For LLM inference, a suitably configured mini-PC will often outperform a storage-focused NAS, but performance depends on the processor, accelerator, memory capacity, runtime and model rather than price alone. 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 | Model-dependent; 32GB on the DS925+ and 64GB on the TS-473A |
| Token generation (7B Q4 model) | Varies with the exact model, quantisation, context length, runtime, memory configuration and accelerator use; publish device-specific figures only from reproducible benchmarks. | Varies with the exact model, quantisation, context length, runtime, memory configuration and accelerator use; publish device-specific figures only from reproducible benchmarks. |
| Max practical model size | A 70B model requires unusually large combined RAM/VRAM capacity; viability depends on quantisation, context length, accelerator memory and whether partial CPU offload is acceptable. | 7B at most on 8GB RAM, slowly |
| Storage integration | Internal NVMe/SATA storage, with optional external or network storage | 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. TS-473A token-generation speed varies with the exact model, runtime, context, memory configuration and whether a supported GPU is installed; a measured range requires a reproducible benchmark. A response to a standard prompt takes 30 to 90 seconds to complete. That is usable for background tasks but frustrating for interactive conversation.
Mini-PC token-generation speed varies by processor generation, power limit, memory bandwidth, runtime, model and accelerator backend; use named-system benchmarks for numerical comparisons. A response arrives in 5 to 10 seconds. A supported discrete GPU can accelerate inference, but token rate depends on the exact GPU, model, quantisation, context, runtime and whether the model fits in VRAM. 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. NAS memory ceilings vary substantially by model; the DS925+ supports 32GB and the TS-473A supports 64GB, while lower-end models may have much lower limits. Available model memory depends on installed RAM, the NAS operating system and the services running concurrently. That restricts you to 7B parameter models at aggressive quantisation levels. Whether a 13B model is practical depends on installed RAM, quantisation, context length and acceptable latency; adequately upgraded NAS hardware can load such models, although CPU-only generation may be slow.
RAM ceiling matters more than CPU speed for LLMs. Usable performance generally requires enough RAM and/or VRAM for model weights plus runtime buffers; heavy paging can severely reduce speed, while some engines support partial accelerator offload. 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 provides Smart Search and Facial Recognition; processing performance depends on the NAS CPU or supported accelerator, library size and job configuration. These jobs run in the background, but Immich identifies machine learning as CPU-intensive, so NAS processor performance can materially affect indexing throughput.
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. An already-running NAS avoids the baseline consumption of another device, but Ollama still adds workload-dependent incremental power use. 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 already own a suitable NAS, no new base-device purchase may be required, but electricity and any RAM or storage upgrades remain marginal costs. If you are buying new hardware specifically for local AI, the calculus changes significantly.
At the time of audit, Scorptec listed the diskless DS925+ at $999; verify live retailer pricing before publication. Current TS-473A pricing varies by retailer; it remains an expandable 4-bay Ryzen NAS but is not the strongest AI NAS currently sold in Australia. 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. 13B viability depends on RAM, quantisation and acceptable speed; NPU acceleration additionally requires a compatible runtime and model. |
| High-end AI mini-PC (Core Ultra 9 or discrete GPU) | ~$1,200 to $2,500 AUD approximate. Some high-memory mini-PC configurations can run quantised 70B models, but viability and speed depend on total RAM/VRAM, memory bandwidth, quantisation, context length and offload strategy. |
| 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. Expandable 4-bay Ryzen NAS with up to 64GB RAM; not the strongest AI NAS currently available in Australia. 8GB base RAM, upgradeable to 64GB |
| QNAP TS-464 (4-bay, Intel Celeron N5095) | 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. Since September 2025, eligible FTTP/HFC Home Fast wholesale services increased to 500/50 Mbps; upload speeds on other access technologies and retail plans can differ. 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.
See also: our complete Synology NAS Australia guide.
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+ and DS1525+ both use the Ryzen V1500B and support up to 32GB RAM; inference suitability depends on installed memory and workload. DS925+ token speed depends on the exact model, quantisation, context length, Ollama version and memory configuration; use a reproducible benchmark before publishing a numerical range. 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. Reserve memory for the NAS operating system and active services; the amount available for inference varies by platform, filesystem and installed packages. 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 $0.30 to $0.40 per kWh and a continuous 15W average draw, power costs approximately $39 to $53 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. Under a specified hardware lifetime and duty cycle, local inference may cost less and can keep inference data on-device, but total cost and privacy depend on maintenance, upgrades, software configuration and whether the local and cloud capabilities being compared are equivalent.
What is the best NAS for running local AI in Australia?
The QNAP TS-473A is an expandable 4-bay option with a Ryzen V1500B and support for up to 64GB RAM, but it is not the strongest AI NAS currently available in Australia; verify current retailer pricing. 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.