Reddit 用户在旧笔记本本地运行 Qwen3.8 27B Q4 编程
r/LocalLLaMA 的一名 Reddit 用户称，在配备 20GB 内存、4GB 显存和第八代 i7 处理器的笔记本上，通过 LM Studio 使用 Qwen3.8 27B Q4 进行编程和游戏模组开发。其测速约为每秒 0.67–0.77 个 token，并称部分任务需要多轮迭代，主要使用 Pi agent，偶尔使用 Opencode。该内容来自单一用户的未经独立验证的叙述，模型身份与性能也未获独立确认。
该帖给出了低端硬件配置和本地 AI 编程工作流的具体实例，但其测速与使用结果仍属于个人经验。
my laptop is very old, like 20G of ram 4G Vram 256G NVMe Gen3 From samsung i7 8th gen
on these specs i got around 0.67 - 0.77 token per second yes, extremely slow, most of you won't even like it, or use it
but free APIs these days are rare, and extremely limited usage, and my country has banned worldwide transactions, so No Claude, No GPT, No Argon
my only way is Local LLMs, and here we are
i achieved some projects with Qwen, actually, it fixed bugs in my old projects, i used it for modding games (like grey hack, Cell to Singularity, Some of itch io games, using Dnspy and Ghidra) it done pretty well, some tasks required iterations
i used LM Studio, and for coding i used Pi agent mostly, and sometimes Opencode
i genuinly loved that model, but lately i have been interested more in Ornith 1.5 122B A10B, i am planning to download it, and use mmap with Q3 K XL, i loved to share my experience here
#Qwen3.8 27B Q4#LM Studio#Pi agent#Opencode#Ornith 1.5 122B A10B#DnSpy#Ghidra