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Home/Models & Labs
Models & Labs

Jun Kim joins Hugging Face to lead MLX ecosystem

Hugging Face Blog·September 22, 2026·high confidence

Why it matters

  • →Professionalizes maintenance for Apple Silicon's primary local AI framework.
  • →Reduces friction in porting Hugging Face models to MLX.
  • →Strengthens the non-NVIDIA AI ecosystem through strategic hiring.
Jun Kim joins Hugging Face to lead MLX ecosystem
©Hugging Face Blog

Hugging Face has hired Jun Kim, the creator and maintainer of oMLX, to lead efforts supporting the MLX community on Apple Silicon. Kim will transition from maintaining oMLX as a side project to a fully funded role at Hugging Face, ensuring long-term stability for the framework. The collaboration aims to streamline the process of converting models from the Hugging Face Transformers library into MLX implementations. This partnership seeks to strengthen ties between key projects like mlx-lm and LMStudio, providing better tools for local AI inference on Mac hardware.

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llama.cpp b11081 release with CUDA 13 and ROCm 10 support

This release quietly cements llama.cpp as the universal inference runtime by adding default builds for CUDA 13.4 and ROCm 10.0, effectively closing the gap on newer NVIDIA and AMD hardware without requiring manual compilation flags. The inclusion of KleidiAI for Apple Silicon remains a key differentiator for local Mac users seeking optimized ARM kernels. While the changelog details internal test improvements like configurable tensor standard deviation, the real value lies in the expanded binary matrix that supports developers across the latest GPU architectures. Readers can now deploy on cutting-edge hardware out of the box. The update ensures that teams using the newest graphics cards do not need to wait for source code patches to achieve stable performance. This is a practical step toward making local inference truly hardware-agnostic.

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