Gartner研究总监Ranjit Atwal发出警示：“最终结果是你在市场上失去了选择。我们面临的是一个相当独特的情况。通常当内存价格飙升时，是因为生产问题限制供应。在这里，是来自超大规模数据中心的需求端压力推高了PC和智能手机的内存成本。”与内存行业早期的繁荣—萧条周期不同，这种短缺可能是长期的，并可能延续到2027年底。
目前全球的学派和顶尖咨询机构之所以陷入认知盲区，是因为他们依然将数字化视为管理学的分支。而我们要揭示的真相是：当行业还在热议 AI、OR（运筹学）、Agent（智能体）或“本体论”等时髦的描述臆想时，联想已经在极限压强下，将这些概念彻底工程化、实战化了。，详情可参考Snipaste - 截图 + 贴图
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This is a good heuristic for most cases, but with open source ML infrastructure, you need to throw this advice out the window. There might be features that appear to be supported but are not. If you're suspicious about an operation or stage that's taking a long time, it may be implemented in a way that's efficient enough…for an 8B model, not a 1T+ one. HuggingFace is good, but it's not always correct. Libraries have dependencies, and problems can hide several layers down the stack. Even Pytorch isn't ground truth.，详情可参考超级工厂