Last spring I noticed something silly: the folder where I keep local AI models had crept past 300GB. Not because I’m a hoarder. Okay, partially because I’m a hoarder. Mostly it’s that every time a new quantized build of a model I like shows up, I download it “just to try,” and nothing ever gets deleted. Multiply that across the Ollama box, my laptop, and the spare machine that handles transcriptions, and you get three machines holding three half-broken copies of the same files.
That’s the point where a NAS stops being a gadget and starts being infrastructure.
I run a small setup here: Ollama on a local box for day-to-day inference, Docker for the boring jobs, nightly backups to a NAS before anything touches a cloud drive. After reviewing 40-plus AI tools this year for this site, I’ve noticed the storage side is the part almost nobody plans for. What follows is the short version of what I learned sizing storage for that setup, with street prices I checked in September 2026.
One honesty note before the picks: I haven’t bench-tested every enclosure on this list. My calls are built on spec sheets I’ve cross-checked, prices I’ve tracked since spring, and the long-running arguments I’ve watched in homelab communities like r/homelab. Where I’m unsure, I’ll flag it.
What a NAS actually does for a local AI setup
A network drive sounds boring next to a new GPU. It’s the boring box that keeps everything else from falling apart. Concretely:
- A shared model library. Ollama models are chunky. A 7B quantized model runs 4-5GB, and the big ones hit 30-40GB or more. Keep the archive on the NAS, keep only what you use daily on local NVMe, and stop buying the same storage three times over.
- Backups that actually run. Every machine on the network gets a nightly copy. When a laptop dies (not if), the restore takes an hour instead of a week.
- Always-on side services. Containers like Open WebUI or a transcription worker run fine on NAS-class hardware around the clock. I run parts of my own automation stack this way, and it quietly works.
- The rest of the homelab. Photo backups and a media library, plus camera feeds if you’re that kind of nerd. Local AI is why you’re here, but the NAS pays for itself on the unglamorous stuff too.
Here’s what a NAS doesn’t do: make inference faster. If a model runs on the GPU inside your main machine, that machine doesn’t care where the file originally lived. The catch is load time. Pulling a 40GB model across a 1Gbps link takes minutes, not seconds. 2.5GbE shortens that a lot. 10GbE basically fixes it, assuming your switch plays along.
Most people land on this pattern: hot models on local storage, the full archive plus backups on the NAS. I’d decide on that pattern before you’ve bought anything.
The specs that actually matter (and the ones that don’t)
Marketing pages love to shout drive counts. A few specs quietly decide whether a NAS still serves you in three years:
- Bays beat capacity. A 4-bay with two modest drives beats a 2-bay with two big ones, because you can add drives as data grows instead of replacing everything.
- Upgradeable RAM. A free memory slot matters more than the amount shipped. Container workloads eat memory. ECC, where offered, is a quiet bonus.
- 2.5GbE as the 2026 baseline. One port is fine. Two is better. Gigabit-only in a 2026 purchase is a compromise you’ll feel every time you move a 40GB file.
- NVMe slots. On most consumer units these are cache slots rather than general-purpose storage. Useful, not transformative.
- CPU class. An Intel N100 or N5095-class chip handles files and Docker comfortably, plus light media transcoding. Heavier video work favors Intel’s Quick Sync.
- Power draw. This box runs 24/7. Expect roughly 10-30W at idle with drives spun up, depending on model and drives. It’s on your electric bill forever.
The short list: what I’d buy
Budget, two bays: Synology DS224+ vs QNAP TS-264
The QNAP TS-264 wins on raw hardware: a newer Celeron, two 2.5GbE ports, dual M.2 slots, and around $450 street. The Synology DS224+ sits near $300 and wins on software calm. Synology’s DSM is the interface people describe as boring in the best possible way; my own nightly jobs run on a NAS precisely because the thing disappears into the background.
If you want headroom for containers and faster networking, take the TS-264. If you mainly want reliable files and backups with zero tinkering, the DS224+ fits better and costs less.
The workhorse: QNAP TS-464 (roughly $550-600)
This is the box I’d buy for a local AI home lab in 2026. Four bays, a quad-core Celeron N5095, two 2.5GbE ports, and a PCIe slot that takes a 10GbE or dual-NVMe expansion card later, once you outgrow the stock setup. QNAP’s Container Station runs Docker comfortably, and the company publishes an official walkthrough for deploying Ollama on the TS-464, which tells you something about who’s buying these.
The honest downside: QTS feels busier than DSM, and you’ll spend an afternoon turning off notification noise. A real tradeoff, not a dealbreaker.
Check the TS-464 price on Amazon
The Synology alternative: DS923+ (around $600)
The DS923+ plays a different game. An AMD Ryzen R1600, ECC memory expandable to 32GB, two M.2 slots, and an optional 10GbE card give it the longest runway of this group for virtualization and long-term ownership. The sting is networking: it ships with two 1GbE ports, so fast file moves require the add-in card. If longevity and ECC matter more to you than out-of-the-box network speed, this is the pick.
Check the DS923+ price on Amazon
The 10GbE wildcard: UGREEN NASync DXP4800 Plus
UGREEN’s first-generation NAS made reviewers sit up: a 5-core Pentium Gold 8505, 8GB of DDR5, and built-in 10GbE networking at an MSRP of $800, with street prices dipping toward $700 after mid-2026 price cuts. PCMag scored it 3.5 out of 5, dinging the thin app catalog rather than the hardware, which is the fair way to put it. The hardware is the best value in this group. The software (UGOS Pro) is younger and less mature than DSM or QTS, and that’s the whole tradeoff.
I’d send a tinkerer here happily. Someone who wants turnkey polish should stick with the two names above.
Check the DXP4800 Plus price on Amazon
| Model | Bays | CPU | RAM | Networking | Street price (Sept 2026) | Best for |
|---|---|---|---|---|---|---|
| Synology DS224+ | 2 | Celeron J4125 | 2GB (1 slot) | 2x 1GbE | ~$300 | Simple, set-and-forget |
| QNAP TS-264 | 2 | Celeron N4505 | 4GB (2 slots) | 2x 2.5GbE | ~$450 | Budget with headroom |
| QNAP TS-464 | 4 | Celeron N5095 | 4GB (2 slots) | 2x 2.5GbE + PCIe slot | ~$550-600 | Local AI home lab |
| Synology DS923+ | 4 | Ryzen R1600 | 4GB ECC, to 32GB | 2x 1GbE (+10GbE card) | ~$600 | Longevity, ECC, VMs |
| UGREEN DXP4800 Plus | 4 | Pentium Gold 8505 | 8GB DDR5 | 10GbE + 2.5GbE | ~$700-800 | Speed per dollar |
Do you even need a NAS?
Straight answer: maybe not yet. If you run one machine and keep a handful of models on it, a fast external drive covers the archive and a second drive covers backups. A 2TB Samsung T7 Shield is under $160 most weeks and moves files faster than a gigabit NAS would.
A NAS starts earning its keep when the second machine shows up, or when backups pile up and the media server and containers move in. My own trigger was three machines keeping separate copies of the same 300GB folder. Yours will be something similar.
Check the Samsung T7 Shield 2TB price on Amazon
And if the real bottleneck is compute rather than storage, start with our guide to mini PCs ready for local AI instead. Storage can wait. The machine that runs the models can’t.
How I’d set it up (the short version)
- Put the model archive on the NAS. Keep the two or three models you use daily on the local NVMe of the machine that runs them, and sync new pulls to the NAS nightly. On Windows, a mapped drive plus a scheduled copy job handles this; it’s the same pattern my own nightly jobs follow.
- Keep inference on the machine with the GPU. Some people point Ollama’s model folder straight at a network share. It works, but first-load times crawl on gigabit, so I’d only do that with 2.5GbE or better, and never for models you touch hourly.
- Optionally run Ollama on the NAS itself in Docker. QNAP documents this officially through Container Station; on Synology, Container Manager gets you there too. Treat it as a light-duty endpoint for small models. NAS-class silicon without a GPU won’t impress anyone on a 70B model, and you shouldn’t expect it to.
- Remember that RAID is not backup. A mirrored volume protects against a dead drive, not a bad update or ransomware. Keep one copy somewhere that isn’t this box. The homelab crowd repeats that rule because someone is always learning it the hard way.
If you’re still deciding how to run the models themselves rather than where to store them, our comparison of Ollama vs LM Studio vs Jan covers the software side.
Key takeaways
- A NAS is the archive and backup layer of a local AI setup, not the inference machine. Hot models live on local NVMe; the NAS holds the rest.
- Four bays with upgradeable RAM is the sweet spot. The QNAP TS-464 is my pick for most AI-adjacent home labs. The DS923+ wins on longevity and ECC.
- 2.5GbE is the baseline in 2026. Built-in 10GbE (UGREEN) or an expansion card matters only if you constantly shuttle models between machines.
- Single machine? A fast external SSD plus one backup drive beats a NAS for now.
- Street prices here were checked in September 2026 and they move weekly, so click through before you decide.
Affiliate disclosure: some links in this post are affiliate links. If you buy through them, we may earn a commission at no extra cost to you. We only recommend hardware we’d consider for our own setup.
Ready to stop losing track of files you already paid for? Start with the two-bay budget pick if you’re cautious, or the TS-464 if you want room to grow. Either way, check live pricing first. NAS street prices swing more than most people expect.