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Apple Silicon
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apple-silicon-0e3988ad·6 events·first seen Aliases: Apple Silicon
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K-Search framework extended to translate CUDA kernel expertise to Apple Silicon MLX via AI-driven evolutionary search
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.
omlx: LLM inference server with continuous batching and SSD caching for Apple Silicon
omlx is an open-source Python project providing an LLM inference server optimized for Apple Silicon, featuring continuous batching and SSD caching managed via a macOS menu bar interface. The project has accumulated nearly 16,000 GitHub stars with strong daily momentum. It targets local inference on Apple hardware, a growing niche as consumer-grade silicon becomes increasingly capable for running open-weights models.
Using Stable Diffusion with Core ML on Apple Silicon
Hugging Face published a guide on running Stable Diffusion models via Apple's Core ML framework on Apple Silicon hardware. The post covers converting diffusion model weights to Core ML format and integrating them into the Diffusers library for on-device inference. This represents an early effort to enable efficient local image generation on consumer Apple hardware without requiring cloud GPU resources.
Swift Diffusers: Fast Stable Diffusion for Mac
Hugging Face published a blog post introducing Swift Diffusers, a native macOS/iOS application for running Stable Diffusion models locally on Apple Silicon hardware. The post covers optimizations leveraging Apple's Core ML framework to accelerate inference on Mac. This represents an effort to bring on-device diffusion model inference to consumer Apple hardware without cloud dependency.
Stable Diffusion XL on Mac with Advanced Core ML Quantization
Hugging Face details the process of running Stable Diffusion XL (SDXL) on Apple Silicon Macs using Core ML with advanced quantization techniques. The post covers how quantization reduces model size and memory requirements to make SDXL feasible on consumer Mac hardware. This represents a practical deployment advance for running large diffusion models at the edge on Apple devices.
Releasing Swift Transformers: Run On-Device LLMs in Apple Devices
Hugging Face released Swift Transformers, a Swift library enabling on-device LLM inference on Apple hardware (iOS, macOS) via Core ML. The library provides a pipeline abstraction for text generation and supports models converted to Core ML format. This extends the Hugging Face ecosystem to Apple's native development environment, lowering the barrier for deploying LLMs on Apple Silicon devices.