A mini-PC built for AI inference will outperform a NAS at every price point. NAS processors are designed for low-power, always-on storage workloads, not compute-intensive LLM inference. If raw AI performance is the priority, a dedicated mini-PC with a modern NPU or discrete GPU wins that comparison without argument. The NAS case only gets interesting when you factor in what you already own, what tasks you actually need to run, and whether those tasks require dedicated hardware at all.
In short: Buy a mini-PC if running LLMs is a primary goal. Use your NAS if you already own one with 8GB or more RAM and want to experiment with smaller models without buying new hardware. The best setups often use both: NAS for storage and file serving, mini-PC for compute-heavy AI inference.
What You Are Actually Comparing
NAS devices run low-power processors optimised for storage throughput, not single-threaded compute speed. Most consumer NAS models use Realtek ARM chips or Intel Celeron processors. Even the more capable models, like the Synology DS925+ with its Ryzen-derived processor, are not designed for sustained inference workloads. QNAP's higher-end models, such as the TS-473A with its AMD Ryzen V1500B, offer better compute headroom, but still lag well behind a modern mini-PC processor.
Mini-PCs purpose-built for AI use Intel Core Ultra processors with integrated NPUs, AMD Ryzen AI series chips, or discrete GPU options. These processors handle matrix multiplication, which is the core mathematical operation behind LLM inference, significantly faster than storage-optimised NAS chips. The result is faster token generation, support for larger model sizes, and the ability to run more capable quantisation levels without grinding to a halt.
Mini-PC vs NAS for Local AI: Core Comparison
| Mini-PC (AI-capable) | NAS (capable end) | |
|---|---|---|
| Processor type | Intel Core Ultra, AMD Ryzen AI, or discrete GPU | Celeron, Ryzen V1500B, or ARM (NAS-grade) |
| NPU support | Yes, on Intel Core Ultra and AMD Ryzen AI 300 series | No, on almost all NAS models |
| Max usable RAM for AI | 16 to 64GB typical | 4 to 16GB depending on model |
| Token generation (7B Q4 model) | 8 to 25 tokens/sec | 1 to 5 tokens/sec |
| Max practical model size | Up to 70B with GPU, 13B comfortably on 16GB RAM | 7B at most on 8GB RAM, slowly |
| Storage integration | External drive or USB only | Native RAID, multiple bays, always-on |
| Idle power draw | 10 to 25W | 8 to 20W (often already running) |
| Entry AU cost (AI-capable) | From ~$600 for a capable mid-range option | From $978 (DS925+) if buying new for AI |
| Noise | Fan noise under load, some fanless options exist | Generally quiet at idle, designed for 24/7 operation |
AI Performance: Where Mini-PCs Win Clearly
Token generation speed is the metric that matters most for conversational AI. On a capable NAS like the QNAP TS-473A running Ollama with a 7B parameter model at Q4_K_M quantisation, expect 1 to 4 tokens per second. A response to a standard prompt takes 30 to 90 seconds to complete. That is usable for background tasks but frustrating for interactive conversation.
A mid-range mini-PC with an Intel Core Ultra 5 or AMD Ryzen AI 9 HX processor generates 10 to 18 tokens per second on the same model. A response arrives in 5 to 10 seconds. If the mini-PC has a discrete GPU with 8GB or more VRAM, that rises to 20 to 50 tokens per second. The difference between 2 tokens per second and 15 tokens per second is not a minor improvement. It is the difference between a tool you use daily and a tool you stop opening after a week.
The other hard constraint on NAS hardware is the RAM ceiling. Most NAS models top out at 8 to 16GB of total system RAM, shared between the OS, running services, and AI inference. In practice, 4 to 6GB is available for the model itself. That restricts you to 7B parameter models at aggressive quantisation levels. Models at 13B parameters are generally not viable on NAS hardware without severe degradation in response quality.
RAM ceiling matters more than CPU speed for LLMs. The model must fit entirely in RAM to run at usable speed. A 7B model at Q4_K_M quantisation requires approximately 4 to 5GB of RAM. Any configuration that forces the model to use disk-based swap instead of RAM drops inference speed to near-zero for practical purposes.
What a NAS Does Well for Local AI
A NAS is not the right primary device for LLM inference, but it handles several AI-adjacent workloads well. Immich running on a NAS handles AI photo search, face recognition, and smart album generation without requiring high inference speed. These tasks run as background jobs, not real-time conversations, so the slower NAS processor is not a meaningful bottleneck.
Running Ollama on a NAS for occasional use is viable. Summarising a document once or twice a day, the slower inference speed is acceptable. It becomes a problem when you want interactive conversation or need to process large volumes of text quickly.
The NAS also wins on storage. A mini-PC running local AI needs somewhere to store model files, training data, and outputs. A 70B model at Q4 quantisation is around 40GB. Storing multiple large model files on a mini-PC with a 512GB SSD becomes a management problem. A NAS with multiple drives handles this naturally and serves model files over the network to any device that needs them.
Power efficiency is another NAS advantage, with an important caveat. If your NAS is already running 24/7 for file serving and backups, running Ollama on it adds almost zero incremental power cost. The NAS is already on. A dedicated mini-PC adds 10 to 25W of always-on draw, which at Australian electricity rates of $0.30 to $0.40 per kWh costs between $26 and $88 per year.
Cost: The Numbers Are Not Simple
The cost comparison depends heavily on whether you already own a NAS. If you do, the marginal cost of running AI on it is zero. If you are buying new hardware specifically for local AI, the calculus changes significantly.
The Synology DS925+ starts at $978 at Australian retailers including Mwave, Scorptec, and Umart, with 4GB of RAM that can be upgraded. The QNAP TS-473A, the NAS best suited for AI inference with its AMD Ryzen processor, starts at around $1,269 at Scorptec and PLE. These are reasonable prices for a capable NAS that also handles storage, but paying that amount purely for AI inference gives you a considerably worse AI experience than a $700 mini-PC.
| Entry mini-PC (Intel N100 class) | ~$350 to $450 AUD approximate. Handles small models at low speed. Not recommended as primary AI hardware |
|---|---|
| Mid-range mini-PC (Core i5/i7 12th/13th gen) | ~$550 to $700 AUD approximate. 7B models at acceptable speed. Good starting point for AI use |
| Capable AI mini-PC (Core Ultra 5/7 or Ryzen AI) | ~$800 to $1,200 AUD approximate. NPU-assisted inference, 13B models viable, recommended for regular LLM use |
| High-end AI mini-PC (Core Ultra 9 or discrete GPU) | ~$1,200 to $2,500 AUD approximate. 70B models viable with GPU, fastest local inference without a full desktop PC |
| Synology DS925+ (4-bay) | From $978 AUD at Mwave, Scorptec, Umart. Ryzen-derived processor, upgradeable RAM. Good for background AI tasks including Immich |
|---|---|
| QNAP TS-473A (4-bay, AMD Ryzen V1500B) | From $1,269 AUD at Scorptec, PLE. Strongest AU-available NAS for inference. 8GB base RAM, upgradeable to 64GB |
| QNAP TS-464 (4-bay, Intel Celeron N5105) | From $1,049 AUD at multiple AU retailers. Capable NAS, weaker processor than TS-473A. Better for Immich and background AI than interactive LLM use |
Do not buy a NAS primarily for AI inference. If AI is the main use case and you do not need NAS storage capabilities, a $700 to $900 mini-PC will outperform a $1,000 NAS on every AI metric that matters for daily use. Buy a NAS because you need a NAS. Treat AI as a secondary benefit, not the primary reason to buy.
The Best Setup: Not Either/Or
For most home users and enthusiasts, the best local AI setup combines both devices. The NAS handles storage, backups, media serving, and background AI tasks like Immich photo recognition. The mini-PC handles compute-intensive LLM inference and acts as the AI endpoint that other devices on the network point to.
This combination works well in practice. Ollama running on the mini-PC can serve models to any device on the network, including phones and tablets. The NAS stores model files on its drives and serves them over the network. The mini-PC stays on only when active inference is needed, while the NAS stays on because it is already running as a file server.
NBN upload constraints also make this setup more compelling than relying on cloud AI services. Australian users on typical NBN 100 plans often have 20 megabits per second upload or less. Round-trip latency to overseas AI API endpoints adds up during multi-turn conversations. Local inference on a mini-PC responds without network dependency, which is a genuine quality-of-life improvement for regular AI users in Australia.
Decision Framework: Which Platform to Choose
Which Platform Suits Which Situation
| Buy a Mini-PC | Use Your NAS | Use Both | |
|---|---|---|---|
| Primary use case | Interactive LLM conversation, code assistance, regular use | Background AI tasks, Immich, occasional document summarisation | Fast inference plus NAS storage and file serving |
| Hardware you own | NAS already handles storage, no new NAS needed | Capable NAS with 8GB or more RAM already owned | Currently only have one device, want a complete setup |
| Model size needed | 13B or larger models reliably | 7B models are sufficient | 13B on mini-PC, smaller background tasks on NAS |
| Response speed requirement | Real-time conversational speed needed | Batch or occasional use, speed is secondary | Fast inference on mini-PC, slow background tasks on NAS |
| Power awareness | Will turn device off when not in use | NAS already running, AI adds zero extra power cost | Both devices justified by other use cases already |
| Budget | Spending specifically on AI hardware | Not willing to add new hardware cost | Willing to invest in a properly capable long-term setup |
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 I run Ollama on a Synology NAS?
Yes, Ollama runs on Synology NAS models that support Docker, including the DS225+, DS425+, DS925+, and DS1525+. The DS925+ is the most capable option, with its Ryzen-class processor and upgradeable RAM. Expect 1 to 3 tokens per second on a 7B model at Q4 quantisation. This is usable for background tasks and occasional use but too slow for regular interactive conversation. See the Ollama on Synology setup guide for full configuration steps.
How much RAM do I need for local AI on a NAS or mini-PC?
The model must fit entirely in RAM to run at practical speed. A 7B model at Q4_K_M quantisation requires approximately 4 to 5GB of RAM. A 13B model requires 8 to 10GB. A 70B model requires 35 to 40GB. On a NAS, subtract RAM used by the OS and running services, which is typically 1.5 to 3GB. A NAS with 8GB total has approximately 5 to 6GB available for inference, enough for 7B models only. On a dedicated mini-PC, 16GB is the practical minimum for 7B models with headroom, and 32GB allows comfortable 13B inference. See the guide on what LLMs run on each RAM tier for detailed breakdowns by model size.
Is running local AI cheaper than paying for ChatGPT Plus?
Over a five-year period, local AI is generally cheaper than a cloud subscription, but it requires upfront hardware cost. ChatGPT Plus costs approximately $30 AUD per month. A capable mini-PC at $900 amortised over five years costs roughly $15 per month in hardware. At Australian electricity rates of $0.30 to $0.40 per kWh and 15W average draw, power adds approximately $13 to $18 per year. Total local AI cost works out to approximately $17 to $20 per month over five years, versus $30 per month for a cloud subscription. Local AI is cheaper in the long run and offers full privacy, but cloud services currently provide access to significantly larger and more capable models.
What is the best NAS for running local AI in Australia?
The QNAP TS-473A (from $1,269 at Scorptec and PLE) is currently the strongest NAS for AI inference available in Australia, with its AMD Ryzen V1500B processor and support for up to 64GB of RAM. The Synology DS925+ (from $978 at Mwave, Scorptec) is a capable alternative with wider availability and a more familiar software environment. Both handle 7B parameter models at acceptable speeds for background and occasional use. For a more detailed comparison of AI-capable NAS hardware, see the best NAS for local LLM guide.
Can a mini-PC replace a NAS entirely?
A mini-PC can handle AI inference and general file storage, but it is a poor replacement for a NAS in a home or SMB environment. NAS devices provide RAID redundancy across multiple drives, specialised storage management software, automated backup scheduling, and hardware designed for 24/7 always-on operation. A mini-PC running a standard OS with a single SSD has none of these capabilities built in. The two devices serve fundamentally different purposes: the mini-PC handles compute, the NAS handles storage resilience. Running both is the most capable setup. Choosing only one should be driven by budget and use case, not the assumption that one device can fully replace the other.
Not sure which NAS models support AI workloads and are actually stocked in Australia? The local AI NAS guide covers RAM ceilings, NPU support, and current AU retail availability.
See the Local AI NAS Guide