Best Mini PCs for Ollama (2026)
The best mini PC for Ollama in 2026 is the Apple Mac Mini M4 Pro — its 64GB of unified memory at 273 GB/s delivers 30–45 tokens/sec on Llama 3.1 8B and can run 70B models at 10–18 tok/s, all within a 30W idle envelope. For Windows users who need x86 compatibility, the Beelink SEI14 with 32GB RAM handles 7B–13B models comfortably at lower cost, though GPU-acceleration in Ollama is limited to iGPU on Intel platforms.
Ranked Picks
4 reviewed01
Top Pick
Apple Mac Mini (M4 Pro, 2024)
Best mini PC for Ollama. Unified memory means the GPU and CPU share the same 64GB pool — Ollama models load once and run fully accelerated. Zero configuration: install Ollama, pull a model, done. 273 GB/s bandwidth makes 70B inference practical at 10–18 tok/s.
02
Apple Mac Mini (M4, 2024)
Entry point for serious local LLMs. 24GB configuration fits Llama 3.1 8B and 13B models with room for context. 120 GB/s bandwidth delivers 25–35 tok/s on 8B. Cannot fit 70B without offloading — upgrade to M4 Pro's 64GB if 70B is a goal.
03
Beelink SEi14 Mini PC (Intel Core Ultra 7)
Best x86 mini PC for Ollama. 32GB DDR5 RAM lets llama.cpp run 7B–13B models fully in RAM. CPU-only inference delivers 8–12 tok/s on 8B — functional for occasional use, not real-time chat. Intel Arc iGPU acceleration requires Ollama's OpenVINO backend (experimental in 2026).
04
GMKtec NucBox M5 Pro Mini PC
Budget entry point. 32GB RAM handles 7B models fine. AMD Radeon 780M iGPU can accelerate smaller models via ROCm on Linux. Windows users get CPU-only inference at 6–10 tok/s. Best if price is the primary constraint.
Hardware Requirements
Minimum 8GB RAM for Llama 3.1 8B (Q4 quantization, ~5GB). 16GB recommended for comfortable 7B inference with context headroom. 32GB+ for 13B models. 64GB for 70B models at Q4_K_M without offloading.
Why This Matters
Unlike desktop GPUs where VRAM and system RAM are separate, mini PCs use unified or shared memory. On Apple Silicon, this is a major advantage — the GPU can use all available memory for model weights at full bandwidth. On x86 mini PCs, the iGPU shares system DDR5/LPDDR5 RAM, which is slower than discrete GDDR6X but faster than CPU-only inference.
Frequently Asked Questions
Q1Can a mini PC run Ollama for real-time chat?
Yes, with the right hardware. The Mac Mini M4 Pro delivers 30–45 tok/s on 8B models — comfortable for real-time chat at typical reading speed (15–30 tok/s feels instantaneous). The Mac Mini M4 base model at 24GB hits 25–35 tok/s. X86 mini PCs with CPU-only inference average 8–12 tok/s — noticeable lag but usable for non-interactive tasks.
Q2Why is Apple Silicon better for Ollama than Windows mini PCs?
Three reasons: unified memory means the GPU accelerates models fully without discrete VRAM limits; memory bandwidth is 120–273 GB/s vs 50–90 GB/s for LPDDR5 in x86 mini PCs; and Ollama's Metal backend for Apple Silicon is production-grade and maintained by the Ollama team. Windows mini PCs rely on iGPU acceleration (OpenVINO/ROCm) that is less mature.
Q3Can a mini PC run Llama 3 70B?
The Mac Mini M4 Pro with 64GB unified memory can run Llama 3 70B at Q4_K_M quantization (~39GB) fully in memory at 10–18 tok/s. The Mac Mini M4 with 24GB cannot fit it without offloading — you'd get 2–4 tok/s. No x86 mini PC reviewed here has the bandwidth to make 70B practical.
Q4How much power does a mini PC use for AI inference?
The Mac Mini M4 Pro idles at 12W and peaks around 35–40W under full LLM load — dramatically lower than a desktop GPU that draws 300–450W. The Beelink SEI14 idles at 8W and peaks at 25–30W under load. For always-on AI inference, a mini PC saves 1,000–3,000 kWh per year compared to a desktop GPU workstation.
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