Unsloth 发布 Qwen3.8-Flash GGUF，75GB 内存可本地运行
Qwen3.8-Flash can now be run locally! 🔥
Unsloth 发布 Qwen3.8-Flash 的 GGUF 量化版本，可在 75GB 内存的本地环境运行这个 125B MoE 模型，并称其表现超过 Claude-Opus-4.6（Max）。同时发布的 Qwen3.8-Flash-Next 支持 CPU 内存与统一内存方案，速度接近显存运行。Unsloth 提供了使用指南和 GGUF 下载链接。被引用的 Qwen 官方介绍显示，Qwen3.8-Flash 为多模态 MoE，采用 GDN + QSA 混合注意力等新架构，每 token 激活 6B 参数，原生上下文 262K，可经 YaRN 扩展至 1M。
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Unsloth 给出了 Qwen3.8-Flash 的本地运行路径与内存门槛，可据此判断消费级硬件能否跑起这个 125B MoE 模型。
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查看原帖⚡Meet Qwen3.8-Flash, a multimodal MoE and an early preview of the Qwen4 architecture, now open-weight! The production version Qwen3.8-Flash will be available soon via QwenCloud API at just $ 0.16/1M input tokens and $ 0.47/1M output tokens. 125B parameters + 51B N-gram embeddings, with just 6B activated per token. Unmatched cost-efficiency. What's new:…
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⚡Meet Qwen3.8-Flash, a multimodal MoE and an early preview of the Qwen4 architecture, now open-weight! The production version Qwen3.8-Flash will be available soon via QwenCloud API at just $ 0.16/1M input tokens and $ 0.47/1M output tokens. 125B parameters + 51B N-gram embeddings, with just 6B activated per token. Unmatched cost-efficiency. What's new: 🥳 - Next architecture: GDN + QSA hybrid attention, Gated Residual, N-gram Embedding & Muon optimizer, serving as a precursor to the architecture used in Qwen4. - Dramatically lower training and inference costs: trained at just 1/9 the cost of Qwen3.7-Plus, while outperforming it across the board with especially strong gains in coding and office tasks. - Strong performance: scoring 58.7 on DeepSWE 1.1, 62.5 on SWE-bench Pro, 73.9 on CoWorkBench, 84.5 on AndroidWorld, and 95.7 on MathVision (with CI). - 262K native context, extensible to 1M with YaRN. We’re also releasing the weights for Qwen3.8-Flash-Next, giving the community an early look at the new architecture we’re exploring for Qwen4.🚀 We can't wait to see what you build with Qwen3.8-Flash!👀👇 - Blog: https://t.co/M5hYypFLgJ - Technical Report: https://t.co/IF0gObIkQO - Hugging Face: https://t.co/6ow8QVAABt - ModelScope: https://t.co/tDOn2jNuFG
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Qwen3.8-Flash can now be run locally! 🔥
The 125B MoE model outperforms Claude-Opus-4.6 (Max).
Run on 75GB RAM via Unsloth GGUFs.
Qwen3.8-Flash-Next enables CPU RAM / unified mem setups to deliver near VRAM speeds.