NAS Drive Compatibility › AI on NAS

AI on your NAS — what each vendor ships and what your hardware can run

Two different questions get mixed up under "AI NAS": does the vendor ship AI features (photo recognition, assistants), and can the box run local LLMs. This guide answers both — the vendor stacks side by side, and the hardware math for model sizes from 3B to 70B. All product facts as of 2026-11, sourced from vendor pages.

Which NAS can run local LLMs?

Model-size × hardware table (3B→70B) and a per-model verdict for current Synology, QNAP, UGREEN and fnOS boxes.

Ollama hardware requirements

RAM vs VRAM, quantization sizes, why 8 GB boxes choke on 8B models, and the NPU-vs-LLM misconception.

AI models you can run on a NAS

Chat, reasoning, coding, vision and embedding models — each one's RAM / VRAM need and which box fits. Filterable library.

AI NAS, exposed: real, nominal, or marketing?

The NPU trap, the name-only AI boxes, and when you must add RAM / a GPU / a separate box. Interactive fit checker.

Does your NAS actually fit AI?

A five-question diagnostic: RAM, GPU, and which AI workloads are a no, a stretch, or a go for your box.

Synology

Photos AI (faces/objects) is official; LLM = Container Manager + Ollama, not a Synology feature

QNAP

AI photos (QuMagie) + dedicated NPU hardware (TS-AI line) + official Ollama tutorials

UGREEN

AI photo/assistant features in UGOS Pro + the HA100 HomeAgent AI-NAS line

fnOS / FygoOS

AI Photos is a headline feature; the DIY hardware means you set the LLM ceiling

The one table everyone actually wants

Model size	Examples	Q4 weight	Total RAM needed	NAS verdict
3B	Llama 3.2 3B, Qwen2.5 3B, Phi-3 mini	~2 GB	4-6 GB	Runs on almost any 8 GB x86 NAS
7-8B	Llama 3.1 8B, Mistral 7B, Qwen2.5 7B	~4.7 GB	8-10 GB	Tight on stock 8 GB boxes; comfortable at 16 GB
13-14B	Qwen2.5 14B, Mistral Nemo 12B	~9 GB	12-16 GB	Needs 16 GB+ and ideally a GPU-class iGPU
32B	Qwen2.5 32B, DeepSeek-R1-Distill 32B	~20 GB	24-28 GB	Needs a 32 GB+ machine — almost no closed NAS qualifies
70B	Llama 3.3 70B	~40 GB	48 GB+	Not a NAS workload — GPU server territory

Quantized (Q4) model sizes per Ollama's public model cards; total RAM includes KV-cache and runtime overhead and assumes the NAS is also being a NAS. Full reasoning: local LLM hardware table.

Three different "AI NAS" products — don't confuse them

1. AI photos on the NAS — face/object recognition in Synology Photos, QNAP QuMagie, UGREEN UGOS AI, fnOS AI Photos. Shipped, official, works on modest hardware. Every vendor has one.

2. AI video analytics NAS — QNAP's TS-AI line (6 TOPS NPU) and similar: purpose-built for camera-event recognition, not chat. The NPU class that powers these does not generate LLM tokens.

3. Local LLM NAS — running Ollama-class models on the box. Nobody ships this as a turnkey consumer feature; it's Docker + RAM + (ideally) an iGPU. QNAP documents it officially; everyone else leaves it to the community.

Where to start

Buying for AI? The hardware table ranks current models. Already own one? Your vendor page below states exactly what is official and what is DIY. And whichever box you pick, the drives still have to fit it — that's the rest of this site: check any model's official drive compatibility list.