Qwen 2.1 图像模型通过token优化实现零依赖启动
Qwen团队用token优化实现零依赖启动，证明agent可自主完成kernel开发，比人工更高效。
阿里巴巴Qwen团队使用token优化技术，从零开始启动Qwen 2.1图像模型，无需任何依赖。
测试显示，尽管消耗大量tokens，该模型与vllm项目的omni kernels相比仍有20%的端到端性能差距。研究人员表示，通过agent完全从零实现kernel实现是完全可行的。
It's great to see @LigengZhu is able to much so much progress and is so token efficient.
I believe kernel optimization would be one of the first field that gets automated by burning tokens.
I ran a few tests to bootstrap @Alibaba_Qwen image 2.1 from scratch with 0 dependencies by burning a ton of tokens, yet I still can't beat @vllm_project omni kernels with 20% gap end2end. Amazing work Zifeng!
It was a great learning experience anyway - at least it proves that kernel implementation from scratch all by agents is absolutely doable. Imagine how much time you need to do so by hands or with agents 1 year ago. The next step is to make it more token efficient.
Stay tuned.
本条情报归属 AITOP「模型」核心栏目，重点聚焦中国 AI 产业与国产大模型生态。在跨区域产业观察坐标中，该动态直接关联「AI 开发者工具链、Coding Agent 与工程化实践」「中美大模型技术代差与商业化对照」研究主线，反映出当前技术演进与商业落地的最新风向。涉及核心实体与技术议题包括：Qwen、vllm、kernel优化、token效率，读者可通过下方标签进入对应专题页查阅历史脉络与关联对比。
信源核验说明：本文原始线索采编自「Xianbao Qian」（@Xianbao_QIAN），经 AITOP 多维评分管线结构化提炼。
