ForgeKernel Live has wrapped up with a 9.8% speedup over the FlashMLA baseline on DeepSeek V4.1 FlashMLA Prefill, measured on NVIDIA B200. Now, we’re open-sourcing ForgeKernel! 🚀
A quick recap:
⌛️ Around 22 hours of autonomous optimization
🛠️ 32 kernel versions across 42 iteration rounds
✅ Version 13 first beat the baseline within 10 hours
📈 Version 31 reached 1.098× performance: 472 µs vs. 518 µs
The official FlashMLA implementation stayed sealed. The agent could run and time its black-box CLI, but could not read its source, documentation, or profiles.
We’re releasing:
1️⃣ The optimized DeepSeek V4.1 fused prefill kernel.
2️⃣ Kernels from our ForgeKernel paper, covering attention, Gated DeltaNet, Kimi Delta Attention, GEMM, and convolution. Across 27 operators, ForgeKernel matches or beats 18 of 22 expert-library kernels and outperforms all 7 published kernels from three kernel-generation systems in our evaluation.
3️⃣ The ForgeKernel harness, so you can optimize your own kernels and explore successive generations of improvement.
Thank you to everyone who followed the live run. We’re excited to see what the community builds next!
🔗 Live: http://openbmb.cn/forgekernel/
💻 Code: http://github.com/OpenBMB/ForgeKernel