non-x blog post: https://z.ai/blog/glm-5.2
And previously: https://news.ycombinator.com/item?id=48518684
GLM-5.2: Frontier Intelligence, Open Weights
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Re: GLM-5.2: Frontier Intelligence, Open Weights
#12Discussion (765 points, 3 days ago, 489 comments) https://news.ycombinator.com/item?id=48518684
Re: GLM-5.2: Frontier Intelligence, Open Weights
#13Looks like their code subscription is sold out. Clicking on the "Subscribe" button just does nothing. Pay-as-you-go API works well.
Re: GLM-5.2: Frontier Intelligence, Open Weights
#14Earlier quoted context omitted.
I've been very pleased with it's performance over the last few days. It's definitely not near Opus 4.8 level but it's very impressive nonetheless and it does do design extremely well.
> it does do design extremely well Better than Opus?
I don't know what people mean when they say design lol, is it for frontends?
Re: GLM-5.2: Frontier Intelligence, Open Weights
#15If I have a fully maxed out MacBook Pro, would it make sense to just switch from Opus 4.8 to this? I've never tried running local models for coding...
HuggingFace says this model has 753B parameters, which will need a lot more RAM than a maxed-out MacBook Pro. With 40B active parameters, running from SSD would need patience.
For an fp4 quantization it should fit with room to spare for KVCache
Re: GLM-5.2: Frontier Intelligence, Open Weights
#16Earlier quoted context omitted.
> it does do design extremely well Better than Opus?
I don't know what people mean when they say design lol, is it for frontends?
Yeah, that's what I mean anyway. Each model has certain design tropes it repeats everywhere, and some of them are very old-school or not really UI best practice.
And then the more ambitious cases where you ask for a feature without being prescriptive with UI needs, the end result is sometimes atrocious with weird font use, colours, etc.
Re: GLM-5.2: Frontier Intelligence, Open Weights
#17Earlier quoted context omitted.
HuggingFace says this model has 753B parameters, which will need a lot more RAM than a maxed-out MacBook Pro. With 40B active parameters, running from SSD would need patience.
I’ve wondered for a while if anyone is working on very wide channel parallel (kind of like RAID 0) SSD for this purpose. Couple that with a tensor processor and that would be interesting.
There's talks about HBF. F for Flash -- HBM packaging and bus width, but using NAND memory.
e.g. https://www.sandisk.com/company/newsroom/press-releases/2026...
Re: GLM-5.2: Frontier Intelligence, Open Weights
#18Earlier quoted context omitted.
HuggingFace says this model has 753B parameters, which will need a lot more RAM than a maxed-out MacBook Pro. With 40B active parameters, running from SSD would need patience.
For an fp4 quantization it should fit with room to spare for KVCache
Aren't Macbooks limited to 128GB RAM?
FP4 would require >350GB RAM + KV cache, so no.
Re: GLM-5.2: Frontier Intelligence, Open Weights
#19Re: GLM-5.2: Frontier Intelligence, Open Weights
#20Zhipu AI is founded by a superstar Tsinghua professor, did an IPO in January (Hong Kong stock exchange) hired half it's past research lab and it's stock is >10x since. This is not a "just distill Claude" thing.
IPO within year of founding?
It it normal for startup in China?