I use Claude Code extensively, but I wanted to test whether the M4 Pro Mac mini on my desk could also serve useful local models for the parts of a development workflow that do not always need a frontier cloud model. The surprising result: GPT-OSS 20B in MLX FP4 generated around 63.9 tok/s with my 16-container OrbStack lab running. Shutting the lab down only moved that to around 64.9 tok/s. MLX was also about 19% faster than GGUF in my back-to-back test, although the MLX measurements were somewhat spikier. I thought it would take a 2TB unified memory 4 node Mac ultra cluster to run local AI. Glad I don’t need to spend $50,000! For code workflows, I see local inference as a complement—not a competitor—to Claude Code: Local/private codebase triage or summaries Fast boilerplate and low-risk transformations Offline experimentation with prompts and agent patterns Preprocessing before escalating a task to Claude Code Keeping a local fallback available The key lesson was that active parameters matter for speed. The 20B+ MoE model beat the 9B dense model because only a fraction of its parameters fire per token. Full testing video: https://www.youtube.com/watch?v=9_-bT62YWAI What local models and runtimes are you pairing with Claude Code on Apple Silicon? submitted by /u/silent_lurker_69
Originally posted by u/silent_lurker_69 on r/ClaudeCode