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by bottlepalm22 hours ago |
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upvoteby txrx000021 hours ago|
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Scaling has hit a wall and will not get us to AGI. Open-source models are only a couple of months behind closed models, and the same level of capability will require smaller and smaller models in the future. This is where open research can help: make the models smaller ASAP. I think it's likely that we'll be able to get something human-level to run on a single 16GB GPU before the end of the decade.
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upvoteby Tade04 hours ago|
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> Scaling has hit a wall and will not get us to AGI.

That was never the aim. LLMs are not designed to be generally intelligent, just to be really good at producing believable text.

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upvoteby tbrownaw21 hours ago|
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> human-level to run on a single 16GB GPU before the end of the decade.

That's apparently about 6k books' worth of data.

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upvoteby txrx000020 hours ago|
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For the weights and temporary state, yes. It doesn't sound like a lot until you remember that your DNA is about 600 books worth of data by the same metric.
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upvoteby octoberfranklin17 hours ago|
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How many humans do you know who can recite 6000 books, word for word, exactly?
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upvoteby drdaeman20 hours ago|
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> Open-source models are only a couple of months behind closed models

Oh, come on, surely not just a couple months.

Benchmarks may boast some fancy numbers, but I just tried to save some money by trying out Qwen3-Next 80B and Qwen3.5 35B-A3B (since I've recently got a machine that can run those at a tolerable speed) to generate some documentation from a messy legacy codebase. It was nowhere close neither in the output quality nor in performance to any current models that the SaaS LLM behemoth corps offer. Just an anecdote, of course, but that's all I have.

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upvoteby fooker22 hours ago|
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> hardware to run them

Costs a few hundred thousand per server, it's a huge expense if you want it at your home but a rounding error for most organizations.

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upvoteby bottlepalm22 hours ago|
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You're buying what exactly for a few hundred thousand? and running what model on it? to support how many users? at what tps?
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upvoteby fooker20 hours ago|
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Not every use case is a cloud provider or tech giant.

Newer Blackwell does 200+ tokens per second on the largest models and tens of thousands on the smaller models. Most military applications require fast smaller models, I'd imagine.

Also, custom chips are reportedly approaching an order of magnitude more for the price. It's a matter of availability right now, but that will be solved at some point.

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upvoteby reactordev22 hours ago|
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I run local models on Mac studios and they are more than capable. Don’t spread fud.
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upvoteby bottlepalm22 hours ago|
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You're spreading fud. There's nothing you can run locally that's on par with the speed/intelligence of a SOTA model.
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upvoteby 383629364822 hours ago|
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You may be correct about the level of models you can actually run on consumer hardware, but it's not fud and you're being needlessly aggressive here.
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upvoteby CamperBob218 hours ago|
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Incorrect as of a couple of days ago, when Qwen 3.5 came out. It's a GPT 5-class model that you can run at full strength on a small DGX Spark or Mac cluster, and it still works pretty well after quantization.
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