Mini PC for AI Comparison — Mac Mini M4 vs AMD AI Mini PCs (2026)

Compare mini PCs for local LLM inference: Mac Mini M4, Beelink GTR9 Pro, GMKtec EVO-X2, GEEKOM A8. Sort by RAM, bandwidth, tokens/sec, price, and power.

Mini PC RAM Bandwidth Max model (Q4) 8B tok/s 32B tok/s 70B tok/s TDP Price Deal
Mac mini M4 Pro 64GB Featured
Apple M4 Pro 12-core
64 GB LPDDR5X unified 273 GB/s 32B 20 tok/s 12 tok/s N/A 30 W $2199 See price →
Beelink GTR9 Pro 128GB
AMD Ryzen AI Max+ 395 (16-core Zen 5)
128 GB LPDDR5X 200 GB/s 70B 28 tok/s 12 tok/s 7 tok/s 100 W $2299 See price →
GMKtec EVO-X2 128GB
AMD Ryzen AI Max+ 395 (16-core Zen 5)
128 GB LPDDR5X 200 GB/s 70B 28 tok/s 12 tok/s 7 tok/s 80 W $1899 See price →
GEEKOM A8 32GB
AMD Ryzen 9 8945HS
32 GB DDR5-5600 90 GB/s 13B 15 tok/s N/A N/A 45 W $599 See price →
Minisforum AI X1 Pro 64GB
AMD Ryzen AI 9 HX 370
64 GB LPDDR5X 160 GB/s 32B 22 tok/s 10 tok/s N/A 65 W $1299 See price →
Intel NUC 13 Pro 64GB
Intel Core i7-1360P
64 GB DDR4 60 GB/s 8B 5 tok/s N/A N/A 40 W $850 See price →

Best for 70B models

Beelink GTR9 Pro or GMKtec EVO-X2 with 128 GB LPDDR5X. Both run Llama 3.1 70B Q4 at ~5–8 tok/s.

Best silent / efficient

Mac mini M4 Pro 64 GB. 273 GB/s unified memory, ~15–25 W under load, and fan noise under 20 dBA.

Best budget entry

GEEKOM A8 32 GB. Handles 7B–13B quantized models and has USB4 for a future eGPU.

Why mini PCs matter for local AI

A modern AI mini PC can run 7B–70B parameter models on your desk for the price of a few months of ChatGPT Plus. RAM capacity and memory bandwidth matter far more than NPU TOPS or CPU core count.

Mac Mini M4 vs AMD AI mini PCs

  • Mac mini M4 Pro: Best efficiency and bandwidth, but capped at 64 GB. Ideal for 30B models and below.
  • Beelink GTR9 Pro: 128 GB ceiling + dual 10GbE. Best for 70B models and homelab networking.
  • GMKtec EVO-X2: Cheapest 128 GB config with OCuLink for a future NVIDIA eGPU.

What about NPU TOPS?

NPU ratings (50–86 TOPS) are marketing for image and video AI, not autoregressive LLM inference. Ollama, llama.cpp, and LM Studio do not route LLM workloads to the NPU.

Find the right GPU → VRAM calculator →

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