问HN：在哪里可以跟踪AI模型的训练成本趋势？
我很好奇，训练人工智能模型的成本（计算、能源、数据等）是如何随时间变化的。<p>有没有公开的资源或数据集跟踪开放权重模型的训练成本（我猜对于封闭模型来说，这些数据很难获取，但如果我错了，我很乐意接受纠正。）<p>我特别想了解哪些架构变化（例如，注意力变体、参数共享、专家混合）导致了主要的成本优化，而不仅仅是来自这些模型背后的公司，而是来自任何训练或复制过这些模型的人。
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I'm curious how the cost of training AI models (compute, energy, data, etc) has changed over time.<p>Are there any public resources or datasets tracking training costs for open-weight models (I'm guessing this data is hard to get for closed models, but happy to be proved wrong.)<p>I'm especially interested in understanding which architectural changes (e.g., attention variants, parameter sharing, mixture-of-experts) have led to major cost optimizations, and NOT just from the companies behind these models, but from anyone who has trained or replicated them.