mlx-010055f1·4 events·first seen Aliases: MLX
Researchers at UC Berkeley extended K-Search, an evolutionary AI-driven kernel optimization framework, with a CUDA-to-MLX translation layer that adapts existing CUDA kernels to Apple Silicon rather than rebuilding from scratch. The system uses an LLM (Gemini 3.5 Pro Preview) to maintain a structured 'world model' decision tree and iteratively generate, compile, and benchmark candidate MLX kernels. Results show 0.97x performance relative to the native MLX Attention kernel and up to 20x prefill speedup over the community mlx-lm implementation on the Mamba SSM kernel. The approach is framed as generally applicable to any ecosystem where CUDA expertise is transferable, not just MLX.
Rapid-MLX is an open-source Python project claiming 4.2x faster inference than Ollama on Apple Silicon, with 0.08s cached time-to-first-token and full tool-calling support via 17 parser implementations. It offers prompt caching, reasoning separation, cloud routing, and a drop-in OpenAI-compatible API, with stated compatibility with Claude Code, Cursor, and Aider. The project has accumulated 3,355 GitHub stars with modest daily growth (+18).
mlx-lm is an open-source Python library for running LLMs using Apple's MLX framework, designed for Apple Silicon hardware. The repository has accumulated 5,817 stars with 43 new stars today, indicating steady community interest. It represents a key piece of the Apple-native ML inference ecosystem.
A Hugging Face blog post discussing a pull request related to converting or integrating Transformers models with MLX, Apple's machine learning framework. The post appears to cover tooling or workflow improvements for running Hugging Face Transformers models on Apple Silicon via MLX. The title suggests a community or automated contribution narrative.