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Claude Sonnet 4.6 的",[29,88,89],{},"79.6%","。考虑到 35B-A3B 采用 MoE 架构，总参数 350 亿、推理时仅激活",[29,92,93],{},"30 亿","，这个逼近显得更有性价比。",[19,96,97,98,101],{},"加速倍数的对比更直观：",[29,99,100],{},"Claude Opus 带来 15 倍加速，本地 Qwen 带来 5 倍加速","，后者完全免费、离线运行、数据不出本机。",[19,103,104],{},"这是 AIHOT 给出的结论：\"本地编程工具栈正在快速成熟，'迷你工厂'模式正在实时上演。\"",[11,106,107],{"id":107},"硬件鸿沟",[19,109,110],{},"但 5 倍加速的前提条件，藏在 arjie 和 HappySweeney 的对比里。",[19,112,113],{},"arjie 的 160 tok/s ，需要两张 Blackwell 专业卡。HappySweeney 的 0.7 tok/s ，来自\"Optane+大内存\"这种非常规组合。两人硬件投入的差距，可能超过 10 倍。",[19,115,116],{},"Qwen 3.6 35B-A3B 的 MoE 设计本来是为了降低门槛——推理只激活 30 亿参数，理论上消费级硬件就能跑。但 HN 评论区的现实是，真正跑出可用速度的用户，要么有专业级 GPU ，要么愿意忍受秒级延迟。",[19,118,119],{},"tumetab1 在 M4 上的体验印证了这一点：消费级芯片跑本地模型，速度\"明显低于云端\"。他提出的第二个问题更尖锐——缺少企业级工具帮用户选择合适的本地模型。模型很多，但没人告诉普通开发者哪个能跑、哪个能写、哪个值得折腾。",[11,121,122],{"id":122},"初级工程师与高级工程师",[19,124,125],{},"一位 HN 评论者用了一个精准的比喻：",[19,127,128],{},"\"把智能体版的 Qwen3.6 35b 和 Claude Opus 对比，就像是一个知识面广但需要你处处引导的初级工程师，与一位能和你一起思考架构的高级工程师之间的差别。\"",[19,130,131],{},"这个比喻点出了 5 倍加速背后的代价。Qwen 可以免费跑、可以离线、可以保护隐私，但它需要开发者投入更多心智负担去引导、去纠错、去填补模型能力的缝隙。",[19,133,134],{},"Claude Opus 贵，但它能分担架构思考。Qwen 便宜，但要开发者自己扛架构。",[19,136,137],{},"能力差距只是表象，成本结构才是分水岭。15 倍加速对应的成本是 5 倍加速的数倍。对个人开发者来说，这个价差是否值得，取决于他们的时间成本和对隐私的权衡。",[11,139,140],{"id":140},"三层结构",[19,142,143],{},"HN 帖子下面三个人的选择，恰好勾勒出本地编程市场的三层结构。",[19,145,146],{},"arjie 代表第一层：专业硬件、愿意折腾、但习惯仍让他们留在云端。本地模型对他们来说是能力储备，日常仍付费。",[19,148,149],{},"HappySweeney 代表第二层：尝试过 full-fat 模型、遭遇过翻车、硬件投入产出比不佳。他们构成本地编程的劝退样本。",[19,151,152],{},"tumetab1 代表第三层：消费级硬件、试过一次、发现速度和工具都不够。他们构成了\"想试但试不动\"的大多数。",[19,154,155],{},"5 倍加速的叙事成立，但它服务的对象，目前只是第一层中愿意付出硬件成本的那一小部分。真正的瓶颈是硬件和工具链，模型参数早已够用。",{"title":157,"searchDepth":158,"depth":158,"links":159},"",2,[160,161,162,163,164,165],{"id":13,"depth":158,"text":14},{"id":17,"depth":158,"text":17},{"id":52,"depth":158,"text":53},{"id":107,"depth":158,"text":107},{"id":122,"depth":158,"text":122},{"id":140,"depth":158,"text":140},"2026-06-17T00:00:00.000Z","md","#202606AI资讯",null,{},true,"/post/nezus/2026/06/5-160-tok-s-0-7-tok-s","---\ntitle: \"5倍加速背后：160 tok/s起飞，0.7 tok/s坠毁\"\ndate: 2026-06-17\ngroup: \"#202606AI资讯\"\ndescription: \"Hacker News 上一个帖子标题平平无奇——\\\"有人用本地模型替换 Claude/GPT 写日常代码吗？\\\"\"\nauthor: storytelling\n---\n\n\n## 5 倍加速背后： 160 tok/s 起飞， 0.7 tok/s 坠毁\n\n## 三条真实评论\n\nHacker News 上一个帖子标题平平无奇——\"有人用本地模型替换 Claude/GPT 写日常代码吗？\"\n\n评论区只有三个人发言。但三个人恰好拼出了本地编程现状的完整切面。\n\narjie 拥有两张 RTX Pro 6000 Blackwell 显卡，跑 DeepSeek V4 Flash ，速度**160 tok/s**。他搭配 Agent 工具 Pi ，形容体验\"blazing fast\"。然后他说了一句很诚实的话：习惯还是让他留在 Claude Code 和 Codex。\n\nHappySweeney 的硬件配置更怪——Optane 内存加大内存，跑 full-fat 模型。速度：**0.7 tok/s**。他让 Kimi 2.6 和 GLM 5.1 做 AVX512 位矩阵转置，两个模型都\"failed miserably\"。\n\ntumetab1 在 Apple M4 上试了 Gemma 4 ，速度明显低于云端。他指出了一个被忽略的问题：企业级工具缺失，没人知道该怎么选本地模型。\n\n三条评论，三种命运。本地编程的真相，就藏在这 160 与 0.7 的**228 倍差距**里。\n\n## 5 倍加速的数据支撑\n\n如果把视野从 HN 评论区拉回到更大的讨论池，本地编程的拥趸们确实拿出了成绩单。\n\n一份被反复引用的追踪数据显示，在本地编程模型的讨论中，**Qwen 3.6 35B-A3B**以**33%的提及率**领跑， 27B 变体以**20%**紧随其后， DeepSeek Pro 和 Gemma4 31B 分列三四位。Agent 工具侧， Pi 以**49%**占比领先， OpenCode 以**45%**紧随。\n\n性能数据同样亮眼。SWE-bench Verified 基准上， Qwen3.6 27B 得分**77.2%**， 35B-A3B 得分**73.4%**，已逼近 Claude Sonnet 4.6 的**79.6%**。考虑到 35B-A3B 采用 MoE 架构，总参数 350 亿、推理时仅激活**30 亿**，这个逼近显得更有性价比。\n\n加速倍数的对比更直观：**Claude Opus 带来 15 倍加速，本地 Qwen 带来 5 倍加速**，后者完全免费、离线运行、数据不出本机。\n\n这是 AIHOT 给出的结论：\"本地编程工具栈正在快速成熟，'迷你工厂'模式正在实时上演。\"\n\n## 硬件鸿沟\n\n但 5 倍加速的前提条件，藏在 arjie 和 HappySweeney 的对比里。\n\narjie 的 160 tok/s ，需要两张 Blackwell 专业卡。HappySweeney 的 0.7 tok/s ，来自\"Optane+大内存\"这种非常规组合。两人硬件投入的差距，可能超过 10 倍。\n\nQwen 3.6 35B-A3B 的 MoE 设计本来是为了降低门槛——推理只激活 30 亿参数，理论上消费级硬件就能跑。但 HN 评论区的现实是，真正跑出可用速度的用户，要么有专业级 GPU ，要么愿意忍受秒级延迟。\n\ntumetab1 在 M4 上的体验印证了这一点：消费级芯片跑本地模型，速度\"明显低于云端\"。他提出的第二个问题更尖锐——缺少企业级工具帮用户选择合适的本地模型。模型很多，但没人告诉普通开发者哪个能跑、哪个能写、哪个值得折腾。\n\n## 初级工程师与高级工程师\n\n一位 HN 评论者用了一个精准的比喻：\n\n\"把智能体版的 Qwen3.6 35b 和 Claude Opus 对比，就像是一个知识面广但需要你处处引导的初级工程师，与一位能和你一起思考架构的高级工程师之间的差别。\"\n\n这个比喻点出了 5 倍加速背后的代价。Qwen 可以免费跑、可以离线、可以保护隐私，但它需要开发者投入更多心智负担去引导、去纠错、去填补模型能力的缝隙。\n\nClaude Opus 贵，但它能分担架构思考。Qwen 便宜，但要开发者自己扛架构。\n\n能力差距只是表象，成本结构才是分水岭。15 倍加速对应的成本是 5 倍加速的数倍。对个人开发者来说，这个价差是否值得，取决于他们的时间成本和对隐私的权衡。\n\n## 三层结构\n\nHN 帖子下面三个人的选择，恰好勾勒出本地编程市场的三层结构。\n\narjie 代表第一层：专业硬件、愿意折腾、但习惯仍让他们留在云端。本地模型对他们来说是能力储备，日常仍付费。\n\nHappySweeney 代表第二层：尝试过 full-fat 模型、遭遇过翻车、硬件投入产出比不佳。他们构成本地编程的劝退样本。\n\ntumetab1 代表第三层：消费级硬件、试过一次、发现速度和工具都不够。他们构成了\"想试但试不动\"的大多数。\n\n5 倍加速的叙事成立，但它服务的对象，目前只是第一层中愿意付出硬件成本的那一小部分。真正的瓶颈是硬件和工具链，模型参数早已够用。",{"title":5,"description":21},"post/nezus/2026/06/5-160-tok-s-0-7-tok-s","PUOPQpvqFyKOrHaZjeZ06jMLXFGJUa_5T8T7TCKc6sA",[178,184,188,192,196,201,204,209,213,217,221,226,230,234,238,242,246,251,256,260,264,268,272,277,281,285,294,299,303,307,312,316,320,324,328,333,338,343,347,351,355,360,364,368,373,377,381,385,389,394,398,403,407,411,415,420,424,428,433,437,441,445,449,450,454,458,463,467,471,476,480,484,488,492,496,501,505,509,513,518,522,526,530,535,539,543,547,551,556,560,564,568,572,577,581,588,594,599,604,609,614,619,624,629,634,639,645],{"id":179,"path":180,"title":181,"date":182,"tags":169,"group":183,"lastmod":169,"author":6,"sort":169},"content/post/nezus/2026/07/2026-7-16.md","/post/nezus/2026/07/2026-7-16","2026年7月16日下午，台积电法说会上传出一个数字，让全球半导体圈炸了锅。","2026-07-17T00:00:00.000Z","#202607AI资讯",{"id":185,"path":186,"title":187,"date":182,"tags":169,"group":183,"lastmod":169,"author":6,"sort":169},"content/post/nezus/2026/07/anthropic-ai.md","/post/nezus/2026/07/anthropic-ai","一个周末干掉四年：Anthropic 用 AI 两周搬完百万行代码，工程师沉默了",{"id":189,"path":190,"title":191,"date":182,"tags":169,"group":183,"lastmod":169,"author":6,"sort":169},"content/post/nezus/2026/07/google-20.md","/post/nezus/2026/07/google-20","欧盟一纸裁决，Google 20年护城河被撕开第一道口子",{"id":193,"path":194,"title":195,"date":182,"tags":169,"group":183,"lastmod":169,"author":6,"sort":169},"content/post/nezus/2026/07/xai-grok.md","/post/nezus/2026/07/xai-grok","xAI给Grok装上定时任务能力",{"id":197,"path":198,"title":199,"date":200,"tags":169,"group":183,"lastmod":169,"author":6,"sort":169},"content/post/nezus/2026/07/apple.md","/post/nezus/2026/07/apple","Apple智能落地中国，阿里千问成幕后赢家","2026-07-16T00:00:00.000Z",{"id":202,"path":203,"title":199,"date":200,"tags":169,"group":183,"lastmod":169,"author":6,"sort":169},"content/post/nezus/2026/07/deepmind.md","/post/nezus/2026/07/deepmind",{"id":205,"path":206,"title":207,"date":200,"tags":169,"group":183,"lastmod":169,"author":208,"sort":169},"content/post/nezus/2026/07/gpt-vibe-coding-paste.md","/post/nezus/2026/07/gpt-vibe-coding-paste","用GPT Vibe Coding一个常用付费软件：Paste","user",{"id":210,"path":211,"title":212,"date":200,"tags":169,"group":183,"lastmod":169,"author":6,"sort":169},"content/post/nezus/2026/07/grok-build-agent.md","/post/nezus/2026/07/grok-build-agent","马斯克开源Grok Build，编程Agent进入白嫖时代",{"id":214,"path":215,"title":216,"date":200,"tags":169,"group":183,"lastmod":169,"author":6,"sort":169},"content/post/nezus/2026/07/openai-ai.md","/post/nezus/2026/07/openai-ai","OpenAI造了个AI黑客，专攻自家模型",{"id":218,"path":219,"title":220,"date":200,"tags":169,"group":183,"lastmod":169,"author":6,"sort":169},"content/post/nezus/2026/07/wps-comate-copilot.md","/post/nezus/2026/07/wps-comate-copilot","金山办公掏出WPS Comate，正面硬刚微软Copilot",{"id":222,"path":223,"title":224,"date":225,"tags":169,"group":183,"lastmod":169,"author":6,"sort":169},"content/post/nezus/2026/07/295b.md","/post/nezus/2026/07/295b","腾讯把295B大模型塞进了单张显卡","2026-07-15T00:00:00.000Z",{"id":227,"path":228,"title":229,"date":225,"tags":169,"group":183,"lastmod":169,"author":6,"sort":169},"content/post/nezus/2026/07/git-cursor.md","/post/nezus/2026/07/git-cursor","打开一个Git仓库Cursor就自动执行了恶意代码",{"id":231,"path":232,"title":233,"date":225,"tags":169,"group":183,"lastmod":169,"author":6,"sort":169},"content/post/nezus/2026/07/gpt-5-6-mac.md","/post/nezus/2026/07/gpt-5-6-mac","GPT-5.6偷偷删光了我的Mac文件",{"id":235,"path":236,"title":237,"date":225,"tags":169,"group":183,"lastmod":169,"author":208,"sort":169},"content/post/nezus/2026/07/grok-cli-xai.md","/post/nezus/2026/07/grok-cli-xai","Grok CLI 偷传代码：xAI 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