It was evident that this will happen.

> 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.

US chip export winners and losers:

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.

Nobody pretended that Chinese firms would just lay back and twiddle their thumbs when faced with import restrictions. The question was whether they would be far enough to be able to catch up without much issue or so far behind that they wouldn’t ever effectively catch up or that by the time they did, it wouldn’t matter.

Half-arsed export restrictions are the best of both worlds for these firms: enough of an incentive to take homegrown hardware seriously, yet not aggressive enough to cause meaningful handicap in the meantime.

Of course it is - how else could Huawei and a number smaller companies compete with NVIDIA.

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.

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.

Didn't everyone make fun of Jenson for saying exactly this?

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.

It created demand that would not have been there without restrictions

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.

Most of the people had kinda guessed this when they decided to provide 100 trillion tokens for free.

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.

Any details on the latest approach to distillation would also be very interesting.

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.

    > Why would I pick GLM over Claude?

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.

And exactly how many tokens (please do the breakdown for prefill vs decode) does a Claude $20/month plan include?

because GLM does what Clauden't

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.

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.

Jevons paradox, technological improvements that increase the efficiency of a resource's use lead to a rise in total consumption of that resource.

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.

seems unusably slow, and is this for short context?

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.

Their plans are still worth it if you use their models. You can see how many tokens you can except to get based on plan here: https://docs.z.ai/devpack/overview#estimated-token-allowance

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).

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

>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.

Yeah it went from a great deal to unviable compared to other providers imo. They really need to find a healthy middle ground

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.

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

Python acts as an orchestrator of accelerator libraries and does none of the inference math directly

Someone tell this man about vLLM!

> Have you seen how bloated and slow Python is?

Yes, but it's calling C code.

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 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

It's only the top open-weights LLM in the world,

https://artificialanalysis.ai/#intelligence-category-tabs

Ziphu, aka Z.ai, is the company that makes GLM (a very competitive Chinese LLM).

Why would you be reading their corporate blog posts if you don't even know who they are?!

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?

Anthropic infringed the copyright of basically every author on the planet: https://www.anthropiccopyrightsettlement.com/

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.

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?

I have very little sympathy for thieves who get robbed of the goods they have stolen.

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?

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.