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My 8gb vram system as i try to load GLM-4.6-Q0.00001_XXXS.gguf:
(media1.tenor.com)
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this post was submitted on 25 Oct 2025
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Im not sure if it's a me issue but that's a static image. I figure you posted where they throw a brick into it.
Also, if this post was serious, how does a highly quantitized model compare to something less quantitized but with fewer parameters? I haven't seen benchmarks other than perplexity which isn't a good measure of capability?
It's a webp animation. Maybe your client doesn't display it right, i'll replace it with a gif
Regarding your other question, I tend to see better results with higher params + lower precision, versus low params + higher precision. That's just based on "vibes" though, I haven't done any real testing. Based on what I've seen, Q4 is the lowest safe quantization, and beyond that, the performance really starts to drop off. unfortunately even at 1 bit quantization I can't run GLM 4.6 on my system
What's higher precision for you? What I remember from the old measurements for ggml is, lower than Q3 rarely makes sense and roughly at Q3 you'd think about switching to a smaller variant. But on the other hand everything above Q6 only shows marginal differences in perplexity, so Q6 or Q8 or full precision are basically the same thing.
As a memory-poor user (hence the 8gb vram card), I consider Q8+ to be is higher precision, Q4-Q5 is mid-low precision (what i typically use), and below that is low precision
Thanks. That sounds reasonable. Btw you're not the only poor person around, I don't even own a graphics card... I'm not a gamer so I never saw any reason to buy one before I took interest in AI. I'll do inference on my CPU and that's connected to more than 8GB of memory. It's just slow ๐ But I guess I'm fine with that. I don't rely on AI, it's just tinkering and I'm patient. And a few times a year I'll rent some cloud GPU by the hour. Maybe one day I'll buy one myself.
That fixed it.
I am a fan of this quant cook. He often posts perplexity charts.
https://huggingface.co/ubergarm
All of his quants require ik_llama which works best with Nvidia CUDA but they can do a lot with RAM+vRAM or even hard drive + rams. I don't know if 8gb is enough for everything.
I think perplexity is still central to evaluating models. It's notoriously difficult to come up with other ways to measure these things.
Unsloth did a test and their dynamic quants were competitive even at 1 bit in aider benchmark https://docs.unsloth.ai/new/unsloth-dynamic-ggufs-on-aider-polyglot
Holy cow!