What is the best local LLM for an M4 Pro?

It depends on your unified memory. A 24 GB M4 Pro runs Qwen 3.6 27B at Q4_K_M well; 48-64 GB M4 Pros can run Llama 3.3 70B at Q4. For OpenClaw agent work, gpt-oss 20B is the reliable pick at any tier.

How much unified memory do I need on an M4 Pro for local LLMs?

24 GB runs 27B-class models at Q4; 48 GB reaches 70B at Q4; 64 GB gives 70B with context headroom. On Apple Silicon the memory you configure is the hard ceiling on model size, so buy as much as your budget allows.

Is the M4 Pro fast enough for local LLMs?

It is usable but not fast. The M4 Pro's ~273 GB/s memory bandwidth delivers roughly 15-18 tokens/sec on a 27B model — fine for chat and background agent tasks, slower than a desktop 24 GB GPU. You trade speed for silence, low power, and zero ops.

M4 Pro or M4 Max for local AI?

Choose the M4 Max if you want more memory bandwidth (faster tokens/sec) and higher memory ceilings for 70B-plus work. The M4 Pro is the better value if 27B-class models and occasional 70B at 48-64 GB are enough.

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Hardware July 7, 2026

Best Local LLM for MacBook Pro / Mac mini M4 Pro (2026)

The Apple M4 Pro (in the 14-inch MacBook Pro and Mac mini) offers up to 64 GB of unified memory at ~273 GB/s. Unified memory means the amount you configure — not a separate VRAM pool — decides which models you can run.

Picking hardware for an OpenClaw host?

Use the local model calculator first, then see our AI training options if you want help matching your workload to the right rig.

Short answer: on an M4 Pro, the model you can run scales with the unified memory you bought. A 24 GB M4 Pro runs Qwen 3.6 27B at Q4_K_M well (~15-18 tok/sec); a 48 GB M4 Pro adds room for Q6/Q8 or a 70B at Q4; 64 GB comfortably runs Llama 3.3 70B at Q4. For OpenClaw, gpt-oss 20B is the reliable agent pick at any tier.

The Memory Math

The M4 Pro is the quiet, low-power sweet spot for local LLMs: no GPU rig, no ops, and enough unified memory (at 48-64 GB) to reach 70B. It trades raw speed for silence and simplicity. Buy the most memory you can — it is the ceiling on model size.

What Actually Fits (Model Picks)

Config	Model	Quant	Speed	Notes
24 GB M4 Pro	Qwen 3.6 27B	Q4_K_M	~15-18 tok/s	Sweet-spot general model
36 GB M4 Pro	Qwen 3.6 27B	Q6_K	~14 tok/s	Higher quality 27B
48 GB M4 Pro	Llama 3.3 70B	Q4_K_M	~8-10 tok/s	Steps into 70B
64 GB M4 Pro	Llama 3.3 70B	Q4_K_M + big context	~8-10 tok/s	70B with headroom

What You Can’t Run

A 24 GB M4 Pro running 70B — 70B at Q4 needs ~40 GB; you need 48 GB+ of unified memory.
Blazing speed — the M4 Pro’s ~273 GB/s bandwidth is roughly half a desktop 24 GB GPU, so tokens/sec are modest.
100B+ MoE at good quants — that is M4 Max / M3 Ultra / big-RAM territory.

🎮 PICK THE UNIFIED-MEMORY TIER

Memory is the ceiling on Apple Silicon: a 24 GB Mac runs 27B comfortably, 48 GB+ reaches 70B. The Mac mini M4 is the cheapest always-on host if you want a dedicated OpenClaw box.

24GBMacBook Pro M-series 24 GB ↗ 48GB+Premium Mac 48 GB+ ↗ MINIMac mini M4 ↗

OpenClaw Setup

Point OpenClaw at your local model through Ollama:

# pull and run your pick, then set it as the OpenClaw default
ollama pull qwen3:27b
openclaw config set agents.defaults.models.chat "ollama/qwen3:27b"

For agent reliability, prefer a model with clean tool-call output (gpt-oss 20B where it fits) and cap context to what your memory holds. See the tool-calling reliability guide.