The authors report performance results for their method on MLX kernels (Attention and Mamba SSM) on Apple Silicon, treating MLX as the target ecosystem—not as a tool under critique or endorsement.
Shiyi Cao, Gal Bloch, Assaf Toledo, Michael Factor, Gil Vernik, Joseph E. Gonzalez ·
Supporting evidence
From CUDA to MLX: How K-Search Brings Decades of Kernel Expertise to Apple Silicon
Original excerpt
We show that our approach reaches near-expert level performance on Apple Silicon with 0.97x speedup compared to the native MLX Attention kernel, and up to a 20x prefill speedup over the community mlx-lm implementation on the Mamba SSM kernel
Context
; we report the numbers, and how much of the gain comes from the translation layer, in the sections below. Although we focus on MLX kernels for Apple Silicon, the method is not specific to MLX and applies to any ecosystem where CUDA expertise is transferable.
About this interpretation
The object-specific interpretation and Chinese translation were checked independently against the source. This is an AI semantic review, not playback verification. Reviewed Oct 5, 2026 · qwen3.8-max-0902
Report an issue