DeepSeek CEO Addresses China's AI Compute Shortfall
Leaked investor call reveals views on US-China AI competition and hardware needs.
In a leaked transcript of an investor meeting, DeepSeek CEO Liang Wenfeng identified limited access to advanced compute as China's main shortfall versus the US in AI development. He claimed four Huawei Ascend chips could match one Nvidia GB300 and dismissed notions of Nvidia's software superiority. The remarks underscore hardware constraints from export controls while noting the growth of a domestic ecosystem. The statements were shared via social media and an archived PDF transcript, confirming focus on raw compute scaling over software edges.
Combined views
6.5K
2 posts, first seen 4h ago
Reactions from ranked influencers
2 posts
interesting how no policy guys found it interesting that Wenfeng dismisses any notion of Nvidia's software advantage or limits to, say, "Mythos class models". Just "4 Ascends for 1 GB300". He won, on his own terms. He stepped onto the frontier, and see: an ecosystem was born.
DeepSeek CEO Liang Wenfeng's leaked call has several notable statements on U.S.-China competition. He acknowledges China is severely compute constrained, says DeepSeek still much prefers NVIDIA over Chinese chips, and implies they're actively buying “non-compliant” chips at a premium if necessary. Some highlights (note this is based on a transcript, the original audio hasn't surfaced): How much compute they have. Liang [01:15:12]: "What is our gap with the US? Really the gap is just one thing: resources. We don't have that many cards — our card count is still fairly small. We currently have about 20,000 cards of H-equivalent compute, and most of it just arrived — in the last one or two months — with many machines possibly still on the way. Our total compute last year was fairly small; this year we are expanding it very aggressively." For reference, leading US AI labs like Anthropic and OpenAI have access to millions of H100e. They're still trying to buy NVIDIA rather than Chinese AI chips. [01:15:12]: "Over the coming months we'll buy machines in large batches — basically all NVIDIA. How many cards do we need? Right now, the more the better. Our strategy is: at a reasonable price, however many cards we can buy, we buy. If I spent all the money within half a year, I'd consider that a good thing. In practice, spending that much money is very hard — you can't buy that many cards, they're hard to get, and prices are high." How they buy NVIDIA under export controls. [01:26:41]: "Our only worry is not being able to buy that many cards. If we could turn all the money into cards, we would not hesitate to turn all of it into cards — and we're willing to pay a certain premium for that." [01:56:36]: "In a normal commercial environment where I could buy NVIDIA cards, domestic substitution would be quite hard; but with NVIDIA cards unbuyable, everyone has no choice but to work on domestic chips." And [02:53:59]: "对我们来讲，我们可以买一些不合规的卡" — "As for us, we can buy some non-compliant cards." On Huawei chips [02:53:59]: "Our purpose in buying Huawei 950s is really to help Huawei get the ecosystem right. 16,000 Huawei 950s are equivalent to only 4,000 B-series cards — not a big quantity, not very significant. Not enough to train a next-generation model; only enough to train our current generation." But elsewhere he sounds more optimistic [01:56:36]: "Huawei's 950 supernode can fully substitute for NVIDIA's GB200/GB300 in performance and price. Everything a GB300 can do, the Huawei supernode can do, with the same latency. The only cost: four Huawei cards equal one NVIDIA card, plus a two-year lag." Sounds like both a quality and production quantity issue. His assessment of the U.S-China gap. [02:53:59]: "Simply put: two years behind the US, done with one-twentieth of the US's compute. That narrative is one to two years behind, on 1/20th of the compute. Going forward we want to rewrite that narrative — still a fraction of their compute, but shrink the gap to six months, three months. We might even surpass them in some specific areas." Why Chinese companies won't be able to catch up anytime soon. [02:53:59]: "But with total compute still an order of magnitude apart, comprehensively surpassing them is unrealistic." [01:26:41]: "To train a model as large [as the top US one], we'd need 50,000 GB300s — or with Huawei 950s, 200,000 cards. And that's training only, not counting research. Even if we spent the entire 50 billion [¥50B = $7.4B], we couldn't train it — even if we could stack the compute together, we couldn't afford to run it." [03:05:48]: "Right now we're certainly stuck on production capacity — this year, next year, the year after, probably still stuck on capacity — but five years out I'm fairly optimistic." Full transcript (Chinese, archived): https://web.archive.org/web/20260723150517/https://2aran.com/resources/liang-wenfeng-investor-meeting/liang-wenfeng-investor-meeting-transcript.pdf All quotes translated by 5.6 Sol.
DeepSeek CEO Liang Wenfeng's leaked call has several notable statements on U.S.-China competition. He acknowledges China is severely compute constrained, says DeepSeek still much prefers NVIDIA over Chinese chips, and implies they're actively buying “non-compliant” chips at a premium if necessary. Some highlights (note this is based on a transcript, the original audio hasn't surfaced): How much compute they have. Liang [01:15:12]: "What is our gap with the US? Really the gap is just one thing: resources. We don't have that many cards — our card count is still fairly small. We currently have about 20,000 cards of H-equivalent compute, and most of it just arrived — in the last one or two months — with many machines possibly still on the way. Our total compute last year was fairly small; this year we are expanding it very aggressively." For reference, leading US AI labs like Anthropic and OpenAI have access to millions of H100e. They're still trying to buy NVIDIA rather than Chinese AI chips. [01:15:12]: "Over the coming months we'll buy machines in large batches — basically all NVIDIA. How many cards do we need? Right now, the more the better. Our strategy is: at a reasonable price, however many cards we can buy, we buy. If I spent all the money within half a year, I'd consider that a good thing. In practice, spending that much money is very hard — you can't buy that many cards, they're hard to get, and prices are high." How they buy NVIDIA under export controls. [01:26:41]: "Our only worry is not being able to buy that many cards. If we could turn all the money into cards, we would not hesitate to turn all of it into cards — and we're willing to pay a certain premium for that." [01:56:36]: "In a normal commercial environment where I could buy NVIDIA cards, domestic substitution would be quite hard; but with NVIDIA cards unbuyable, everyone has no choice but to work on domestic chips." And [02:53:59]: "对我们来讲，我们可以买一些不合规的卡" — "As for us, we can buy some non-compliant cards." On Huawei chips [02:53:59]: "Our purpose in buying Huawei 950s is really to help Huawei get the ecosystem right. 16,000 Huawei 950s are equivalent to only 4,000 B-series cards — not a big quantity, not very significant. Not enough to train a next-generation model; only enough to train our current generation." But elsewhere he sounds more optimistic [01:56:36]: "Huawei's 950 supernode can fully substitute for NVIDIA's GB200/GB300 in performance and price. Everything a GB300 can do, the Huawei supernode can do, with the same latency. The only cost: four Huawei cards equal one NVIDIA card, plus a two-year lag." Sounds like both a quality and production quantity issue. His assessment of the U.S-China gap. [02:53:59]: "Simply put: two years behind the US, done with one-twentieth of the US's compute. That narrative is one to two years behind, on 1/20th of the compute. Going forward we want to rewrite that narrative — still a fraction of their compute, but shrink the gap to six months, three months. We might even surpass them in some specific areas." Why Chinese companies won't be able to catch up anytime soon. [02:53:59]: "But with total compute still an order of magnitude apart, comprehensively surpassing them is unrealistic." [01:26:41]: "To train a model as large [as the top US one], we'd need 50,000 GB300s — or with Huawei 950s, 200,000 cards. And that's training only, not counting research. Even if we spent the entire 50 billion [¥50B = $7.4B], we couldn't train it — even if we could stack the compute together, we couldn't afford to run it." [03:05:48]: "Right now we're certainly stuck on production capacity — this year, next year, the year after, probably still stuck on capacity — but five years out I'm fairly optimistic." Full transcript (Chinese, archived): https://web.archive.org/web/20260723150517/https://2aran.com/resources/liang-wenfeng-investor-meeting/liang-wenfeng-investor-meeting-transcript.pdf All quotes translated by 5.6 Sol.