From CUDA to MLX: K-Search Brings Kernel Optimization Expertise to Apple Silicon

IBM Research extended K-Search, the evolutionary kernel search framework from UC Berkeley Sky Lab, with an MLX backend and a structured CUDA-to-MLX translation layer that adapts existing CUDA kernels into Apple Silicon kernels. On an MLX attention kernel the approach reached 0.97x the speed of Apple's native MLX Attention kernel, up from 0.26x without the translation context, and an evolved Mamba SSM kernel reached up to 20x prefill speedup over the community mlx-lm implementation on an M1 Max. The authors attribute the gain mainly to the quality of context and constraints given to the LLM rather than its ability to write Metal code, and say the method is not specific to MLX.