> The largest model we tested has 405 billion parameters, but others, such as DeepSeek, have gone much further, with over 600 billion parameters. Very quickly

> The largest model we tested has 405 billion parameters, but others, such as DeepSeek, have gone much further, with over 600 billion parameters. Very quickly skimming, I have some trouble taking this post seriously when it omits that the larger DeepSeek one is a mixture-of-experts that will only use 12.5% (iirc) of its components for each token. The best summary of text energy use I've seen is this (seemingly more rigorous, although its estimates are consistent with the final numbers made by the present post): epoch.ai/gradient-updates/how-much-energy-does-chatgpt-use Estimates for a given response widely for a "typical" query (0.3 Wh; 1080 joules) and a maximal-context query (40 Wh; 144k joules). Assuming most uses don't come close to maximizing the context, the energy use of text seems very small compared to the benefits. That being said, the energy use for video generation seems substantial I would be interested in seeing the numbers corresponding to how LLMs are typically used for code generation