5 ms·
All these answers are good, but I can share more concrete numbers… Meta released their OPT model which they claim is comparable to the GPT-3 model. Guidance fo
by vineyardmike 4y ago
All these answers are good, but I can share more concrete numbers…
Meta released their OPT model which they claim is comparable to the GPT-3 model. Guidance for running that model [1] suggests a LOT of memory - at least 350GB of gpu memory which is roughly 4 A1000s, which are pricy.
Running this on AWS with the above suggestion would cost $25/hr - just for one model running. That’s almost $0.50 a minute. If you imagine it takes a few seconds to run the model for one request… easily you’ll hit $0.05 per request once you factor in the rest of the infra (storage, CDN, etc) and the engineering cost, and the research cost, and the fact that they probably have a scale to hundreds of instances for heavy traffic and that may mean less efficient purchased servers.
OpenAI has a sweetheart deal with Azure, but this is roughly the cost structure for serving requests. And this doesn’t include the upfront cost of training.
https://alpa.ai/tutorials/opt_serving.html https://alpa.ai/tutorials/opt_serving.html
- mr_00ff00 4y agoReally makes you appreciate the brain, which presumably operates with some sort of similar demand.
- unsupp0rted 4y agoHard to tell. Similar to how it takes a lot of resources for a human to hang from monkey bars but for a sloth it takes basically no resources at all, because the sloth comes out of the box designed for it.
- nocsi 4y agoHuman babies come out of the box designed for hanging from monkey bars as well. https://youtu.be/jXJLaGguQiU https://youtu.be/jXJLaGguQiU
- canadianfella 4y ago[dead]
- smnrchrds 4y agoAnother mind-boggling thing about brain is how little power it uses to do all the complex things it does.
- wordpad25 4y agocalories are a unit of energy, so it’s a straight forward comparison if we assume that a computer can be powered by 100 watts, over a day it will use 2.4 kW h, which is about 2000 Calories GPU will consume a lot more, but we aren’t that far off in efficiency
- pattrn 4y agoDoesn't that assume 100% of a human's daily calories burn is due to brain activity?
- unsupp0rted 4y agoThe brain uses about 20% of a human's calories. It's not 100%, but it's a substantial fraction.
- sannee 4y agoThe other components of the human body are also required for brain function.
- _8j50 4y agoTo have ML produce meaningful content you need tp give it some input or a sense of what the outcome should be and this is after billions of trial and errors. Yet people these days believe something like the brain was bruteforced by nature into an accidental existence.
- ericathegreat 4y agoSome input: The organism's environment. Outcome should be: The organism successfully produces offspring Natural selection is doing exactly what you describe.
- _8j50 4y agoExcept natural selection can't start over. It onlu works if there are always a high rate of survivors and even if that was not an issue consider 4 billion years and a generous generation life of one year (natural selection cycle), 4 billion isn't a whole lot even for small features when you don't have an enormous population and birth rate. Let's say there were 100000 humans at some point and only a 1000 fatal features (being generous) it's not just the replacement rate of defective humans that needs to exceed the elimination rate, a certain percent of replacements must be free of all fatal defects and survive. Also, consider how there should be many failed species that attempted to evolve into a human like species or a primate. You can't always luck out, at some point the entire branch has to fail, requiring subsequent attmepts meanwhile the fatal conditions that required the evolution will not go away.
- KingMob 4y ago> It onlu works if there are always a high rate of survivors There doesn't have to be a high rate of survival if the reproductive rate compensates for losses. E.g., if 80% of wild rabbits are eaten, but the remaining 20% can give birth to 5 bunnies per parent per lifetime, the population will be stable. I have no idea where you're getting your beliefs, but most of it is wrong in both the math and biology.
- 4y ago
- imtringued 4y agoThe brain doesn't use a synchronous digital architecture. It is asynchronous. Spiking neural networks implemented in neuromorohic hardware are equally efficient. They consume milliwatts for a million neurons.
- awesomeMilou 4y agoDo you have links on novel hardware architectures for neuromorphic hardware? In my country , the leading research group for neuromorphic computing does not cite any novel hardware approaches, only what existing hw architectures are most suitable.
- iosystem 4y agoHow do you know that the universe isn't just rendering everything.
- mike_hearn 4y agoDon't forget training costs, labor costs used for RLHF and (most likely) the money required for such large volumes of training data.
- ramblerman 4y agoIt's interesting that the requirements for a text model are so much greater than for images. Stable diffusion can run on a home pc, while it seems you need a super computer for GPT3. I'm not sure that would have been my intuition.
- sadpasture 4y agoI think it has to do with text being much more precise. Your stably diffused cartoon avatar having 6 finger is not nearly as noticeable as a language model's chat mispelling every second word. So you need less resources to get to a human acceptable result
- andbberger 4y agono, diffusion models are just more efficient
- hansvm 4y agoDoesn't ChatGPT fine-tune one of the smaller GPT-3s, not the 175B parameter model?