M4 Mac Mini跑Ollama：600美元的本地LLM神机

基础款M4 Mac Mini是2026年性价比最高的本地LLM设备。

Ollama on M4 Mac Mini 2026: The Best $600 Local LLM Machine

The base M4 Mac Mini (16GB unified memory, ~$599) is the default recommendation for local LLMs in 2026. Apple's unified memory gives it bandwidth that no PC at this price matches, and Ollama runs natively.

Why It Wins

• 16GB unified memory - the whole RAM pool is available to the model
• ~120 GB/s memory bandwidth - the key metric for LLM token speed
• Silent, tiny, 10W - runs 24/7 on your desk
• Native Metal support - llama.cpp and Ollama use the GPU cores automatically

Real Speeds (measured, Q4 quantized)

All faster than any Windows PC in the same price range, and most are usable for real work.

Setup

# 1. Install Ollama
curl -fsSL https://ollama.com/install.sh | sh

# 2. Pull models
ollama pull qwen2.5:7b
ollama pull llama3.1:8b

# 3. Run
ollama run qwen2.5:7b

What You Can Do With It

• Coding assistant - 7B-14B models handle autocomplete and small refactors well
• RAG on your documents - combine with Chroma (28,900 stars) for private document QA
• 24/7 home assistant - serve the OpenAI-compatible API at localhost:11434
• Learning and experimentation - fine-tune small models, test prompts freely

Limits

• 16GB RAM caps you at ~14B models with reasonable context
• 32GB version (~$800) adds 30B-class models - worth it only if you need them
• Not for training - inference only

FAQ

Mac Mini M4 vs M4 Pro for LLMs? M4 Pro adds memory bandwidth (~270 GB/s) and up to 48GB RAM, roughly doubling speeds and model size headroom. The base M4 is 80% of the value at half the price.

Should I buy this over a cloud API? For heavy daily use, a $600 machine pays for itself in API savings within a year while keeping data local.

Can it run image models too? Yes - flux schnell and SD models run, though slower than text LLMs.