Mac Mini + Ollama: A Practical Guide to Running LLMs Locally

Mac Mini for Running LLMs

Which config should you get?

Real benchmarks, real answers.

💻 What Did I Test?

M4 / 16GB / 256GB M4 / 24GB / 512GB M4 Pro / 48GB / 1TB

Each ran for 72 hours straight.

✨ TL;DR

Running 7B models? 16GB is enough. Qwen2.5 7B runs smoothly at 30+ tokens/sec.

Running 14B models? You need at least 24GB. Mistral 14B works but occasionally stutters.

Running 32B models? 48GB minimum. Qwen2.5 32B is usable — slightly slow but gets the job done.

Running 72B models? Forget it. You’d need 128GB+. Mac Mini can’t handle it.

💰 Best Value Pick?

My recommendation: M4 / 24GB / 512GB (~$850)

• Runs 7B and 14B models comfortably
• Covers most daily needs

For power users: M4 Pro / 48GB / 1TB (~$1,400)

• Handles 32B models
• Doubles as a dev workstation

⚠️ Pitfalls to Avoid

Don’t buy 256GB storage. Model files are huge — Qwen2.5 7B alone is 4.5GB. You’ll fill it up fast.

RAM matters more than CPU. At the same price point, always prioritize more RAM. RAM determines the largest model you can run.

SSD speed makes a difference. External drives work, but model loading will be slower.

🔥 Benchmark Data

7B Model Inference Speed:

• 16GB: 32 tokens/sec
• 24GB: 35 tokens/sec
• 48GB: 38 tokens/sec

14B Model Inference Speed:

• 16GB: ❌ Can’t load
• 24GB: 12 tokens/sec
• 48GB: 18 tokens/sec

32B Model Inference Speed:

• 24GB: ❌ Can’t load
• 48GB: 8 tokens/sec (usable but slow)

📱 Best Companion Tools

Ollama — One-command install:

ollama pull qwen2.5:7b

Open WebUI — ChatGPT-like web interface. Browse and chat, same experience.

Dify — Build local AI workflows. Completely free, fully private.

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