We built a complete production-grade inference service from scratch on a cluster of more than 100,000 Chinese-made AI accelerators. All production inference for GLM-5.3-Flash runs on this system.
Zhipu has built a production-grade inference service for GLM-5.3-Flash, running on a cluster comprising over 100,000 domestic AI accelerators. The practical application of GLM-5.3-Flash has given rise to a fundamental feedback loop: the model contributes to optimizing the inference infrastructure, while the optimized system continues to serve the model.
Interesting that the tone of announcements between US and Chinese providers is converging.
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.
Necessity is the mother of invention. The shortsighted protections put on chips, etc., by the US has forced Chinese AI industry to adapt or die. Guess what their response to this fitness function has been? Kudos to Z.ai on their inventions and excellent write-up, which reads like humans wrote it.
> Today, GLM-5.3 has become an indispensable daily coding partner for everyone on the team, and it is moving steadily toward replacing us. If this trend continues, given enough compute and enough time, its endpoint is a system that can design and train its own successor entirely autonomously. This is known as Recursive Self-Improvement, or RSI.
You're tragically misinformed; it isn't. Several metrics are actually growing exponentially. But if you want emprical information, you can just have al look at the nature of the late AI incidents.
Ironically, many benchmarks being maxxed out, and quite quickly, so new ones have to be created.
It was not solved. ~OpenAI~ Buckmaster and Alpöge found one (or a few) singularities in the forced version of the Navier-Stokes equations. Then magically 2 weeks later OpenAI found them too. Again, I am not saying this is not a great feat. I am just saying that everyone should be a bit more careful when making statements about RSI.
You prove your own point no? You are asking for a benchmark to prove AGAINST ASI. Surely the burden of proof for such a scientific fiction concept should be the other way around.
> This is known as Recursive Self-Improvement, or RSI.
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.
I was gonna ask how people found their coding plans, and realized, have they massively ramped up the prices? Seems the middle plan is ~$80/month now, didn't that used to be like $20/month? Cheapest plan is ~$20/month currently.
They must have hit really hard scaling limits if the prices were hiked so much so quickly.
I paid $360 annual for Max plan and currently averaging about 1BN tokens a day with their frontier GLM-5.3 model. This was clearly unsustainable for them and they've dropped this package.
Well, there's essentially two major ways to use these models: Pair programming or fully autonomous fire-and-forget code generation. The second strategy needs essentially zero input, so the number of tokens you can blow is practically only limited by API speed.
It's not my main model (that would be Fable 5.1 Extra) but it's been doing agent-driven search and optimisation of a cross-trading ranking model (it's for work).
I would suggest you to hook fable or 5.6 to check it regularly and its work because it gets lost easily on stuff it was not trained on. I'm doing some custom inference engine optimization and it's a workhorse but it can easily lose its way and if you don't recheck it you will get wrong answers in the end.
I also have a legacy pro plan and the only limitation is if you are trying to work in the morning from Europe because you are in the 3x usage overlapping China time but after 12 or so you basically can run it at least for me at least 3 parallel sessions all the time.
>I was gonna ask how people found their coding plans
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.
I believe the that the companies who claim to not train on my data are more likely to not train on my data than the companies who refuse to even claim they won't.
Also why Meta gets a +1, just charge less money on the training path.
I’m not sure that follows. You’re assuming that all those claims have the same weight, without considering the size, jurisdiction, reputation or even the general vibe of the company making that claim.
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.
It just gives a taste of what we are all going to have to pay soon, once the model providers actually have to make money. And the era of "let's charge a dollar for every 10 dollars running the infra actually costs" is rapidly coming to an end.
And you can bet GLM is still ridiculously subsidized, just not as ridiculously as Anthropic and OpenAI.
This isn't true, you can pay for GLM 5.3 from a provider like Neuralwatt or Friendli who have no incentive to subsidize or loss-lead their inference APIs
It's hard to know, since no one advertises the actual token limits (partially cause they're prolly complex / adaptive). So it seems much more likely that they just offer different pricing tiers than you're used to. Like, the $80 plan is still ~$80 of subscription quota, regardless of what else is offered.
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.
Different angle on the same model: the full GLM-5.3 (744B MoE, 4-bit experts, 434 GB on disk) runs on a single MacBook Pro M5 Max with 128 GB
by streaming the experts from NVMe SSDs instead of keeping them in memory.
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.
Well, other than the infrastructure they got from illegally routing millions of paying customers' requests through Anthropic's Opus 4.8 in a distillation attack...
That is such a canard, IMO. FWIW, Anthropic and OpenAI encrypt "thinking" token outputs in their models, while Chinese labs don't. If anything, it's more likely that everyone is using open-weight models in their synthetic training data generation pipelines. It's way easier to distill from logits than it is to distill from hard tokens.
Are you joking...? Sorry if so! Just in case: It's illegal in both the PRC and the USA.
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.
This article left me with one immediate question: "WTF is GLM?".
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...
z.ai is a fairly well known AI lab out of China and their GLM models are probably the most popular outside of Anthropic or OpenAI’s. I don’t think it’s presumptuous for them to not introduce themselves in a post on their own blog, I think you’re just a bit out of the loop here.
We built a complete production-grade inference service from scratch on a cluster of more than 100,000 Chinese-made AI accelerators. All production inference for GLM-5.3-Flash runs on this system.
Most of the people had kinda guessed this when they decided to provide 100 trillion tokens for free.
Zhipu has built a production-grade inference service for GLM-5.3-Flash, running on a cluster comprising over 100,000 domestic AI accelerators. The practical application of GLM-5.3-Flash has given rise to a fundamental feedback loop: the model contributes to optimizing the inference infrastructure, while the optimized system continues to serve the model.
Interesting that the tone of announcements between US and Chinese providers is converging.
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.
Any details on the latest approach to distillation would also be very interesting.
Necessity is the mother of invention. The shortsighted protections put on chips, etc., by the US has forced Chinese AI industry to adapt or die. Guess what their response to this fitness function has been? Kudos to Z.ai on their inventions and excellent write-up, which reads like humans wrote it.
> Today, GLM-5.3 has become an indispensable daily coding partner for everyone on the team, and it is moving steadily toward replacing us. If this trend continues, given enough compute and enough time, its endpoint is a system that can design and train its own successor entirely autonomously. This is known as Recursive Self-Improvement, or RSI.
Statements dreamed up by the utterly deranged.
Given the rate of improvement, why is this deranged?
because the rate of improvement is fairly stalled?
You're tragically misinformed; it isn't. Several metrics are actually growing exponentially. But if you want emprical information, you can just have al look at the nature of the late AI incidents.
Ironically, many benchmarks being maxxed out, and quite quickly, so new ones have to be created.
Yeah, it's been over a week since a Millennium Problem was solved. AI has hit a wall.
It was not solved. ~OpenAI~ Buckmaster and Alpöge found one (or a few) singularities in the forced version of the Navier-Stokes equations. Then magically 2 weeks later OpenAI found them too. Again, I am not saying this is not a great feat. I am just saying that everyone should be a bit more careful when making statements about RSI.
Do you have anything that proves this one way or another that isn't based on vibes or shoddy benchmarks?
You prove your own point no? You are asking for a benchmark to prove AGAINST ASI. Surely the burden of proof for such a scientific fiction concept should be the other way around.
No, they asked for a reliable measurement to prove that model development has stalled. Nobody is talking about ASI except you.
Nah, they're fine making that small leap. RSI is the new marketing term for the sci fi singularity.
>> because the rate of improvement is fairly stalled?
> 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.
if you can be replaced by an algorithm, how useful were you really?
Very useful. That’s a weird question.
I aspire to be at least as useful as bogosort.
> This is known as Recursive Self-Improvement, or RSI.
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.
You are a singularity. Just a loop that processes a bunch of input and creates output.
Everything you think you are is just what you can imagine in a single moment. Nothing more, nothing less.
I was gonna ask how people found their coding plans, and realized, have they massively ramped up the prices? Seems the middle plan is ~$80/month now, didn't that used to be like $20/month? Cheapest plan is ~$20/month currently.
They must have hit really hard scaling limits if the prices were hiked so much so quickly.
I paid $360 annual for Max plan and currently averaging about 1BN tokens a day with their frontier GLM-5.3 model. This was clearly unsustainable for them and they've dropped this package.
1 billion tokens a day?!! I've done a lot of work these past 2 weeks with GLM-5.3. Like, a lot. And I've just passed 300 million tokens in total.
Can I ask where are you using all those tokens?
Well, there's essentially two major ways to use these models: Pair programming or fully autonomous fire-and-forget code generation. The second strategy needs essentially zero input, so the number of tokens you can blow is practically only limited by API speed.
Something like this I guess: https://youtu.be/U-Rqv9dOB1U
That's easy to do with many agents independently told to find bugs in a large codebase.
300M for two weeks is surprisingly low. What are you doing that need so few tokens?
It's not my main model (that would be Fable 5.1 Extra) but it's been doing agent-driven search and optimisation of a cross-trading ranking model (it's for work).
I would suggest you to hook fable or 5.6 to check it regularly and its work because it gets lost easily on stuff it was not trained on. I'm doing some custom inference engine optimization and it's a workhorse but it can easily lose its way and if you don't recheck it you will get wrong answers in the end.
I also have a legacy pro plan and the only limitation is if you are trying to work in the morning from Europe because you are in the 3x usage overlapping China time but after 12 or so you basically can run it at least for me at least 3 parallel sessions all the time.
>I was gonna ask how people found their coding plans
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.
The way I look at it, their coding plan doesn’t retain data or use it for training making it one of the cheaper plans for me.
https://docs.z.ai/legal-agreement/privacy-policy
You believe any of these companies care about the law? They care about winning and building the self improving AI as quickly as possible.
I too am sceptical but I’ll take my chances. At least it’s helping the open weights.
I believe the that the companies who claim to not train on my data are more likely to not train on my data than the companies who refuse to even claim they won't.
Also why Meta gets a +1, just charge less money on the training path.
I’m not sure that follows. You’re assuming that all those claims have the same weight, without considering the size, jurisdiction, reputation or even the general vibe of the company making that claim.
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.
Yeah it went from a great deal to unviable compared to other providers imo. They really need to find a healthy middle ground
It just gives a taste of what we are all going to have to pay soon, once the model providers actually have to make money. And the era of "let's charge a dollar for every 10 dollars running the infra actually costs" is rapidly coming to an end.
And you can bet GLM is still ridiculously subsidized, just not as ridiculously as Anthropic and OpenAI.
This isn't true, you can pay for GLM 5.3 from a provider like Neuralwatt or Friendli who have no incentive to subsidize or loss-lead their inference APIs
This introduces other incentives to cut corners and over-quantize.
What provider are you using currently?
It's hard to know, since no one advertises the actual token limits (partially cause they're prolly complex / adaptive). So it seems much more likely that they just offer different pricing tiers than you're used to. Like, the $80 plan is still ~$80 of subscription quota, regardless of what else is offered.
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.
Different angle on the same model: the full GLM-5.3 (744B MoE, 4-bit experts, 434 GB on disk) runs on a single MacBook Pro M5 Max with 128 GB by streaming the experts from NVMe SSDs instead of keeping them in memory.
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).
seems unusably slow, and is this for short context?
Well, other than the infrastructure they got from illegally routing millions of paying customers' requests through Anthropic's Opus 4.8 in a distillation attack...
That is such a canard, IMO. FWIW, Anthropic and OpenAI encrypt "thinking" token outputs in their models, while Chinese labs don't. If anything, it's more likely that everyone is using open-weight models in their synthetic training data generation pipelines. It's way easier to distill from logits than it is to distill from hard tokens.
https://x.com/EricSimons/status/2099252922098061714
We weep for Dario, that he had to suffer such a devastating attack against his Terms of Service.
What is "illegal" about it?
breaking Anthropic TOS and misleading users
Breaking TOS isn't illegal per se. It just allows for denial of services, and may define terms by which the provider can reclaim costs.
Are you joking...? Sorry if so! Just in case: It's illegal in both the PRC and the USA.
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.
Yes, wont somebody please think of the shareholders whose IP had been stolen...
What does any of this has to do with the legality of distilling Claude?
Nulla poena sine lege?
Eh, even if this was true, then they're merely stealing from thieves. Anthropic did break a ToS or two to get training data themselves.
This article left me with one immediate question: "WTF is GLM?".
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...
z.ai is a fairly well known AI lab out of China and their GLM models are probably the most popular outside of Anthropic or OpenAI’s. I don’t think it’s presumptuous for them to not introduce themselves in a post on their own blog, I think you’re just a bit out of the loop here.
I don't get the outrage. Do you post this kind of stuff on every topic on hackernews that you are not knowledgeable about?
It's only the top open-weights LLM in the world,
https://artificialanalysis.ai/#intelligence-category-tabs
It's presumptuous for them to assume that a reader of their blog is familiar with their product?
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
A ai model family similar to Codex, Gemini or Claude.
Where GLM-5.3-Flash is the newest "small / fast" model.
>As we develop GLM, the model sometimes exhibits capabilities that surprise us, and even unsettle us.
Come on now
Also, why would they introduce themselves on their own blog?