China Tech Talk

I had a chance to go over Tau Scaling and reusable rocket with Lei. As Lei is the person who shares my point of view the closest on Tau Scaling and Huawei’s efforts on this front, we really spent a lot of time explaining why what Huawei did in their design and production is quite special.
Even if transistor cell itself can continue to scale smaller, that does not apply to memory cell sizes or analog components like copper connections and such.
This episode was recorded before I went to China. Now that I’m back and Kirin 9050 has been unveiled, it’s a good time to publish my discussions on it.


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I am currently in Beijing and have the pleasure of getting together with some friends in Tsinghua University in the AI school. I was welcomed by Alex and Qian Yu. We sat down talked a lot about the academia, AI research in China, getting talent to come to China and future of AI both in LLM and embodied AI.
It’s quite the interesting experience to come here and see Zai in one corner, Unigroup building in another corner, the school for automation (using unreal new technology), Galbot robot booth, Infinigence, Vidu AI, Robotera and many other AI and semi startups.
Pay attention to the tips they have about the transitioning toward Ascend from Nvidia chips by researchers. Also, check the part about researchers in China finally finding Kimi K3 to be good enough for vibe coding compared to GPT and Claude. There are still some concerns about inference sped for Kimi. I think they need to get more compute.


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I was fortunate to be invited by Angelica Oung last week to talk about China’s AI progress in light of Kimi K3, DUVi lithography new, CXMT IPO and the KOSPI market crash.
Things have changed a little bit since the podcast, but CXMT remains very important, K3 remains the top Chinese model and lithography is still a big story.


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My quick thoughts on DSV4-Flash: Huge improvement over the original. It took 3 to 4 months, but we essentially got something close to GLM-5.2 in software engineering tasks.
I did some testing today on VS Code using cline plugin. DSv4-Flash is essentially free, so I’m going to keep it as a backup to GLM & Kimi’s latest models, since those coding plans are going to get used up quickly.
Going forward, I see 2 paths for open src/weight models:
* Smaller models that can run locally with some souped up hardware.
* Larger models that can need SuperNode to run and can compete with closed source flagship models.
All the developers are going to want to run these smaller LLMs locally. You don’t have to worry about AI company getting your data and can fine tune them anyway you want. This is going to drive an acceleration in “souped up” local hardware. Very bullish in my opinion for memory chips.
For the larger models, I think there really isn’t a lot of room for middle ground. You need to make them larger to compete fully with the Anthropic and OpenAI models.


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I’m staring a new series of what I call quick takes. These will be shorter (15-20 minutes) than scheduled podcast. They will be recorded whenever something interesting comes up in the tech world and I don’t want to wait to talk to someone about it.
So the first thing I did here is Kimi K3 and GLM-5.2 and what this tells us about China’s AI development. I think the overall story I want to put out there is the self improvement part of it and also models likely expanding over time and improved training data and flywheel with the Chinese labs.
By this point next year, we could very well (imo) see 5T+ parameter models coming out of China.


This is a public episode. If you would like to discuss this with other subscribers or get access to bonus episodes, visit tphuang.substack.com