Mac mini M4 Pro для локального инференса LLM: тест под AI-агентов OpenClaw

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

Why a Single GPU Isn't Enough for Inference

Running an LLM locally depends not only on the GPU's computing power but also on the amount of video memory (VRAM) and memory bus width. According to Selectel, it's the lack of VRAM that most often prevents running a medium-sized model on a regular PC: the weights simply don't fit into a discrete GPU's memory, and fast token generation requires high memory bandwidth.

Apple Silicon's unified memory removes part of this limitation. In the M4 Pro chip, the CPU and GPU work with one shared memory pool, so the entire amount of RAM is available for the model's weights, rather than a separate, small VRAM pool. This, as the author notes, solves the main problem of budget builds "for reasonable money" — without buying a separate server-grade GPU.

"Apple

Silicon's unified memory partially solves the VRAM shortage problem for reasonable money," the Selectel piece on Habr states.

What Exactly Was Tested

Selectel runs the Mac mini M4 Pro as a compact server for AI agents, not as a desktop computer. Key elements of the breakdown:

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

The piece isn't limited to synthetic benchmarks: the authors show the full path from setting up the environment to the point where the compact Mac stops keeping up. That very 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

It's advantageous to run AI agents like OpenClaw locally: it means data privacy, no API fees, and full control over the model. But an agent needs a constantly available inference backend, and running a separate workstation with a top-tier GPU just for that is expensive, noisy, and power-hungry.

The Mac mini in the role of 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 is exactly what checks where this compromise really works, and where it hits a ceiling — whether in generation speed or in the size of the model that can physically be loaded.

What This Means

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