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- Hacker News
- >> 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 ?
by sifar - > 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 :)
- 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.
by egeozcan - The main goal is AGI, and the underlying prerequisite theme is continuous learning.
Everyone is trying to figure out how to achieve this prerequisite. I'm thinking of agent harnesses. That's what everyone is trying to do at this point.
That's the same problem I'm trying to solve: https://github.com/rush86999/atom
by rush86999 - Curious what the fundamental limit on Huawei's capacity is. China has shown if nothing else they know how to scale when they want to. If it came down to just building more of what they know how to do, it would be happening. Is there more to it?by zmmmmm
- Huawei chips need advanced 3d packaging in order to keep up. Since the process is too complex the yield is still bad.
Not to mention there is a lot of demand from various factors, not deepseek only. Huawei itself is a major consumer.
by npn - The production capacity constraint seems to come from SMIC who make the Ascend processors for Huawei. Huawei's memory comes from CXMT who seem to have plenty of capacity, with Apple looking to buy memory from them. Huawei then combines processors and memory into chiplets similar to what NVIDIA does with their GPUs.
The reason SMIC are capacity constrained is at least in part because they've been blocked from buying ASML's EUV machines, and are therefore having to make do with previous generation lower resolution DUV machines. These DUV machines can be coaxed into making surprisingly competitive 5-7nm chips, but at the expense of using many more production steps ("multi patterning") which limits productivity.
- 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.by seanmcdirmid
- Perhaps there is an opportunity for China to close the compute gap by renting compute from hyperscalers through a complex web of shell entities similarly to how the US procured titanium for the SR-71 during the Cold War.
https://theaviationgeekclub.com/in-1960s-russia-sold-titaniu...
https://nationalinterest.org/blog/buzz/titanium-russia-was-s...
by toomuchtodo - 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 restrictionsby culi
- Some deleted it (again?). But the comments of Liang could be found somewhere else.
[1] https://aiproem.substack.com/p/must-read-deepseek-liang-wenf...
by sinuhe69 - > Objectively speaking, if I can spend two billion this year, it would indicate that our procurement department has achieved outstanding performance. The main gap between us and the United States lies in resources, while the disparity in personnel is minimal—there is virtually no difference, as we are essentially the same team of people, possibly from China.
> With the largest models available today, we simply cannot afford to train them
It seems they're largely talking about literally purchasing NVIDIA H200 chips. Important context is that Trump first started the trade war with China largely focusing on banning anything that could improve the Chinese domestic semiconductor industry. It was a blatant attempt to prevent China from progressing up the value chain to high tech. China's response is the reason they went from a miniscule player in EVs to the world's largest manufacturer (same for other high tech industries like LIDAR, solar, etc). In his second term, Trump blocked NVIDIA from selling chips to China. China again responded with astounding progress on their domestic semiconductor industry which led to Trump backing down on the ban. However, China shocked everyone by banning their own companies from buying NVIDIA in order to support the domestic semiconductor industry. Obviously China is still years away from EUV but it now produces most of its own >14nm chips and is rapidly growing
by culi - Wow, that is fascinating, I didn't realize China was now blocking foreign chips, lol. It's not a definitive indicator, but I feel that doesn't bode well for US dominance in this area -- when your competitor thinks they'd be helping _you_ by using your resources, that's not great.
- Everything in this transcript reads so very different from what megalomaniacs in charge of Anthropic/OAI have to say
- Did you also read the transcripts of meetings of Anthropic/OpenAI's investors?
Maybe you should read its IPO Filing. Since the doc isn't available at the moment, may be try SpaceX's one to see how an official doc of a company of another "megalomaniac" looks like, especially the section "CAUTIONARY STATEMENT REGARDING FORWARD-LOOKING STATEMENTS"
https://www.sec.gov/Archives/edgar/data/1181412/000162828026...
by jryle70 - Not sure why people keep lumping OAI and Anthropic together. Really, Anthropic are the evil ones. You can make the case OAI are evil too if you want, but Anthropic are very clearly significantly worse and they aren't even in the same ballpark.
Notice how OAI signed the recent open-source/open-weights letter with all of the other big tech companies, but Anthropic are the only ones who didn't? Notice how their employees are getting huge heat on X for dropping gems like this: https://x.com/Mononofu/status/2080937562739531837
This is how their brains work. They think everyone in the world except them are stupid and gullible, will fall for their incessant lying, gas-lighting and fearmongering, and can't be trusted with AI. They believe that only they deserve the keys to the AI castle. They've created a literal cult out of their culture while their employees are serving as useful idiot ideologues for the execs who are power and wealth hungry.
They've also just increased their political spending from 20mil to 40mil - and that's just what's on the books.
by nullbio - The repository was force-pushed so the link doesn't work anymore, but the file is still available at: https://github.com/demo-zexuan/liang-wenfeng-investor-meetin...by progval
- Thank you. These documents are a particularly valuable insight into the kind of thinking going on at DeepSeek. The part about how inference should be priced at a level that's enough to return capex in 10 months, but no higher, is really interesting. Liang Wenfeng simply has different motivations than we're used to over here in the West.by trollbridge
- 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 also want to go down that route? Is having the absolute frontier really that important, given that the performance difference is just transient and costly?
by nsoonhui - They want to achieve AGI first because, once it is achieved, no one knows what the world will look like.by kburman
- there's a lot of propganda from these state backed enterprises. I think the fraction of the cost label is debatable given the evidence of mass gpu smuggling through third parties like Singapore which China can't exactly openly admit to. Unless of course we're talking about distilling, which is probably a lot cheaper than training a model from scratch (there's also the fact that labour is still relatively cheap in China compared to the US which may or may not matter e.g. Anthropic claim against Alibaba > The campaign allegedly used nearly 25,000 fraudulent accounts to run 28.8 million exchanges with Claude between April and June 2026 (although their campaign could have been in part or all automated via agents, not sure)by testaburger
- My understanding after reading Liang’s comments during the investment meeting is that Liang firmly believes in AGI and he bets everything to reach goal. Once it reaches AGI, the game would flip totally. How he didn’t paint it out, and with the potential severe impact on the labor and consumer market, the true economic impact is difficult to predict. Liang is more like religious about this goal.
He also admits that it’s still a long way to it and along the way you have to recoup some money, too. But that is not their main motive, because focus too much on this short term goal will lower their probability of AGI success and it’s trivial to what AGI can bring. Liang stressed on restraining and emphasized that it’s part of their culture.
Thus, they continue invest in AI because they believe in breakthrough and not just being better.
by sinuhe69 - The paper discusses this, and is refreshingly honest. They do not expect nor aim to be a top player. They're not aiming for a path to world domination, but a path forward to continuing to play their part in pursuing the development and advancement of LLMs - nothing more, and nothing less. They mention that commercialization is, at best, a distant goal. Given DeepSeek already is commercialized, I assume that refers more to commercialization in the sense of making substantial profits and the like.
It's probably the same mindset that enables them to just cancel fund raising in response to the leak.
- Deepseek is funded by their hedge fund, high flyer. They intentionally cap their token prices to basically recoup server costs. The meeting transcript describes it as a moral commitment, that they don’t care about trends like image and video generation, and world model “hype”. They only care about reasoning, chain of thought and continuous learning.by janalsncm
- U.S. policymakers believe that even if the gap is small—like six months to a year—whoever reaches AGI first (whatever that means) could gain such an overwhelming advantage over their perceived adversary that it would effectively kneecap them. (You can look at the kinds of things they mention—cyber, WMDs—to get a sense of what they mean.) Jensen Huang disagrees and has said AI is a marathon.by WiSaGaN
- Article grabbed at random that provides some more context (tho could use more):
https://www.cyberkendra.com/2026/07/deepseek-pauses-fundrais...
"The Hangzhou AI lab has told prospective investors in its second fundraising round that it is suspending the deal, people familiar with the matter told Bloomberg on Saturday, days after remarks attributed to founder Liang Wenfeng about US-China AI competition circulated widely online."
And:
"Tencent's technology outlet published a 118-item version covering AGI strategy, chip supply, pricing, and retention. In it, Liang reportedly framed China's disadvantage as an arithmetic problem rather than a talent one: "The biggest gap between us and the US is in resources.""
"The specifics were unusually candid. Liang is said to have told investors he needed 200,000 Huawei 950 chips to train a frontier model but received 16,000, adding that "Huawei's problem is still insufficient capacity" and expecting the crunch to last at least three years. He also floated narrowing the gap with US labs to three to six months using a fraction of their computing."
- this bodes well for continuing to refine smaller models and open sourcing them.
There's a delusion that what America's AI companies are doing is "best"; the chinese should realize that the forefront is bloated and there's likely hundreds of speed ups viable. Pushing open weights will continue to grind down the bloat.
by cyanydeez - And that's why OpenAI bought all the future ram contracts
- I wonder if it would be viable for the Chinese to pursue a huge buildout of less sophisticated logic fabs. My experience with GPGPU is that it seems to intensely skew towards memory bandwidth, and has a much lower compute intensity than graphics (by which I mean rasterization and shaders).
This seems to be holding true for AI as well. I'm sure if you have an excess of compute and a dearth of bandwidth, you can trade the former for the latter, but still, this is a fundamenta property.
A lot of talk has been said about how companies are doing 'financial tricks' to extend the useful life of GPUs by showing lower depreciation - but what if these are not tricks at all - new GPUs don't really have that much more bandwidth, and while they might be clever in some other ways, they are limited in how much they can improve fundamentals.
This has been reflected in how memory vendors' stock price has exploded, but NVIDIA stayed stagnant.
Since the Chinese are far closer to the US in building SOTA memory chips, it's possible that their disadvantages are far overstated.
by torginus - I think the way to parse the current title "DeepSeek pause fundraise after comments on compute gap to US leaked (transcript) [pdf]" is that there was a leak that DeepSeek will pause fundraising because they perceive there is a compute gap with the US.
I am also guessing that the majority of the people who read this title will think that DeepSeek is pausing this fundraising because some comments they made about the compute gap were leaked. That is not the case.
by credit_guy