K-Search Adapts CUDA Kernel Expertise to Apple Silicon MLX Backend

Berkeley AI Research extended the K-Search evolutionary kernel framework with an MLX backend and a CUDA-to-MLX translation layer, letting it adapt existing CUDA kernels for Apple Silicon.

The team reports a 0.97x speedup relative to the native MLX Attention kernel and up to a 20x prefill speedup over the community mlx-lm implementation on the Mamba SSM kernel.

The method uses Gemini 3.5 Pro Preview to both reason about optimizations and write candidate kernels.