I agree with you that quality is the most important question, for similar reasons.

I don't think that current models are at expert level, but they do seem to be reliably good enough to be useful and pass standardised tests and be generally quite solidly in the "good enough you have to pay close attention for a while before you notice the stupid mistake" area that makes them very irritating for anyone running job interviews or publishing books etc.

And worse, I also think the numbers you're replying to are, at best, off by a few decimal places.

If I take the 0.36 bananas (which was already suspicious) and USD 0.1 / kWh, I get 0.004 USD. If I scale that up to by 1/0.72 to get a megatoken, that's still only 5/9ths of a cent.

If I make the plausible but not necessarily correct assumption that OpenAI's API prices reflect the cost of electricity, none of their models are even remotely that cheap. It's close enough to the cost of their text-embedding-3-small (per megatoken) to be within the fudge-factor of my assumption about how much of their prices are electricity costs, but text-embedding are much much weaker than transformer models, to the point they're not worth considering in the same discussion unless you're making an academic point.

> It's wrong or at best, does not make sense. The brain barely spends any more energy above its baseline when thinking hard (often not much more than 5%).

Indeed.

Now I'm wondering: how much power does the human brain use during an epileptic fit? That seems like it could plausibly be 70% of calories for a the few seconds of the seizure? But I've only got GCSE grade C in biology, so even with what I picked up the subsequent 25 years of general geeking, my idea of "plausible" is very weak.

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pama a day ago | parent [-]

> If I make the plausible but not necessarily correct assumption that OpenAI's API prices reflect the cost of electricity, none of their models are even remotely that cheap

This assumption is very wrong. The primal cost factor in inference is the GPU itself. NVidia’s profit margins are very high; so are OpenAI’s margins for the API usage, even after taking into account the costs of the GPU. You can understand their margins if you read about inference at scale, and the lmsys blog in my parallel answer is a decent eye opener if you thought that companies sell tokens close to the price of electricity.

	▲	pama 20 hours ago \| parent [-]
		An alternative and perhaps easier way to estimate the relative importance of the GPU cost vs the electricity cost is to estimate how many years of constant use of the GPU at full power you need for the cost of industrial-scale electricity to catch up to the cost of the industrial scale GPU pricing. The H200 had 700W max power draw and about 40k USD cost (price varies a lot); typical lowest rental price a year ago was 2USD/h, possibly a bit lower by now. In 1h you could not even spent 1kWh electricity with them in optimal compute conditions, yet, at scale, you can negotiate prices lower than 0.05 USD per kWh of electricity at some parts of the US. Alternatively, assume 0.05 USD per kWh, and use the GB200 NVL72 that draws 120kW at peak. That is a cost of 6USD/hour or $52.6k per year. Even if one were to use the hardware for 10 years straight without problems at peak performance, the cost of electricity is way cheaper than the cost of the hardware itself (you have to ask NVidia for a quote, but expect a multi-million dollar tag and they have no shortage of customers ready to pay.)