Running large language models locally has gone from a niche experiment to a legitimate alternative to cloud AI - especially as privacy concerns grow and inference costs add up. The challenge? Most consumer desktops are bulky, loud, and power-hungry. Mini PCs have emerged as the sweet spot: compact form factors with enough CPU/RAM headroom to run Ollama, llama.cpp, or LM Studio with 7B to 13B models comfortably. In this guide, I break down the best mini PCs for local AI inference in 2026, based on real-world use in my own self-hosted stack.
Why Mini PCs for Local AI?
Before diving into specific picks, it's worth understanding why mini PCs have become popular for local LLM inference over traditional desktops or dedicated GPU boxes:
- Power efficiency: Modern mini PCs running Intel Core Ultra or AMD Ryzen AI chips idle at 8-15W and peak around 35-65W - far less than a desktop with a discrete GPU.
- Noise: Fanless or near-silent operation makes them ideal for always-on home lab use.
- Size and cost: They fit anywhere and typically run $150-$500 - much cheaper than a GPU upgrade for pure CPU inference.
- NPU/iGPU acceleration: Newer chips include Neural Processing Units and fast integrated graphics with dedicated VRAM. A Ryzen 9 8945HS with AMD Radeon 780M, for example, can offload embedding layers to the iGPU for noticeably faster token generation.
The tradeoff: you won't be running 70B models at usable speeds, and you don't get discrete GPU VRAM. For 7B models quantized to Q4 or Q5, though, these boxes are surprisingly capable.
What to Look For
When evaluating a mini PC for AI inference, prioritize in this order:
- RAM capacity and speed: Models load into RAM. A 7B Q4 model needs ~5-6GB; a 13B Q4 needs ~9-10GB. You want 32GB minimum, 64GB if you're serious. Also check if RAM is soldered or socketed - many budget mini PCs solder RAM at 16GB with no upgrade path.
- CPU architecture: AMD Ryzen AI (8000 series) and Intel Core Ultra (Series 1/2) both include NPUs. For pure inference, more cores at higher frequencies matter more than NPU specs - the NPU ecosystem for LLMs is still maturing.
- Integrated GPU VRAM sharing: Some mini PCs let you allocate up to 16GB of system RAM to the iGPU via BIOS. Combined with Vulkan or ROCm support, this can meaningfully accelerate inference vs. pure CPU.
- Storage: Model files are large. A 7B model is ~4GB; a 13B is ~8GB. You'll want at least a 1TB NVMe SSD, ideally with a second M.2 slot.
- TDP and thermals: Higher TDP headroom = sustained performance under load. Check that the chassis can sustain 35-45W without throttling.
Best Mini PCs for Local AI Inference in 2026
1. Beelink GTi14 - Best Overall
The Beelink GTi14 (~$479) is the top pick for local AI work in 2026. It ships with an Intel Core Ultra 5 125H - a 14-core chip (6P + 8E) with Intel Arc iGPU and an NPU rated at 11 TOPS. More importantly, it supports up to 96GB DDR5 RAM via two user-upgradeable SO-DIMM slots. That's a game-changer for large model inference.
In practice, running Ollama with llama3:8b on a 32GB config, you'll see 12-18 tokens/second - usable for a local coding assistant or chat bot. Bump to 64GB and you can load a 13B model with room to spare. The Intel Arc iGPU supports OpenCL and has early ROCm-like support via Intel's oneAPI stack, though CUDA alternatives like llama.cpp's Vulkan backend are more reliable today.
Pros: Upgradeable RAM, Intel Arc GPU with XeSS, strong sustained performance, dual M.2 slots, Thunderbolt 4
Cons: Price is higher than AMD alternatives; Arc GPU driver maturity still lags NVIDIA
2. Minisforum UM790 Pro - Best AMD Option
AMD fans should look at the Minisforum UM790 Pro (~$359), which packs the Ryzen 9 7940HS - an 8-core chip with AMD Radeon 780M integrated graphics. The 780M is currently one of the best iGPUs for AI inference: it supports ROCm (with community patches), Vulkan, and can have up to 16GB of the system's unified memory allocated to it in BIOS.
With llama.cpp compiled with Vulkan support and 32GB RAM (16GB allocated to GPU), the UM790 Pro can run a 7B Q4 model at 20-25 tokens/second - noticeably faster than CPU-only inference. Minisforum also ships with two M.2 slots and socketed RAM, making upgrades easy.
Pros: Best iGPU for Vulkan-accelerated inference, upgradeable RAM, competitive price, excellent thermals
Cons: ROCm support requires manual setup; 7940HS is a Zen 4 chip without dedicated NPU
3. GMKtec NucBox K8 - Best Budget Pick
If you want to get started with local AI without a big spend, the GMKtec NucBox K8 (~$299) is a strong budget option. It runs the Ryzen 7 8845HS - AMD's Ryzen AI 300 predecessor with solid integrated Radeon 780M graphics and a 16 TOPS NPU. RAM is soldered at 32GB (unfortunately), but that's enough for 7B-13B Q4 models without upgrades.
The K8 is particularly notable because its 8845HS runs cooler and more efficiently than the 7940HS, allowing for better sustained performance in continuous inference workloads. For an Ollama homelab serving a family or small team, this delivers strong value.
Pros: 32GB soldered RAM standard, AMD Radeon 780M iGPU, competitive price, Ryzen AI NPU
Cons: RAM is not upgradeable; only one M.2 slot on some variants
4. ASUS PN64 - Best for Expandability
The ASUS PN64 (~$499) is an Intel Core i7-1360P-based mini PC from a tier-1 OEM, which means better long-term driver support, more robust thermals, and a barebones option for those who want to supply their own RAM and storage. It supports up to 64GB DDR4 and has two M.2 slots plus a 2.5" SATA bay - ideal if you're building a multi-purpose home lab node that also handles local AI.
CPU inference performance is solid for 7B models. The Iris Xe iGPU won't match the AMD Radeon 780M for Vulkan inference, but if you're running Proxmox VMs alongside your AI workload, the PN64's stability and ASUS firmware support give it an edge over off-brand mini PCs.
Pros: Reputable OEM, barebones option, SATA bay for bulk storage, excellent build quality
Cons: Intel Iris Xe GPU weaker for AI workloads; more expensive for equivalent CPU performance
5. Beelink EQ12 - Best Entry-Level
For an always-on AI assistant that stays within a tight budget, the Beelink EQ12 (~$189) punches well above its price. It runs the Intel N100 - a 4-core efficiency chip designed for low-power continuous operation (6W TDP). You won't be running 13B models here, but for Phi-3-mini, TinyLlama, or Qwen2-1.5B (sub-3B models quantized to Q4), it performs acceptably as a low-power always-on assistant.
Think of it as the Raspberry Pi alternative for x86 local AI: $189, 16GB RAM (soldered), a fanless or near-silent chassis, and the ability to serve small LLMs 24/7 without impacting your electricity bill.
Pros: Extremely low power draw (~6W idle), nearly silent, very affordable
Cons: N100 is too slow for 7B+ models at useful speeds; RAM soldered at 16GB
Comparison Table
| Model | CPU | Max RAM | iGPU | Price | Best For |
|---|---|---|---|---|---|
| Beelink GTi14 | Intel Core Ultra 5 125H | 96GB DDR5 | Intel Arc (8 Xe cores) | ~$479 | Best overall, 13B models |
| Minisforum UM790 Pro | Ryzen 9 7940HS | 64GB DDR5 | Radeon 780M (12 CUs) | ~$359 | Best AMD / Vulkan inference |
| GMKtec NucBox K8 | Ryzen 7 8845HS | 32GB (soldered) | Radeon 780M (12 CUs) | ~$299 | Budget Ryzen AI pick |
| ASUS PN64 | Intel Core i7-1360P | 64GB DDR4 | Intel Iris Xe (96 EU) | ~$499 | Expandability + home lab |
| Beelink EQ12 | Intel N100 | 16GB (soldered) | Intel UHD (24 EU) | ~$189 | Entry-level, sub-3B models |
Setting Up Ollama on a Mini PC
Once your mini PC arrives, getting Ollama running takes under 5 minutes on Linux:
- Install Ollama:
curl -fsSL https://ollama.com/install.sh | sh - Pull a model:
ollama pull llama3:8borollama pull phi3:minifor the EQ12 - Run it:
ollama run llama3:8b - For GPU acceleration on AMD: compile llama.cpp with
-DLLAMA_VULKAN=onand use it as a backend instead of Ollama's default
For a proper home lab setup, I run Ollama as a systemd service and expose it to my local network so Open WebUI (running in a Docker container) can connect to it. This gives me a ChatGPT-like interface for any device on my network.
Storage: Don't Skimp
Model files stack up fast. A reasonable model collection (llama3:8b, mistral:7b, phi3:mini, codellama:7b) eats about 25-30GB. I'd recommend a Samsung 870 EVO 1TB (~$79) or an equivalent NVMe drive as your primary or secondary storage. On mini PCs with a second M.2 slot, a fast NVMe speeds up model load times significantly - especially for larger quantizations.
My Current Pick
For my own self-hosted AI stack, I'm running the Minisforum UM790 Pro with 64GB RAM, Ollama + Open WebUI in Docker, and llama.cpp with Vulkan enabled for the Radeon 780M. For 7B models at Q4_K_M quantization, I'm getting 22-26 tokens/second - fast enough for a responsive local coding assistant and completely private. No API keys, no usage limits, no data leaving the house.
If budget is no constraint and you want to run 13B models comfortably, the Beelink GTi14 with 64GB or 96GB DDR5 is the one to beat. The Intel Arc iGPU has been improving steadily, and the Core Ultra 125H's performance-per-watt is excellent.
Final Verdict
Local AI inference on mini PCs is no longer a hobbyist curiosity - it's a legitimate setup for privacy-first developers, home lab enthusiasts, and anyone tired of API rate limits. The Minisforum UM790 Pro hits the best price-to-performance sweet spot for Vulkan-accelerated inference. The Beelink GTi14 is the premium pick. And the Beelink EQ12 is the perfect entry point if you just want to dip a toe in without spending much.
Whichever you pick, pair it with a fast NVMe, max out the RAM, and install Ollama - you'll be surprised how capable local AI can be in 2026.