In the news
From CUDA to MLX: How K-Search Brings Decades of Kernel Expertise to Apple Silicon
Berkeley AI Research · Published · 3 min read
In 30 seconds
- What happened
- Researchers extended K-Search, an AI-driven kernel optimizer, with an MLX backend to automatically translate CUDA kernels to Apple Silicon, achieving near-expert performance.
- Why it matters
- Engineers optimizing ML inference on Apple Silicon who need performance-critical kernels like attention or state-space models without months of manual tuning.
- Watch out
- Results shown on specific models and hardware; generalization to other kernel types and Apple chips remains unclear. Translation layer requires careful hardware constraint mapping.
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- kernel
- cuda
- edge
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