Comment by culi | Hacker News Reader

Comment by culi | Hacker News Reader

> 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

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.

They've achieved self-sufficiency in >14nm chips in remarkable timing. Unfortunately for DeepSeek, it's the <14nm chips that are needed for massive training tasks. I wouldn't be surprised if China backs down and lets them purchase the chips given that they are still years away from being able to make them themselves. Either that or the gov't steps in and forces them to share resources

And even if Huawei's Ascend 910C can compete with NVIDIA's H200, CUDA is still a large moat

Bypassing the CUDA moat is, in fact, one of the major tasks Deepseek set for itself. Their efforts in this area are likely one of the main reasons for their slow release cadence, culminating in their v4 inference setup that runs on Huawei Ascend chips.

there are already two independent drop in cuda emulators. zluda is open source with extra features like game physics, scale is production ready and focused on inference workloads. both of them can run pytorch.

and for a company full of brilliant engineers with zero respect for american law its not that hard to make one more by themselves.

China is already making it's own chips. The one most commonly discussed is the Huawei Ascend 950 (which exists and is in production). NVIDIA say the 950 is about equivalent to an NVIDIA H100, and DeepSeek in this leaked meeting are saying that about 4 950 cards are equivalent to 1 NVIDIA "B" one (Blackwell B200 I think).

Apparently production is constrained though, with DeepSeek saying they were only able to get an allocation of 16,000 950 cards. They have plenty of money, but are limited by the number of cards available to buy, and therefore the size of models they can train. They mentioned having a 20,000 GPU cluster. Other companies like Kimi, with a ~3T param model, clearly have a lot more compute, probably all NVIDIA.

DeepSeek have their own "TileLang" software, which sounds a bit like the Triton kernel compiler, and isolates them from the diffences between NVIDIA and Huawei chips - they are deliberately avoiding any CUDA dependency.

I don't know that it's clear that the motivation is that it's "helping" their competitors directly. Maybe the motivation is "if we rely on these, then the next time a US president arbitrarily decides to block us from buying them, we won't have the infrastructure already in place to be able to work around it". It seems more betting on a shorter-term cost with less uncertainty in the long term rather than a shorter-term win with a lot harder to quantify risks in the long term.

This was the Chinese government burning the boats[0]. Beyond the symbolism and ensuring everyone's commitment, they want to direct the firehose of AI money towards a local champion (Huawei). Money, and experience bourne of being in the trenches developing, manufacturing, deploying and debugging on actual workloads will supercharge how quickly Huawei get good, compared to when they had to fairly compete against a well-resourced Nvidia

0. Though it's not absolute - there's still the Singapore-based clusters loop-hole, that the Chinese government may choose the degree to which it turns a blind eye to, if progress is slow.

The money and resources aren't going to Huawei exclusively, but to a whole range of companies. This selection changes over time as new companies show promise, or previously selected companies prove themselves to be incompetent.

For example there are like 6 different technical tracks of EUV development. They're not committing to one technical direction, it's exploring all of them.

I think the point is more than by banning the NVIDIA hardware they are forcing local development of potentially competitive hardware, basically giving Huawei a subsidy or leg-up.

It's notable because when Trump first banned the chips, it massively impacted China's economy. Now China is the one voluntarily not buying them

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