alt Hacker NewsThis is absolutely still shy of Sol and Fable, but only just by a hair. Ridiculous results. There's still not a compelling economic reason to drop OpenAI courtesy of the ludicrous reset addiction that's taken place, but it feels like we're on the precipice.
How are you all toying with running this kind of thing in a mega quantized way locally? Two weeks out from released weights, but this is still just GLM 5.2 with post-training magic.
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I can run this at home. No guardrails, this is not shy of Sol and Fable, this crushes them in my book. It's not just about evals, but what I can do with the damn model.
The thing that blows me away is it does this at one quarter the total parameter count of K3 (and 40% active parameter count). There's plenty of room at the bottom.
> How are you all toying with running this kind of thing in a mega quantized way locally?
Sure, let me answer that in excessive detail. I briefly tried running the UD IQ3_S quant of GLM-5.2, which is 288 GiB of weights (301 GB). Setup was: llama.cpp, 1x NVMe SSD (Evo 980), 64 GiB DDR5-5200, i9-13900HX, and 1x RTX Pro 6000. Token generation around 0.7 t/s. Not remotely usable interactively, but something I could plausibly push a codebase into and come back to a review in a couple of days.
There's potential for that hardware to go much faster, but current local inference backends make poor use of the memory hierarchy. Ideally I would have: always-active weights, KV and hot expert cache in VRAM; warm expert victim cache in host RAM; and disk as a last resort. Instead it's 1/3rd of the layers fully pinned in VRAM (all experts), and 2/3rds running wholly on the CPU with mmap()'d weights. The CPU cores spend most of their time sleeping on disk fills.
llama.cpp has backed itself into a bit of a corner architecturally by trying to support all models on all possible backends. If you look into how their "MoE offload" feature works (not viable for me because it requires enough host RAM to permanently pin the weights) you very quickly realise it's "oops, all bubbles!" due to the static compute graph splits. There are more focused frameworks like DS4 [1] and Colibri [2] which have better support for streaming weights from disk, and support GLM-5.2.
Obviously I wouldn't recommend my setup for huge models like GLM-5.2. Supposedly it can just about be squeezed into 3x GB10, or run comfortably on 4x GB10 (tensor-parallel) for multi-user serving. I'm not sure whether that qualifies as local, but it's at least not a rack.
> This is absolutely still shy of Sol and Fable
Not sure about Sol as I haven't used it, but, at least for security work -- does it matter? It's not like you will be allowed to use Fable (or access Mythos) for anything cybersecurity-related unless your name is "Dario Amodei" or you are one of his rich friends. So regardless of how good Fable/Mythos is here it's a completely moot point for normal people, because they can't use it for that anyway.
Have you seen the news about decrypting the hidden COT in U.S. models? [0] The decoded logs revealed instances where Claude memorized answers to test questions beforehand while making its final output look like it had derived the answer step-by-step—hiding the memorization from the user.
> this is just GLM 5.2 with post-training magic
Isn't post-training turning out to be the most important part?
DwarfStar (https://github.com/antirez/ds4) supports GLM 5.2 and DeepSeek. Not only for toying, but for getting work done.
> This is absolutely still shy of Sol and Fable, but only just by a hair
What's crazy is that this is a relatively small model - approx. 750B total, 40B active params, while Sol and Fable are one or two tiers above that (Kimi 3 and Qwen 3.8 also ~3T params).
Realistically, you're looking at least 2x DGX sparks to run this at a 2 bit quant, but quantization really lobotomizes models so it's just better to run DSv4 flash at full precision.
4x DGX sparks should let you run this at 4 bit at least and there are some folks who ran GLM 5.2 on this configuration in r/LocalLlama
Each time I try to use GLM it is under heavy load and I get downgraded to the older model. So much so that I have given up trying to stop wasting my own time.
I rather pay a few bucks more and not have to deal with that nonsense
Fable finished training 6+ months ago.
At this point, Anthropic only needs to release models to the public when the competition forces them to.
OpenAI also has a better model (Astra) that they haven't released yet.
the difference is that with open models jailbreaking is trivial if you know what you are doing so this makes a frontier open model infinitely more useful for certain tasks seeing as closed frontier models will just refuse (and jailbreaking them is a waste of time when you have good open models).
in some cases (mainly reverse engineering) I have observed GLM 5.2 jailbreaking itself with no effort on my part, the thinking trace revealed that it did some mental gymnastics to pretend it was a crackme or capture the flag competition.
> This is absolutely still shy of Sol and Fable, but only just by a hair.
Even if there was a small/medium gap, the fact that this is a free model beats both of the above on pure economics.
> but this is still just GLM 5.2 with post-training magic.
So exactly the same as Opus 5 and GPT 5.6 Sol. It's all "post-training magic".
OpenAI and Anthropic are both seeking trillion IPOs, while Chinese labs are pumping out open-weight models that are free for US providers to host and monetize.
These Chinese models cost less of US SOTA models to run, even if they are less capable. Providers can just run them, offer cheap tokens, and pocket the margin.
I just don't see how you justify a trillion valuation for US AI labs when the underlying models are being commoditized this fast.