Specs at a glance
| CPU | Apple M4 / M4 Pro — 10–14 cores |
| GPU / NPU | 10–20 core integrated GPU + Neural Engine |
| RAM options | 16 / 24 / 32 / 48 / 64 GB unified memory |
| Storage | NVMe SSD 256 GB – 2 TB |
| Power draw | 11–60 W |
| Form factor | 127 × 127 × 50 mm |
| Local LLM capability | Up to 70B Q4 |
| Agent score | 10/10 |
| Price point | €699–2200 |
Overview
The Mac Mini M4 with 24+ GB unified memory is the single best small-form-factor machine for running local LLMs in 2026. Apple's unified memory architecture means a 70B parameter Llama 3.3 quantised to 4-bit fits comfortably in 48 GB and runs at ~7-10 tokens per second — usable for agent workloads. The Neural Engine accelerates certain model classes meaningfully. Pair with NanoClaw for Apple-native sandboxing or ZeroClaw for fully offline operation. The downside is Linux: Asahi Linux is genuinely impressive but not yet production-grade. If your stack assumes Linux, the M4 Mac Mini is more friction than it's worth. If you're macOS-native or willing to run macOS as the host, it's the desk machine to beat.
Best for
- Local LLM inference on 13B–70B models
- ZeroClaw production setups
- NanoClaw native macOS deployment
- Anyone wanting Apple Silicon performance per watt
Not for
- Linux-only stacks (yes, Asahi Linux exists, but it's a project)
- True portability (still mains power, still desk-bound)
- Cost-sensitive deployments (Linux mini PC offers more RAM/€)
Compatible self-hosted agents
Tested working on Mac Mini M4 / M4 Pro (with the caveats from “Best for” / “Not for” above):
Where to buy
Manufacturer page: https://www.apple.com/mac-mini/. We don't have an active affiliate programme with this vendor — see our disclosure page for the full list of partners we do work with.
See: all pocket AI hardware · edge AI hardware buyer's guide · how we test.