This content describes a native DFlash implementation on MLX for Apple Silicon, significantly accelerating token generation in Qwen models. The speculative decoding technique achieves speedups of up to 3.3x while maintaining identical output quality.
Phosphene is a free, open-source desktop panel for Apple Silicon Macs that generates video with synchronized audio using Lightricks' LTX 2.3 model. Its key differentiator is generating video and audio in a single diffusion pass, ensuring perfect timing between visual and auditory elements.
![Phosphene local video and audio generation for Apple Silicon open source (LTX 2.3) [P]](/cdn-cgi/image/width=3840,quality=75,format=webp/https://preview.redd.it/vutakjb0vgyg1.png?width=140&height=59&auto=webp&s=08ecb95fd65ade25c924988f1992e9abe3d79f62)
A user compares the performance of the Gemma 4-26b-a4b model in MLX and GGUF versions on an M1 Max with 32GB RAM. Tests with a 3k token prompt indicate that GGUF is slightly faster in both prompt processing and tokens per second.
This research systematically optimizes real-time diffusion model inference on Apple M3 Ultra, exploring various techniques like CoreML conversion, quantization, and model distillation. The study achieved 22.7 FPS for 512x512 img2img transformation by combining CoreML conversion of SDXS-512 with a 3-thread camera pipeline.
This content describes building a free, complete AI art generation and evaluation pipeline that runs entirely locally on a MacBook Apple Silicon. It leverages tools like ComfyUI/SDXL and Vulca, eliminating the need for cloud APIs or GPU servers.
The article discusses the significant change in running local AI models on Apple Silicon Macs, a task that previously required dedicated NVIDIA GPUs. This transformation is driven by Apple Silicon's unified memory architecture, which efficiently utilizes shared RAM across components.
This research evaluates cross-family speculative decoding for Polish LLMs on Apple Silicon, extending the MLX-LM framework with Universal Assisted Generation (UAG) for cross-tokenizer compatibility. Experiments show that context-aware token translation significantly improves acceptance rates for Bielik 11B on Polish language datasets.
The author describes encountering 19 GB of memory compression while running a large LLM with MLX on an M1 Max, initially mistaking it for a leak. The fix involved a single MLX API call to properly manage macOS unified memory for large models idling between inferences.
DeepSeek-V4 has been ported to Apple's MLX framework, enabling the large language model to run on Apple Silicon Macs. The functional port, a community effort by @Prince_Canuma, still requires optimization for improved performance.