I think electricity doesn't matter that much (yet) because China is bottlenecked on chips. I think the incentives/directives to build on Huawei also doesn't matter that much yet because it's still such a small percentage of compute relative to NVIDIA, even for Chinese AI companies. (But this too could matter more from 2027-2030 and on.)
Yeah, to be clear I'm pretty excited about confidential computing and startups building on it, like Tinfoil, for some use cases. I just wanted to point out it's far from adequate for some important threat models (e.g., securing model weights for data centers located abroad, I think). (It's also not super widely adopted in AI yet, but that seems to be changing, at least for inference workloads.)
Where did you get this information? I think it's wrong -- I'm pretty sure they used the Export Administration Regulations (EAR) under the Export Control Reform Act (ECRA), which is under Commerce, not ITAR which is under the State Department. See for example https://harvardlawreview.org/blog/2026/06/is-access-to-fable...
Confidential computing is not secure against a potential attacker who has physical access to the hardware. The CC security guarantees explicitly assume the attacker has no physical access.
Thanks. I'm like 95% sure that you're wrong, and that GLM-5 was trained on NVIDIA GPUs, or at least not on Huawei Ascends.
As I wrote in another comment, I think so for a few reasons:
1. The z.ai blog post says GML-5 is compatible with Ascends for inference, without mentioning training -- it says they support "deploying GLM-5 on non-NVIDIA chips, including Huawei Ascend, Moore Threads, Cambricon, Kunlun Chip, MetaX, Enflame, and Hygon" -- many different domestic chips. Note "deploying". https://z.ai/blog/glm-5
2. The SCMP piece you linked just says: "Huawei’s Ascend chips have proven effective at training smaller models like Zhipu’s GLM-Image, but their efficacy for training the company’s flagship series of large language models, such as the next-generation GLM-5, was still to be determined, according to a person familiar with the matter."
3. You're right that z.ai trained a small image model on Ascends. They made a big fuss about it too. If they had trained GLM-5 with Ascends, they likely would've shouted it from the rooftops. https://www.theregister.com/2026/01/15/zhipu_glm_image_huawe...
Thanks. I'm like 95% sure that you're wrong (as is the parent), and that GLM-5 was trained on NVIDIA GPUs, or at least not on Huawei Ascends.
I think so for a few reasons:
1. The Reuters article does explicitly say the model is compatible with domestic chips for inference, without mentioning training. I agree that the Reuters passage is a bit confusing, but I think they mean it was developed to be compatible with Ascends (and other chips) for inference, after it had been trained.
2. The z.ai blog post says it's compatible with Ascends for inference, without mentioning training, consistent with the Reuters report https://z.ai/blog/glm-5
3. When z.ai trained a small image model on Ascends, they made a big fuss about it. If they had trained GLM-5 with Ascends, they likely would've shouted it from the rooftops.
4. Ascends just aren't that good
Also, you can definitely train a model on one chip and then support inference on other chips; the official z.ai blog post says GLM-5 supports "deploying GLM-5 on non-NVIDIA chips, including Huawei Ascend, Moore Threads, Cambricon, Kunlun Chip, MetaX, Enflame, and Hygon" -- many different domestic chips. Note "deploying".
Where did you read that it was trained on Ascends?
I've only seen information suggesting that you can run inference with Ascends, which is obviously a very different thing. The source you link also just says: "The latest model was developed using domestically manufactured chips for inference, including Huawei's flagship Ascend chip and products from leading industry players such as Moore Threads, Cambricon and Kunlunxin, according to the statement."
Where did you read that it was trained on Ascends? I've only seen information suggesting that you can run inference with Ascends, which is obviously a very different thing.
It's more like it moved from 2028-2032 to 2030-2035 (depending on the author).