Autor: Jakub Rusinowski · Ostatnia aktualizacja: 10 lipca 2026
Last Updated: May 2026 — Mini PCs have become the sleeper hit of the local AI world. Compact, quiet, and energy-efficient, they let you run capable LLMs 24/7 without a gaming tower. This guide compare
Last Updated: May 2026 — Mini PCs have become the sleeper hit of the local AI world. Compact, quiet, and energy-efficient, they let you run capable LLMs 24/7 without a gaming tower. This guide compares the best options across every budget.
Mini PCs occupy a sweet spot that dedicated gaming rigs can't match:
The tradeoff: you trade raw throughput (tokens/sec) for efficiency. A Mac Studio M4 Ultra at 65W can match or beat a power-hungry RTX 4090 rig on many workloads — at a fraction of the electricity cost.
| Device | Memory | TDP | Est. tok/sec (8B Q4) | Price (2026) | Best For |
|---|---|---|---|---|---|
| Mac Mini M4 (16GB) | 16GB unified | 20W | ~55 t/s | $599 | Everyday AI, students |
| Mac Mini M4 Pro (24GB) | 24GB unified | 30W | ~75 t/s | $1,399 | Developers, 27B models |
| Mac Mini M4 Pro (48GB) | 48GB unified | 35W | ~90 t/s | $1,999 | Power users, 70B models |
| Mac Studio M4 Max (128GB) | 128GB unified | 65W | ~120 t/s | $3,999 | Professionals, 405B models |
| Minisforum UM890 Pro | 64GB DDR5 RAM | 54W | ~12 t/s (CPU) | $499 | Budget option, AMD Ryzen AI |
| Beelink EQ14 | 16GB DDR5 RAM | 15W | ~5 t/s (CPU) | $199 | Ultra-budget, small models only |
| Minisforum AtomMan G7 Ti | 32GB DDR5 + RTX 4060 | 100W | ~65 t/s (GPU) | $899 | Best Windows mini PC for AI |
> Note: Apple unified memory means the full RAM is available to the GPU — no VRAM separation. 16GB Mac Mini effectively gives you ~11–13GB for model weights.
Apple's Mac Mini M4 is the best-value local AI machine in 2026 for most users. The M4 chip's Neural Engine and GPU share unified memory, giving you:
Recommended purchase: The 24GB configuration is worth the extra $400 for developers — it unlocks a completely different tier of models.
The M4 Pro chip adds more GPU cores and a wider memory bus, translating to meaningfully faster inference:
If you're running a personal API server or doing heavy coding assistance work, the M4 Pro with 48GB is the sweet spot for performance per watt.
The M4 Max with 128GB unified memory is a serious workstation. It can run:
At 65W, it consumes less power than most gaming GPUs at idle. For professional use or running a local AI server for a team, this is unmatched value.
The only mini PC with a discrete GPU worth considering for AI work. The integrated RTX 4060 8GB gives you dedicated VRAM:
Best for: Windows developers who need CUDA, or users already in the Windows ecosystem.
A CPU-only machine running AMD Ryzen AI 9 HX 370. Large RAM pool but slow inference:
Best for: Always-on server use where latency doesn't matter, or as a file/model storage server paired with a GPU workstation.
The entry-level option. Intel N150 processor with Intel UHD graphics:
Getting started on any Mac Mini takes under 10 minutes:
# 1. Install Ollama
curl -fsSL https://ollama.com/install.sh | sh
# Or download from https://ollama.com/download/mac
# 2. Run a model matching your RAM
# 16GB Mac Mini (runs 8–12B models comfortably)
ollama run llama3.1
# 24GB Mac Mini (runs 27B models well)
ollama run gemma3:27b
# 48GB Mac Mini M4 Pro (runs 32B–70B models)
ollama run deepseek-r1:32bTo use your Mac Mini as an always-on AI server accessible from other devices on your network:
# Allow Ollama to listen on all interfaces (add to ~/.zshrc or ~/.bashrc)
export OLLAMA_HOST=0.0.0.0:11434
# Restart Ollama
ollama serveThen access from any device: http://your-mac-mini-ip:11434
# 1. Download Ollama for Windows from https://ollama.com/download/windows
# 2. Run the installer
# 3. Open PowerShell and run your first model
ollama run llama3.1For the best GUI experience on Windows, install LM Studio from lmstudio.ai — it auto-detects the GPU and handles VRAM allocation.
This matters for always-on servers:
| Device | Idle | AI Load | Monthly (24/7) |
|---|---|---|---|
| Mac Mini M4 16GB | 6W | 18–22W | ~$1.50–2.50 |
| Mac Mini M4 Pro 48GB | 8W | 28–35W | ~$2.50–3.50 |
| Mac Studio M4 Max 128GB | 12W | 55–65W | ~$5–7 |
| Minisforum AtomMan G7 Ti | 15W | 85–100W | ~$8–10 |
| RTX 4090 Gaming PC | 80W | 350–450W | ~$35–45 |
*Estimated at $0.12/kWh average US electricity rate.*
Apple Silicon machines pay for themselves in electricity savings over 2–3 years compared to a GPU workstation running 24/7.
→ Choose Mac Mini M4 16GB ($599) if: You want the best entry-level AI assistant for daily use. Runs all 7–13B models well. Silent and efficient.
→ Choose Mac Mini M4 Pro 24GB ($1,399) if: You're a developer who wants to run 27B models locally, or you want to serve multiple users simultaneously.
→ Choose Mac Mini M4 Pro 48GB ($1,999) if: You need to run DeepSeek R1 32B or multiple large models without compromise.
→ Choose Minisforum AtomMan G7 Ti (~$899) if: You need Windows/CUDA compatibility, or you want a GPU-equipped mini PC for gaming + AI.
→ Choose Minisforum UM890 Pro (~$499) if: You want a cheap always-on server for less latency-sensitive tasks, or you need 64GB RAM on a budget.
Can I add more RAM to a Mac Mini later? No — Apple Silicon RAM is soldered to the chip and cannot be upgraded. Choose the right configuration at purchase.
Is a Mac Mini faster than an RTX 4060 PC for AI? For Apple Silicon, the 24GB Mac Mini M4 Pro roughly matches an RTX 4060 Ti 16GB for inference speed, but runs at ~1/8th the power draw.
Can I use an eGPU with a Mac Mini for AI? Apple Silicon Macs don't support eGPU acceleration for AI/ML workloads — only Intel Macs did. On Windows mini PCs, eGPU via Thunderbolt 4 works but adds latency.
What's the best model for a 16GB Mac Mini? Llama 3.1 8B Q4_K_M (6GB, ~55 t/s) or Gemma 3 12B Q4_K_M (8GB, ~35 t/s) are excellent choices.