duration of this AI cycle – but AI won't wait for DRAM fabs to be built. This note looks at ways the industry might work around memory shortages, notably: de-speccing (reducing rack scale central memory LPDDR5 and selectively reducing high bandwidth memory), which shifts some of the bottleneck from DRAM to NAND and at the margin favors larger scale-up domains (ALAB, MRVL); disaggregation of AI workloads, breaking tasks into memory intensive and less memory intensive portions such as prefill, benefiting compute solutions that can serve disaggregated workloads;