Covers Chinese-language content from October 2026 on running open-source models locally: memory and VRAM requirements for quantized DeepSeek and Qwen versions, offline use of Gemma, measured speeds on different hardware, and trade-offs in choosing a graphics card. Suited for people who want to run models on their own machines.
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Unsloth's quantized DeepSeek V4.1 Flash runs with llama.cpp on 64GB of RAM, and the Engram table can be moved to an SSD and read on demand.
More aggressive 1.75 to 2 bpw quantization plus SSD memory mapping lets a 12GB graphics card paired with 46 to 64GB of RAM run the 763B MoE model.
A machine with 128GB of unified memory can run a 235-billion-parameter model, or a 284-billion-parameter model quantized to 1.6 bits at about 60GB. It has more room than a single graphics card's VRAM.
15GB of VRAM can run Silero VAD, Whisper, llama.cpp, and Qwen3-TTS at the same time, building real-time voice chat entirely locally.
Connect with Tailscale and set the Qwen model on your home graphics card as the default model for a remote Agent. This uses electricity in place of API credits and queue cooldowns.
Gaming cards have their FP16 accumulation throughput limited to half speed. This affects fine-tuning, image generation, and long prompt prefill, while chat is barely affected. 30-series cards can be unlocked with an open-source module.
The Gemma family suits on-device applications. Gemma 4 generates responses and EmbeddingGemma 2 handles semantic search, and it's already used for offline notes and file search on Mac.
SuSu_酥酥👅10-24ZHOpen-source kernel module unlocks the half of Tensor compute locked on 30-series graphics cards#1💥卧槽！6年前的3090被“破解”了！Tensor算力直接翻倍，官方锁了一半的性能被开源一键释放！ NVIDIA长期把30系显卡的FP16/BF16 Tensor指令发射速率限制在专业卡一半左右。本地部署ImageOriginal post657KLikes 738实践哥 Li10-10ZHUse Tailscale to connect your local Qwen to Muse as the default model#2🎉 全体起立！有显卡的注意了！ Muse/Grokbot真正的无限token来了，只需要5毛钱电费，本地部署ImageOriginal post201KLikes 643huangserva10-30ZHRun the new quantized DeepSeek version locally on 64GB of RAM#3🤯 彻底疯了！本地大模型直接变天！ Unsloth 刚刚放出 DeepSeek-V4.1-Flash 最新 Dynamic V3.1 量化版，现在只要 64GB 内存就能原地起飞！本地部署Original post182KLikes 2.1KSuSu_酥酥👅10-24ZHRun the 763B DeepSeek quantized version locally on a 12GB graphics card with an SSD🔥牛逼了！12GB显卡本地跑763B！DeepSeek-V4.1-Flash-1017量化版炸裂登场，前沿质量几乎零损失！ ISTA-DASLab团队发布GSQ-RCO非均匀量化版，Strata引擎原生支持。本地部署ImageOriginal post171KLikes 1.8KAYi10-11ZHGoogle Foresight uses Gemma 4 to take offline meeting notes on a Mac卧槽这谷歌波是真掀桌了， 他们刚出了个 Mac 端的 AI 笔记应用，免费，没有任何订阅。 叫做AI Edge Foresight，录音，转写，整理，提问，全在 Mac 本机离线跑，一分钱不收。本地部署ImageOriginal post141KLikes 1.1KAYi10-14ZHA 128GB unified memory mini PC runs a 235-billion-parameter model locally苏姿丰在台上单手托起一个 1499 美元、饭盒大小的迷你主机，正面硬刚老黄那台 4000 美元的 AI 盒子， 没有连数据中心，没有调云端接口，也没外接显卡坞，这个厚度跟平装书差不多的铁盒，当场在本地跑起了 2350 亿参数的大模型。本地部署ImageOriginal post134KLikes 473
lifcc10-14ZHSpeed test results for using a jailbroken PS5 as a local AI server老黄（NVIDIA）看了都要连夜报警的程度 😭 国外有个老哥把越狱的 PS5 爆改成了本地 AI 服务器 不仅做成了 PS5 原生 App本地部署ImageOriginal post121KLikes 1.1KSuSu_酥酥👅10-09ZHDeploy the enhanced Qwen 27B with llama.cpp on a 24GB card😱逆天！强化版 Qwen3.8-27B 竟比肩 Opus 5.5、GPT-6 Astra！ 明明只是 27B，却用 390 万条代码做专项后训练，思考长度砍近一半，LiveCodeBench 小集 83→90。本地部署ImageOriginal post115KLikes 1.5K
数字生命卡兹克10-16ZHRTX Spark Local Test: 27B Model at 30 Tokens per Second等了很久的英伟达RTX Spark电脑今天晚上8点终于开售了，在线下举办了首发体验会，真的是人山人海... 然后RTX Spark 128G+1T的，华硕ProArt创16酷卖37999，本地部署ImageOriginal post103KLikes 349
大罗SEO10-03ZHOpen-Source Project Runs a 128B Large Model on 12G VRAM at Up to 96 Tokens per Second这个项目太吊了 12G显存就能跑 Qwen 3.8 Flash 的 128B 版本，速度能到 96token/s本地部署Original post96KLikes 1.1KAndy L10-09ZHDeepSeek V4 Flash at 284B Quantized to About 60GB Runs on 128GB Unified Memory重磅！微软这波有点猛！ 据称现场演示 DeepSeek V4 Flash 本地运行！本地部署ImageOriginal post94KLikes 216
Austin10-17ZHSpark Machine vs. 4090: Which GPU to Choose for Running Large Models and Image Generation Locally别再买 DGX Spark 、RTX Spark 这种纯垃圾了。 跑 LLM 比不上多卡 2080ti ，甚至不如多卡V100。本地部署Original post87KLikes 431雪瑜10-11ZHA Mac Local File Content Search App Built with the EmbeddingGemma2 Small Model卧槽，Github上有人做了个基于Google EmbeddingGemma2 小模型的搜索Mac App本地部署ImageOriginal post80KLikes 798SuSu_酥酥👅10-30ZHRunning a Quantized DeepSeek V4.1 Flash Locally on 64GB RAM with llama.cpp🤯炸了！64GB 内存直接炸开最强开源模型！DeepSeek-V4.1-Flash-1017 本地高性能彻底落地！ Unsloth 正式放出 DeepSeek-V4.1-Flash-1017 全新 GGUF，本地部署ImageOriginal post77KLikes 942Xiao Tan10-24ZHWhich Local Workloads Suffer From Gaming GPUs' Half-Speed Compute Cap? Chat Barely Feels It3090 Ti 和 A6000 是同一块芯片。 区别在卡里的一个标志位。 一个写着 FULL_SPEED，一个写着 HALF_SPEED。 Reddit 上有个哥们翻了很多代码，把这事讲明白了。本地部署Original post69KLikes 924实践哥 Li10-10ZHUsing Tailscale to Network a Locally Run Qwen 3.8 Flash Next for Unlimited Agent Calls我有一个想法，让Muse使用PI接入本地的Qwen 3.8 FlashNext，真正的无限Token，只需要一点点电费，不限额。本地部署ImageOriginal post64KLikes 27A collection of Grok Bot practical tips shared by Chinese-speaking users in October 2026: how to assign tasks, how to save quota, and how to work with Claude Code and Codex. Suited for people who just got Grok Bot and don't know where to start.
A collection of hands-on Claude Code tips from the Chinese-language community in October 2026: settings that save quota, sub-agent division of labor, code review workflows, and how to use Skills, plugins, and Mods. Suited to people who want to use it more reliably and economically.
A roundup of hands-on Codex content from the Chinese-speaking community in Oct 2026: how to spend less quota, how to configure Skills and MCP, how to pair it with other Agents, and several official updates from this month. Suitable for subscribers who want to get more value out of their plan.