This is the takeaway here: That's how they have been serving it at scale as Ox-Alpha. This is a definitional moment.-
Further quote:
"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."
They are high end really expensive Huawei ascend GPUs.
It is kinda bruteforcing the performance on a older semiconductor processing tech, so total production is pretty low.
By this they probably mean RTX series GPUs? If so, then they are not comparing the hardware efficiency with the A100 / H100, etc. that are commonly used for training models
Another self-inflicted own courtesy of US government policy.
While I think China would always get to hardware self-sufficiency eventually, all export controls have done is (1) accelerate China's development, and (2) divert revenue that would've otherwise gone to NVIDIA/AMD/etc instead.
Long term it's irrelevant. The only relevant thing is that there's lots of money in chips that can do high performance inference. You see all kinds of competitor products in development or already on the market even here in the US where there are no such restrictions. Cerebras comes to mind. It's natural and expected that eventually Nvidia will either have to keep way ahead or competition will catch up with specialized products.
That doesn't mean by any stretch of the imagination Nvidia will disappear. But the entire stock market valuation, not just tech, has had me scratching my head for a while.
Cerebras "competes" with Nvidia in the same way a Vespa scooter competes with a Ford F-150. Groq and Tenstorrent are in a similar boat, ASICs don't really threaten CUDA.
Curiously, there is not a single real CUDA competitor anywhere in the world. We almost had one with OpenCL, but all of the American stakeholders abandoned it right before the crypto/AI takeoff. All of which means that Nvidia sets their own margins, exploiting American investors and taxpayers while letting China avoid their dominance. So the American economy subsumes the bulk of Nvidia's arbitrarily-priced debt, and the Chinese economy can direct SOEs to pour billions in liquid cash into real GPGPU research.
I'm an American and I'm pretty fond of Nvidia, but Jensen was right about this policy; it gives China everything they need to actually replace CUDA. It's reminiscent of America's attempts to deprive China of ARM and Texas Instruments IP, only to end up swimming in unlicensed clones after refusing to sign an IP deal.
Revoked or not, just ever having those controls signals to the Chinese ecosystem that you're not necessarily a reliable supplier (Would you trust US export policy to remain stable for the next ~decade given the state of US politic?) and to the Chinese government just how strategically important you see these components.
This isn't the kind of thing you can hash out in public and go back and forth on. Once you put it out there, the other party will take steps to make sure they don't have to rely on us in the long run.
The export controls were not revoked, only reduced, and not before, but after China refused to buy low performing chips. Top gear was and is still sanctioned, as is any EUVL equipment.
I don't see a situation where subscription payers move outside American LLMs (chatgpt, claude, gemini)
And I don't see a situation where serious API payers are OK with handing the Chinese state all their data. Like manufactures of decades past did and learned a hard, even existential, lesson for it. The state mantra has been "Collect and Copy" for a long time now, tech just hasn't had that moment to experience it yet.
So that leaves local hosting/leasing, but one of those has totally non-practical economics and the other doesn't have enough compute to meet any kind of real demand.
I also have yet to meet a single person who isn't neck-deep in the tech space mention a Chinese LLM. It's 100% the big American three.
If anything it's custom chips from the labs that threatens Nvidia.
These open models serve as price / performance pressure. Not all tasks require frontier models and cheap open models can be quite good for in-app assistants, if you're building that sort of thing. We also aren't sure the subscriptions will continue to be sustainable. They're currently subsidized to the tune of 50-70x. As someone who is hitting limits weekly that would easily cost me over $10k month per sub.
Google probably serves more tokens then OAI and Anthropic combined, even if many of those tokens aren't from explicit gemini requests, but from AI overviews and other service integrations.
xAI is already selling spare compute, and basically exists just to gas spacex's perceived valuation.
Casual consumers are using American models because their usage is low. As usage scales, the economics heavily favor open weight models. The API pricing from American companies is absurd. This is particularly true in an enterprise setting.
Open weight model hosts don't have the compute to meet enterprise demand. A large part of why these models are so cheap is because overall demand for them is incredibly low. Back in May, Gemini alone was doing about a month's worth of Openrouter tokens every day.
I am not ok with handing all my data to American companies that are best friends with the American surveillance state. I still remember the Snowden revelations. Chinese companies are a much better option in that regard.
you don't have to hand them your data, the models are available so you can run them on bedrock yourself (or use another US housed inference service). and for what it's worth in my job i have access to data that gives a picture of the way companies are doing inference, and they're using a lot of chinese models (deepseek-v4 is a huge percentage of inference requests for example)
Lead doesn't really matter anymore. I just ported a very old cuda library to rocm, so it can be run on MI300s. 2 years ago this would have been a nightmare. Today it was an afternoon.
Presumably the efficiency numbers they're quoting are for the high concurrency state they were serving.
RAM was probably the bottleneck for the amount of context they were offering.
I assume it would run a little faster with lower concurrency but "RIP nVidia" is a little premature. The cutting edge inference hardware is amazingly powerful
It's also in this very announcement, in the first paragraph:
> Before release, we tested GLM-5.3-Flash anonymously as ox-alpha on OpenCode and OpenRouter to gather user feedback. It quickly became the most popular model of the week — with all of this traffic served on Chinese AI chips.
Most US companies that have anything to do with government, finance, medical, etc. already have contractual or regulatory obligations which prevent them from using Chinese hardware or services, even before the AI boom. That's a huge market.
Nvidia will do just fine. (Disclaimer: not a shareholder. At least, not directly.)
>> "They are already there on open weight models and Jensen knows that it is only a matter of time until China catches up with GPUs or other AI accelerators."
It is also why Nvidia becoming a bank for other AI companies who are unable to find VCs to fund them isn't really a good thing and that is bearish.
Not really. Chinese AI companies were never using NVidia AI chips.
This announcement doesn't really mean anything at all. It means the very few people who are already using Z.ai's API will continue to do so, but the vast majority of money going to Nvidia is through the massive amount of business going to Anthropic, OpenAI, and other western cloud providers and inference providers, who are mostly using NVidia chips for inference.
Also, NVidia chips are still sold out and supply constrained.
This is the takeaway here: That's how they have been serving it at scale as Ox-Alpha. This is a definitional moment.-
Further quote:
"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."
https://z.ai/blog/glm-5.3-flash
Anyone knows what are those Chinese chips? Can they be bought? (Assuming im not i the US, And actually im in a 3rd world country).
Perhaps these may be Huawei Ascend chips.
https://en.wikipedia.org/wiki/HiSilicon#Ascend_910
https://medium.com/@huaweiclouddevelper/a-brief-introduction...
They are high end really expensive Huawei ascend GPUs. It is kinda bruteforcing the performance on a older semiconductor processing tech, so total production is pretty low.
> comparable to mainstream NVIDIA GPUs
By this they probably mean RTX series GPUs? If so, then they are not comparing the hardware efficiency with the A100 / H100, etc. that are commonly used for training models
Another self-inflicted own courtesy of US government policy.
While I think China would always get to hardware self-sufficiency eventually, all export controls have done is (1) accelerate China's development, and (2) divert revenue that would've otherwise gone to NVIDIA/AMD/etc instead.
Long term it's irrelevant. The only relevant thing is that there's lots of money in chips that can do high performance inference. You see all kinds of competitor products in development or already on the market even here in the US where there are no such restrictions. Cerebras comes to mind. It's natural and expected that eventually Nvidia will either have to keep way ahead or competition will catch up with specialized products.
That doesn't mean by any stretch of the imagination Nvidia will disappear. But the entire stock market valuation, not just tech, has had me scratching my head for a while.
Cerebras "competes" with Nvidia in the same way a Vespa scooter competes with a Ford F-150. Groq and Tenstorrent are in a similar boat, ASICs don't really threaten CUDA.
Curiously, there is not a single real CUDA competitor anywhere in the world. We almost had one with OpenCL, but all of the American stakeholders abandoned it right before the crypto/AI takeoff. All of which means that Nvidia sets their own margins, exploiting American investors and taxpayers while letting China avoid their dominance. So the American economy subsumes the bulk of Nvidia's arbitrarily-priced debt, and the Chinese economy can direct SOEs to pour billions in liquid cash into real GPGPU research.
I'm an American and I'm pretty fond of Nvidia, but Jensen was right about this policy; it gives China everything they need to actually replace CUDA. It's reminiscent of America's attempts to deprive China of ARM and Texas Instruments IP, only to end up swimming in unlicensed clones after refusing to sign an IP deal.
The export controls were revoked before it triggered Chinese protectionism: https://www.silicon.co.uk/e-innovation/artificial-intelligen... / https://archive.vn/B2pah
Revoked or not, just ever having those controls signals to the Chinese ecosystem that you're not necessarily a reliable supplier (Would you trust US export policy to remain stable for the next ~decade given the state of US politic?) and to the Chinese government just how strategically important you see these components.
This isn't the kind of thing you can hash out in public and go back and forth on. Once you put it out there, the other party will take steps to make sure they don't have to rely on us in the long run.
The export controls were not revoked, only reduced, and not before, but after China refused to buy low performing chips. Top gear was and is still sanctioned, as is any EUVL equipment.
1 reply →
And it doesn't matter, it still pushed China to speed-up their AI related hardware development.
> The export controls were revoked before
Zai is on another "export control" list outside the broader 1. Doesn't help.
I don't see a situation where subscription payers move outside American LLMs (chatgpt, claude, gemini)
And I don't see a situation where serious API payers are OK with handing the Chinese state all their data. Like manufactures of decades past did and learned a hard, even existential, lesson for it. The state mantra has been "Collect and Copy" for a long time now, tech just hasn't had that moment to experience it yet.
So that leaves local hosting/leasing, but one of those has totally non-practical economics and the other doesn't have enough compute to meet any kind of real demand.
I also have yet to meet a single person who isn't neck-deep in the tech space mention a Chinese LLM. It's 100% the big American three.
If anything it's custom chips from the labs that threatens Nvidia.
These open models serve as price / performance pressure. Not all tasks require frontier models and cheap open models can be quite good for in-app assistants, if you're building that sort of thing. We also aren't sure the subscriptions will continue to be sustainable. They're currently subsidized to the tune of 50-70x. As someone who is hitting limits weekly that would easily cost me over $10k month per sub.
What about the current situation, where serious API payers are increasingly OK with using open-weight models running on US providers?
https://www.ft.com/content/32a70a3c-7d28-40b4-808e-36edb58c7...
I can easily see a situation where most non American AI usage is on Chinese models on Chinese chips though.
Genuine question but who do you put as the "three" in big three.
Because I genuinely can't tell if you mean Google or SpaceX/X.ai lol.
Google probably serves more tokens then OAI and Anthropic combined, even if many of those tokens aren't from explicit gemini requests, but from AI overviews and other service integrations.
xAI is already selling spare compute, and basically exists just to gas spacex's perceived valuation.
Casual consumers are using American models because their usage is low. As usage scales, the economics heavily favor open weight models. The API pricing from American companies is absurd. This is particularly true in an enterprise setting.
Open weight model hosts don't have the compute to meet enterprise demand. A large part of why these models are so cheap is because overall demand for them is incredibly low. Back in May, Gemini alone was doing about a month's worth of Openrouter tokens every day.
1 reply →
I am not ok with handing all my data to American companies that are best friends with the American surveillance state. I still remember the Snowden revelations. Chinese companies are a much better option in that regard.
you don't have to hand them your data, the models are available so you can run them on bedrock yourself (or use another US housed inference service). and for what it's worth in my job i have access to data that gives a picture of the way companies are doing inference, and they're using a lot of chinese models (deepseek-v4 is a huge percentage of inference requests for example)
Ox Alpha is a smaller model and it was running very slowly. Chinese AI accelerators are coming along, but nVidia’s lead is huge.
Lead doesn't really matter anymore. I just ported a very old cuda library to rocm, so it can be run on MI300s. 2 years ago this would have been a nightmare. Today it was an afternoon.
Exactly. Coding for inference is solved. CUDA is no longer a moat.
It was being served for free. They were almost certainly being overloaded.
Presumably the efficiency numbers they're quoting are for the high concurrency state they were serving.
RAM was probably the bottleneck for the amount of context they were offering.
I assume it would run a little faster with lower concurrency but "RIP nVidia" is a little premature. The cutting edge inference hardware is amazingly powerful
Ox Alpha was also serving 10T+ tokens a day for free.
When it first launched on OpenRouter I was getting nearly 70 Tokens/second.
> and it was running very slowly
... I'm at a loss for words here. It was being served for free. To the entire world.
GPT-5.6 Luna is also served for free to the entire world with a tokens per second rate nearly 10X higher.
> ... I'm at a loss for words here
No need to be so dramatic. I think it's great that they're developing chips, but the whole "RIP nVidia" claim was overly dramatic.
2 replies →
Has there been any confirmation about what that model even is?
Edit: Ah:
> This stealth model was developed and operated by ZAI, revealed to be ZAI GLM-5.3-Flash.
It's also in this very announcement, in the first paragraph:
> Before release, we tested GLM-5.3-Flash anonymously as ox-alpha on OpenCode and OpenRouter to gather user feedback. It quickly became the most popular model of the week — with all of this traffic served on Chinese AI chips.
Most US companies that have anything to do with government, finance, medical, etc. already have contractual or regulatory obligations which prevent them from using Chinese hardware or services, even before the AI boom. That's a huge market.
Nvidia will do just fine. (Disclaimer: not a shareholder. At least, not directly.)
> Most US companies that have anything to do with government, finance, medical, etc... That's a huge market.
Compared to the rest of the world?
I don't have any pie charts in front of me, but yes, I would estimate it's a decently big slice of the world market.
bigger than RotW
God I wish I could’ve shorted NVIDIA right now
It's earnings day for them...
Which 9/10 times hasn't been great anyway (stock reaction).
2 replies →
Whats stopping you? You could buy puts right now.
Get a 210 strike put contract and if your thesis is that nvidias current 10 day slide continues you could make some money.
Unless NVidia craters you are likely to lose money given the IV crush that will happen today.
yay, I called it! :) (in the other thread)
Not really a brag: it ran like shit. Very slow (~20tps, VERY high latency) and it would timeout all the time.
I'm sure the chips are fine, but they clearly didn't have enough capacity for the demand they had (that 100T/day claim was asbolute bs)
seems unlikely that they'll get nearly as much demand now that it isnt free
Sure, although I still expect it to become the most used model on openrouter.
[flagged]
This is no surprise [0] [1].
>> "They are already there on open weight models and Jensen knows that it is only a matter of time until China catches up with GPUs or other AI accelerators."
It is also why Nvidia becoming a bank for other AI companies who are unable to find VCs to fund them isn't really a good thing and that is bearish.
[0] https://news.ycombinator.com/item?id=49431231
Not really. Chinese AI companies were never using NVidia AI chips.
This announcement doesn't really mean anything at all. It means the very few people who are already using Z.ai's API will continue to do so, but the vast majority of money going to Nvidia is through the massive amount of business going to Anthropic, OpenAI, and other western cloud providers and inference providers, who are mostly using NVidia chips for inference.
Also, NVidia chips are still sold out and supply constrained.