Qwen Image 2.1 kernel optimization
This research shows how AI agents can automate complex technical tasks like kernel optimization, potentially reducing development time significantly. For developers and researchers in AI optimization, this represents a practical approach to automating low-level system programming tasks that traditionally require extensive manual work.
Ligeng Zhu tested bootstrapping Alibaba Qwen Image 2.1 from scratch with 0 dependencies by burning tokens.
The results showed a 20% performance gap compared to vLLM project's omni kernels.This demonstrates that kernel implementation from scratch by agents is absolutely doable.
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.
This report is categorized under AITOP's "Models" desk, focusing on China's AI industry and domestic foundation model ecosystem. Within our cross-regional analytical framework, it directly maps to AI developer toolchains, coding agents, and engineering workflows and US-China foundation model benchmarks and commercialization comparison. Key entities and technical topics under continuous tracking include Qwen Image 2.1, vLLM, kernel optimization, agents.
Source attribution: curated from Xianbao Qian (@Xianbao_QIAN).
