⚡ Rapid-MLX: Optimized Local Inference Engine for Apple Silicon

In short, it tries to provide a highly optimized local AI inference engine specifically for Apple Silicon. The project focuses heavily on performance, claiming significantly faster speeds than Ollama and extremely low cached Time To First Token (TTFT). The hard part is not just running models on a Mac, but maximizing the compute of the unified memory architecture while supporting complex features. It includes prompt caching, reasoning separation, and 17 tool-calling parsers, all wrapped in a drop-in OpenAI replacement API that works directly with tools like Cursor and Aider. Local inference is gaining traction as developers seek lower latency and better privacy. If you use a Mac for AI-assisted coding, this engine's deep optimization for Apple hardware makes it a compelling piece of infrastructure.