@sitinme: There's a pretty interesting open-source project called Cider, specifically designed to accelerate local AI inference on Macs with Apple Silicon chips. Many people buy a Mac mini or MacBook Pro and want to run models locally, but often encounter issues like insufficient speed and high memory usage. Actually...

There's a pretty interesting open-source project called Cider, specifically designed to accelerate local AI inference on Macs with Apple Silicon chips. Many people buy a Mac mini or MacBook Pro and want to run models locally, but often encounter issues like insufficient speed and high memory usage. In fact, the computing power of Mac chips is not weak, especially the M-series. The problem is more that these hardware capabilities are not fully utilized. What Cider does is to unleash these potentials as much as possible, making local models run faster and consume less. Simply put, Cider is a local inference acceleration framework for Apple Silicon, compatible with the MLX ecosystem. Models like Qwen, Llama, Mano-P, etc. that are integrated with MLX can try to use it for acceleration. According to official data, inference speed can be significantly improved in some scenarios, especially in multi-task, high-concurrency, and local visual model inference scenarios, where the effect is more noticeable. The practical value it brings is also straightforward: for example, if you want to run a visual language model locally, let AI view the screen, operate the interface, perform automated testing, or process data that is not convenient to upload to the cloud, Cider is suitable. The whole process can be done locally as much as possible, data does not leave the device, which is more friendly for personal privacy and enterprise internal scenarios. Installation is also simple: clone the project and run pip install -e . to use it. M5+ chips can get full acceleration, and M4 can also be automatically adapted.