So... is this literally a... umm, sorry, I'm just genuinely (really, no sarcasm intended) which terminology to use... finetune of DeepSeek V4-Pro or post-trained version of DeepSeek V4-Pro Base? Because I haven't fully dived into the tech report (so I may update my opinion as well as my comment), but this far the architectural solutions seem to be largely similar to DeepSeek ones.
Maybe I'm wrong, but that's just the first impression.
EDIT: I take my words back (which happens rarely) - although they do build upon DeepSeek's work, their contribution far exceeds merely post-training the base model in a different way. They did introduce something new to the architecture, though I still can't find the full tech report, with Hugging Face and GitHub links returning 404 right now.
EDIT-2: Now when I think about it, I'm not quite sure if they're going to release in the open the full report with methodology, as well as the model weights, at all.
> The training and deployment of LongCat-2.0 are built on large-scale clusters of tens of thousands of AI ASIC superpods. Compared to the mature Nvidia GPU ecosystem, the supporting software community is still less developed. We have therefore put significant effort into building a stable, secure, and scalable infrastructure.
huh? who knows what they did, it's not like any of it is audited. it sounds like they started with deepseek v4 pro, and made a bunch of random changes to it, and called the parts of it different things?
> If you could run a nuclear reactor with U-235 as fuel or Pu-241 (both mixed with 95% U-238), which one would you choose and why?
For a human this would not be tricky at all. For an LLM it could be, because this question certainly does not exist in any sort of training, because Pu-241 does not exist in pure form, it only exist as a minor component of reactor-grade plutonium, where Pu-239 would dominate, with Pu-240 coming second and Pu-241 coming third.
In any case, LongCat-2.0. gave a very well reason but incorrect answer that Pu-241 is preferable.
I then tested on Qwen 3.7 Plus, and it correctly answered that U-235 is preferable because of its much higher delayed neutron fraction. I then went to Gemini Flash, which answered the same, with much more confidence, and with much stronger arguments, and the speed of the answer was much higher.
Overall I rate Gemini Flash the best, Qwen 3.7 Plus an acceptable second, and LongCat-2.0 an ok'ish third, if you have nothing better.
"Choose U-235 if the goal is safe, boring, practical electricity generation.
Choose Pu-241 only if the goal is specifically to consume/recycle plutonium in a reactor designed and licensed for that fuel.
In brutal shorthand: Pu-241 is a better “fissile isotope” in some nuclear-physics ways, but U-235 is a much better reactor fuel in the real world."
If only I knew anything about nuclear reactors. But it sounds to me that the answer is also correct.
I asked a question with "Search" enabled, with the app set to English, and got results back in Chinese. Interesting view into how the LLM responds to its context.
The bad ass “resume” of the founder - sounds like the Chinese guy from the Silicon Valley tv show (who ends up ruling the world from somewhere in the jungle):
Wang Xing (Chinese: 王兴; born 18 February 1979) is a Chinese businessman, who co-founded Meituan and has been serving as chief executive officer of Meituan since January 2010. He previously served as chief executive officer of Fanfou from 2007 to 2010.
I'm sure it also takes more compute effort to be at the frontier, rather than being able to distill and poach ideas from the frontier. No mistake that it's the same handful of labs taking turns at or near the frontier.
Lets wait for them to open source it. I dont think a company like that would just copy and paste deepseeks work. Let alone Longcat's preview version was released on the same day along with deepseek v4 pro.
> Both the full training run and the large-scale deployment are built entirely on AI ASIC superpods. Pretraining spans millions of accelerator-days across more than 35 trillion tokens,
To think that Nvidia would not have any competition is quite laughable and Jensen knew that China would catch up.
This is the reason why restricting GPUs as a temporary blockade does not work and they would just make all the Chinese AI labs find clever workarounds to serve AI compute as cheap as possible, including building their own hardware.
Like Bitcoin has done with ASICs, AI will soon need them for training and inference (TPUs are also ASICs) and Jensen knew this by buying Groq.
Today is not a good day if you are Anthropic or OpenAI.
US restriction on China is not just GPU, but total blockade of anything semi related all the way from fab equipment to final chips. Same restriction will work on any other country. But it does not work on China. Not only there are astonishing number of crazy smart AI researchers in China, but also the entire semi supply chain, from fab machines to GPU/XPU chips and software ecosystems, is advancing extremely rapidly. China will be the only country where every step of the AI supply chain from materials, fab equipment, lithography, 7nm to 3nm logic fabs, HBM, packaging, photonics, GPU/CPU/XPU, software ecosystem, frontier AI labs, power production and power generation equipment are all within a single country.
I would love to see a 1.6T total with something like 3B active. I'm running an M4 Max and I'm still heavily bandwidth-limited -- I can hardly run anything at speed!
The N-gram embedding model thing is absolutely crazy. They had a previous model at a much smaller rate that used N-gram embedding as well which I had submitted on Hackernews when it had released[0] because N-gram embedding seems like an amazing idea.
There was an comment on r/localllama that I had read which said Imagine having deepseek v4 has n-gram embedding and 1.3 (ternary) or 1 bit model combined, it was when deepseek v4 hadn't released.
I think that there is a lot of research and proof's being released. There is now a ternary bit model called bonsai which exists and N-gram embedding large model like Longcat-2.0 existing as well. So there could be a model in future which could leverage both of these if their synergy made sense.
I asked about tiananmen square and it said "Too many requests, try again later" - this was my first question. I understand this is one data point but still ;/
34 comments
[ 3.1 ms ] story [ 57.0 ms ] threadMaybe I'm wrong, but that's just the first impression.
EDIT: I take my words back (which happens rarely) - although they do build upon DeepSeek's work, their contribution far exceeds merely post-training the base model in a different way. They did introduce something new to the architecture, though I still can't find the full tech report, with Hugging Face and GitHub links returning 404 right now.
EDIT-2: Now when I think about it, I'm not quite sure if they're going to release in the open the full report with methodology, as well as the model weights, at all.
This is the real news story. It looks like they may have used Huawei Ascend 910C chips: https://nitter.net/teortaxesTex/status/2071708141037781407#m
In any case, LongCat-2.0. gave a very well reason but incorrect answer that Pu-241 is preferable.
I then tested on Qwen 3.7 Plus, and it correctly answered that U-235 is preferable because of its much higher delayed neutron fraction. I then went to Gemini Flash, which answered the same, with much more confidence, and with much stronger arguments, and the speed of the answer was much higher.
Overall I rate Gemini Flash the best, Qwen 3.7 Plus an acceptable second, and LongCat-2.0 an ok'ish third, if you have nothing better.
"Choose U-235 if the goal is safe, boring, practical electricity generation. Choose Pu-241 only if the goal is specifically to consume/recycle plutonium in a reactor designed and licensed for that fuel.
In brutal shorthand: Pu-241 is a better “fissile isotope” in some nuclear-physics ways, but U-235 is a much better reactor fuel in the real world."
If only I knew anything about nuclear reactors. But it sounds to me that the answer is also correct.
Which humans have you been hanging out with? :-D
I could not make sense of the question at all, and I have a PhD in Computer Science and decades of SWE experience :-D :-D
A bonus would be tok/s on common hardware.
https://en.wikipedia.org/wiki/Wang_Xing
Wang Xing (Chinese: 王兴; born 18 February 1979) is a Chinese businessman, who co-founded Meituan and has been serving as chief executive officer of Meituan since January 2010. He previously served as chief executive officer of Fanfou from 2007 to 2010.
That is a tiny tiny system. OpenAI uses _milions_ of GPUs for training
On the other hand, this probably reuses the existing deepseek v4 architecture and weights. Maybe didn't need that much compute.
To think that Nvidia would not have any competition is quite laughable and Jensen knew that China would catch up.
This is the reason why restricting GPUs as a temporary blockade does not work and they would just make all the Chinese AI labs find clever workarounds to serve AI compute as cheap as possible, including building their own hardware.
Like Bitcoin has done with ASICs, AI will soon need them for training and inference (TPUs are also ASICs) and Jensen knew this by buying Groq.
Today is not a good day if you are Anthropic or OpenAI.
There was an comment on r/localllama that I had read which said Imagine having deepseek v4 has n-gram embedding and 1.3 (ternary) or 1 bit model combined, it was when deepseek v4 hadn't released.
I think that there is a lot of research and proof's being released. There is now a ternary bit model called bonsai which exists and N-gram embedding large model like Longcat-2.0 existing as well. So there could be a model in future which could leverage both of these if their synergy made sense.
[0]: https://news.ycombinator.com/item?id=46803687
And those aiming to fit with Q2 or Q1. It's not even worth it to destroy the models to claim it's still alive after cutting all the limbs.
Response: Hello, I can't answer this question at the moment. Let's switch topics and chat about something else.
:-D