显存有限（特别是 8GB 或 12GB）的用户正在讨论运行本地大语言模型的策略。他们正在探索诸如 Qwen 3.5 9B 或 Qwen 微调的 MoE 等较小微调模型，并争论是否有必要升级到 24GB 显存以获得更好的性能。这次讨论突显了当前消费者硬件在模型效率方面的限制和期望的进步。 AI
影响 硬件有限的用户正在寻求高效的模型和策略来运行本地大语言模型，这表明对优化解决方案的需求。
排序理由 Reddit 用户关于运行大语言模型的硬件限制的讨论。
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<!-- SC_OFF --><div class="md"><p>I want to hook up a local model to Cline, but it seems the best model is still just Qwen 3.5 9B. <em>Please</em> can we have a Qwen 3.8 9B that gets close to Qwen 3.6 27B?</p> </div><!-- SC_ON -->   submitted by   <a href="https://www.red…
<!-- SC_OFF --><div class="md"><p>Seems like we're limited to qwen finetuned MoEs for now. Looking at the current landscape - focus seems to be on dense models (muse glimmer 30b, qwen 3.8 27b) for smaller setups. </p> <p>Is upgrading to 24GB VRAM the only option?</p> </div><!-- S…