Apple Silicon Guide
Run local AI effectively on M-series Macs
- Apple Silicon can be an excellent local AI platform when configured deliberately
- Unified memory requires different planning than discrete GPU VRAM setups
- Prioritize runtime/tooling compatibility before choosing model families
- Use quantization + speed pages to choose stable model profiles
- Measure real workload behavior, not just synthetic one-off tests
Why Apple Silicon for Local AI
Apple Silicon is attractive for local AI when you value power efficiency, low noise, and a stable desktop environment.
Strengths
Strong performance-per-watt, fast local iteration, and a straightforward setup for many inference workflows.
Limits
Ecosystem support differs from CUDA-first workflows. Always verify runtime support before committing to a model stack.
Unified Memory Model
Apple Silicon uses unified memory shared by CPU and GPU. This changes how you think about VRAM versus system memory.
Planning Memory Budget
Budget memory for model weights, context growth, and background processes. Practical stability comes from headroom, not maximum fill.
What to Measure
Track throughput, latency, and swap behavior while increasing model size or context length.
Recommended Software Stack
Pick tools with active Apple support and predictable runtime behavior.
MLX-Based Workflows
MLX-based model builds can be a strong default on Mac. Validate each model variant with your real prompts before adopting.
Desktop App Layer
Use a local app with model management and API serving so you can connect coding, writing, and automation tools consistently.
Model Selection Strategy
Choose model size based on reliability under your typical context and workload, not just one-off benchmark runs.
Daily Driver Approach
Keep one stable model for daily usage and one larger model for periodic high-quality tasks.
Quantization First
Use quantization requirements and speed pages to select a format that fits your Mac's memory profile.
Workflow Tips
Small workflow adjustments improve stability and throughput on Mac significantly.
Context Discipline
Keep prompts focused and archive old context when possible to avoid unnecessary memory growth.
Task Segmentation
Use smaller models for classification/extraction and reserve larger models for final synthesis.
Frequently Asked Questions
Related Guides & Resources
Ready to Get Started?
Check our step-by-step setup guides and GPU recommendations.