Which Mac Mini should you buy for running local AI in 2026? The answer depends on what you're running — and most guides get this wrong because they don't test the models themselves.
I've been running Ollama and Claude Code on a Mac Mini for months. Here's the buying guide based on real usage, not spec-sheet comparisons.
The Full Lineup
Apple sells the Mac Mini with M4 or M4 Pro chips. No M4 Max — that's Mac Studio and MacBook Pro only. Here's every config that matters for AI work:
| Config | RAM | Storage | Price | AI Verdict |
|---|---|---|---|---|
| M4 | 16GB | 256GB | $599 | Skip. Not enough for real LLM work. |
| M4 | 16GB | 512GB | $799 | Still 16GB. Still skip. |
| M4 | 24GB | 512GB | $999 | Budget entry point. |
| M4 | 32GB | 1TB | $1,199 | Best value for local AI. |
| M4 Pro 12/16 | 24GB | 512GB | $1,399 | Faster chip, same RAM as $999 M4. |
| M4 Pro 14/20 | 24GB | 512GB | $1,599 | Even faster, still only 24GB. |
| M4 Pro 12/16 | 48GB | 1TB | $1,799 | Best for 70B models. |
| M4 Pro 14/20 | 48GB | 1TB | $1,999 | Faster 70B inference. |
| M4 Pro 14/20 | 64GB | BTO | ~$2,199+ | Multiple large models at once. |
The counterintuitive finding: the M4 Pro 24GB configs ($1,399 / $1,599) are questionable buys for LLM work. You get a faster chip but the same RAM as the $999 M4. For LLMs, RAM determines which models fit in memory. A $999 M4 loads the exact same models as the $1,599 M4 Pro.
That said — the M4 Pro does have ~30-50% higher memory bandwidth than the M4, which directly affects tokens per second during inference. If you're running the same 14B model all day and want faster output, the Pro chip matters. If you're buying for model variety, spend the money on RAM instead.
What Actually Runs on Each Config
24GB M4 — The Budget Entry ($999)
Models that run well:
- Llama 3.3 8B — 18-22 tok/s, good for general tasks
- Mistral 7B — Fast inference, solid coding assistant
- Qwen3-Coder-Next (3B active params) — The MoE model that hit ~70% SWE-Bench runs with only 3B active, fits easily
- Ollama + Claude Code — The local API routing setup works here (Claude Code bills per API token, not a flat subscription — I spend roughly $3/month at my usage level)
24GB is tight. macOS takes 6-8GB, leaving ~16-18GB for models. You can run up to ~14B parameter models at Q4 quantization. Anything larger and you're swapping to disk, which kills inference speed.
32GB M4 — The Sweet Spot ($1,199)
Same chip as the 24GB but with real headroom:
- Everything the 24GB runs, but faster (less memory pressure)
- Qwen3-Coder 14B — ~10-12 tok/s, fits comfortably with room for system overhead
- Mixtral 8x7B — MoE model, runs with Q4 quantization
- Multiple smaller models loaded simultaneously in Ollama
This is my recommendation for most developers. $200 more than the 24GB gets you from "it barely fits" to "it runs comfortably."
48GB M4 Pro — The Power User ($1,799)
This is where 70B models become possible:
- Llama 3.3 70B (Q4 quantized) — fits in memory, ~5-8 tok/s
- Qwen 72B — Runs with Q4 quantization
- DeepSeek-V3 (distilled variants) — Smaller versions fit
- Multiple mid-size models loaded simultaneously
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The jump from $1,199 to $1,799 opens up an entirely different tier of models. If you need 70B+ parameter models, this is the entry point.
64GB M4 Pro — For Teams (~$2,199+ BTO)
Unless you're running an inference server for a team or need multiple 70B models loaded at once, the 48GB Pro covers every practical solo use case.
The Specs That Actually Matter
RAM Is Everything
For LLMs, the formula is simple: model size in GB ≈ RAM needed. A 14B model at Q4 quantization needs ~8GB. A 70B model needs ~40GB. Your Mac Mini's unified memory is the hard ceiling.
Buy the most RAM you can afford. You can't upgrade it later. The chip speed difference between M4 and M4 Pro matters for inference speed, but RAM determines whether a model runs at all.
Memory Bandwidth Matters Too
Apple's unified memory architecture is why the Mac Mini works for AI at all. CPU, GPU, and Neural Engine share one memory pool. No PCIe bottleneck, no copying between VRAM and system RAM.
The M4 Pro has ~30-50% higher memory bandwidth than the base M4. For LLM inference, this translates directly to faster token generation. If you're running the same model repeatedly, the Pro chip gives you noticeably faster output.
Disk Speed: Mostly Irrelevant
For model loading, the internal SSD is fast enough on every config — models load in seconds regardless. But there's a caveat: if your model barely fits in RAM and the system starts swapping to disk, SSD speed becomes very relevant. This is another reason the 16GB configs are a skip — you'll be swapping constantly.
Mac Mini vs Mac Studio for AI
| Mac Mini (M4 Pro 48GB) | Mac Studio (M4 Max 36GB+) | |
|---|---|---|
| Price | $1,799 | from $1,999 |
| Max RAM | 64GB (M4 Pro) | 128GB (M4 Max) |
| For local AI agents | Perfect | Overkill for solo dev |
| For 70B models | Yes (quantized) | Yes (full precision) |
| For fine-tuning | Limited | Better |
| For inference server | Solo use | Team / production |
The Mac Studio starts at $1,999 with M4 Max (14-core, 36GB, 512GB). It also comes in M3 Ultra configs with up to 192GB for massive workloads. The price gap to the Mac Mini is smaller than most people think — only $200 between a 48GB Mac Mini and the base Mac Studio.
Bottom line: Mac Mini if you know 64GB is enough. Mac Studio if you need M4 Max performance or want a path to 128GB.
My Recommendation
Best value: M4 with 32GB / 1TB ($1,199). Runs everything a solo developer needs. Comfortable headroom for 14B models and below. This is the one I'd buy today.
Budget entry: M4 with 24GB ($999). Works, but tight. Good if you're just exploring local AI and want to keep costs down.
Large models: M4 Pro 12-core with 48GB / 1TB ($1,799). 70B models, multiple simultaneous models, heavier workloads. The 14-core variant at $1,999 gets you faster inference if the budget allows.
Skip: The 16GB configs ($599 / $799) — not enough for real AI work, constant swapping. And think carefully about the M4 Pro 24GB ($1,399) — you're paying $400 more than the M4 24GB for bandwidth, not capacity.
The Mac Mini is the best value hardware for local AI in 2026. Not because Apple designed it for AI — because unified memory architecture happens to be exactly what LLM inference needs. Fast memory access, large pool, small form factor.