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GLM Built Its Own Inference Infrastructure

59 pointsby 2h agoz.ai
35 comments
48m agoHN ↗

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.

9m agoHN ↗

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

45m agoHN ↗

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.

42m agoHN ↗

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

29m agoHN ↗

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.

20m agoHN ↗

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

34m agoHN ↗

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.

33m agoHN ↗

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.

23m agoHN ↗

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.

10m agoHN ↗

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?

30m agoHN ↗

You believe any of these companies care about the law? They care about winning and building the self improving AI as quickly as possible.

26m agoHN ↗

I too am sceptical but I’ll take my chances. At least it’s helping the open weights.

38m agoHN ↗

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...

11m agoHN ↗

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.

22m agoHN ↗

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.

10m agoHN ↗

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

38m agoHN ↗

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.

23m agoHN ↗

Given the rate of improvement, why is this deranged?

10m agoHN ↗

because the rate of improvement is fairly stalled?

8m agoHN ↗

Do you have anything that proves this one way or another that isn't based on vibes or shoddy benchmarks?

5m agoHN ↗

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.

7m agoHN ↗

if you can be replaced by an algorithm, how useful were you really?

5m agoHN ↗

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.

20m agoHN ↗

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...

16m agoHN ↗

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

15m agoHN ↗

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.

14m agoHN ↗

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?

9m agoHN ↗

A ai model family similar to Codex, Gemini or Claude.

Where GLM-5.3-Flash is the newest "small / fast" model.

15m agoHN ↗

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).