NAS for AI Model Storage: Sizing a Library of GGUFs, Checkpoints, and Datasets

A couple of years ago, a GGUF collection was a novelty. Today, someone serious about running local LLMs accumulates models fast. A handful of different 7B quantizations, maybe a 13B or two, a 70B base model and a fine-tuned 70B, some image-to-text checkpoints, and a few datasets for evaluation — and you are suddenly at 500GB. A year in, terabytes. The NAS question sounds like one question (“how much storage?”), but it is really three: How many terabytes do you actually need? What network speed gets models off the disk fast enough? Does caching help at all, or is the network the bottleneck? And a fourth, often forgotten: Where do your irreplaceable fine-tuned checkpoints live when the local box dies?

This guide sizes a NAS for real AI workloads — not just “big storage,” but storage that serves models to GPU workers fast enough that the network is not the stall. It is the on-ramp to the 10GbE vs 2.5GbE breakdown and the cloud-backup angle that makes sense of the RunPod data-loss guide.

Core principle: Capacity → Network → Cache

Three layers control NAS reality for AI workloads:

1. Capacity first. The model library grows predictably. Capacity is the easy math — you need enough disk to hold what you are going to pull off it.
2. Network second. A 1TB model loads over 1GbE in ~2.5 hours; over 10GbE in ~15 minutes. The network is where the rubber meets the road for practical model loading speeds.
3. Caching last. NVMe cache in front of HDD spindles helps for random reads; for sequential model loading (which is what happens when you pull a GGUF), the cache is mostly irrelevant — the network speed is the constraint.

Get the capacity right or you are done before you start. Get the network right or you are waiting forever. The cache is a nice-to-have if the budget allows.

How much storage do you actually need?

The honest answer depends on your model hoarding discipline. Here is the capacity math:

Model Type	Typical Size	Example
8B, Q4_K_M	~4–5 GB	Llama 3.1 8B at Q4
8B, Q8_0	~8 GB	Same model, full-weight
13B, Q4_K_M	~8 GB	Llama 2 13B at Q4
70B, Q4_K_M	~37–40 GB	Llama 3.1 70B at Q4 baseline
70B, Q8_0	~70–80 GB	Same, near-lossless
Fine-tune checkpoint (per epoch)	20–150 GB	70B base model checkpoint
Dataset (compressed)	50–500 GB	PILE subset, C4 sample, evaluation set

A light setup — 5 active models plus one dataset — lands at ~200–300 GB.

A serious hobbyist — 10 models across sizes, 2–3 active fine-tunes, a few datasets — hits ~1.5–2 TB.

A small research group — 20+ model variants, multiple checkpoints per run, versioned datasets — can push 4–8 TB.

The key insight: Most people underestimate by 2–3×. That 500 GB purchase fills up faster than expected when you add variants, checkpoints, and the next big model release from Meta or Mistral.

Recommendation: Buy at least 2× what you think you need right now. If you are at 500GB of models today, a 4TB NAS is a one-year horizon, not a five-year buffer. That 8TB NAS is a two-to-three-year buy, not five.

NAS Capacity & Ecosystem: Synology vs QNAP

The two credible choices for a homelabber are Synology and QNAP. Both ship NAS hardware with 4–8+ bay options. The trade-off is ecosystem maturity vs. price.

Aspect	Synology	QNAP
Entry 4-bay (DS424/TS-464)	~$400–$600	~$300–$500
Operating system	DSM (Synology OS) — very polished	QTS or QuTS Hero (Linux-based) — steeper curve
Community support	Excellent (r/synology, r/homelab)	Good (smaller but growing)
HDD / SSD flexibility	Any SATA / SAS drive	Any SATA / SAS drive
NVMe M.2 cache support	Yes	Yes
Upgrade path	Straightforward (swap drives, keep running)	Mostly straightforward; some quirks on major updates
Price-per-TB aggregate	Mid-range (ecosystem premium)	5–10% cheaper

Community-cited reality (r/homelab, r/datahoarder, 2024–2025): Synology owners cite ease of DSM OS and app ecosystem; QNAP owners cite better value and flexibility. Neither is wrong. Synology is the lower-friction choice if you want “open the box and have it work”; QNAP rewards the person willing to learn Linux-based configuration.

For AI workloads specifically, both are fine. The OS does not care what data is on disk; the constraint is HDDs (spinning) or NVMe (fast random) and network speed. Pick based on your comfort with Linux and how much you value polish in the UI.

Drive selection: HDD tier and optional NVMe cache

HDDs: Buy NAS-rated drives (WD Red, Seagate IronWolf, Synology-branded). They carry MTBF ratings for array environments, not gaming rigs. A 4TB NAS drive costs ~$80–$120; scaling to 8TB lands at ~$120–$180 per drive. Avoid surveillance drives (optimized for sequential video, not array parity). For a 4-bay NAS with 4× 8TB drives, you are at ~$500–$700 in HDD alone.

NVMe cache (optional): A 500GB–1TB NVMe in the M.2 slots speeds up random access and metadata operations. For AI workloads — where you are mostly doing sequential model loads from spinning media — the cache matters less than the network speed. Do not skip a 2.5GbE network upgrade to buy a cache drive. If budget allows after the network is right, add it. Community-cited choices: Samsung 980 Pro, Crucial P5 Plus.

Browse NVMe drives on Amazon →

Network: The real constraint

A 70B model at Q4 is ~40 GB. Let us do the math:

• Over 1GbE (theoretical max ~125 MB/s, real-world ~100 MB/s): ~6–7 minutes
• Over 2.5GbE (real-world ~300 MB/s): ~2.5 minutes
• Over 10GbE (real-world ~1,200 MB/s): ~35 seconds

If you are pulling models to a GPU box hundreds of times a week, the network speed compounds into hours or weeks of your life. The full argument lives in the 10GbE vs 2.5GbE breakdown. The short version:

• 1GbE: Viable for one or two model pulls per week. Not acceptable for rapid iteration or benchmarking.
• 2.5GbE: The practical minimum for an active lab. Requires 2.5GbE-capable NIC (most modern boards support it; add a card for ~$50–100 if not).
• 10GbE: Overkill for casual hobby use; essential if you are pulling models 10+ times per day or moving multi-TB datasets between machines.

Most home AI labs land on 2.5GbE as the sweet spot: affordable, materially faster than 1GbE, and readily available.

NVMe caching: When it actually matters

The question: Does a 1TB NVMe cache in front of the HDD pool speed up model loading?

Honest answer: Not meaningfully, if your network is the bottleneck. Here is why:

• If you are loading a model over 2.5GbE at ~300 MB/s, the NVMe cache is irrelevant — the network is the limiter.
• The cache helps random I/O and metadata lookups. Model loading is mostly sequential (read start to finish).
• Once a model is cached, it is likely to be loaded again (good). But the first load is over the network anyway.

Exception: If you have multiple workers pulling the same model in quick succession (benchmarking, batch inference, concurrent users), the cache holds the model warm and the second, third, and fourth pulls can come from NVMe at much higher speed. In a single-worker setup, the value is lower.

Recommendation: Skip the cache to fund the network upgrade first. If 2.5GbE is already in place and budget is available, add a 1TB NVMe cache. Otherwise, it is nice-to-have, not need-to-have. A 2.5GbE network without cache beats a 1GbE network with cache.

Backup: Irreplaceable checkpoints

A GGUF is easy to regenerate — it is on Hugging Face or Civitai. A fine-tuned 70B checkpoint that took you 3 weeks and cost $200 in compute to train is not. If you have irreplaceable checkpoints, they must live in two places.

The NAS is one. The cloud is the other. The question: How much risk are you willing to carry? Options:

1. AWS S3 + Glacier (most durable): ~$0.023/GB/month for standard, ~$0.004/GB for Glacier (12-month minimum). A 1TB of irreplaceable checkpoints costs ~$4/month (standard) or ~$0.50/month (Glacier, long-term).
2. Backblaze B2 (cheaper, community-standard for this niche): ~$0.006/GB/month for storage. 1TB = ~$6/month storage. Community-cited (r/datahoarder, 2024–2025) as reliable and transparent.
3. No backup (risky): Your box fails, the drive fails, and three weeks of training vaporizes.

The RunPod data-loss guide makes the case for cloud checkpoints. The same logic applies locally: the cloud is not for production inference, it is for never losing what you trained. Set up a nightly rsync to Backblaze B2 (both Synology and QNAP can do this via their native tools or a cron script) and sleep better.

Picks, by constraint

Best balance: Synology DS424 + 4× 8TB HDD + 2.5GbE

The Synology DS424 is the community standard for homelabbers who want it to just work. ~$500 new, plus $600 in drives, plus a 2.5GbE network upgrade if not already present ($50–100). Total outlay: ~$1,150–$1,200. You get 32TB of raw capacity (16–24TB usable, depending on RAID level), DSM to manage it, and a network path that does not constrain model loading. This is the “buy once, do not relitigate” choice.

Browse Synology 4-bay NAS on Amazon →

Budget-conscious: QNAP TS-464 + 4× 8TB HDD + 2.5GbE

The QNAP TS-464 ships at $350–$400 and is community-cited as reliable and good value. Steeper initial setup than Synology if you are not familiar with Linux; same drive math ($600), same network upgrade. You save ~$100–150 on the NAS box and spend it back in time learning QTS or QuTS Hero (or save it if you already know Linux). Good if the budget is tight and you do not mind the steeper initial configuration. Fewer surprises once set up.

Browse QNAP 4-bay NAS on Amazon →

Serious researcher / multi-worker: 8-bay + 10GbE + 1TB NVMe cache

An 8-bay Synology (DS2423+) or QNAP (TS-932PX or equivalent) pushes 64+ TB of raw capacity. Add 10GbE (built-in or via expansion card), a 1TB NVMe cache, and redundant drives (RAID 6). Cost: ~$1,200–$1,500 (NAS) + ~$1,000 (8× 8TB drives) + ~$150 (NVMe cache) + ~$300 (10GbE card/modules) = ~$2,700–$3,300 all-in. For a research group or someone training multiple models in parallel, it is the no-compromise choice. The 10GbE pays for itself in time saved.

Browse large-capacity NAS on Amazon →

Cloud backup only (lightweight): Backblaze B2 + rsync + cron

For the checkpoints you never want to lose, skip the fancy NAS backup subsystem entirely. Set up a nightly rsync to Backblaze B2 via their S3-compatible API. It costs ~$0.006/GB/month and requires no additional hardware beyond what you already have (the NAS itself). Put the script in cron on your Synology or QNAP and forget about it. For a 1TB of irreplaceable checkpoints, it is ~$6/month, or ~$72/year. Cheaper than a drive failure and infinitely better than losing the data.

Who This Is Not For

This guide assumes you have models and datasets you want to store and load reliably. It is the wrong guide if:

• You are just archiving old backups. A cheap USB HDD is fine; you do not need a NAS.
• You need NAS for video streaming or media serving. The protocol stack is different (DLNA vs. NFS/SMB for models). This guide is model-specific.
• Your network is already 10GbE everywhere and you have no budget constraint. Buy the 8-bay, max everything out, and move on. This guide trades off cost vs. performance for the budget-aware.
• You expect NVMe caching to solve latency problems. It does not. The network does. Fix the network first.
• You have zero irreplaceable checkpoints and no plans to fine-tune. A single large USB-C drive is cheaper than a NAS. This guide assumes you are building a library, not archiving.

Bottom line

A serious AI hobbyist’s model library hits terabytes fast. The right NAS decision is:

1. Buy 2× what you think you need right now. Capacity fills faster than expected.
2. Upgrade the network to 2.5GbE minimum. It is the constraint that matters most; it compounds into days of your life.
3. Synology for ease, QNAP for budget. Both work; the gap is learning curve and polish, not capability.
4. NVMe cache is optional. Network speed is not.
5. Backup irreplaceable checkpoints to Backblaze B2 or S3. The NAS is not a backup. Two copies, or it does not exist.

The NAS is not glamorous, but it is the thing that prevents you from re-downloading a 200GB model at 2 AM because you forgot to save it locally, or re-training a 70B checkpoint because a drive failed. Buy once, think twice a year, sleep soundly.