| ▲ | IshKebab 4 hours ago |
| That's never going to happen. By the time you can run current frontier models on your $10k desktop the frontier will have massively advanced and people will want those models instead. |
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| ▲ | bunderbunder 3 hours ago | parent | next [-] |
| I’m not so sure about that. Already AI vendors are back to cutting prices to try and keep customers from cutting back on their usage. My own employer is working hard at pivoting to much smaller fine-tuned models for established use cases, and seeing model performance improvement in addition to large inference cost reductions. Being able to run them locally hasn’t exactly been a disaster for devex, either. It may turn out that demand for SOTA frontier models isn’t so limitless after all. |
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| ▲ | pixl97 3 hours ago | parent [-] | | Every product follows demand curves. At a price of 0 you could find infinite usage. This has nearly zero relation to how much it costs to provide the product. | | |
| ▲ | bunderbunder 3 hours ago | parent [-] | | Except of course it relates. All else being equal, we will prefer $X COGS over $2X COGS because that helps us with both profit margins and price competition. | | |
| ▲ | pixl97 2 hours ago | parent [-] | | It relates in the sense there's a minimum cost of production without losses, not the actual price people are willing to pay. | | |
| ▲ | bunderbunder 2 hours ago | parent [-] | | Framing it in terms of the price people might be willing to pay for a single product in isolation frames the point I was making, which was about price competition, right out of the picture. Maybe I'd be willing to pay $10 for product A if I had other options. But if there's a product B for $3 that's not quite as nice but still ticks all my boxes, then product instantly becomes a lot less attractive. |
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| ▲ | blurbleblurble 3 hours ago | parent | prev | next [-] |
| It's going to happen very soon, which is why these frontier labs are scrambling to shut down open source language models. There's an existential risk threatening their obscene returns. |
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| ▲ | usef- 3 minutes ago | parent | next [-] | | I feel like cost competitiveness of local has been going backwards if anything, compared to API providers. Do you see any reason this might change? | |
| ▲ | 14 minutes ago | parent | prev | next [-] | | [deleted] | |
| ▲ | mrlonglong 2 hours ago | parent | prev | next [-] | | It is for that reason they are being archived and torrented as a very large middle finger. | |
| ▲ | IshKebab 2 hours ago | parent | prev [-] | | > It's going to happen very soon Why? You can't just assert it. There are very good reasons to think it won't happen soon, and you've given no reasons to think it will happen soon. | | |
| ▲ | blurbleblurble 5 minutes ago | parent | next [-] | | Because everything is converging on a backlog of huge efficiency gains established in research, waiting to be combined. Looped transformers, a whole host of diffusion techniques and new quantization techniques, maturation of ternary distillation and new ways to separate logic from stuff that can be looked up. It would surprise me if most frontier models were actually even that big at that point in terms of active params. I highly doubt it. | |
| ▲ | dwedge 2 hours ago | parent | prev [-] | | You asserted that it was never going to happen first | | |
| ▲ | geysersam 18 minutes ago | parent [-] | | But he gave a reason for that. "Before that happens the frontier will move". Why do you think it will happen anyway? Do you think the frontier will not move fast enough that local models are unable to catch up, or do you think people will prefer local models at a point. Or something else? |
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| ▲ | hypfer 3 hours ago | parent | prev | next [-] |
| I'm not sure if this prediction will hold true. We're not seeing the progress in those "frontier models" that we have previously seen. There's certainly still gas left in tank tank, but we're way into the diminishing returns by now. Cloud inference still beats hardware investments by orders of magnitude of course, but that's only if your data doesn't really matter to you. |
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| ▲ | gehsty 3 hours ago | parent | next [-] | | It’s a constant tension in computing that has been around since mainframes and clients… Neither is going to disappear. My general feeling is normal people care more about how thin and light something is than their privacy, so if data center powered LLMs will have a strong future. | | |
| ▲ | hypfer 3 hours ago | parent [-] | | Hmm I'm not 100% sure about that, given that edge is very viable, and the geopolitical climate has changed quite significantly. I agree that datacenters are not going to go away, but I have doubts that the buildup that has happened is really going to pay off for most operators. |
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| ▲ | airspresso 3 hours ago | parent | prev | next [-] | | We are certainly not in the diminishing returns phase for LLM progress. No sign of that yet. | | |
| ▲ | bunderbunder 3 hours ago | parent | next [-] | | I’ll grant that for specialized applications like coding agents and mathematics, but even there I suspect that most the real gains are actually taking place in the harness. But I suspect returns may have already diminished into negative territory for at least some other use cases. One of my least favorite job responsibilities in this brave new era is figuring out how to avoid performance and behavior regressions when an older model were using for some application reaches end of life. It’s getting uncommon for me to look at our benchmark results and say, “Oh, good, it does better on one of the newer models!” | | |
| ▲ | pixl97 3 hours ago | parent | next [-] | | >suspect that most the real gains are actually taking place in the harness. Part of the reason harnesses work well is you can run a lot of agents in parallel. That doesn't slow down demand. | | |
| ▲ | hypfer 3 hours ago | parent | next [-] | | That is true, but the eventual realization that more machines doing more coin flips in parallel does not mean "more work gets done" might. LLMs are amazing tech, but they're terrible without oversight. More agents faster just makes reality collapse on them quicker. But yeah, you're right, temporarily, this will still push demand. But the topic was about "diminishing returns" as in "tech getting better". Not as in "customer spending". | | |
| ▲ | pixl97 3 hours ago | parent [-] | | It's kind of weird because more machines working together does mean more work gets done. Coin flips and weighted coin flips are totally different things. Any biases weights towards reality push you closer to reality when you use them. New models keep being able to use more and more agents on longer time frames. Your hypothesis doesn't look like what we're measuring. | | |
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| ▲ | bunderbunder 3 hours ago | parent | prev [-] | | I had actually been thinking more about all the non-LLM functionality that go into the harnesses. I'm not going to name names and I haven't done any rigorous testing, but my general impression is that choice of harness matters more than choice of model. In terms of basic task completion success specifically, not code aesthetics. | | |
| ▲ | pixl97 2 hours ago | parent [-] | | A perfect harness will not extract gold from a dumb model. It's a system that builds on each other, though we've not probed that frontier much to have a good intuition on what effects what. |
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| ▲ | 3eb7988a1663 2 hours ago | parent | prev [-] | | One thing that I really want to know - the better models from today vs a year ago - what has changed. They have already pre-trained on all available public data. Scooping up the last percentage of archaic texts which were never digitized is not going to move the needle. Is it just that the providers are generating tons of synthetic datasets on coding tasks so that the models get more exposure to the right thing to do? Every time someone points out an LLM stupidity they add some training data to patch over the weakness (trivial to generate "there are two 'l's in llama")? |
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| ▲ | hypfer 3 hours ago | parent | prev [-] | | Well I mean if I wanted to be extra pedantic, I would argue that we've been in that phase since LLMs were first introduced. Before that, we had 0.
After that, we had more than 1. A leap as far as that is hard to recreate. But that wasn't my point. That's just trolling. The actual point is that LLMs aren't gaining new capabilities anymore. They just get more reliable at the ones they already have; turning what was a coin flip to some higher probability. That's (intuitively speaking, not strictly mathematically speaking) kinda the mathematical definition of diminishing returns. |
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| ▲ | 48488448 3 hours ago | parent | prev [-] | | they really dont want to hear this bro lol | | |
| ▲ | hypfer 3 hours ago | parent [-] | | I can see that by those reddit-style vote swings, but who are "they", exactly? Who is so emotionally invested into random comment sections being purely positive about their pet.. uuuuuuuh.. tech? Very weird. |
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| ▲ | kingleopold 4 hours ago | parent | prev [-] |
| This + your ROI in $10k device will be always lower than busy datacenter, its literally math. They sell free compute to others when you dont use it, you will never sell at that level or even you magically sell home compute, you will not compete at price |
