Qwen3.7-Max Ran for 35 Hours on Unknown Hardware and Achieved a 10× Speedup

Summary

- Advertisement - Alibaba gave Qwen3.7-Max a kernel optimization task on a hardware platform the model had never encountered before. No documentation or profiling data. No example kernels for the architecture. Just a task description, an existing implementation, and an evaluation script. The model ran for 35 hours. It made 1,158 tool calls. It wrote, compiled, profiled, and rewrote the kernel repeatedly, diagnosing failures, fixing bugs, identifying blocks, and redesigning the architecture multiple times without anyone watching. After 30 hours it was still finding meaningful improvements. The final result was a 10x speedup over the reference implementation. For context: GLM 5.1 ran the same task and reached 7.3x. Kimi K2.6 reached 5x. DeepSeek V4 Pro reached 3.3x. The models that stopped early did so because they issued no tool calls for five consecutive rounds, they concluded they couldn’t make further progress and stopped. Qwen3.7-Max didn’t stop. What the task actually was The kernel in question is Extend Attention, a production component in SGLang, a widely used inference framework. Specifically it handles attention between newly generated tokens and a prefix KV-cache of up to 32K entries, a memory-bound, latency-critical operation that directly affects how fast LLMs serve responses. The hardware was T-Head ZW-M890 PPUs, a processor architecture that wasn’t in any training data. The model had no prior knowledge of how it behaved. It started cold. Over 35 hours it performed 432 kernel evaluations. Each cycle meant writing code, compiling it, running it, reading the profiling output, deciding what to change, and trying again. The model diagnosed compilation failures it hadn’t seen before, identified performance bottlenecks through runtime feedback rather than prior knowledge, and redesigned the kernel architecture multiple times when incremental improvements stopped working. This matters because it tests something different from standard benchmarks. Most evaluatio...