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DeepSeek pause fundraise after comments on compute gap to US leaked (transcript) [pdf]

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Re: DeepSeek pause fundraise after comments on compute gap to US leaked (transcript) [pdf]

#91

Earlier quoted context omitted.

All the Chinese reporting I see point to the second (majority) interpretation. Liang being furious about his private investor talk leaked online is the news here. e.g. https://x.com/_FORAB/status/2081034500101017616?s=20

Those could be subsequent developments, but that's not what the linked transcript was about. The transcript was a discussion of the DeepSeek founder (Liang Wenfeng) with investors, and he does not mention any leaks, or any frustration. He simply says that he is constrained by the supply of cards, and he has no problem of getting funding, but has no reason to raise further funding because he can't transform the cash i…

he's pausing because of the leak. the transcript content has nothing to do with it.

Re: DeepSeek pause fundraise after comments on compute gap to US leaked (transcript) [pdf]

#92
I don't think his pitch when asking money from investors should mean too much for us. He wants the funds, and he needs to point to a deficiency that those funds should cover. We cannot know for sure but he may be exaggerating, or let's just say, talking strategically.

This is also me who wants to believe that we can make all this very efficient, so take my warning with a grain of salt.

Re: DeepSeek pause fundraise after comments on compute gap to US leaked (transcript) [pdf]

#93
post #88

>> As you can understand, during V3 training, NVIDIA GPUs were still used, but the NVIDIA ecosystem was no longer employed. Ironic that these large LLMs are eroding Nividia's moat. In the next paragraph he talks about Nvidia digging its own grave. I wonder if Nividia is aware of this and the frequent release cycle is a response to this development ?

> Ironic that these large LLMs are eroding Nividia's moat.

That's not what the quoted part meant. During v3 development they only had access to hardware limited variants of H series GPUs. Those had less interconnect bandwidth IIRC. So, at the time, the low-level wizards that ds employed bypassed the official APIs (i.e. the nvda ecosystem) and hand wrote alternatives to say nccl, to better use those limited GPUs. I remember them publishing some of it as well. It had to do with allocating memory, moving stuff around, etc. Basically bypassing some limitations by going lower than the official APIs support.

There is no eroding of their moat, as long as they sell GPUs. ANd they're selling GPUs like crazy. The moat speaks for itself, if I may :)

Re: DeepSeek pause fundraise after comments on compute gap to US leaked (transcript) [pdf]

#95
post #88

>> As you can understand, during V3 training, NVIDIA GPUs were still used, but the NVIDIA ecosystem was no longer employed. Ironic that these large LLMs are eroding Nividia's moat. In the next paragraph he talks about Nvidia digging its own grave. I wonder if Nividia is aware of this and the frequent release cycle is a response to this development ?

> Ironic that these large LLMs are eroding Nividia's moat. That's not what the quoted part meant. During v3 development they only had access to hardware limited variants of H series GPUs. Those had less interconnect bandwidth IIRC. So, at the time, the low-level wizards that ds employed bypassed the official APIs (i.e. the nvda ecosystem) and hand wrote alternatives to say nccl, to better use those limited GPUs. I re…

They talk about developing and using TileLang which they ported to Huawei's 950 GPUs.

And it makes sense, as these LLMs become more capable in coding abilities - people will use them to develop their own abstractions to work on different HW. You cannot have it otherwise. If SaaS companies get threatened that their SW doesn't have a moat why do you expect Nividia's SW to have moat ? The computing algorithms are not even proprietary. It is only a matter of whether someones cares about it and is committed. This should be encouraging for new AI chip development companies.

The very progress that Nividia enables also has a negative feedback that threatens it.

Re: DeepSeek pause fundraise after comments on compute gap to US leaked (transcript) [pdf]

#96
post #32
post #10

Earlier quoted context omitted.

Trump reversed course on the NVIDIA ban. It's now China that is blocking their companies from buying NVIDIA chips. So the shell entities would be to get around Chinese, not USian restrictions

That's not true. First there is still a licensing and quota scheme on the US side for the H200s. Secondly China blocked them for use in inferencing. Thirdly Chinese companies don't want them for training because newer chips are more cost effective.

> Thirdly Chinese companies don't want them for training because newer chips are more cost effective.

Please read the FTA. Deepseek is literally explicitly talking about wishing they could get their hands on H200s

> Secondly China blocked them for use in inferencing.

This seems to be false unless you can provide a source. I tried looking into it

> First there is still a licensing and quota scheme on the US side for the H200s.

A major reversal from Trump's outright ban. As I stated.

Re: DeepSeek pause fundraise after comments on compute gap to US leaked (transcript) [pdf]

#97

Here's something I really don't understand: If as alleged Chinese open weight models are catching up with US anyway, and the performance is near US frontier model level but Chinese can do it with a fraction of cost, and eventually AI model will be commodified, wouldn't that means that the billion or even trillion dollars that US labs spend have only diminishing returns and the lead is only temporary? So why Deepseek…

[deleted]

Re: DeepSeek pause fundraise after comments on compute gap to US leaked (transcript) [pdf]

#98

Earlier quoted context omitted.

Most of that is paywalled, but this one paragraph in the Bloomberg article suggests it might be more to do with investors leaking information: "The suspension stemmed in part from Liang’s frustration over online reports about his comments to investors during his first financing deal" The part of the transcript I'd seen floating around online was this part from around 1 hour 26 min: "With the largest models available…

amusingly ive been working on ultra sparse llm inference/ training/ model design because nature loaths a dense graph/matrix and cause i think it shoukd be possible. i actually stood up a 20-25 percent faster than sota causal fast attention kernel yesterday, will be standing up cuda/metal/armv8 kernels too and thats gonna be fun. i genuinely think these models should be like 0.1 percent sparse for same capabilities we…

Curiosity:

For most of the past five years, I've known ways to do better than Anthropic, OpenAI, and friends in many ways, at least on paper. I know I was right about many of them since many would show up 6-24 months later tools from the major providers, or otherwise become standard practice.

A central problem is the Mythical Man-Month. True, I could do those, beating then-state-of-the-art, but only given 2-5 years. I suspect many other people knew about them too and could do so as well. As I noted above, throwing people and dollars caused many of those to be built in less time than I could have regardless.

Other methods, I'm less confident about (>50%, So the TL;DR is that my knowledge was not at all helpful towards e.g. competing with OpenAI, Anthropic, or even building a small business.

However, where it was useful was in predicting where the industry was going. This is true in investing (but not easily, at least with my skill set), but in developing startups and systems, there were capabilities which I (correctly) assumed would be there, whereas there were many arguments that "AI will never be able to ____."

If I know how to do something, it will almost certainly happen, regardless of whether I'm the one who does it.

To be clear, my expertise is almost certainly nowhere as deep as yours. I'm not providing a direct analogy, or claiming others know what you do or can do the same. My point was really that if you believe you can have these models be 0.1 percent sparse for same capabilities we associate with them today:

a) You're probably right. They were built quickly for capabilities. A slower process can almost certainly lead to much smaller models too. That's a radical statement: Historically people claiming a 1000x improvement somewhere were crackpots, but that's very possible in an industry as fast-changing as this one.

b) Someone at Anthropic or OpenAI might be working on building out extent tools right now. Even if so, there are indirect ways to capitalize on that knowledge.

c) Critically, that predicts a future where Fable is $1/month instead of $100/month, and that's something which CAN be acted upon in planning.

It also suggests -- much less strongly -- the existence of much more sophisticated models at $100/month. There are open discussion in planning about whether models plateau, continue improving, singularity, or otherwise. That changes the biases there.

Re: DeepSeek pause fundraise after comments on compute gap to US leaked (transcript) [pdf]

#99

Earlier quoted context omitted.

Their yields on high performance chips that could do training is really bad, and they aren’t getting more of the outdated ASML machines that they could use to scale up even with bad yields. It will still take China a few years or a decade to build out the tech needed to fab high performance chips economically on their own.

They can already make chips economically. Yields are worse than TSCM but good enough since they no longer need to pay the "Qualcomm tax". Huawei's phones are profitable. The issue is rather that Chinese capacity comes from a low quantity, and scaling capacity while simultaneously indigineousizing parts takes time — years. Foreign capacity was too good and too cheap so they never succeeded in scaling capacity, because…

They can make chips economically or they can make high performance chips. They just can’t do both at the same time yet.

The chip supply chain in the west is under heavy lock and key and impossible to reverse engineer, so they have to develop all from scratch. And that’s what they are doing, it will just take some time.

Re: DeepSeek pause fundraise after comments on compute gap to US leaked (transcript) [pdf]

#100

Here's something I really don't understand: If as alleged Chinese open weight models are catching up with US anyway, and the performance is near US frontier model level but Chinese can do it with a fraction of cost, and eventually AI model will be commodified, wouldn't that means that the billion or even trillion dollars that US labs spend have only diminishing returns and the lead is only temporary? So why Deepseek…

They are not catching up to US models. The only Chinese models that attain a modicum of competence are all, sooner or later, are discovered to be trained by exploiting US models (in fact Deepseek itself admitted so about 1 year back).

Chinese models are not innovating anything, they are just doing what China does everywhere else: copying the West… poorly but cheaper.

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