US chip export restrictions may actually be an advantage for China's AI Infrastructure. Chinese companies are forced to speed up developing their own AI chips
> 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.
you can also derive some stats from the ~10T tokens a day on 100k devices, 100M / device / day, but then one has to account for the multi-gpu model size, and I need coffee before I go there
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
Because having unrestricted access to both American and Chinese hardware is better than only Chinese hardware. Long term or short term. More suppliers the better.
Where the US sees itself penalizing China with an export restriction, China sees the US gifting it with zero-political-cost “protective” import tariff.
You can’t really hurt a country that has a culture with a positive attitude toward growth.
Not sure, but there are definitely people around the president on both sides, some who think they can addict the Chinese to our silicon, like its the new opium war or something
Yup. That was really short sighted. And good for China. And actually the overall global market market since supply will augment and competition will decrease pricing as well.
China themselves recognize this. After Trump relaxed sanctions and allowed NVIDIA H200 sales to China on a case by case basis, the Chinese government stepped in to essentially block it!
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.
And this wouldn't have happened if we had tried to get them to buy our hardware rather than trying to gatekeep. Protectionism never works in the long term.
There seems to be a belief that the post-war 20th century order would be a fixed feature rather than a contingency of history. That the US would be #1, Europe #2, and the rest of the world would remain "developing" in rural poverty forever. That somehow China could be prevented from catching up. Now they are, and behind them India. I think people are going to be even more surprised in the latter half of the 21st century when South America and Africa start catching up as well.
Brazil is one to watch if they manage to achieve political stability, as is Nigeria if it can transition from being a petrostate.
US companies should now be more worried about Chinese companies flooding the market with their, hopefully, very affordable GPU's. The scale at which they can manufacture stuff is unmatched anywhere else. Nvidia can kiss goodbye to their 75%+ profit margins.
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.
China is gated by not having EUV machine access. They're also bottlenecked by ASML's DUV machine production like everyone else. There are already talks of banning China from even purchasing DUV machines from ASML.
So until China solves the ASML problem, there won't be any flooding.
pretty sure they'll be fine for a while, between the build out and import bans, I don't expect demand to slow enough to let supply catch up
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.
Look at how China does it. They'll happily sell us everything we want - more than enough of it, cheap enough, to put all of our own manufacturers out of business.
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.
Same thing with Trump not helping Ukraine and berating NATO. He thought he held all the cards, but now Ukraine has a thriving battle-tested drone industry, UK and France have stepped in to replace the US with advanced missiles and anti-missile systems, stepping up their own production and transferring IP to Ukraine.
Now, the US is left out in the cold with little influence left, themselves now the ones with an anti-missile shortage.
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.
very few people comprehend - how much of an asteroid level event for western AI labs this is.
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.
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.
Ziphu (who make GLM) use Huawei Ascend processors made by SMIC. Huawei use a combination of domestic memory from CXMT and leftover (pre-sanctions) memory from Samsung.
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.
I am surprised at the lack of open-weights models in the >35B, but <200B range. I keep thinking about devices like the NVIDIA Spark and AMD Ryzen Halo, which have their 128GB of combined memory, but there are so few models made for that range. Nearly all the open weights distillations are for larger customer bases with <24GB VRAM.
American exceptionalism states that America is special and unique so everyone else must be a copycat. American ai labs don't need this kind of optimization and fable will outright refuse to do it.
I might be missing something but when I went to their site they are more expensive than Claude. Why would I pick GLM over Claude? Is it they just offer more tokens in their plans?
What are you referring to? Given the audience, my instinct is to assume "plan" refers to the GLM Coding Plans, which are all cheaper than their Anthropic counterparts. As far as I can tell, the API costs are also all cheaper than their roughly equivalently capable Anthropic models.
Not to digress from the core argument of Claude vs GLM being open weights….
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.
For one you would have to use Claude if you pick it. But seriously there is no way for you to determine if one is a better offer than the other, when the usage/tokens/credits are vague, detached, and won't tell you much without trying both.
If only this infrastructure could handle all the traffic. I've tried using glm via z.ai - and it's a snail kind of slow.
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.
That it's slow doesn't mean it can't handle the traffic, just that this speed is the optimal tradeoff to them. They benefit from serving more tokens by exploiting parallelism across users at a lower number of tokens per second per user, instead of serving each individual user as quickly as possible. When there's a drop in traffic, they probably shut down GPUs rather than giving you higher speed.
Given the huge amount of money being spent on AI chips in the US, what prevents US AI labs from doing the same level of software optimization? It could be a solve for some of the capacity constraints.
They have already been doing it for months https://openai.com/index/openai-broadcom-jalapeno-inference-... . OpenAI on their custom chip brought up lightspeed deepseek as experiment by using AI in the exact same way as this zAI blogpost. And the kernel optimization contests/etc have all been havily done through AI based optimization loops for half a year+.
> what prevents US AI labs from doing the same level of software optimization?
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.
This is a really funny sounding post. They sound like they just found out that increasing your automation gives you increased capabilities at faster speeds. They also sound like they just realized AI makes hard things easier.
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?
It's not that they "just found out" - what they are saying is that while they were previously dogfooding because it's good practice, now that their models are so much stronger they are using them because it helps accelerate.
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.
Most of the fastest inference and training code in production today is written in Python. There are no global locks on the GPU except the ones you put there
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.
Wouldn't it be refreshing if OpenAI and Anthropic were this open, and spelled out how they were using their own models during development and rollout?!
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.
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.
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.
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.
Buckmaster and Alpöge has found a forced finite-time singularity for the 3D incompressible Euler equations (and two other types) building on the work by Diego Córdoba and Luis Martínez-Zoroa with the assistance of Anthropic and OpenAI models. Then OpenAI found a forced finite-time singularity for the Navier-Stokes equations.
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?
> 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.
Share the resulting code from any one of those please? I've tried so many times to find a setup that facilitates parallel work + high quality results, but it's just impossible regardless of harness or model. Leave the agents alone for too long, and the entire thing just balloons out of control, and next you know you're sitting there with half a million LOC where 80% isn't even needed.
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.
Kind of feels like this applies to every single model, from Astra to Qwen, they all eventually lose track of the plot unless you feed it some human's input that can steer them right every now and then. The only difference is how often you need to do so, and also how often you want to do so heavily influences how good quality the results will be.
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.
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 table assumes cache hit rate of 95% or better. Am I understanding this correctly that people really are doing such repetitive prompts (compared to each other, across the concurrent user base at that time) that only 5% or less need actually be computed by the intended LLM?
Every tool call is essentially entire prompt so far sent again with the response and that's why cache rates are so high for agentic workloads.
This really bites when using expensive models since most models are 1/10 for cached input.
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.
Yes I sometimes think the "don't train on my data" is actually a good signal for "this data/person is probably better to train on because they want to keep something private". The whole copyright system should have stopped these guys from training on everyone's data and it did not, if you think they care about the privacy checkbox I think you're dreaming personally, based on their past behavior.
>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.
Way to restrictive in terms of tokens provided. I am on their largest plan, and quickly run into their limits. And that is using it selectively in addition to codex.
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.
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.
Maybe my post sounded harsher than I intended, and yeah, it's probably on me that I'm not familiar with GLM. Actually the other major Chinese LLM Kimi does ring a bell, maybe it's because three-letter acronyms are a dime a dozen and annoy me because I'm confronted with them regularly at work too (people at my company seem to love acronyms), but that's obviously on me too...
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!
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...
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.
I'm not defending their actions, but we should be clear about where the law currently stands: Anthropic was found to infringe because of the torrenting, not because of the training.
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.
What does any of this have to do with the legality of distilling Claude?
> 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
I'm always wondering when "distillation" comes up how feasible it is, or if it's just BS.
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?
If you understand what they have achieved here, then the notion that they are bottle-necked on training data is absurd.
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?
> 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.
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.
You can’t really hurt a country that has a culture with a positive attitude toward growth.
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.
Brazil is one to watch if they manage to achieve political stability, as is Nigeria if it can transition from being a petrostate.
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.
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.
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.
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.
Signed, a customer.
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.
Yes, but it's calling C code.
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).
> 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.
Ironically, many benchmarks being maxxed out, and quite quickly, so new ones have to be created.
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%.
https://docs.z.ai/legal-agreement/privacy-policy
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
https://artificialanalysis.ai/#intelligence-category-tabs
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.
https://x.com/EricSimons/status/2099252922098061714
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?