| ▲ | whazor 8 hours ago |
| But economically, it is still much better to buy a lower spec't laptop and to pay a monthly subscription for AI. However, I agree with the article that people will run big LLMs on their laptop N years down the line. Especially if hardware outgrows best-in-class LLM model requirements. If a phone could run a 512GB LLM model fast, you would want it. |
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| ▲ | m4rtink 5 hours ago | parent | next [-] |
| Are you sure the subscription will still be affordable after the venture capital flood ends and the dumping stops? |
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| ▲ | nl 5 hours ago | parent | next [-] | | 100% yes. The amount of compute in the world is doubling over 2 years because of the ongoing investment in AI (!!) In some scenario where new investment stops flowing and some AI companies go bankrupt all that compute will be looking for a market. Inference providers are already profitable so with cheaper hardware it will mean even cheaper AI systems. | | |
| ▲ | AyyEye 2 hours ago | parent | next [-] | | You should probably disclose that you're a CTO at an AI startup, I had to click your bio to see that. > The amount of compute in the world is doubling over 2 years because of the ongoing investment in AI (!!) All going into the hands of a small group of people that will soon need to pay the piper. That said, VC backed tech companies almost universally pull the rug once the money stops coming in. And historically those didn't have the trillions of dollars in future obligations that the current compute hardware oligopoly has. I can't see any universe where they don't start charging more, especially now that they've begun to make computers unaffordable for normal people. And even past the bottom dollar cost, AI provides so many fun, new, unique ways for them to rug pull users. Maybe they start forcing users to smaller/quantized models. Maybe they start giving even the paying users ads. Maybe they start inserting propaganda/ads directly into the training data to make it more subtle. Maybe they just switch out models randomly or based on instantaneous hardware demand, giving users something even more unstable than LLMs already are. Maybe they'll charge based on semantic context (I see you're asking for help with your 2015 Ford Focus. Please subscribe to our 'Mechanic+' plan for $5/month or $25 for 24 hours). Maybe they charge more for API access. Maybe they'll charge to not train on your interactions. I'll pass, thanks. | |
| ▲ | jeremyjh 2 hours ago | parent | prev | next [-] | | Datacenters full of GPU hosts aren't like dark fiber - they require massive ongoing expense, so the unit economics have to work really well. It is entirely possible that some overbuilt capacity will be left idle until it is obsolete. | |
| ▲ | oa335 3 hours ago | parent | prev | next [-] | | > Inference providers are already profitable. That surprises me, do you remember where you learned that? | |
| ▲ | blibble 43 minutes ago | parent | prev [-] | | > The amount of compute in the world is doubling over 2 years because of the ongoing investment in AI (!!) which is funded by the dumping when the bubble pops: these DCs are turned off and left to rot, and your capacity drops by a factor of 8192 |
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| ▲ | anonzzzies 2 hours ago | parent | prev [-] | | They will go down. Or the company will be gone. |
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| ▲ | seanmcdirmid 5 hours ago | parent | prev | next [-] |
| Running an LLM locally means you never have to worry about how many tokens you've used, and also it allows for a lot of low latency interactions on smaller models that can run quickly. I don't see why consumer hardware won't evolve to run more LLMs locally. It is a nice goal to strive for, which consumer hardware makers have been missing for a decade now. It is definitely achievable, especially if you just care about inference. |
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| ▲ | ignoramous 8 hours ago | parent | prev [-] |
| > economically, it is still much better to buy a lower spec't laptop and to pay a monthly subscription for AI Uber is economical, too; but folks prefer to own cars, sometimes multiple. And how there's market for all kinds of vanity cars, fast sportscars, expensive supercars... I imagine PCs & Laptops will have such a market, too: In probably less than a decade, may be a £20k laptop running a 671b+ LLM locally will be the norm among pros. |
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| ▲ | joshred 7 hours ago | parent | next [-] | | Paying $30-$70/day to commute is economical? | | |
| ▲ | zmmmmm 5 hours ago | parent | next [-] | | if you calculate depreciation and running costs on a new car in most places - I think it probably would be. | | |
| ▲ | adrianN an hour ago | parent [-] | | If Uber were cheaper than the depreciation and running costs of a car, what would be left for the driver (and Uber)? | | |
| ▲ | zmmmmm 28 minutes ago | parent | next [-] | | a big part of the whole "hack" of Uber in the first place is that people are using their personal vehicles. So the depreciation and many of the running costs are sunk costs already. Once you paid those already it becomes a super good deal to make money from the "free" asset you already own. | |
| ▲ | cjbgkagh an hour ago | parent | prev [-] | | The depreciation would be amortized to cover more than one person. I only travel once or twice per week, it cost me less to use an Uber than to own a car. |
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| ▲ | ignoramous 6 hours ago | parent | prev [-] | | > Paying $30-$70/day to commute is economical? When LLM use approaches this number, running one locally would be, yes. What you and other commentator seem to miss is, "Uber" is a stand-in for Cloud-based LLMs: Someone else builds and owns those servers, runs the LLMs, pays the electricity bills... while its users find it "economical" to rent it. (btw, taxis are considered economical in parts of the world where owning cars is a luxury) |
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| ▲ | subjectsigma 7 hours ago | parent | prev [-] | | > Uber is economical, too One time I took an Uber to work because my car broke down and was in the shop and the Uber driver (somewhat pointedly) made a comment that I must be really rich to commute to work via Uber because Ubers are so expensive | | |
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