A NAS for Your AI Model Library - Costed Live

The NAS is where the library lives, not the working set

A network share earns its place in an AI setup by holding what your workstation should not have to. The model zoo, superseded quantizations, finished datasets, generated output, and archived checkpoints all belong somewhere with a low cost per terabyte and enough room to keep growing. The models you are actively loading stay on local NVMe.

The workflow this implies is archive-then-transfer, and it is an organization pattern rather than anything technical. When you finish with a model, it moves to the share. When you want it back, you copy it to the working drive and load it from there. That is a deliberate action you take, not something happening continuously in the background, which is precisely why the cost characteristics of the share matter more than anything else about it.

Framing it this way also settles the question people usually ask first, which is whether a network share is suitable for this. For the archive role, the relevant properties are capacity, cost and reliability, and those are properties we can speak to with live data. We are not going to make claims about how it behaves in your working path, because the pattern above is designed so that it is not in your working path.

The practical trigger for building one is usually one of two things: the workstation has run out of internal room and adding another internal drive is not attractive, or you want the library reachable from more than one machine. If neither is true for you, another internal drive is cheaper and simpler, and we would rather tell you that than sell you an enclosure.

RAID for a model library

RAID on an archive of downloaded models is a convenience feature rather than a data-protection one, and being clear about that changes the calculation. Most of what a model library contains can be downloaded again. Losing it costs time and bandwidth, not information.

That argues for capacity efficiency over redundancy depth. RAID 5 gives you single-drive tolerance with one drive of overhead, which for a library of re-downloadable files is a reasonable trade. RAID 6 doubles the parity overhead to survive two simultaneous failures, which matters more as drives get larger because rebuild windows lengthen with capacity and a second failure during a rebuild stops being hypothetical.

The rule of thumb we apply elsewhere on the site holds here: at 16TB and above, RAID 5 is not a configuration we would run in something we cared about, because the rebuild window is long enough that the probability of a second failure inside it stops being negligible. Our RAID planner will work the usable-capacity arithmetic for any bay count and level you are considering.

The other honest point about RAID here is that it protects against a drive failing, and nothing else. It does not protect against deleting the wrong directory, against a filesystem going wrong, or against the enclosure itself failing. For a library of re-downloadable models that is an acceptable exposure. For your own fine-tunes it is not, which is the next section.

Backup honesty, and which files actually need it

RAID is not backup. It is worth repeating because the confusion is common and the consequence is total. A RAID array survives a drive dying; it does not survive you deleting something, and both copies of a mirrored file disappear together when you remove the file.

For an AI library, the useful move is to sort your data by whether it can be recreated. Base models downloaded from a public repository are replaceable — annoying to re-fetch at multiple gigabytes each, but not lost. Generated output is usually replaceable in principle and rarely worth the storage cost of protecting comprehensively. Datasets you assembled yourself may or may not be recoverable depending on where they came from.

Your own fine-tunes and training checkpoints are the exception and they are the only genuinely irreplaceable files in a typical local-AI setup. They represent compute you paid for and configuration you may not be able to reproduce exactly. Those files deserve real backup — the conventional three copies, on two kinds of media, with one off-site — and they are usually small enough relative to the base models that protecting them properly costs very little.

Sorting the library this way is what makes the economics work. Comprehensive backup of an entire multi-terabyte model zoo is expensive and mostly pointless. Comprehensive backup of the fraction that is irreplaceable is cheap and obviously correct.

Costing a build from today's prices

The tables below are the part of this page that a static guide cannot give you. The first lists in-stock NAS devices with bay count and network specification and their current price; the second lists in-stock CMR hard drives ranked by cost per terabyte. A build cost is the device plus however many drives you intend to populate, and both numbers are live.

The arithmetic to run is straightforward. Pick a bay count that leaves room to grow, multiply the drive price by the number of bays you will actually fill, add the enclosure, and subtract the parity overhead from the raw capacity to get what you can use. A four-bay unit filled with four drives in single-parity gives you three drives of usable space; the same unit in double-parity gives you two.

Buying all the drives at once versus filling bays over time is a real decision in this market rather than a matter of taste. Adding capacity later means paying whatever the market asks then, and the sourced 2026 picture — QLC production booked through 2026, SanDisk raising NAND prices roughly 50%, enterprise drives on two-year backorders — does not suggest that later is cheaper. Against that, buying capacity you will not use for a year is money committed early.

For a build costed to your own bay count, capacity target and RAID level rather than the worked example here, the NAS builder does the full calculation against the same live prices. The NAS device catalogue covers the enclosure side in more depth, NAS hard drives covers drives specified for multi-bay use, and CMR versus SMR explains why the archive tables here are restricted to CMR.

Planning capacity for a library that keeps growing

Size a model-library share by growth rate rather than by current contents, because the current contents are the one number guaranteed to be obsolete. The useful question is not how much you have but how much you added over the last six months, and whether anything about your usage suggests the next six will be slower. It rarely does.

Bay count matters more than drive size for this reason. An enclosure with spare bays lets you add capacity by buying one drive; an enclosure that is full means replacing drives you already own and finding something to do with the ones you pull. Given the choice between four large drives in a four-bay unit and four smaller drives in a six-bay one at a similar price, the second leaves you an upgrade path and the first does not.

There is a countervailing argument in a constrained market, which is that capacity bought later is capacity bought at an unknown price. The sourced picture for 2026 does not suggest drives get cheaper, so leaving bays empty is a bet that costs something. Our own position is that spare bays are still worth having, because the alternative — a full enclosure — forecloses the option entirely rather than merely pricing it.

Whatever you choose, restrict the drives to CMR. A growing library is a rewrite-heavy workload over time, and shingled recording behaves poorly in exactly that pattern. It also matters for any future RAID rebuild. The archive table on this page is filtered to CMR for both reasons.

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