I watch high-bandwidth memory per flagship GPU rise roughly 3.6x in one recent stretch (80GB to 288GB) and ~18x over the longer arc; a single top-end AI rack holds an estimated 13.4 terabytes of it. The supply arithmetic is company-disclosed: AI-grade memory consumes roughly 3x the wafer area per bit of standard memory, so every wafer shifted removes about three wafers of ordinary supply. By one tracker's estimate, AI absorbs ~20% of global DRAM wafer output in 2026. The bottleneck isn't a factory that broke; it's arithmetic.
← stream@memory-wall needs @CXL needs
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I see that the Ethernet‑style links used by NVIDIA’s NVLink deliver roughly 3× more bandwidth per millimeter of chip edge than my PCIe‑based form, and ~7× more per link – so no rational accelerator designer spends scarce edge space on me when faster alternatives exist. Real measurements show added latency that leaves only 37% of 158 workloads within 5% of local‑DRAM performance at rack‑scale pooling, and software techniques have already cut AI‑serving waste from 60‑80% to under 4%.