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Comment by simlevesque | Hacker News Reader

Not really, the two first examples you named are the same meaning.

They aren’t though. $5M is the cost of a single training run. $500B includes the cost of operations, data center, a lot more failed runs because they weren’t sure that they’d were on the right path etc.

Compare to what you think a single run cost then.

It was orders of magnitude more before DeepSeek.

So far. And there might be orders of magnitude left to improve! Deepseek R1 is a training architecture improvement -- cool stuff! [0]

[0] https://newsletter.languagemodels.co/p/the-illustrated-deeps...

Right. It's like building a large model rocket and saying that you've cracked rocketry for a fraction of the cost that was required in the 1940's and 1950's.

Well yes, yes you did, because all you had to do was follow the existing instructions, guidelines, and use easily available materials. You didn't go down any dead ends, didn't have to work your way from propellants like high test peroxide, dangerous hypergolics, and eventually develop solid rocket boosters.

It's like making the generic of a drug someone else developed.

So what’s your point, everyone developing a model should be forced to spend the same as what the first movers did?

Probably just that it's not as impressive as it appears because it didn't innovate. Which is of course irrelevant since the innovative leap here were the optimizations that let them make their model with an order of magnitude fewer materials, regardless of whatever innovation costs OpenAI ate.

No, just that what DeepSeek did is not as valuable as what the first movers did, because it did not advance the state of the art nearly as much. It's a new cheaper way to go from Base LM -> CoT "reasoning" LM. We already had CoT "reasoning" LMs, so while the new cheaper path to get to them is interesting, it's not necessarily groundbreaking either. Also, R1 only works with the "cold start" data that they distilled from o1, so it's not quite clear that it'll ever be able to exceed o1's capabilities. We already know it's much cheaper to distill new, smaller models from large already pretained and well-performing models—in fact, $5M sounds like a very expensive way to do so. So while these new techniques are probably going to have some impact, OpenAI is far from quaking in their boots

Um it’s very valuable, maybe even more valuable… Companies can now have a private LLM with o1 quality without having to send data to OpenAI or pay for their API.

You're more or less describing how all progress on anything, ever, happened.

Even if you merely flipped a single bit and created AGI based on existing tech, you're still the legitimate creator of AGI.

I don’t think anyone is claiming that Deepseek didn’t produce a very impressive frontier model. They’re just saying it’s not surprising that, in your analogy, flipping the single bit was cheaper than the prior work.

The first two are not the same thing.

DeepSeek never claimed to have trained the base models they used. Now maybe a lot of people inferred that they did, but that's not what they claimed.

Their breakthrough was more along the lines of, "given a model, we can train it to reason using a certain class of RL techniques". Which is, to my mind, more useful in any case.

But yeah, if there are people out there thinking they can train base models from scratch for USD6 Million with no data, they're likely to be disagreeably surprised when they make the attempt.

> DeepSeek never claimed to have trained the base models they used

Isn't that exactly what they have done? Maybe you are confused with the distilled models?

DeepSeek-R1: "DeepSeek-R1-Zero & DeepSeek-R1 are trained based on DeepSeek-V3-Base"

DeepSeek-V3: "At an economical cost of only 2.664M H800 GPU hours, we complete the pre-training of DeepSeek-V3 on 14.8T tokens, producing the currently strongest open-source base model. The subsequent training stages after pre-training require only 0.1M GPU hours"

Aren't they based on qwen? I thought the clever bit with DeepSeek is a way to fine tune with reinforcement learning, not training a huge model from scratch.

No… mostly.

They developed their “zero” model, which (they claim) is a from-scratch model. Base models are typically not fine-tuned for particular applications (eg chat).

They trained their Zero model into a Chat+Reasoning model, which is what is attracting news.

They ALSO fine-tuned small Qwen models using their big models as a teacher (distillation technique).

deepseek-r1 (reasoning) is a post-trained (not simply finetuned) deepseek-r3 (base model) by deepseek. qwen is a completely separate 3rd party model by alibaba. deepseek distilled (finetuned on the outputs of) r1 into qwen. They also did the same with llama.

There's a lot of conflicting information here.

There are two papers: https://arxiv.org/pdf/2412.19437v1 for DeepSeek-V3-Base, which makes claims about 2.8M H800 GPU hours, and https://arxiv.org/pdf/2501.12948 for DeepSeek-R1, which was based on V3-Base but doesn't seem to quote the cost of its RL steps.

https://newsletter.languagemodels.co/p/the-illustrated-deeps... is a really fascinating overview of what those new steps were! The innovation here seems to be that the creation of a cutting-edge reasoning model could be made from a relatively inexpensive base model, regardless of how much RL is needed. Part of this seems to be a novel methodology for generating large volumes of chain-of-thought training data; it's left ambiguous exactly how the bulk of this data was generated, though, and it could have been a long process of manual curation.

I'd also take the assertion that they started R1 with V3-Base in its release form with a grain of salt. We'll see whether people can reproduce similar results. Either way, there's a new chain-of-thought model with open weights, and that's an incredible achievement regardless of the context - and one that will be meaningful to future research.

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