As AI becomes more capable, we’re starting to see it integrated into NAS systems — smart photo albums, auto-tagging, anomaly detection, natural language search (“find photos from my beach trip last summer”), and more. But I feel like we’ve only scratched the surface of what AI + NAS could be.
We’d love to hear how you actually think about this. Here are a few questions to get the conversation going:
Questions for the community:
What’s the one thing you most wish AI could do for you on your NAS? (File organization? Auto-backup sorting? Security monitoring? Something else?)
Do you have privacy concerns about AI analyzing your personal files? Would you be comfortable with on-device AI but NOT cloud-based AI processing?
Do you prefer AI to run fully automatically in the background, or do you want to approve every action it takes?
Whether you’re all-in on AI or still on the fence — all perspectives welcome. Let’s map out what AI on NAS should actually look like for real users!
My biggest concern with any of this is going to be CPU loading. My TS-873A gets overloaded regularly from doing backups, Qsirch indexing, multi-media indexing, etc.
It would be helpful if QNAP first optimized CPU usage before adding more AI features that would suck more CPU time.
Thanks for your suggestion! We’ll make sure the related features won’t affect your experience. We’d also love to hear what you’d expect AI to help with on your NAS — whether it’s data management, sync & backup, or anything else you have in mind. Any feedback is greatly appreciated!
Agree with NA9D to improve the current used of CPU on existing “AI” tools first than to add more things (Personally Qmagie, Qsirch indexing and so on are disable dur to high CPU usage) . on my side the security of the NAS analyze by AI would be an help if it can aggregate all logs files and then make a summary of what is good and most interesting what is wrong.
Another point that i think could be interesting is on photos, analyze metada and then provide an help to classify the photos by date or localization.
last thing, i prefer on site AI than in the cloud.
Adding a slightly different angle on the CPU concern — I’m not sure AI features would actually add much to existing CPU load. Modern AI models (even the smaller ones) are generally too large to run efficiently on CPU; they rely on GPU or NPU for acceleration. On older NAS units without a capable GPU/NPU, I’d expect the AI features simply wouldn’t be enabled at all, so they shouldn’t compete with Qsirch / media indexing / backups for CPU usage.
That said, optimizing CPU usage for existing features is always welcome — just probably a separate topic from AI.
On cloud vs. local: both have their place. Personally I’d prefer local inference as the default, with at least a small open model running on-device (something like Gemma4 or qwen3.5) so basic tasks — log summarization, natural-language search, light automation — can run fully locally for privacy. But I’d also want the flexibility to route heavier tasks to a more capable cloud model when needed. Cloud AI has another real benefit too: it’s the only way users on older NAS units (which can’t host a model locally) would get access to AI features at all. So offering both paths means nobody is left out.
One feature I’d really like to see in the future AI roadmap is agent capability — not just one-shot Q&A or tagging, but an agent that can take multi-step actions on the NAS (e.g. “find all duplicate photos from 2023 and move them to a review folder,” or “summarize this week’s anomalies from the logs and fire a alert if anything looks serious”). That’s where I think AI on NAS gets genuinely useful.
Actually, that is not factually true. My company has an AI tool that allows edge AI algorithms to work on our 32 bit and even 16 bit micro-controllers. Algorithms amount to a few kB of memory and are very efficient and small. What’s behind that is taking a whole lot of data and signal processing math that is done in the cloud first to give you your algorithm. But we can, for example, do preventative diagnostics on an entire HVAC system (is the coil blocked, is there a coolant leak, is the motor running correctly, etc.). No accelerator is needed. Look into the subject of TinyML.
Not every AI model out there is running Tensor-Flow or LLM or whatever. So please don’t generalize like that. It’s out of the scope of this forum but I have a LOT of information to back up what I say as I sell this stuff daily.
For things like full natural language processing (not just keywords - that can be done in a 32 bit MCU), or Image recognition, there you need accelerators.
Adding things like more vision AI w/o an accelerator to a QNAP would absolutely add to the CPU load. I have a QAI-U100 and the difference in CPU loading when that things is recognizing images vs. if I didn’t have it connected is huge.
Certain neural accelerators are also used for specific things. Some are tailored more towards vision processing, others for other sort of AI tasks. It’s a very broad scope. So depending on what a user has, it certainly could affect CPU load on the QNAP.
