▲ | mgraczyk 3 days ago | ||||||||||||||||||||||||||||||||||||||||
As others have pointed out, this is false. Google has made their models and hardware more efficient, you can read the linked report. Most of the efficiency comes from quantization, MoE, new attention techniques, and distillation (making smaller models useable in place of bigger models) | |||||||||||||||||||||||||||||||||||||||||
▲ | jjani 3 days ago | parent | next [-] | ||||||||||||||||||||||||||||||||||||||||
- The report doesn't name any Gemini models at all, only competitors'. Wonder why that is? If the models got so much more efficient, they'd be eager to show this. - The report doesn't name any averages (means), only medians. Why oh why would they be doing this, when all other marketing pieces always use the average because outside of HN 99% of Joes on the street have no idea what a median is/how it differs from the mean? The average is much more relevant here when "measuring the environmental impact of AI inference". - The report doesn't define what any of the terms "Gemini Apps", "the Gemini AI assistant" or "Gemini Apps text prompt" concretely mean | |||||||||||||||||||||||||||||||||||||||||
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▲ | oulipo2 2 days ago | parent | prev [-] | ||||||||||||||||||||||||||||||||||||||||
sure, but the issue is if you make the model 30x more efficient, but you use it 300x more often (mostly for stuff nobody wants), it's still a net loss | |||||||||||||||||||||||||||||||||||||||||
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