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English(EN) Run GLM 5.2 Locally (2026): 2-bit on a 256GB Mac or 4090 box

GLM 5.2 模型现已通过量化在消费级硬件上运行

作者 PulseAugur 编辑部 · [3 个来源] ·

拥有 7530 亿参数和 100 万 token 上下文窗口的 GLM 5.2 模型现已可用于在消费级硬件上本地部署。虽然完整模型需要超过 1.5 TB 的存储空间，但量化版本可在至少拥有 256 GB RAM 的机器上运行。在 256 GB Mac Studio 或类似配置的强大 GPU 上运行 2 位量化模型是可行的，但性能可能限制在每秒 3-9 个 token。为了获得最佳质量和速度，建议使用 512 GB 系统运行 4 位量化模型，但用户须知，对于大多数应用而言，托管 API 解决方案通常更具成本效益且速度更快。 AI

影响 使强大 LLM 的本地、私有或离线使用成为可能，惠及个人用户和技术爱好者。

排序理由 社区驱动的努力，旨在通过量化权重在消费级硬件上本地运行大型模型。

在 dev.to — LLM tag 阅读 →

AI 生成摘要 · Google Gemini · 来自 3 个来源。 我们如何撰写摘要 →

GLM 5.2 模型现已通过量化在消费级硬件上运行

本文如何被排名

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Research
社区驱动的努力，旨在通过量化权重在消费级硬件上本地运行大型模型。
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3 independent sources
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model release, product
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完整方法见我们的编辑标准。

报道来源 [3]

  1. r/LocalLLaMA TIER_1 English(EN) · /u/segmond ·

    在预算硬件上运行 GLM5.2 < $2500。

    <!-- SC_OFF --><div class="md"><p>Too many times I hear people whine about not being ble to run SOTA models or claim it would require $50k, or $100k. </p> <p><a href="https://www.ebay.com/itm/398079051468">https://www.ebay.com/itm/398079051468</a> Epcy Motherboard &amp; CPU - $46…

  2. r/LocalLLaMA TIER_1 English(EN) · /u/phwlarxoc ·

    GLM 5.2 运行在消费级硬件上

    <!-- SC_OFF --><div class="md"><p>I tried out the unsloth quants of GLM 5.2 on still &quot;consumer-ish&quot; hardware:</p> <p>32C Zen5 Threadripper Pro 9975 WX, Asus WRX90E-SAGE-SE PCIe Gen5, 512GB DDR5 ECC RAM @ 4800MHz, dual RTX 5090.</p> <p>This machine was put together pre-R…

  3. dev.to — LLM tag TIER_1 English(EN) · Owen ·

    本地运行 GLM 5.2 (2026)：256GB Mac 或 4090 盒式机上的 2 位

    <blockquote> <p>Zhipu put the GLM 5.2 weights on HuggingFace under an MIT license, so the question stopped being "can I download a frontier coding model" and became "will it run on the machine I already own." For a single Mac Studio or a desktop with one GPU and a lot of RAM, the…

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