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当你诞生AI时,记取,它比钻石贵!
我试图在我的筹划机构诞生一个微型的东谈主工智能筹划测试台,以下是每次BOM和操作用度:
1)500个最新GPU芯片组:约1500万好意思元
2)左近开辟和所需系统:约600万好意思元
3)5年软件因循和许可证:3百万好意思元
4)5年电力用度:400万好意思元
5)2名全职实际室职工,为期5年:100万好意思元
这种微型东谈主工智能筹划测试台的预算总资本:2900万好意思元
用这样2900万好意思元,我不错招募大约30名伯克利加州分校或斯坦福大学或剑桥大学或麻省理工学院讲明注解。 或者我不错招60名清华大学或北京大学的正讲明注解。
是以,哪个更紧迫? 讲明注解 vs AI?
如若你因循AI,你基本上即是因循英伟达和中国左近材料供应商(在台湾和中国大陆)。
如若你因循讲明注解,你基本上即是因循咱们后代的学生。
即是这样苟简!
When you build AI, remember, it is more expensive than diamond!
I tried to build a small AI research testbed at my research organization, here is the cost per BOM and operation:
1) 500 newest GPU chipsets: about US$15M
2) Peripheral devices and required systems: about US$6M
3) Software supports and licenses for 5 years: US$3M
4) Electricity power cost for 5 years: US$4M
5) 2 full-time lab staffs for 5 years: US$1M
Total cost of budget for such Small AI research testbed: US$29M
With such US$29M, I can recruit about 30 Full Professors of UC Berkeley or Stanford University or University of Cambridge or MIT Professor. Or I can recruit 60 full professors of Tsinghua University or Peking University.
So, which one is more important? Professor vs. AI?
If you support AI, you are basically supporting Nvidia and Chinese peripheral material vendors (in Taiwan and Mainland China).
If you support professors, you are basically supporting our future generations of students.
That is so simple!开云体育(中国)官方网站