Rohan Paul@rohanpaul_ai

2026-06-02 16:40·30天前

AI 摘要

为应对AI驱动的巨大需求，SK hynix计划在五年内将其晶圆产能翻倍，但仍预计供应紧张局面将持续至2030年。2026年第一季度，其在DRAM市场占比28.8%，在用于AI的HBM市场则以58%的份额领先。HBM因采用垂直堆叠封装以提供更高带宽，但受限于先进DRAM、封装和测试等物理因素，产能难以快速扩张。目前，SK hynix正与Nvidia、TSMC合作开发下一代HBM4基础芯片。

SK hynix just said AI memory demand is now so large that it will double wafer capacity within 5 years， yet still expects supply to stay tight until 2030.

A wafer is the round silicon starting plate that becomes thousands of memory chips， so doubling wafer capacity basically means SK hynix is trying to expand the physical base of its chip output， not just run current lines harder.

AI supply is increasingly constrained by the physical rhythm of memory manufacturing， where wafers， packaging， yields， and supply agreements move far slower than GPU roadmaps.

The pressure comes from HBM （High-bandwidth memor）， the stacked memory used beside Nvidia GPUs.

HBM is hard to scale because it needs advanced DRAM， stacking， packaging， testing， and close work with GPU designers， which is why SK hynix is working with Nvidia and TSMC on HBM4 base dies.

--- The global memory market.

The global memory market has 2 main layers： DRAM， which includes the memory used next to CPUs and AI GPUs， and NAND flash， which is the storage inside SSDs， phones， and data centers.

In DRAM， the market is extremely concentrated， with Samsung at 38.5%， SK hynix at 28.8%， and Micron at 22.4% in 1Q26， meaning the top 3 control about 90% of global DRAM revenue.

In HBM， which is a premium submarket inside DRAM， the AI-specific memory used beside Nvidia GPUs， SK hynix is the market leader， with 58% share in 1Q26， while Samsung and Micron each had 21%.

HBM， or High Bandwidth Memory， is a special form of DRAM built for extreme data movement.

The difference is physical design.

Normal DRAM chips usually sit on memory modules or near the processor， and data moves through relatively narrower connections.

HBM stacks multiple DRAM dies vertically and places them very close to the GPU through advanced packaging， which creates a much wider data path.

Rohan Paul@rohanpaul_ai · X

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2026-06-02 16:40·30天前

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AI 摘要

SK hynix just said AI memory demand is now so large that it will double wafer capacity within 5 years， yet still expects supply to stay tight until 2030.

AI supply is increasingly constrained by the physical rhythm of memory manufacturing， where wafers， packaging， yields， and supply agreements move far slower than GPU roadmaps.

The pressure comes from HBM （High-bandwidth memor）， the stacked memory used beside Nvidia GPUs.