Unsloth AI· @UnslothAI · X·· 2026-08-14精选AI 评分76
AI 导读
Unsloth 发布 Qwen3.8-27B 的 Dynamic GGUF 量化版本，4-bit 量化可在 17-19GB 内存设备上本地运行，并同时上传 NVFP4 量化。
推荐理由
原文给出了本地运行的具体内存门槛、量化版本和硬件对照表，读者可以据此判断能否在自己的设备上跑通这个 27B 模型。
正文 · 原文
Qwen3.8-27B can now be run locally! ✨
Run on 17GB RAM via Unsloth Dynamic GGUFs.
Qwen3.8-27B is by far the strongest model for its size. We also uploaded NVFP4 quants.
GGUF: https://huggingface.co/unsloth/Qwen3.8-27B-GGUF
Guide: https://unsloth.ai/docs/models/qwen3.8
We promised open weights for Qwen3.8. Now, time to meet them! 🎉 ⚡ Qwen3.8-27B: - A native multimodal dense model. With just 27B parameters, it outperforms Qwen3.7-Plus overall and shines in real-world coding & office workflows. - 262K native context, easily extendable to 1M tokens via YaRN. - Built for builders. Highly efficient, high-quality, and licensed under Apache 2.0. 🚀 The open weights for Qwen3.8-2.4T-A95B (Max-level) have also been released recently. Whether you're shipping lightweight applications with Qwen3.8-27B locally or building agents with Qwen3.8-2.4T-A95B, they're yours now! Download, deploy, and build something we haven't imagined yet. 👀👇 - Hugging Face： https://huggingface.co/collections/Qwen/qwen38 - ModelScope： https://www.modelscope.cn/collections/Qwen/Qwen38在 X 查看被引用的帖子
来源：Unsloth AI · x.com