English(EN) I Ran an LLM Locally on My ASUS ROG Ally and Here's What I Actually Learned

ASUS ROG Ally因内存限制难以部署本地LLM

作者 PulseAugur 编辑部 · [1 个来源] · 2026-07-01 22:57

一位用户尝试在ASUS ROG Ally游戏掌机上本地运行大型语言模型（LLM），发现了显著的硬件限制。主要挑战是图形处理单元（GPU）可访问内存（UMA帧缓冲）不足，导致系统默认使用速度慢得多的CPU处理。在BIOS中增加UMA帧缓冲被证明是最有效的优化方法，这凸显了在本地部署LLM时理解特定硬件架构的重要性。 AI

影响强调了本地部署LLM的硬件限制，暗示其更适合特定用例而非替代云服务。

排序理由关于在消费级硬件上部署特定AI模型的用户体验报告。

在 dev.to — LLM tag 阅读 →

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dev.to — LLM tag TIER_1 English(EN) · Peter Grishechkin · 2026-07-01 22:57

我在 ASUS ROG Ally 上本地运行了一个 LLM，我学到了什么

<h2> TL;DR: </h2> <p>I ran an LLM locally on my ASUS ROG Ally for a few weeks, expecting a fun tinkering project and got a real lesson in hardware limits instead. The fastest model wasn't the best choice, the "obvious" memory fixes mostly didn't work, and the actual value showed …

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我在 ASUS ROG Ally 上本地运行了一个 LLM，我学到了什么

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