Which NAS hardware can run a language model?
Three things decide it. The processor must be x86 or a capable ARM chip with Docker support. Memory must hold the model, at about 0.6GB per billion parameters at 4-bit plus overhead. And the operating system must support containers or offer an AI app.
Most AI NAS and AI Ready systems meet all three. Budget storage NAS units with ARM chips and 2GB of fixed memory, such as the QNAP TS-233 or TerraMaster F4-212, do not.
How fast is a local LLM on a NAS?
Without a graphics card, speed depends on memory bandwidth and the processor. On modern AI NAS hardware, 8B class models reply at a comfortable reading pace. 14B models are usable. 32B and larger models work but answer slowly. On older or low-power processors, even small models feel sluggish.
A graphics card that holds the whole model is many times faster, which is why GPU slots and OCuLink ports matter for anyone who uses a local model every day.
Which models make sense on a NAS?
| NAS memory | Models that fit well (Estimated) |
|---|---|
| 16GB | Llama 3.1 8B, Qwen3 8B, Gemma 3 4B |
| 32GB | Gemma 3 12B, Qwen3 14B, Phi-4 |
| 64GB | Gemma 3 27B, Qwen3 32B |
| 96GB | Llama 3.3 70B |
What does it cost to run?
Mostly electricity. AI NAS systems built on laptop-class processors draw modest power at idle and more while answering. A NAS with a desktop graphics card fitted draws considerably more under load. There is no subscription for the model itself: open models from the Ollama library are free to download and use within their licences.
Does the NPU help?
Not for language models yet. NAS NPUs speed up photo recognition, transcription and the vendor's own search tools. Open-source runtimes such as Ollama run on the CPU or a GPU.
Common questions
Can a Synology NAS run a local LLM?
Current Plus models can run small models through Container Manager, slowly. See our Synology guide.
Is a NAS better than a PC for local AI?
A PC with a strong graphics card is faster. A NAS wins on running all day at low power next to your files.
What is the best first model to try?
An 8B model such as Llama 3.1 8B or Qwen3 8B. Both are around 5GB at 4-bit, run on any AI NAS with 16GB of memory and give a good sense of speed and quality before you try something larger.
Can several people use a local LLM at once?
Yes, through a chat interface such as Open WebUI with separate accounts. Each active conversation needs its own memory for context, and answers slow down when several people ask at the same moment, so size memory for your busiest time.
Will a local LLM slow down file sharing?
While it is answering, the model uses most of the processor and memory bandwidth, which can slow other tasks. File sharing usually stays usable, but heavy jobs such as backups or media transcoding are best scheduled away from busy chat times.
