Synology NAS for AI Developers: A Practical Storage Buying Guide

Choosing a NAS for AI work comes down to four decisions, in this order: how much usable capacity you need in two years, how many drive bays that takes with redundancy, how fast the network link must be, and whether hard drives alone will do or SSDs are needed for part of the job. Processor and memory in the NAS matter far less than they do in the AI machine, because the NAS stores data and does not run the model. For a single developer a 4-bay unit is usually enough; for a team sharing datasets, 5 to 8 bays with 10GbE; beyond that, a rackmount unit. This guide explains how to work each decision out.

Step 1: How much capacity do you need?

Add up what you will store, then allow for growth.

CALC. A three-person team: models 500GB, datasets 2,000GB, checkpoints 300GB, machine backups 1,000GB. Total 3,800GB. Add 20 percent for snapshots: 3,800 x 1.2 = 4,560GB. Double it for two years of growth: 9,120GB, about 9.1TB usable. The growth factor is a planning assumption; AI teams usually underestimate it.

Step 2: How many bays?

Usable capacity is less than the sum of the drives, because some is given to redundancy.

CALC: single redundancy gives (bays - 1) x drive size; double redundancy gives (bays - 2) x drive size. Real usable space is a little lower after formatting. For the 9.1TB example, four 8TB drives leave ample room. Two points of judgment: larger arrays of large drives take a long time to rebuild after a failure, which is the argument for double redundancy at 8 bays, and empty bays are cheap headroom, since capacity can be added later.

Step 3: How fast a network link?

Check three things before ordering: which ports the NAS has built in, whether it accepts a 10GbE card and which one, and whether your switch and AI machines have matching ports. A fast NAS behind a 1GbE switch is a 1GbE NAS. See building a small AI lab for sizing with several machines.

Step 4: Hard drives, SSDs or both?

• Hard drives for capacity: model library, datasets of large files, backups, archives. Use NAS-rated or enterprise drives, and check them against the compatibility list for the NAS model. See IronWolf vs IronWolf Pro vs Exos.
• SSD cache on a hard-drive volume when several users browse and open many files. It helps repeated and small reads; it does little for one large sequential copy.
• SSD volume when datasets are millions of small files, or several machines read the same data at once.

For most AI developers, hard drives with the models in use copied to the workstation's own NVMe is the cost-effective design. See NAS vs local SSD for AI models.

Which NAS class fits which team?

Prices are for the NAS without drives (listed 11 Nov 2026; prices change; ask for a current quotation). Entry models with lighter processors are fine for storage and backup; choose a Plus model or above if you want snapshots on a suitable file system, containers or a 10GbE upgrade path, and confirm each feature on the specification sheet for that model.

Buy the AI machine for its memory. Buy the NAS for its bays and its network port. Those two specifications decide how long each one stays useful.

What else belongs in the budget?

• Drives. Often cost as much as the NAS itself.
• A second copy. Another NAS, a backup appliance or a cloud target. See backing up AI models, datasets and checkpoints.
• A UPS, so the NAS shuts down cleanly in a power cut.
• The switch and cables to match the NAS port speed.
• A spare drive on the shelf for a fast replacement.

When is a NAS not enough?

When a GPU server or cluster needs storage that keeps pace with 25GbE or faster links, or parallel access from many nodes, the requirement moves to all-flash and enterprise storage. We cover that end in connecting a DGX Station to enterprise NAS storage. For a comparison across brands, see the best NAS for Singapore SMEs.

Buying in Singapore

Alongside the NAS models in the table: drives, Seagate IronWolf Pro from S$390 and Seagate Exos X24 from S$835; 10GbE cards for models with a compatible slot, the Synology E10G18-T1 (S$202.80) and E10G30-T2 (S$387.60); a 10GbE switch, the HPE Networking Instant On 1960 12-port 10GBASE-T (S$2,444.40); and a backup target, the Synology ActiveProtect DP340 (S$6,526.80) (listed 11 Nov 2026; prices change; ask for a current quotation). Tell us your data sizes and how many AI machines you have and we will check ports and card compatibility before quoting. To start, use Request for Quotation on the product page, call (65) 6978 3502, email Sales@sourceit.com.sg or use our contact page.

Frequently asked questions

What size NAS does an AI developer need? Add up the model library, datasets, retained checkpoints and machine backups, add an allowance for snapshots and double it for growth. For a solo developer this usually points to a 4-bay NAS; for a small team, 5 to 8 bays.

Does the NAS need a powerful processor for AI? No. The NAS stores and protects data; the AI machine runs the model. Bays, drive capacity and network speed matter more than NAS processing power.

Is 10GbE necessary on a NAS for AI work? Not for a single developer copying models occasionally, where 2.5GbE is workable. It is worthwhile when datasets move regularly or several AI machines share the NAS.

Should a NAS for AI use SSDs or hard drives? Hard drives suit most AI storage: large model files, datasets and backups. Add an SSD cache or SSD volume for datasets of many small files or several simultaneous users.