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> Like even with cryptocurrency, there were a lot of people mistaking the issue of scalability - namely that "as a store of value" crypto would consume incredible amounts of other resources (and a lot of people got stuck trying to figure out how somehow "a hash" could be reclaimed for useful resources) to do less then alternatives, with "the energy usage itself is the problem".

To be fair, technology-wise they mostly solved this problem via proof-of-stake.

From an individual point of view you still expense enormous resources as a miner / validator in a proof-of-stake system. It's just that now the resources come in the form of lost opportunity costs for your staked tokens (eg staked Ethereum).

But from aggregated perspective of society, staked Ethereum is essentially free.

That has some parallels to how acquiring regular money, like USD, is something individuals spend a lot of effort on. But for the whole of society, printing USD is essentially free.

> Because environmentally, I can install solar panels to do zero-carbon training (and if LLMs are as valuable as they're currently being priced, this is a no-brainer - if people aren't lying about solar being "cheaper then fossil fuels").

There's still opportunity costs for that energy. Unless you have truly stranded electricity that couldn't be used for anything else.

> Finding optimizations for LLMs is good because it means we can build cheaper LLMs, which means we can build larger LLMs then we otherwise could for some given constraint, which means we can miniaturize (or in this case specialize) more capable hardware. The thing which really matter is, can the energy usage be meaningfully limited to a sensible scaling factor given the capability that makes them useful?

I agree with that paragraph. It's all about trade-offs. If we can shift the efficiency frontier, that's good. Then people can decide whether they want cheaper models at the same performance, or pay the same energy-price for better models, or a combination thereof. Or pay more energy for even better model

It’s good that we can build cheaper LLMs, but the problem companies guzzling energy for LLM training won’t use less energy, they’ll just have better models

That energy is still on the order of a household’s yearly electricity, not that of Argentina as cryptos were, and that’s just for training.

Inferring is much cheaper and arguably provides quite a lot of value (even though I also think it is overhyped), for very little energy consumption, probably more is lost due to inefficiency for any physical product.

I got news for you.

The 1M GPUs that meta purchased running at full (or less probably) load 24/7 is more than the energy of a single household.

The energy cost is in training, not inference.

Which is the same order of magnitude? Also, how often do they train from scratch?

I don’t understand your first question, sorry.

But for the second:

Llama 1, 2, and 3 all have different architectures and needed to be trained from scratch. Llama 1 was released February 2023.

Same training story for openAI’s Sora, dalle, and 4o. All of mistral’s models Mamba, Kan, and Each version of rwkv (they’re on 6 now)

Not that this list is a result of survivor bias. It’s only looking at their published models too. Not the probably 1000s of individual training experiments that go into producing each model.

Which is still absolutely nothing compared to something like youtube’s servers, which is absolutely nothing compared to something like the food industry.

Like, if a couple of millions of people can use chatgpt in the manner they do today, would it matter if a house’s yearly energy budget was used up for that? Or 10?

I guess because training AI doesn’t use as much energy as the largest energy consumers on the planet, we just shouldn’t even worry about it.

No, because the article is about sacrificing quality by using less energy. But the quality is already bad for non-toy purposes, so there is plenty of demand for higher-energy higher-quality systems.