A comprehensive setup guide for running Meta's Llama 4 Scout (a 109B MoE model) locally on Apple Silicon Macs using Ollama with the MLX backend. Covers hardware requirements by Mac tier (M1–M4 Ultra), quantization selection (Q4–Q8) matched to unified memory size, Ollama installation and MLX backend verification, environment variable tuning, context window sizing, real-time memory monitoring, custom quantization via mlx-lm from HuggingFace weights, Python integration using both the native Ollama package and OpenAI-compatible API, and a simple RAG pipeline example. Includes troubleshooting for OOM errors, slow generation, and MLX backend activation failures.
Table of contents
How to Run Llama 4 Scout on Apple Silicon via Ollama MLXTable of ContentsWhy Llama 4 Scout Belongs on Your MacPrerequisites and Hardware RequirementsUnderstanding the MLX Backend in OllamaInstalling and Configuring Ollama with MLXQuantization Guide by Mac TierRunning Llama 4 Scout: First Inference and TestingTuning Throughput and Memory UsagePython Integration via Ollama's APITroubleshooting Common IssuesImplementation ChecklistBeyond Scout: Maverick and Fine-TuningSort: