使用Epyc CPU和双Tesla P40的$2500预算方案可在llama.cpp上运行GLM5.2 Q2–Q4版本
英文摘要
Reddit user u/segmond shares a sub-$2500 hardware configuration capable of running GLM5.2 at Q2, Q3, or Q4 quantization. The build features an Epyc motherboard/CPU combo ($460), two NVIDIA Tesla P40 24GB GPUs ($230 each), and 512GB DDR4 RAM ($1000), totaling approximately $1920 before adding a PSU, storage, and cooling (~$580). Inference is expected to be slow but functional with llama.cpp, and the same setup can run other large models like KimiK2.6, DeepSeek, and MiniMax. The poster notes the system is not suited for agent-based tasks but works for planning and debugging purposes, emphasizing that resourcefulness can make local SOTA model inference accessible without extreme budgets.
中文摘要
Reddit用户u/segmond分享了一套成本低于2500美元的硬件方案，可运行GLM5.2的Q2、Q3或Q4量化版本。该配置包含Epyc主板/CPU套装（460美元）、两块NVIDIA Tesla P40 24GB GPU（每块230美元）以及512GB DDR4内存（1000美元），基础部件总计约1920美元，加上电源、存储和散热约580美元即可完成。通过llama.cpp进行推理速度较慢，但能够正常工作；该方案还能运行KimiK2.6、DeepSeek和MiniMax等其他大模型。作者指出此配置不适合运行agent任务，但可用于规划和调试，并强调具备动手能力就无需极高预算便可实现本地顶尖模型推理。
关键要点
Full build cost is under $2,500, with core components (Epyc board + CPU, 2× Tesla P40 24GB, 512GB DDR4) costing around $1,920.
整套方案成本低于2500美元，核心组件（Epyc主板+CPU、2块Tesla P40 24GB、512GB DDR4）花费约1920美元。
Can run GLM5.2 at Q2, Q3, or Q4 quantization via llama.cpp, albeit with slow inference speed.
可通过llama.cpp运行GLM5.2的Q2、Q3、Q4量化版本，但推理速度较慢。
The same hardware also supports KimiK2.6, DeepSeek, MiniMax, and similar large models.
同样的硬件也支持KimiK2.6、DeepSeek、MiniMax等类似大模型。
Not suitable for agent-based workloads; best used for planning and debugging tasks.
不适用于基于agent的工作负载，最适合用于规划和调试任务。