Mini-PC vs NAS for Local AI — Which Hardware Should You Choose?

A NAS makes sense for local AI if you already own one, your workloads are bursty, and you want file access plus AI in one device. A mini-PC wins when you need consistently fast inference, want the option to add a GPU later, or are building a dedicated AI system from scratch. The hardware type matters less than matching the platform to how you will actually use it.

What Matters for Local AI Inference

Before comparing hardware, you need to know what local AI inference actually requires. Three factors determine whether hardware is usable:

• RAM (not VRAM). LLMs run in RAM when there is no GPU. A 7B model at Q4 quantisation needs approximately 5-6GB of RAM just for the model weights, plus overhead for the context. A 13B model at Q4 needs 8-10GB. If the model does not fit in RAM, it pages to disk and becomes unusably slow.
• CPU throughput. Inference speed is measured in tokens per second. A fast modern CPU produces 5-20 tok/s on a 7B model. That is readable in a chat interface. Below 3 tok/s it becomes frustrating. ARM-based CPUs (most consumer NAS) produce 1-4 tok/s on 7B models. Functional but slow.
• Cooling headroom. Sustained inference at full CPU load for minutes or hours requires the hardware to maintain clock speeds. A system that throttles under thermal load will slow down mid-conversation. NAS units are optimised for storage workloads, not sustained compute.

NAS Strengths for Local AI

A NAS has genuine advantages for local AI that a dedicated AI box cannot replicate:

Mini-PC Strengths for Local AI

Performance Comparison. Real Inference Speeds

Inference speed is measured in tokens per second (tok/s) using Ollama. These figures are for CPU-only inference with the Llama 3 7B model at Q4_K_M quantisation. The most common home use case. GPU figures use a single discrete GPU.

The practical threshold for comfortable text chat is 8-10 tok/s. Below that, responses feel slow. Above 20 tok/s, the model is faster than most people read. The QNAP TS-473A sits right in the usable zone. A Beelink EQR6 is noticeably faster and costs less than most AI-capable NAS units. A GPU changes the equation entirely.

Price Comparison. Hardware in Australia (2026)

Note that mini-PCs listed above do not include storage. Add a 2TB NVMe SSD for model storage ($120-180) and plan to either use an existing NAS for files or add a USB drive. A QNAP TS-473A includes the NAS functionality but costs $1,269 versus$1269-$1818 for an equivalent-performance mini-PC. If you already own a NAS, the AI-capable QNAP units represent the best total-cost-of-ownership argument.

Which to Choose: Decision Framework

The right answer depends on what you already have and what your AI workload looks like:

Choose a NAS if:

• You already own a QNAP TS-473A, TS-873A, or Synology DS925+. These are good enough for home AI without additional hardware
• Your primary use is AI photo management (Immich, Synology Photos AI). The storage integration is the whole point
• You want one device for everything and are comfortable with 7-15 tok/s inference speed
• Power efficiency matters. The always-on cost is lower when AI is one function among many

Choose a mini-PC if:

• You are buying hardware specifically for local AI and do not need integrated storage management
• You want 13B+ model performance without a GPU
• You want the option to add an eGPU later via Thunderbolt or USB4
• You are already frustrated with the speed of AI on your existing NAS
• Budget matters. A $550 Beelink outperforms most AI-capable NAS at lower cost

Avoid both and go GPU if:

• You want real-time 13B+ model inference for coding, document analysis, or image generation
• You are running multiple users simultaneously against the same model
• The 80-120 tok/s difference between CPU and GPU inference matters for your workflow

Australian Buyers: What You Need to Know

Mini-PCs from Beelink and Minisforum are available through Amazon AU with standard delivery and Australian Consumer Law (ACL) protections applying to purchases from Australian-registered sellers. Some marketplace listings are grey imports. Check the seller's location before buying. A retailer registered in Australia is required under the ACL to provide a remedy for defective goods regardless of any manufacturer warranty terms.

NAS hardware from Synology and QNAP is available through all major Australian retailers. Mwave, PLE, Scorptec, and Computer Alliance all stock the AI-capable models. Both brands are distributed through BlueChip IT in Australia, which means consistent stock levels and 1-3 day delivery nationally. GPU hardware (RTX 3060, RTX 4070) is widely stocked through the same retailers at standard Australian pricing.

For power cost estimates by state and hardware type, see the AU power cost of running local AI guide. For the full cost-of-ownership comparison including hardware amortisation and cloud AI alternatives, see the NAS vs cloud AI cost comparison.

Related reading: our NAS buyer's guide, our NAS vs cloud storage comparison, and our NAS explainer.

Free tools: NAS Sizing Wizard and AI Hardware Requirements Calculator. No signup required.

Can a Synology NAS run Ollama?

Yes, but with caveats. Synology NAS units with Intel x86 CPUs (DS425+, DS925+, DS1525+) can run Ollama via Docker. ARM-based Synology models (DS223, DS423) cannot run Ollama. ARM support is limited in Ollama's standard builds. The DS925+ with its AMD Ryzen R1600 is the most capable Synology for text inference, producing 7-12 tok/s on 7B models. See the Ollama on Synology guide for setup instructions.

Is a mini-PC better than a NAS for running local AI?

For raw inference speed, yes. A $550-700 mini-PC like the Beelink GTi14 or EQR6 produces 15-28 tok/s on 7B models, compared to 7-15 tok/s for the best AI-capable NAS. But a mini-PC does not replace your NAS. You still need storage management, RAID, and file access. The right choice depends on whether you are augmenting an existing NAS setup or building a dedicated AI box from scratch.

Do I need a GPU for local AI at home?

Not for most home use cases. A modern CPU handles 7B models at conversational speed (8-20 tok/s) without a GPU. GPU inference is worth adding if you need fast 13B+ model responses, are running image generation (Stable Diffusion), or want to serve multiple users simultaneously. A GPU via eGPU enclosure costs $500-1,500+ in Australia. Use the NAS vs cloud AI cost comparison to check whether cloud API costs would be cheaper at your usage level.

What is the cheapest hardware that can run a 7B LLM usably in Australia?

A Beelink EQR6 (AMD Ryzen 7 6800H, 32GB DDR5) available through Amazon AU for $450-600 will run Llama 3 7B at Q4 quantisation at 18-28 tok/s. Well above the comfortable threshold. For a NAS-integrated approach, a second-hand QNAP TS-473A or TS-464 with 8-16GB RAM is the lowest-cost entry point with acceptable performance.

Can I add a GPU to a NAS for faster AI inference?

No consumer NAS has a PCIe slot that supports a desktop GPU. QNAP makes PCIe expansion units for enterprise models, but these are not designed for GPU inference workloads. If you want GPU-accelerated AI, a mini-PC or desktop with a Thunderbolt 4 / USB4 port paired with an eGPU enclosure is the closest home-friendly option. The QNAP TVS-h674 has Thunderbolt ports that may support eGPU enclosures, but this is an experimental configuration.