Rohan Paul· @rohanpaul_ai · X·· 2026-09-26AI 评分59
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Rohan Paul 引述 Columbia Business School 报告《Financing the AI Buildout》,测算 2025–2032 年新增 182.7 GW 美国数据中心容量约对应 107 万机架、7700 万块 GPU(按 GB300 NVL72 配置),需要 3.725 万亿美元成熟年收入,折合约 5.5 美元每 GPU 小时(满载)。
正文 · 原文
The $800B–$1T AI capex wave is a beautiful choreography of chips, power, credit, and capital.
Hyperscalers bring the credit quality, outside investors bring the capital, and frontier AI firms get access to far more compute than they could finance on their own.
From Columbia Business School report "Financing the AI Buildout" ans that.
How much revenue each unit of deployed compute would need to generate for 182.7 GW of additional U.S. data-center capacity between 2025 and 2032 This Columbia Business School report "Financing the AI Buildout" ans that. 182.7 GW is its central buildout scenario which is the U.S. data-center capacity between 2025 and 2032. Using the GB300 NVL72 configuration, that capacity works out to roughly 1.07 million racks / 77 million GPUs, which lets the author express the required $3.725 trillion of mature annual revenue as about $5.5 per installed GPU-hour at full utilization. This required unit revenue sits within the $6–$10+ per GPU-hour for current high-end NVIDIA capacity, so the central scenario does not require some radically higher compute price to make the arithmetic work.在 X 查看被引用的帖子
来源:Rohan Paul · x.com