4 ms·
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 c
by kaba0 2y ago
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.
- dartos 2y agoI 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.
- kaba0 2y agoWhich is the same order of magnitude? Also, how often do they train from scratch?
- dartos 2y agoI 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.
- kaba0 2y agoWhich 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?
- dartos 2y agoI 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.
- deleted 2y ago[deleted]