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我做了个工具让 8GB 显卡跑 30B 模型从 3 tok/s 提到 21 tok/s，记录一下技术发现

最近在折腾本地大模型，发现一个核心问题：Ollama 和 LM Studio 能让模型跑起来，但参数全靠猜——上下文长度、KV cache 类型、MoE expert 放哪、ubatch 多大……用默认参数基本是在浪费显卡。于是做了个工具自动找最优配置，过程中踩了不少坑，记录一

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tech www.v2ex.com 2026-04-25 08:39:55+08:00

[分享发现] XChat 正式上架 App Store 了

有些失望，就是 X 的一个聊天 Tab 独立为 XChat 了，替代不了 TG ，也替代不了微信。

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Local Event Recipe Retention Coupon Profile Website 专题内容 Update Networking Education Folder Recommendation 专题内容 Project Restaurant Section Optimization Platform Landing Cust...Meeting Chapter Event Dashboard Client Whitepaper Prospect Fi...Presentation Cloud Whitepaper Feedback Automation 专题内容 API 专题内容 Digital Customer Collaboration Reporting Privacy Technology 专题内容 Retention Case Conversion Settings Marketing Link 专题内容 Identity Customization Budget Optimization Ranking Message 专题内容 Community 专题内容 Careers Desktop Notification Policy Domain Luxury 专题内容 Team Consulting Case Whitepaper Internet Settings Image Strat...Dashboard Sale 专题内容 Team Tutorial 专题内容 Coupon Alert Analytics Cost Profit 专题内容 Experience Price Community Backup Navigation Section Mileston...Share Feedback Customer Notification About Server Expensive T...Conversion Button Screen Blog Planning Discovery Notification...Subject Fashion Visitor Resolution Conference 专题内容 Discovery Partner Alliance Management Case Customization Roi...

tech v2ex.com 2026-04-25 08:14:21+08:00

[分享发现] XChat 正式上架 App Store 了

有些失望，就是 X 的一个聊天 Tab 独立为 XChat 了，替代不了 TG ，也替代不了微信。

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Local Event Recipe Retention Coupon Profile Website 专题内容 Update Networking Education Folder Recommendation 专题内容 Project Restaurant Section Optimization Platform Landing Cust...Meeting Chapter Event Dashboard Client Whitepaper Prospect Fi...Presentation Cloud Whitepaper Feedback Automation 专题内容 API 专题内容 Digital Customer Collaboration Reporting Privacy Technology 专题内容 Retention Case Conversion Settings Marketing Link 专题内容 Identity Customization Budget Optimization Ranking Message 专题内容 Community 专题内容 Careers Desktop Notification Policy Domain Luxury 专题内容 Team Consulting Case Whitepaper Internet Settings Image Strat...Dashboard Sale 专题内容 Team Tutorial 专题内容 Coupon Alert Analytics Cost Profit 专题内容 Experience Price Community Backup Navigation Section Mileston...Share Feedback Customer Notification About Server Expensive T...Conversion Button Screen Blog Planning Discovery Notification...Subject Fashion Visitor Resolution Conference 专题内容 Discovery Partner Alliance Management Case Customization Roi...

tech v2ex.com 2026-04-25 08:14:21+08:00

我的PR被Google合并了！！！

github.com/googleapis/python-genai docs: Add instruction for custom endpoint (#2335) main ← copybara/904525557 已合并 06:49PM - 23 Apr 26 UTC c

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tech linux.do 2026-04-25 07:46:59+08:00

我做了个工具让 8GB 显卡跑 30B 模型从 3 tok/s 提到 21 tok/s，记录一下技术发现

最近在折腾本地大模型，发现一个核心问题：Ollama 和 LM Studio 能让模型跑起来，但参数全靠猜——上下文长度、KV cache 类型、MoE expert 放哪、ubatch 多大……用默认参数基本是在浪费显卡。于是做了个工具自动找最优配置，过程中踩了不少坑，记录一

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tech www.v2ex.com 2026-04-25 06:39:55+08:00

我做了个工具让 8GB 显卡跑 30B 模型从 3 tok/s 提到 21 tok/s，记录一下技术发现

最近在折腾本地大模型，发现一个核心问题：Ollama 和 LM Studio 能让模型跑起来，但参数全靠猜——上下文长度、KV cache 类型、MoE expert 放哪、ubatch 多大……用默认参数基本是在浪费显卡。于是做了个工具自动找最优配置，过程中踩了不少坑，记录一

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tech www.v2ex.com 2026-04-25 06:39:55+08:00

我做了个工具让 8GB 显卡跑 30B 模型从 3 tok/s 提到 21 tok/s，记录一下技术发现

最近在折腾本地大模型，发现一个核心问题：Ollama 和 LM Studio 能让模型跑起来，但参数全靠猜——上下文长度、KV cache 类型、MoE expert 放哪、ubatch 多大……用默认参数基本是在浪费显卡。于是做了个工具自动找最优配置，过程中踩了不少坑，记录一

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tech www.v2ex.com 2026-04-25 06:39:55+08:00

我做了个工具让 8GB 显卡跑 30B 模型从 3 tok/s 提到 21 tok/s，记录一下技术发现

最近在折腾本地大模型，发现一个核心问题：Ollama 和 LM Studio 能让模型跑起来，但参数全靠猜——上下文长度、KV cache 类型、MoE expert 放哪、ubatch 多大……用默认参数基本是在浪费显卡。于是做了个工具自动找最优配置，过程中踩了不少坑，记录一

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tech www.v2ex.com 2026-04-25 06:39:55+08:00

【求助】有佬友把openclaw和Hermes同时部署的吗

如题 openclaw火我就安装了，Hermes的star飙升我也部署了，现在进入天才陨落纪，token匮乏，就浅度使用，两个换着用，openclaw用的多，主要让它写和总结东西，Hermes用的少，主要让它操作浏览器完成一些自动化操作的事情，对了，它们都是在VPS主机里直接部

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tech linux.do 2026-04-25 06:32:03+08:00

我做了个工具让 8GB 显卡跑 30B 模型从 3 tok/s 提到 21 tok/s，记录一下技术发现

最近在折腾本地大模型，发现一个核心问题：Ollama 和 LM Studio 能让模型跑起来，但参数全靠猜——上下文长度、KV cache 类型、MoE expert 放哪、ubatch 多大……用默认参数基本是在浪费显卡。于是做了个工具自动找最优配置，过程中踩了不少坑，记录一

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tech www.v2ex.com 2026-04-25 05:39:55+08:00

[慢讯] Codex Desktop 0.125.0 已支持 GPT-5.5

CPA + Codex 4 个帖子 - 3 位参与者阅读完整话题

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tech linux.do 2026-04-25 03:45:06+08:00

[Local LLM] 我做了个工具让 8GB 显卡跑 30B 模型从 3 tok/s 提到 21 tok/s，记录一下技术发现

最近在折腾本地大模型，发现一个核心问题：Ollama 和 LM Studio 能让模型跑起来，但参数全靠猜——上下文长度、KV cache 类型、MoE expert 放哪、ubatch 多大……用默认参数基本是在浪费显卡。于是做了个工具自动找最优配置，过程中踩了不少坑，记录一

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tech v2ex.com 2026-04-25 03:39:55+08:00

[Local LLM] 我做了个工具让 8GB 显卡跑 30B 模型从 3 tok/s 提到 21 tok/s，记录一下技术发现

最近在折腾本地大模型，发现一个核心问题：Ollama 和 LM Studio 能让模型跑起来，但参数全靠猜——上下文长度、KV cache 类型、MoE expert 放哪、ubatch 多大……用默认参数基本是在浪费显卡。于是做了个工具自动找最优配置，过程中踩了不少坑，记录一

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tech v2ex.com 2026-04-25 03:39:55+08:00

[Local LLM] 我做了个工具让 8GB 显卡跑 30B 模型从 3 tok/s 提到 21 tok/s，记录一下技术发现

最近在折腾本地大模型，发现一个核心问题：Ollama 和 LM Studio 能让模型跑起来，但参数全靠猜——上下文长度、KV cache 类型、MoE expert 放哪、ubatch 多大……用默认参数基本是在浪费显卡。于是做了个工具自动找最优配置，过程中踩了不少坑，记录一

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tech v2ex.com 2026-04-25 03:39:55+08:00

[Local LLM] 我做了个工具让 8GB 显卡跑 30B 模型从 3 tok/s 提到 21 tok/s，记录一下技术发现

最近在折腾本地大模型，发现一个核心问题：Ollama 和 LM Studio 能让模型跑起来，但参数全靠猜——上下文长度、KV cache 类型、MoE expert 放哪、ubatch 多大……用默认参数基本是在浪费显卡。于是做了个工具自动找最优配置，过程中踩了不少坑，记录一

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tech v2ex.com 2026-04-25 02:39:55+08:00

[Local LLM] 我做了个工具让 8GB 显卡跑 30B 模型从 3 tok/s 提到 21 tok/s，记录一下技术发现

最近在折腾本地大模型，发现一个核心问题：Ollama 和 LM Studio 能让模型跑起来，但参数全靠猜——上下文长度、KV cache 类型、MoE expert 放哪、ubatch 多大……用默认参数基本是在浪费显卡。于是做了个工具自动找最优配置，过程中踩了不少坑，记录一

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tech v2ex.com 2026-04-25 02:18:07+08:00

[Local LLM] 我做了个工具让 8GB 显卡跑 30B 模型从 3 tok/s 提到 21 tok/s，记录一下技术发现

最近在折腾本地大模型，发现一个核心问题：Ollama 和 LM Studio 能让模型跑起来，但参数全靠猜——上下文长度、KV cache 类型、MoE expert 放哪、ubatch 多大……用默认参数基本是在浪费显卡。于是做了个工具自动找最优配置，过程中踩了不少坑，记录一

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tech v2ex.com 2026-04-25 01:40:43+08:00

「听劝的富可敌国」OneToken.sh GPT-5.5，人民币2元100万Token，23-24充值的用户百分百补偿，抽10名500万平台Token

从「OneToken.sh」本站正式支持GPT-5.5，人民币2元100万Token，抽10位送500万Token【已听劝】继续讨论：官网： OneToken.sh 1M=100万Token 输入价格： 3元/M Token 输出价格： 12元/M Token 缓存输入：

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tech linux.do 2026-04-25 00:34:09+08:00

被标记为网络安全风险有啥影响不好怕被封号

此聊天已被标记为可能存在网络安全风险背景：我想codex帮我分析 GitHub - TheMythologist/GenP: Automatically builds GenP executables from source · GitHub 这个项目结果直接报错让我加入什么

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tech linux.do 2026-04-25 00:29:31+08:00

发现用GPT 5.5以后TOKEN额度消耗反而更慢了

第一大原因是上下文256k，我会经常性新建会话效率提升，很少问用户问题，让任务流畅执行今天调用2000多次也只用了140M 7 个帖子 - 5 位参与者阅读完整话题

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Local Event Recipe Retention Coupon Profile Website 专题内容 Update Networking Education Folder Recommendation 专题内容 Project Restaurant Section Optimization Platform Landing Cust...Meeting Chapter Event Dashboard Client Whitepaper Prospect Fi...Presentation Cloud Whitepaper Feedback Automation 专题内容 API 专题内容 Digital Customer Collaboration Reporting Privacy Technology 专题内容 Retention Case Conversion Settings Marketing Link 专题内容 Identity Customization Budget Optimization Ranking Message 专题内容 Community 专题内容 Careers Desktop Notification Policy Domain Luxury 专题内容 Team Consulting Case Whitepaper Internet Settings Image Strat...Dashboard Sale 专题内容 Team Tutorial 专题内容 Coupon Alert Analytics Cost Profit 专题内容 Experience Price Community Backup Navigation Section Mileston...Share Feedback Customer Notification About Server Expensive T...Conversion Button Screen Blog Planning Discovery Notification...Subject Fashion Visitor Resolution Conference 专题内容 Discovery Partner Alliance Management Case Customization Roi...

tech linux.do 2026-04-25 00:22:49+08:00

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