GLM Built Its Own Inference Infrastructure—and It's Coming for Your Job
GLM-5.3-Flash was trained on over 100,000 Chinese-made AI accelerators, and much of the infrastructure work was done by an Infra Agent powered by GLM-5.3. The model went from initial adaptation to production in under two weeks, tripling throughput. The article details the 'dense feedback' loop that made this possible and hints at early recursive self-improvement.
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.
- zicohacks
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
- dada216
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.
- Havoc
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.
- throwa356262
"We implemented a series of aggressive memory optimizations, including..."
This whole thing sounds like industrial scale auto-research, but done by people who actually know what they are doing.
- KronisLV
Time to tackle consumer GPUs next, since I’m not getting that Intel Arc B770.
- chung8123
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?
- konart
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.
- 9cb14c1ec0
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.