For the continuous-power examples used in this article, annual cost is around $40 to $130; the actual cost of a mini-PC depends on its measured average wall power and the customer's electricity tariff. A mid-range mini-PC drawing 25 watts continuously consumes 219 kilowatt-hours per year. At New South Wales rates that is roughly $70 annually. At South Australian rates, closer to $95. A NAS already running for storage avoids the cost of powering a separate device, but AI inference still adds workload-dependent energy use that should be measured at the wall. The real cost question is not the device wattage but whether you leave the hardware on all the time, and which state you are in.
In short: The selected hardware examples in this article cost roughly $40 to $130 per year under the stated power and tariff assumptions. That is far less than a $360 annual ChatGPT Plus subscription. If your NAS is already running, AI inference adds almost nothing to the power bill. A dedicated mini-PC adds a real but manageable cost. A GPU workstation running continuously is the only scenario where power becomes a meaningful ongoing expense.
How Much Power Does Local AI Hardware Actually Use?
Power draw varies considerably by hardware class. A NAS running Ollama in the background sits at 8 to 25 watts at idle, depending on the model, how many drives are populated, and whether those drives are actively spinning or have spun down. Synology lists the DS925+ at 37.91 watts during access and 12.33 watts in HDD hibernation under its test conditions. The QNAP TS-473A draws similarly. A 2-bay model like the DS225+ typically runs at 5 to 10 watts at rest. QNAP lists the TS-464 at 21.618 watts in disk standby mode and 40.536 watts in typical operating mode with drives fully populated.
Mini-PCs purpose-built for AI inference draw more power than NAS hardware, particularly under active inference load. Mini-PC idle and inference power cannot be determined from processor family alone; it varies with the exact system, OEM power limits, model, inference backend and configuration, and should be measured at the wall. A discrete-GPU workstation can draw substantially more power, but inference draw varies widely with the GPU, power limit, model, utilisation and the rest of the system.
| NAS (2-bay, 2 drives) | 5 to 12W idle, 15 to 25W active |
|---|---|
| NAS (4-bay, 4 drives) | 15 to 25W idle, 30 to 45W active |
| Mini-PC (Intel N100 class) | Varies by the exact system, configured power limits, model and inference backend; use a measured wall-power average for cost calculations. |
| Mini-PC (Core i5/i7 13th gen) | Varies by the exact system, configured power limits, model and inference backend; use a measured wall-power average for cost calculations. |
| Mini-PC (Core Ultra 5/7) | Varies by the exact system, configured power limits, model and inference backend; use a measured wall-power average for cost calculations. |
| Desktop workstation with discrete GPU | wall power varies widely with the GPU, CPU, power limits and workload; measure the complete system under the intended inference workload. |
Australian Electricity Rates by State (2026)
Residential electricity rates in Australia vary considerably by state and retailer. South Australia has the highest residential electricity costs in the country, Retail electricity prices reflect wholesale, network, environmental and retail costs, which vary by jurisdiction and tariff. The ACT and Queensland tend to have the lowest general usage rates, though Queensland government rebate programs can further reduce effective costs depending on the year and your eligibility. These figures are approximate 2026 general usage rates for residential customers. Your actual rate depends on your retailer, tariff structure, and whether you are on a flat rate or time-of-use plan.
Australian Electricity Rates and Annual AI Hardware Cost (2026 Approximate)
| Rate (per kWh) | 15W device 24/7 | 25W device 24/7 | 50W device 8h/day | |
|---|---|---|---|---|
| New South Wales | $0.29 to $0.37 | $38 to $48/yr | $63 to $80/yr | $42 to $54/yr |
| Victoria | $0.26 to $0.35 | $34 to $46/yr | $57 to $77/yr | $38 to $51/yr |
| Queensland | $0.26 to $0.31 | $34 to $41/yr | $57 to $68/yr | $38 to $45/yr |
| South Australia | $0.38 to $0.48 | $50 to $63/yr | $83 to $105/yr | $55 to $70/yr |
| Western Australia | $0.31 to $0.34 | $41 to $45/yr | $68 to $75/yr | $45 to $50/yr |
| Tasmania | $0.27 to $0.33 | $36 to $44/yr | $59 to $72/yr | $39 to $48/yr |
| ACT. ActewAGL standard flat Home tariff (from 1 July 2026) | $0.369536/kWh | $49/yr | $81/yr | $54/yr |
Always-On vs On-Demand: The Cost Difference
Whether you run your hardware 24 hours a day or only during active use makes a substantial difference to the annual power bill. A NAS that is already running 24/7 for file storage adds virtually zero incremental cost for running Ollama. A powered-on NAS may still have idle or hibernating drives and a lightly loaded processor; inference can wake components and increase processor utilisation. Adding an occasional inference request to that baseline load costs almost nothing measurable.
A dedicated mini-PC is a different calculation. If it runs only during a typical working day (8 hours per day), a 25-watt device consumes 73 kilowatt-hours per year rather than 219 kilowatt-hours. At Queensland rates, that is approximately $19 per year rather than $57. At South Australian rates, $33 rather than $94.
The worst case for power cost is a GPU-equipped workstation running inference continuously. A system drawing 250 watts around the clock consumes 2,190 kilowatt-hours per year. At NSW rates of $0.35 per kWh, that is approximately $765 annually. GPU rigs for local AI inference should be powered on only when actively in use unless there is a compelling operational reason to keep them on.
| NAS already running 24/7 | Near-zero incremental power cost for AI inference |
|---|---|
| Mini-PC 25W, 8h/day weekdays only | $14 to $25/yr using the article's stated tariff range |
| Mini-PC 25W, 24/7 | $57 to $105/yr depending on state |
| Mini-PC 50W average, 8h/day | $38 to $70/yr depending on state |
| GPU workstation 250W, 4h/day | $95 to $175/yr depending on state |
| GPU workstation 250W, 24/7 | $570 to $1,050/yr depending on state |
Local AI vs Cloud Subscription: Five-Year Cost Comparison
Cloud AI subscriptions cost between $28 and $40 AUD per month for a single user on a premium plan. That is $336 to $480 per year, or $1,680 to $2,400 over five years. API access for heavier use adds cost on top.
A mid-range mini-PC at $900 AUD, used 8 hours per day at 45 watts average draw, consumes approximately 131 kilowatt-hours per year. At NSW rates of $0.35 per kWh, that is $46 per year in power. At SA rates of $0.42 per kWh, $55 per year. Amortised hardware cost over five years is $180 per year. Total five-year cost of local AI: approximately $1,130 in NSW and $1,175 in South Australia under these assumptions. That compares to $1,680 to $2,400 for a single cloud subscription over the same period.
The cost advantage of local AI grows with multi-user households. Local AI serves any device on the network from one hardware investment. A household with three people, each paying for a cloud AI subscription, is spending $1,000 to $1,440 per year combined. If one mini-PC provides acceptable capacity for all three users and remains suitable for five years, the stated $1,200 purchase and $70 annual power assumptions total roughly $1,550 over that period.
The cost comparison only holds if you use the hardware regularly. A $900 mini-PC is not worth the investment for someone who uses AI for ten minutes per week. Local AI hardware makes financial sense for daily users, multi-user households, and privacy-sensitive workloads where cloud services are not appropriate. For light or occasional use, a cloud subscription at $30 per month remains the more practical option.
What Costs More Than Expected
Several factors push real-world power costs above the nominal device figures. The power difference between active and sleeping drives is model-, drive- and workload-dependent and can be substantially greater than 5 to 10 watts in a four-bay NAS. If drives are spinning down between AI requests then spinning up again each time, that cycling adds wear but saves power. Choosing between continuous drive spin and sleep-on-demand is a trade-off worth configuring deliberately.
Cooling is relevant in Australian summers. Poor ventilation can increase temperatures and fan activity and may trigger thermal throttling; the resulting change in wall power is system- and workload-dependent. This is particularly relevant in Queensland, Western Australia, and New South Wales during summer when ambient room temperatures can exceed 30 degrees without air conditioning. Properly ventilated hardware runs cooler, draws less power, and lasts longer.
Always-on devices running multiple services also draw more than the AI inference figures suggest. A NAS simultaneously running Plex, Immich, Docker containers, and Ollama draws considerably more than a NAS running Ollama alone. The power figures in this guide assume the AI workload is the primary load on the device. For accurate cost tracking, measure actual draw with a plug-in watt meter rather than relying on manufacturer specifications, whose defined test conditions may differ from the intended real-world workload.
Related reading: our NAS buyer's guide, our NAS power consumption guide, and our NAS vs cloud storage comparison.
Free tools: NAS Power Calculator and AI Hardware Requirements Calculator. No signup required.
Related reading: our NAS explainer.
How much does it cost to run Ollama on a NAS in Australia?
If your NAS is already running 24/7 for file storage, the incremental cost of running Ollama is near zero. The processor and drives are already active. The additional power draw from a 7B model running occasionally is too small to measure meaningfully against the baseline device draw. If you are turning the NAS on specifically for AI inference, a 4-bay NAS at 25 watts continuous costs $57 to $105 per year depending on your state's electricity rate. See the Ollama on Synology setup guide for full configuration steps.
Which Australian state has the cheapest electricity for running local AI?
There is no single cheapest state across every tariff structure. Current examples include Aurora Energy's 24.75c/kWh Tasmanian Residential Single Rate and the 27.97c/kWh SE Queensland flat DMO usage cap, while ActewAGL's standard ACT flat Home rate is 36.9536c/kWh. South Australia has the most expensive residential electricity at approximately $0.38 to $0.48 per kWh, which adds meaningful cost for any always-on hardware. If you are weighing up always-on vs on-demand operation for a dedicated mini-PC, the case for turning the device off when not in use is stronger in South Australia than in other states.
Is buying a mini-PC for local AI cheaper than paying for ChatGPT Plus?
Over five years, yes, for regular daily users. A capable mini-PC at $900 AUD including power costs totals approximately $1,100 to $1,200 over five years in NSW. Five years of ChatGPT Plus totals approximately $1,800. For multi-user households, the gap widens significantly. The local option requires upfront cost and the trade-off that local models are not as capable as the largest cloud models. For heavy daily users, local AI hardware pays off. For occasional users, a $30 per month cloud subscription remains more practical.
How do I measure the actual power draw of my AI hardware?
A plug-in watt meter (also called an energy monitor or power meter) is the most accurate approach. Plug-in power meters are available from Australian retailers; for example, Jaycar currently lists the MS6115 Mains Power Meter at $19.95. Plug the meter between the wall socket and the device, let it run for 24 hours under typical usage, and read the kilowatt-hour consumption directly. Manufacturer figures use defined test configurations that may differ from a system running background services, active drives and a sustained compute workload.
Does running AI inference all day shorten hardware lifespan?
Sustained inference workloads run hardware hotter and harder than typical NAS storage workloads. Mini-PCs purpose-built for compute handle sustained load well if ventilation is adequate. NAS hardware is designed for 24/7 storage operation at light compute loads. Sustained inference increases processor utilisation and heat, but any effect on service life depends on temperatures, cooling, component design and operating conditions. If running inference on a NAS, schedule heavy batch jobs for cooler parts of the day and ensure the device has adequate airflow. For continuous heavy inference workloads, a dedicated mini-PC with proper cooling handles the load more gracefully than a NAS not designed for that purpose.
Deciding between a NAS and a dedicated mini-PC for local AI? The full comparison covers performance, RAM ceilings, real AU costs, and which platform fits which use case.