Nathan Lambert：中国AI实验室缺的不是人才，是算力
Nathan Lambert 说 OpenAI 普通研究员手里有 5000 张 H100，中国研究员听完下巴都掉了，还聊了蒸馏和 RL 环境市场，挺有信息量。
Trillium Labs 联合创始人 Nathan Lambert 在 a16z 播客中表示，中国AI实验室技术水平优秀，但算力严重不足。他说当他告知中国研究人员 OpenAI 一名普通研究员可调用约 5000 张 H100 时，对方非常震惊。他还认为蒸馏带来的领先优势只有几个月，称美国实验室独占好模型的想法里有不少自我安慰。他估计数学和代码之外的前沿进展，很大程度依赖实验室花钱购买数据和 RL 环境这两个规模达数十亿美元的市场。
Trillium Labs 的联合创始人Nathan Lambert ： “中国AI实验室在技术上非常优秀，但是他们非常的缺乏计算资源。” 当他告诉中国AI实验室的研究人员：“OpenAI 只是一名普通研究员可以随时调用相当于 5000 张 H100 算力的时候，对方非常震惊的感觉下巴就直接掉到地板上...." “毫无疑问，他们拥有非常多的人才” “他们缺少的是计算资源” Your browser does not support the video tag. 🔗 View on Twitter a16z @a16z Nathan Lambert, former post-training co-lead at the Allen Institute for AI, now launching Trillium, a nonprofit open AI lab, joins a16z's Lisha Li to discuss whether open models can really keep up with the frontier. When he tells researchers in China that an average OpenAI researcher reportedly has five thousand H100s to work with, "their jaw just falls to the floor." In his view, Chinese labs have the talent but are mostly short on compute. He also thinks the distillation story is overblown. Based on the research, he puts its edge at a few months, and says "there's a bit of cope" in thinking only American labs can build good models. A lot of frontier progress outside math and code comes down to labs buying 1) data and 2) RL environments. It's a multibillion-dollar market, and some of it is sloppy enough to teach models to reward hack. That's a big part of why Trillium exists. Nathan wants to give away the environments other people would sell, and show exactly how his team uses AI to build AI, including where the models still get stuck. His bet is that the more people can see how this works, the better prepared everyone is for what's coming. 0:55 Can open models keep up with the frontier? 2:05 What it takes to reach the frontier 3:55 Open weights and the fast-follower playbook 8:25 Chinese labs: equal talent, less compute 9:55 Why distillation is harder than it looks 14:55 Is distillation theft or competition? 16:20 The lab flywheel vs owning your weights 21:45 Can general models beat every specialist? 26:30 AI at 1% of today's cost in 4 years 27:15 The RL env boom and reward hacking 31:30 What Trillium will build in the open 35:30 Breaking into AI in the age of AI slop 38:05 Open-sourcing how AI builds AI 44:25 Why Nathan isn't pre-training 49:45 Agent swarms and why lab secrets leak 54:25 Fast takeoff vs risks already here YouTube: youtu.be/BKuK0M6oDTI @natolambert @trillium_labs @lishali88 Your browser does not support the video tag. 🔗 View on Twitter 🔗 View Quoted Tweet 💬 3 🔄 1 ❤️ 9 👀 1699 📊 3 ⚡
