• Intel Optane, built on Phase Change Memory, was a real shipping product with near-DRAM speed and near-Flash density, but Intel wound it down anyway: it arrived before AI's memory-bandwidth appetite existed to justify it, so the industry bet on HBM instead.
• New memory technologies historically take about two decades to reach production reliability: DRAM (Dennard, IBM 1966) took until the late 1970s to go mainstream, and Flash (Masuoka, Toshiba, NOR 1984/NAND 1987) didn't until the SSD/USB boom of the 2000s.
• The piece argues AI changes memory's optimization target: because neural nets tolerate noise, chipmakers can stop chasing near-zero bit error rate and instead pack more bits per cell at lower conductance, trading reliability for large density and energy gains.
• It splits AI memory into two needs: near-permanent dense 'parameter memory' for weights, where PCM/RRAM fits once freed from the BER obsession, versus fast dense 'working memory' where gain cells, 3D-stacked HBM-on-logic, and eDRAM are current front-runners.

• LLM — A large language model — the neural network behind tools like Claude and ChatGPT, trained on huge amounts of text to predict what comes next.
• context window — The maximum amount of text a model can consider at once — its working memory for the current conversation or task.

• articleScaling the Memory Wall: The Rise and Roadmap of HBMDylan Patel
• articleLong Live the Short King: Why 4-hi HBM WinsMyron Xie
• article[AINews] Memory prices up 500% in 12 monthsLatent.Space

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