Select your NAS model and an AI task to get an instant compatibility verdict, including which quantisation levels fit in your RAM, estimated performance, and what would make it faster.
This tool checks whether consumer NAS hardware can run local AI workloads: text LLMs via Ollama, AI photo tagging (Immich, Synology Photos, PhotoPrism), Stable Diffusion image generation, and Whisper transcription. Covers 20+ current Synology, QNAP, Ugreen, and Asustor models available in Australia.
Based on default RAM. Upgrade headroom noted. Last verified: March 2026.
| Model | RAM Default / Max | 7B LLM (Q4) | AI Photo Tagging | Image Gen |
|---|---|---|---|---|
| Synology DS224+ | 2GB / 6GB | No | Yes | No |
| Synology DS423+ | 2GB / 6GB | No | Yes | No |
| Synology DS723+ | 2GB / 32GB | Upgrade needed | Yes | No |
| Synology DS923+ | 4GB / 32GB | Upgrade needed | Yes | No |
| Synology DS1522+ | 8GB / 32GB | Yes (Q4/Q5) | Yes | No |
| QNAP TS-264 | 8GB / 16GB | Yes (Q4/Q5) | Yes | No |
| QNAP TS-464 | 8GB / 16GB | Yes (Q4/Q5) | Yes | No |
| QNAP TS-h886 | 8GB / 64GB | Yes (Q4/Q5) | Yes | No |
| QNAP TVS-h874 | 16GB / 64GB | Yes (all) | Yes | GPU card needed |
| Ugreen DXP2800 | 8GB / 16GB | Yes (Q4/Q5) | Yes | No |
| Ugreen DXP4800 Plus | 8GB / 32GB | Yes (Q4/Q5/Q8) | Yes | No |
| Ugreen DXP6800 Pro | 16GB / 64GB | Yes (all) | Yes | No |
| Asustor AS5402T | 4GB / 8GB | Upgrade to 8GB | Yes | No |
| Asustor AS6704T | 4GB / 16GB | Upgrade to 8GB | Yes | No |
| Asustor Flashstor 12 Pro | 8GB / 32GB | Yes (Q4/Q5/Q8) | Yes | No |
7B LLM requires 8GB RAM minimum (5.2GB model + 1.5GB OS overhead). "Upgrade needed" = hardware is capable with a RAM upgrade. Image gen requires a discrete GPU.
Full-precision (F16) AI models store each weight as a 16-bit float. Q4_K_M compresses each weight to roughly 4 bits — about one-third the RAM at minimal quality loss for most conversational tasks. A 7B model goes from 16GB at F16 to 5.2GB at Q4_K_M. For NAS hardware with limited RAM, Q4_K_M is the practical choice: you get 90–95% of the quality at 33% of the RAM. Q8_0 is the sweet spot for quality if your hardware supports it.
Use your NAS for AI if you want one box for storage and light inference — photo tagging, occasional chat, transcription. These are background tasks that run between normal NAS duties, and modern NAS CPUs handle them adequately.
Use a dedicated mini-PC (Beelink, Minisforum) if AI is the primary workload, you need GPU acceleration for image generation, or you need faster LLM inference for regular interactive use. A $400–600 mini-PC with a recent AMD Radeon iGPU will outperform any NAS for AI tasks by a significant margin.
The two are not mutually exclusive. A NAS for storage plus a mini-PC for compute is a common and sensible setup.
AU electricity costs (average 28–35c/kWh) make 24/7 AI inference more expensive than it looks. A NAS pulling 35W running Ollama continuously adds ~$90–$110/year to your power bill. Use the NAS Power Cost Calculator to calculate your exact running cost. AU homes also have NBN upload constraints — if self-hosting AI APIs for remote access, your home upload speed (typically 20–50 Mbps) becomes the bottleneck, not the NAS CPU. Hardware sold in Australia carries a mandatory 2-year ACL warranty regardless of manufacturer terms.
Can You Run a Local LLM on a NAS? — AI NAS Hardware Requirements Explained — AI Photo Search: Synology vs QNAP vs Ugreen — Private AI on NAS vs Cloud AI (AU cost) — AI Hardware Requirements Calculator
Methodology: NAS hardware specs from manufacturer spec sheets, March 2026. CPU PassMark scores from PassMark Software (±10%). AI model RAM requirements from Ollama and llama.cpp community benchmarks. Token speed estimates are ranges based on community-reported performance — real-world speeds vary with background load and model version. Image generation benchmarks based on AUTOMATIC1111 on CPU-only hardware. Report an error.