HBM consumes approximately three times the wafer capacity of standard DRAM to produce equivalent bits. This fundamental physics constraint – not market dynamics or investment decisions – explains why AI’s memory shortage will persist for years regardless of how much capital pours into the sector.
The B200 integrates 192 GB of HBM3E memory, priced at approximately $14-17 per gigabyte. But HBM isn’t ordinary memory. It requires through-silicon vias (TSVs) that stack memory dies vertically, enabling unprecedented bandwidth of 8 TB/s – double the previous generation.
This architectural complexity means every GB of HBM consumes 3x the manufacturing resources of standard DDR5. When memory makers shift production toward HBM to serve AI demand, they reduce conventional memory output by an equivalent 3x multiple. The cascade effect ripples through the entire memory market.
OpenAI’s Stargate project alone may require 900,000 DRAM wafers per month by 2029 – roughly 40% of current global DRAM output. Converting existing capacity to HBM doesn’t scale linearly; it scales at one-third efficiency.
Framework Analysis
This is the AI Memory Chokepoint at the physics level. The 3x capacity consumption isn’t a manufacturing inefficiency to be optimized – it’s inherent to the TSV technology that enables HBM’s bandwidth advantage.
Through-silicon vias require drilling holes through silicon dies, filling them with copper, and precisely aligning multiple stacked layers. Each step adds complexity and reduces yield. The technology cannot be easily converted from standard DRAM production lines. New HBM capacity requires purpose-built facilities with different equipment and processes.
The structural constraint explains memory maker caution. Building HBM capacity means 3x the opportunitycost of standard memory. If AI demand disappoints, that capacity cannot be easily repurposed. The economics of semiconductor manufacturing punish overcapacity brutally.
Strategic Implications
The 3x factor means the memory shortage is more structural than cyclical. Even aggressive capacity expansion cannot rapidly close the demand-supply gap. SK Hynix has told analysts the shortfall will persist through late 2027. New conventional memory fabs won’t come online until 2027-2028.
For enterprises, this creates planning certainty of a different kind: sustained memory constraints. AI infrastructure decisions must account for component availability, not just pricing. Those who secured supply agreements early hold multi-year advantages.
For GPU designers, it means memory remains the binding constraint on system performance. Nvidia’s full-stack strategy – from $40K chips to $3M racks – partly addresses this by optimizing memory utilization across larger systems rather than individual chips.
The Deeper Pattern
Physical constraints trump economic incentives when conversion isn’t possible. No amount of investment can change the 3x capacity consumption of HBM manufacturing. The AI infrastructure buildout must work within these physics-imposed limits.
Key Takeaway
HBM’s 3x capacity consumption relative to standard memory is a physics constraint, not a market inefficiency. This fundamental limit shapes the pace and economics of AI infrastructure expansion for years to come.
Gennaro is the creator of FourWeekMBA, which reached about four million business people, comprising C-level executives, investors, analysts, product managers, and aspiring digital entrepreneurs in 2022 alone | He is also Director of Sales for a high-tech scaleup in the AI Industry | In 2012, Gennaro earned an International MBA with emphasis on Corporate Finance and Business Strategy.
