Apple Silicon Local AI Buyer's Guide: Mac mini to Mac Studio

Published: 2026-02-25 · 8 min read

Running AI models locally is no longer a hobbyist move. With Apple Silicon's unified memory architecture, even a $599 Mac mini can run capable open-source models — no GPU rig required. This guide maps every current Apple desktop tier to the free models it can actually run, so you can buy the right machine instead of the most expensive one.

All specs are current as of early 2026 (M4 Mac mini, M4 Max / M4 Ultra Mac Studio). All models listed are free and runnable via Ollama.

The Full Tier Breakdown

Memory vs. Model Size: The Visual Map

The single most important number is unified memory. Here's how it maps to what you can actually run:

Which Tier Should You Actually Buy?

Here's the honest breakdown by use case:

• Personal automation, simple agents, light coding: Mac mini M4 16GB ($599) — Llama 3.2 3B and Phi-4 mini handle most day-to-day tasks cleanly.
• Coding assistant, research agent, document work: Mac mini M4 32GB or M4 Pro 24GB ($799–$1,399) — 14B-class models give you real reasoning without hitting limits.
• Serious multi-agent ops, 70B-class reasoning: Mac mini M4 Pro 48GB ($1,599) — this is the floor for running Llama 3.3 70B and DeepSeek-R1 32B without compromise.
• Production inference server, parallel model serving: Mac Studio M4 Max 64GB ($2,399+) — the 546 GB/s bandwidth means real throughput, not just capacity.
• Research lab, full-precision frontier models: Mac Studio M4 Ultra ($3,999+) — the only desktop that runs 405B and 671B models usably.

The Free Model Stack (via Ollama)

All models below run locally via Ollama on macOS. Zero API costs, zero data leaving your machine.

• Llama 3.1 / 3.2 / 3.3 — Meta's flagship open series. 8B, 70B, 405B. Best all-around.
• DeepSeek-R1 — 7B, 14B, 32B, 70B, 671B. Exceptional reasoning. Chinese open weights.
• Phi-4 / Phi-4 mini — Microsoft. Punches above weight at small sizes. Great for low-memory machines.
• Qwen2.5 — Alibaba. 7B, 14B, 32B, 72B. Strong coding and instruction following.
• Gemma 3 — Google. 4B, 12B, 27B. Good balance of speed and quality.
• Mixtral 8x7B / 8x22B — Mistral's mixture-of-experts. Fast at inference relative to parameter count.
• CodeLlama — Meta's code-focused variant. 7B, 13B, 34B, 70B.

Getting Started

Install Ollama, pull a model, and you're running local AI in under 5 minutes:

# Install Ollama
brew install ollama

# Pull and run a model
ollama run llama3.2        # 3B — fast, 16GB+
ollama run phi4            # 14B — smart, 32GB+
ollama run llama3.3:70b    # 70B — frontier, 48GB+
ollama run deepseek-r1:32b # 32B reasoning, 48GB+

Once running, Ollama exposes an OpenAI-compatible API at localhost:11434 — drop it into any tool that accepts an OpenAI endpoint.

Questions about building a local AI stack for your team? Reach out.