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OpenAI据称今年将亏损50亿美元,算力成本或高达70亿美元,占总运营成本八成以上,大模型有多烧钱?

关于“OpenAI据称今年将亏损50亿美元,算力成本或高达70亿美元,占总运营成本八成以上,大模型有多烧钱?”这个问题,我们可以从以下几个方面进行分析:

  1. 算力成本的构成

    • OpenAI的日常运营成本大约为70万美元,其中算力成本占据了很大一部分。具体来说,维护ChatGPT的正常运行每天的成本约为70万美元,而每次查询的成本约为36美分。
    • 例如,GPT-3.5-turbo模型的定价为每1000个代币0.002美元,这意味着处理大量请求时,算力成本会迅速累积。
  2. 大模型的训练和运行成本

    • 训练大型AI模型(如GPT-MoE-1.8T)需要大量的GPU资源,通常需要数月的时间和数千个GPU的支持。以Ampere架构的A100 GPU为例,训练一个大型模型可能需要3到5个月的时间。
    • OpenAI使用的HGX A100服务器总计约有28,936个GPU,每台服务器的成本约为20万美元,这使得整体硬件投资非常庞大。
  3. 与其他公司的比较

    • 相较于Google的搜索服务,每次查询的成本约为1.06美分,而OpenAI的每次查询成本高达36美分,显示出大模型在算力上的高消耗。
    • Google的搜索服务在2022年的收入为1624.5亿美元,而OpenAI的运营成本和亏损则显得更加突出。
  4. 未来的成本预期

    • 根据预测,到2030年,AI模型的成本可能会从1亿美元上升到5亿美元,这表明随着技术的发展和应用的普及,算力成本将持续增加。

综上所述,OpenAI的亏损和高昂的算力成本主要源于其大模型的训练和运行需求,这些需求不仅需要大量的硬件资源,还需要持续的资金投入来维持其运营。大模型的“烧钱”程度在行业内是显而易见的,尤其是在与其他技术服务的成本比较中更为突出。

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