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GLM-5.2 量化模型本地运行测试
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提供了量化后具体尺寸、精度和速度数据，值得关注开源模型本地部署可行性。核心解读
Unsloth AI 将 GLM-5.2 模型量化至 2-bit GGUF 格式，大小从 1.51TB 压缩至 238GB（-84%），保留约 82% 准确率，可在 256GB Mac Studio M3 Ultra 上以约 21.6 tok/s 速度本地运行，并与 Claude 4.8 Opus 和 GPT-5.5 进行了单次输出对比。
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AK (@_akhaliq) 转发了 Unsloth AI (@UnslothAI) 的帖子：
1-bit GLM-5.2 GGUF vs. Claude 4.8 Opus vs. GPT-5.5
We gave 3 models the same prompt and compared one-shot outputs.
The 1-bit GLM-5.2 GGUF ran locally on a Mac Studio M3 Ultra with 256GB RAM at ~21.6 tok/s.
Which output do you like best?
GGUF: https://huggingface.co/unsloth/GLM-5.2-GGUF https://t.co/UoXsCSh4Gn
https://video.twimg.com/amplify_video/2069417260104749056/vid/avc1/1920x1080/FzbVZMPg7R2r1Yi_.mp4?tag=28
> **引用原帖 Unsloth AI (@UnslothAI):**
> GLM-5.2 can now be run locally!🔥
> The 2-bit model retains ~82% accuracy after we shrunk it from 1.51TB to 238GB (-84% size).
> Run on a 256GB Mac or RAM/VRAM setups.
> GLM-5.2 is the strongest open model to date.
> Guide: https://t.co/bI7FeeKHDd
> GGUF: https://t.co/BMkxswdj5N https://t.co/qIPuU63W9D
> https://x.com/UnslothAI/status/2067588262156501497