> Compared with our initial baseline on the same hardware, we achieved a 3× improvement in end-to-end serving performance, reaching hardware efficiency and per-token cost comparable to mainstream NVIDIA GPUs. This demonstrates that Chinese chips can support frontier-model inference efficiently and economically at scale.
The other part is that it’s a bit of a meme here to say that the chip restriction is actually helping China (or shall I say, coordinated effort?). For once, we know that China has put a lot of pressure on the US to relax these controls multiple times. In addition to large chip smuggling networks (e.g. 22% of NVidia’s worldwide revenue magically comes from Singapore, and the ratio has been growing).
Lastly, assuming acceleration in AI (which we ARE seeing), there might not be time to China to catch up. The best estimate right now is that the first EUV chips from China will not come out before 2030. By that time who knows how powerful AI will be.
All I’m saying is that the discussion is so one sided and a bit baselesss with no nuance, that it seems either a meme/groupthink in the community or coordinated. If anything, the data suggests that the US should increase its export controls and better track the tech supply chain if it wants to further curb Chinese progress.
Or, to put it bluntly, you are a proponent of the trade war.
Winners: Huawei, SMIC, CXMT,Chinese ASML-competitors, OpenAI, Anthropic, Amazon, Microsoft, Google, Meta.
Losers: Chinese AI labs, Nvidia, AMD, TSMC, Micron, SK Hynix, Samsung, Intel.
Any company that depends on Nvidia hardware such as OpenAI, Anthropic, AWS are winners. It means less competition for Nvidia chips and services. If you think Nvidia chips are expensive now, imagine if Chinese companies can buy them freely. Also for American AI labs, it also means they can stay ahead of Chinese AI labs in compute capacity.
The American hardware makers lost the lobby fight in Washington.
In the short term maybe yes, in the long term, maybe they are the winners, they can build on top of cheap inference stack and eventually win on pricing
I don't think your information is entirely accurate.
Just logic.
In addition to Huawei who make the Ascend series that Ziphu are using, there are also at least a half dozen or so other Chinese companies also making their own AI accelerators.
Almost everyone knew that these sanctions would backfire within a few years. You can't really put sanctions that have noticeable negative effects on bigger economies. They only work for small to medium economies. I believe sanctions on any economy in top 10 would fail.
So until China solves the ASML problem, there won't be any flooding.
Which they are in progress on: https://www.reuters.com/world/china/how-china-built-its-manh...
A bit late for that now that they are moving into early production with their own.
Nvidia are likely more concerned about AMD taking market share, and I suspect that geopolitics will leave US/China GPUs with largely non overlpping customer bases.
Brazil is one to watch if they manage to achieve political stability, as is Nigeria if it can transition from being a petrostate.
So I think it can be summarized as “it all depends” and “skill issue.”
Seems to work for them.
You seem to misunderstand what Protectionism is. This is not an example of it not working. If anything, it is any example of it working. Because Protectionism is about protecting your industry from foreign competition - exactly what China decided to do.
Now, the US is left out in the cold with little influence left, themselves now the ones with an anti-missile shortage.
You can’t really hurt a country that has a culture with a positive attitude toward growth.
china has cheap abundant power, now they can make their own inference chips (which was supposed to be a chokepoint), their models yeah can be 6 months behind the frontier - but most people don't need frontier models - small models r more than enough.
my only wish was labs like Mistral would make their own inference chips or partner up eg with established / new chip makers or companies like Oxide.
GLM has in the past been more technical rather than speculation about future development on RSI etc.
Also curious whether those 100k accelerators are entirely locally made. If that's genuinely end to end on all components including lithography, memory, design etc then that is quite a feat.
Just like the rest of the world, including the US (Intel, Micron), SMIC are currently using ASML lithography equipment (DUV, not EUV), but Shanghai Aishengna are now moving into early production with their own DUV machines, with SMIC and CXMT as early customers.
There is also a state sponsored Chinese EUV development underway.
Also, Deepseek V4 Flash can be run relatively well in hybrid 2-bit quantization on 128gb devices, with way better results than you'd expect for a typical 2-bit quant.
Those are currently the 'smartest' options for that memory level.
"We implemented a series of aggressive memory optimizations, including..."
> Why would I pick GLM over Claude?
GLM: 80 USD (pro), 168 USD (max) -> with "limited-time event" discount this becomes 56 USD and 117.6 USD
I also don't understand why are they so much costlier, and I would also like to give it a try.
Anthropic's Pro is $20 and corresponds to Z.ai's Lite at $18
Anthropic's 5x Max is $100 and corresponds to Z.ai's Pro at $80
Anthropic's 20x Max is $200 and corresponds to Z.ai's Max at $168
I have both plans. Claude monthly €20 and Z’s €18 monthly. Running GLM-5.3 high on their monthly plan will hit quotas absurdly fast compared to Opus 5 High on Claude code. It’s almost unusable for AI driven development. I ended up using the Z plan for using GLM-5.3 as a detailed security reviewer and adversarial feedback. For that, it is much better than Opus which will flag and bail out for even simple security tasks that are aimed at defense.
But, it was enough for a customer like me who tried them at good faith to walk away and find their competitors..
I like the diversity of LLMs as of today and prefer to not tie myself to one big plan with any vendor. If they don’t prefer me as a customer, then I will accept that, and move away.
GLM's "Max" plan is (was?) equivalent to 3x Claude's 20x ($200) plan.
And at the same time you have pretty strict limits to your usage, so in many cases you can't even let it work all night, as you will reach your limit faster than that.
Because they don’t have to. Most of the time money would buy you newest and/or more hardwares so there’s low/minimal interest to optimize the code or approach.
Creators of known unreliable programs be surprised their programs are unreliable.
But what really kills me is the idea that these companies are using Python for production inference. I mean really? Have you seen how bloated and slow Python is? Do global locks really sound like a strategy for fast dynamic computation?
If you look at how many years the whole NVIDIA and CUDA ecosystem has been evolving, it's certainly impressive how they've just stood up and optimized this CUDA-free 100,000 node cluster in just a few months.
All I can recall reading from OpenAI about what they have actually done in the name of "RSI" is using one of their models to help automate the training process.
One drive gives about 2 tok/s; striped across four drives it reaches 3.5 tok/s with byte-identical output, and our best internal build with a not-yet-published patch does 4.2.
Method and numbers: https://github.com/argonautlabsai/argodrive (built on antirez/ds4).
Ironically, many benchmarks being maxxed out, and quite quickly, so new ones have to be created.
> Do you have anything that proves this one way or another that isn't based on vibes or shoddy benchmarks?
They clearly aren’t talking about RSI here, but that model development has stalled in general.
TL;DR Buckmaster and Alpöge haven't solved Navier-Stokes blow up.
How information can get so distorted when it's trivial to fact check?
Some call this "The singularity" (e.g. Hinton).
This is actually a core danger postulated by the, let's call it, "worrying" scenario - see AI 2027 (to be clear, I think its timeline is not realistic).
> Statements dreamed up by the utterly deranged.
Evidently, and tragically, it will take catastrophes to show that deranged are the ones deriding the worried crowd.
They must have hit really hard scaling limits if the prices were hiked so much so quickly.
Can I ask where are you using all those tokens?
I now exclusively use https://omp.sh/ as my harness:
I set it up so it never works in the main branch so subagents etc don't step on each others toes, and only merges back when complete: https://github.com/Daviey/mario/blob/main/.omp/hooks/pre/wor...
A good AGENTS.md is essential: https://github.com/Daviey/mario/blob/main/AGENTS.md
I then provide specifications for what I want, making sure it is unit tested.
The max plan will provide ~1,100 USD of GLM-5.3 or ~260 USD of GLM-5.3-flash per month for 168 USD. I can personally attest to these numbers through omp (~97% cache hit rate).
Unless you are able to highly parallelize (your work, you won't be able to hit your hourly or weekly quota using the flash model simply because it's so slow.
They give you ~3x more flash tokens, which maybe comes out to ~2x more actual work after accounting for the extra thinking it does to achieve the same result. The mental model, for not getting angry, is 5.3 is fast mode by default, and you can disable fast mode for 2x the work output at 1/3-1/10th the speed.
They're serving me 5.3 at ~40 tok/s and 5.3-flash at 30 tok/s (according to omp).
That is shocking. Is it per-token I wonder?
I’m getting 97%.
Also why Meta gets a +1, just charge less money on the training path.
If you factor that in, then there are clearly different tiers: one you can trust, and one that may well just be saying that to increase market share with little reputational or legal consequences if they are found to be lying.
These are not equal.
To be fair, none of us are sure of anything and I think that’s the part that’s most irritating
Very good - but I'm on a legacy plan. And coming up on a renewal that would put me on the watered down current plan. But with 50% legacy discount think it may be worthwhile. If I go to a competitor I'd be paying market rate.
>They must have hit really hard scaling limits if the prices were hiked so much so quickly.
Not really scaling - their plans were initially comically subsidized even more so than what the western providers are doing. More advert for an upstart than commercially priced.
And you can bet GLM is still ridiculously subsidized, just not as ridiculously as Anthropic and OpenAI.
For [API usage](https://openrouter.ai/z-ai/glm-5.3-flash#providers) they charge a bit more than the very cheapest providers of GLM-5.3-Flash, but not so much that a big price difference would make sense.
Honestly, I have no idea what z.ai is either (I'm aware of an AI-enabled editor called Zed, but that's under zed.dev), so it's a bit presumptuous from them to assume that everyone is familiar with their product...
Appreciate the clarification. For me it was the "F" in "WTF" that tipped me. Other than that, it's more than fair for you to not know what GLM is. Things are moving so fast that I would be surprised if anyone can keep track of it all. Cheers, have a grand day!
Also I feel like the obvious way to read the very first sentence is that GLM is a language model
> As we develop GLM, the model sometimes exhibits capabilities that surprise us
Why would you be reading their corporate blog posts if you don't even know who they are?!
Where GLM-5.3-Flash is the newest "small / fast" model.
Come on now
Also, why would they introduce themselves on their own blog?
No real reason to respect any terms they might want to impose. Besides, if you want to break TOS, just have an agent do it; "everyone" running these things agrees there's no corporate or moral liability for what your AI does.
In the PRC, they[1] leaked tons of national secrets on the PRC's latest AI campaigns, the inner workings of their "opinion monitoring" (read: performative panopticon) and "stability" (read: violent oppression) departments, Chengdu's whole CCTV network, direct-energy weapons plans, espionage activities in Syria to hunt down Uyghur refugees, and god knows what else that Anthropic didn't divulge to us common folk.
In the US, it's very clearly an attempt to rip off a competitor. I'm not sure how else you could possibly see it. Even if you're a distillation fan in general (which A. why and B. plz don't), they did this through a network of Japanese and Signaporean shell accounts, presumably at least some of which were abusing Anthropic's subscription service in a ToS double-whammy, as it would be exorbitantly expensive otherwise. They also had to hack around Anthropic's API to get CoT traces, which seems impossible to explain away as anything innocent.
I've been beating the "China isn't necessarily an enemy, it's gonna take us all to handle AI" drum for literally years, but this attack was just... gross. Gross in scale and gross in arrogance. Not a good sign for the dawning alignment crisis, to say the least :(
TL;DR: Use these services if you want, but know that you're supporting aggressive escalations and companies that very clearly don't give a flying fuck about violating the law, much less your ToS. So... buyer beware, I guess.
[1]: For clarity, Z.ai was not alone in this, nor were they most egregious attack -- Moonshot.ai (kimi) took that coveted prize. DeepSeek was involved, too.
> use these services if you want, but know that you're supporting aggressive escalations and companies that very clearly don't give a flying fuck about violating the law, much less your ToS
From my European point of view the same risk/concerns apply when using US providers
Alignment is meaningless; as you've noticed, humans aren't all that "morally aligned".
If the tool needs safety measures it should be kept in a safe enclosure like we do with CNC machines, furnaces, and so on.
The Antrophic article mentions "16 million" conversations, GLM models are in the 700-300 billion parameter ranges and while the frontier sizes aren't know but Gemini suggests Astra and Mythos are at around 10 trillion. That'd amount to extracting 40k parameters per conversation without a lot of errors if it was just a distillation (from an unknown source/algorithm as opposed to distilling your own model).
Now, I can imagine these conversations being used as a verification step that they're not missing stuff in their training, and that their models are capable of most of the same things, but that's mostly confirming that they've stolen the same data from the public as Antrophic/OpenAI has stolen already.
Or am I missing something here that makes real "distillation" feasible?
I mean, if they get to distill other's IP, why can't others distill their IP?
I wonder how you imagine that China built their own space station? Reliant on using American made duct tape, perhaps?
Do you realize how reasoning models are being trained nowadays? You design/build simulation environments to run agents in, with the environment providing the RLVR "verification" scoring. So why won't Ziphu use GLM to build their own RL training environments? Do you think they are not doing this?