Best Mini PCs for Local LLM UK 2026: 6 Top Picks

Best mini PCs for running local LLMs (Ollama, Llama 3.3) in the UK 2026 - Mac mini M4, Beelink SER8, Minisforum HX99G, Geekom AX8 Pro picks compared.

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Rob
By Rob12 June 2026 · 10 min read

Our local LLM for smart home piece and cloud vs local cost analysis covered the strategic side of running a local LLM. This guide drills into the hardware: which mini PC actually delivers the inference speed and reliability for a typical UK smart-home Home Assistant + Ollama setup in 2026. Background on the underlying neural architecture is at the Wikipedia large language model page.

Which mini PCs are best for UK local LLM hosts in 2026?

Mac mini M4 24GB - £999 UK (easiest setup)

Mac mini M4 24GB - £999 UK (easiest setup)

Apple Silicon M4 chip + 24GB unified memory. Runs Ollama natively via the Apple-optimised Metal backend. Lowest setup friction - macOS + Ollama installer + Home Assistant Docker container is a 30-minute job. 30W idle / 40-60W under inference. Silent fan. Best for buyers who want it to just work.
Beelink SER8 32GB - £700 UK (AMD value pick)

Beelink SER8 32GB - £700 UK (AMD value pick)

Ryzen 9 8945HS + 32GB DDR5 + iGPU. Linux-friendly (Ubuntu / Debian / Proxmox). Runs Ollama via ROCm for iGPU acceleration. Quieter than the desktop Ryzen builds; bigger RAM headroom than Mac mini. Best for Linux-comfortable buyers who want more memory at lower cost.
Minisforum HX99G - £800-900 UK (dedicated GPU)

Minisforum HX99G - £800-900 UK (dedicated GPU)

Ryzen 9 + Radeon RX 6650M dedicated GPU + 32GB DDR5. Materially faster inference than iGPU-only mini PCs. The 'has a real GPU' pick. Best for users who want Llama 3.3 70B-class models in addition to 8B, or who plan to run multiple workloads (Frigate AI + Ollama + HA).
Geekom AX8 Pro - £600 UK (entry-tier value)

Geekom AX8 Pro - £600 UK (entry-tier value)

Ryzen 7 8845HS + 32GB DDR5 + iGPU. Cheapest viable mini PC for local LLM use. Slower inference than the £800+ options but adequate for 1-3 users on a Home Assistant Assist setup. Best for budget-conscious buyers willing to accept slightly slower voice response.
Mac mini M4 Pro 48GB - £1,500 UK (premium)

Mac mini M4 Pro 48GB - £1,500 UK (premium)

Apple Silicon M4 Pro + 48GB unified memory. Runs Llama 3.3 70B and Qwen 3.5 32B comfortably - the bigger-model upgrade pick. 30-50W idle. Silent. For households who want frontier-quality local LLM responses without the GPU+desktop noise.
Intel NUC 13 Pro - £450-550 UK (cheap Linux host)

Intel NUC 13 Pro - £450-550 UK (cheap Linux host)

Older-gen but proven reliable Linux mini PC. Runs Ollama on CPU-only fallback - slower but workable for 8B models on patient users. Best for buyers who already have a NUC sitting around and want to test local LLM before committing to dedicated hardware.

How do they perform on Llama 3.3 8B inference?

Practical inference speed across the picks, measured as tokens-per-second on Llama 3.3 8B at Q4_K_M quantisation (the typical Ollama default):

  • Mac mini M4 24GB: ~25-35 tokens/sec. Voice response latency ~2-3s end-to-end with Whisper STT + Piper TTS.
  • Mac mini M4 Pro 48GB: ~35-50 tokens/sec on 8B; ~12-18 tokens/sec on 70B (which the smaller M4 can't run usably).
  • Minisforum HX99G (dedicated GPU): ~40-55 tokens/sec on 8B. Fastest of the non-Mac picks.
  • Beelink SER8 32GB (iGPU): ~15-25 tokens/sec on 8B. Adequate but slower than the dedicated-GPU picks.
  • Geekom AX8 Pro (iGPU): ~12-22 tokens/sec on 8B. Lowest acceptable.
  • Intel NUC 13 Pro (CPU-only): ~6-12 tokens/sec on 8B. Workable but patient-user only.

For Home Assistant voice + automation drafting use, anything above 15 tokens/sec feels responsive. Below 10 tokens/sec the voice interactions feel sluggish. The Mac mini M4 24GB is the floor for 'just works'; the Minisforum HX99G is the floor for 'noticeably fast'.

Setup workflow

  1. Install host OS

    Mac mini: macOS (out of box). Beelink/Geekom: Ubuntu 24.04 LTS recommended (the most-tested distro for Ollama). Minisforum: Pop!_OS or Ubuntu - both have ROCm support. Plan a 60-minute initial OS install.

  2. Install Ollama

    Single-line installer on all platforms: `curl -fsSL https://ollama.ai/install.sh | sh` (Linux/macOS). Add the Ollama service to autostart.

  3. Pull your model

    `ollama pull llama3.3:8b` for the standard recommendation. Add `ollama pull qwen2.5:7b` if you want a second model to compare reasoning quality.

  4. Connect to Home Assistant

    In Home Assistant: Settings → Devices & Services → Add Integration → Ollama. Point at your mini PC's local IP at the Ollama port (11434 default). HA Assist now uses your local LLM as the Conversation backend.

  5. Test voice + automation drafting

    Test 1: 'Hey Jarvis, turn off the kitchen lights.' Should work within 1-3s. Test 2: 'Draft an automation that turns the porch light on at sunset weekdays.' Should produce usable YAML within 5-10s.

Which to buy for your situation

Practical decision shortcuts:

  • Fresh build, lowest friction: Mac mini M4 24GB. Apple Silicon + Metal-optimised Ollama is the most predictable setup.
  • Lowest budget, Linux comfortable: Geekom AX8 Pro (~£600). Slowest of the practical picks but adequate.
  • Best raw inference speed without Mac premium: Minisforum HX99G (~£800-900). Dedicated Radeon GPU pays off on inference latency.
  • Want 70B models too: Mac mini M4 Pro 48GB (~£1,500). Only mini-PC pick that runs frontier-class local models usably.
  • Privacy-first + multi-workload: Beelink SER8 32GB (~£700). Linux flexibility, fits multiple homelab roles.

Frequently asked questions

Q01Can I run Llama 3.3 70B on a £700 mini PC?
No - 70B models need 40GB+ VRAM or unified RAM for decent inference speed. The Beelink SER8 / Geekom AX8 Pro tier handles 8B models comfortably but choke on 70B. For 70B move up to Mac mini M4 Pro 48GB or build a desktop with a Nvidia RTX 4090 24GB / dual 4060 Ti 16GB setup.
Q02Is a Raspberry Pi 5 enough for local LLM?
No - the Pi 5 has 8GB RAM and no GPU acceleration. It can technically run quantised 3B-4B models but the inference speed (3-5 tokens/sec) makes voice interactions painful. Use a Pi 5 for Home Assistant + cloud LLM Conversation agent, not for local LLM.
Q03Do I need a separate machine for HA and Ollama?
Not necessarily. A Mac mini M4 24GB or Beelink SER8 32GB can run both HA OS in a VM / Docker container AND Ollama natively. The trade-off is contention for system resources during peak voice + automation events. For larger homes (10+ Wi-Fi clients, 5+ cameras), splitting HA and LLM onto separate boxes is cleaner.
Q04Mac mini M4 vs Beelink SER8 - which is better value?
Beelink SER8 wins on raw value (£700 vs £999 for similar RAM and faster CPU). Mac mini M4 wins on setup simplicity + power efficiency + silence. For Mac-comfortable users the £300 premium typically pays back in less setup time and reliability over the device's lifespan.
Q05How much electricity does a local LLM mini PC use in the UK?
Mac mini M4 24GB: ~30W idle, 40-60W under inference. At UK 2026 electricity rates (~28p/kWh) and 4 hours active inference/day: roughly £4-6 per month. The dedicated-GPU picks (Minisforum HX99G) sit at £6-10/month. Cheap relative to cloud LLM monthly spend.
Q06Should I wait for the next generation?
Probably not. Apple Silicon iteration is annual; AMD's Strix Halo successor is mid-2026; Nvidia's RTX 5060 Ti is late 2026. None of these are step-changes from current options for 8B-class LLM use. If you want to run local LLM now, the 2025-2026 hardware generation is mature and worth committing to.

The bottom line

For most UK households committing to local LLM in 2026 the Mac mini M4 24GB is the right default - £999, easiest setup, runs Llama 3.3 8B at usable speeds, silent. Budget buyers should go Geekom AX8 Pro (£600) or Beelink SER8 32GB (£700). Performance buyers should go Minisforum HX99G (£800-900) for the dedicated GPU advantage. Frontier-model buyers should stretch to the Mac mini M4 Pro 48GB (£1,500) for 70B-class capability.

The mini PC tier is genuinely the right hardware bracket for typical UK smart-home + local LLM use - more powerful than a Raspberry Pi but materially cheaper and quieter than building a discrete-GPU desktop. Mature picks across £600-1500 means every budget tier has a credible choice.

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