Mac mini M4 Pro for Local LLM Inference: A Test Built Around OpenClaw AI Agents

In July 2026, Selectel published a test of the Mac mini M4 Pro on local inference of mid-size language models, checking whether Apple Silicon's unified memory can replace an expensive discrete GPU for running AI agents like OpenClaw.

Why a GPU alone isn't enough for inference

Running an LLM locally is limited not only by a graphics card's compute power, but also by the amount of video memory (VRAM) and the width of the memory bus. According to Selectel, a shortage of VRAM is the most common obstacle to running a mid-size model on an ordinary PC: the weights simply don't fit into a discrete card's memory, and fast token generation requires high memory bandwidth.

Apple Silicon's unified memory removes part of this constraint. In the M4 Pro chip, the CPU and GPU share a single memory pool, so the entire RAM capacity is available for model weights rather than a separate, small VRAM. As the author notes, this addresses the main problem of budget builds "for a reasonable price" — without buying a dedicated server-grade GPU.

"Apple

Silicon's unified memory partially solves the VRAM shortage problem for a reasonable price," the Selectel article on Habr says.

What exactly was tested

Selectel ran the Mac mini M4 Pro as a compact server for AI agents, not as a desktop computer. The key elements of the write-up:

• Hardware — Mac mini M4 Pro on an Apple Silicon chip with unified memory
• Workload — mid-size language models, local inference
• Scenario — a portable server for the OpenClaw AI agent
• Scope — setting up the environment from scratch and finding the real limits of the configuration
• Author and venue — Selectel, published on Habr, July 2026

The article doesn't stop at synthetic benchmarks: the authors walk through the full path from environment setup to the point where the compact Mac stops coping. That boundary — where the practical ceiling of such a build lies — is the main result of the test.

Why this matters for AI agents like OpenClaw

There are good reasons to run AI agents like OpenClaw locally: data privacy, no API fees, and full control over the model. But an agent needs an always-available inference backend, and dedicating a separate workstation with a top-end graphics card to it is expensive, noisy, and power-hungry.

A Mac mini as a quiet, portable server looks like a compromise: one small box with enough unified memory can serve an agent without a dedicated GPU workstation. Selectel's test checks exactly where this compromise works in practice and where it hits a ceiling — either in generation speed or in the size of the model that can physically be loaded.

What this means

With unified memory, Apple Silicon turns an inexpensive Mac mini M4 Pro into a viable local server for LLM inference: for mid-size models, it is enough to run AI agents like OpenClaw without an expensive graphics card. But the configuration has a clear ceiling beyond which a full-fledged GPU is still needed — and knowing where that ceiling lies matters more than any marketing promises.