OpenBMB open-sources ForgeKernel, after previously saying it looks forward to more results from the Forge series
On October 28, 2026, ModelBest's OpenBMB reposted a message from Yuxuan Li about the ForgeKernel kernel optimization framework, saying it looks forward to the forthcoming paper and more results from the Forge series. The framework belongs to the Forge series and is said to rank first in tests on NVIDIA SOL-ExecBench (FlashInfer-Bench) and Huawei CANN-Bench. On October 30, ModelBest announced the open-sourcing of ForgeKernel; its agent completed 42 rounds of iteration in about 22 hours, producing 32 kernel versions, and achieved 1.098x performance on NVIDIA B200 for the DeepSeek V4.1 FlashMLA Prefill implementation, at 472 microseconds versus a baseline of 518 microseconds. The open-source release includes the optimized fused prefill kernel, the kernels covered in the paper spanning attention, Gated DeltaNet, Kimi Delta Attention, GEMM and convolution, as well as the ForgeKernel harness that can be used to optimize one's own kernels.
he open-sourcing of ForgeKernel; its agent completed 42 rounds of iteration in about 22 hours, producing 32 kernel versions, and achieved 1.098x performance on NVIDIA B200 for the DeepSeek V4.1 FlashMLA Prefill implementation, at 472 microseconds versus a baseline of 518 microseconds. The open-source release includes the optimized fused prefill kernel, the kernels covered in the paper spanning attention, Gated DeltaNet, Kimi Delta Attention, GEMM and convolution, as well as the ForgeKernel harne
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