For most small AI teams, neither is the first thing to buy. If the AI machine copies the models and datasets it is using onto its own NVMe drive, a NAS built on hard drives does the storage job well, and the budget is better spent on capacity and backup. Flash in the NAS earns its place in specific cases. An SSD cache, a pair of small SSDs added to a hard-drive NAS, speeds up repeated access to frequently used and small files at modest cost, and does little for one-off copies of huge files. An all-flash NAS makes every read and write fast, including random access to millions of small files by several machines at once, at several times the cost per terabyte. Choose by the access pattern of the workload.
How do the three approaches compare?
| Hard drives only | Hard drives with SSD cache | All-flash | |
|---|---|---|---|
| Large sequential reads and writes | Good | Same as hard drives | Fastest, up to the network limit |
| Repeated reads of the same files | Slow | Fast once cached | Fast |
| Random reads of many small files | Slow | Better for the frequently used portion | Fast across the whole dataset |
| First read of cold data | Slow | Slow: not yet cached | Fast |
| Many users at once | Degrades quickly | Better | Best |
| Cost per usable TB | Lowest | Slightly higher | Several times higher |
| Capacity for a given budget | Highest | High | Lowest |
| Noise, heat, power | Highest | Similar | Lowest |
How does an SSD cache work?
The NAS keeps copies of the most frequently accessed data blocks on fast SSDs. When that data is requested again, it is served from the SSD instead of the hard drives. Two modes:
| Mode | What it speeds up | SSDs needed | Risk |
|---|---|---|---|
| Read-only cache | Repeated reads | One or more | Low: the cache holds only copies; if the SSD fails, nothing is lost |
| Read-write cache | Reads, and bursts of small writes | Two or more, mirrored | Higher: recent writes may exist only on the cache until flushed, so SSD failure or power loss can damage the volume |
For this reason a read-write cache should use mirrored SSDs with power-loss protection, on a NAS protected by a UPS. See why your NAS needs a UPS. Cache SSDs also wear, since they are written to constantly; choose drives rated for the duty and monitor their health.
What does a cache not do?
- It does not speed up the first read. Data has to be accessed before it is cached.
- It does little for large sequential transfers. Copying a 60GB model once passes straight through to the hard drives.
- It does not add capacity.
- It cannot cache everything. If the working set is larger than the cache, benefits fall away.
Which AI workloads benefit from which?
| Workload | Access pattern | Best fit |
|---|---|---|
| Model library, copied to workstations as needed | Large, sequential, occasional | Hard drives |
| Backups and archives | Sequential writes | Hard drives |
| Checkpoints saved during training | Large sequential writes | Hard drives; fast network matters more |
| Team browsing a shared project area | Many small, repeated reads | Hard drives with SSD cache |
| Document store indexed for RAG | Read once fully, then changes only | Hard drives; cache helps little |
| Image dataset of millions of small files, read in random order every training pass | Random, across the whole set | Local NVMe on the workstation, or all-flash NAS if shared |
| Several GPU workstations training from shared data at once | Random, concurrent | All-flash, with 10GbE or faster |
| Vector index or database | Random reads and writes | Local NVMe; not a hard-drive NAS |
Training is the case to think about carefully. A training run reads the whole dataset in a shuffled order on every pass. That defeats a cache, because everything is equally likely to be needed, and it is punishing for hard drives. The usual answer is not to buy flash for the NAS but to copy the dataset to the workstation's NVMe for the duration of the run. See NAS vs local SSD for AI models and why data locality matters.
A cache makes a hard-drive NAS feel faster for what people do repeatedly. All-flash makes it fast for everything. Most AI teams need the first for their staff and local NVMe for their GPUs.
What decides whether all-flash is justified?
- More than one machine needs fast random access to the same data at the same time.
- Copying data locally is impractical: the dataset is too large or changes too often.
- The network is 10GbE or faster end to end. Flash behind a gigabit link is wasted. See 10GbE vs 2.5GbE.
- The capacity needed in flash is affordable; a common pattern is a small all-flash volume for active projects and a large hard-drive volume for everything else.
How flash and hard-drive budgets compare is covered in HDD vs SSD for AI datasets, and the wider supply picture in the AI storage crisis.
What should you check before buying?
- Whether the NAS has M.2 slots for cache, or needs an adapter card, and which SSDs are on its compatibility list.
- Whether M.2 slots can be used as a storage volume or only as cache on that model.
- Endurance rating and power-loss protection of the SSDs.
- Memory in the NAS: large caches use RAM to track what is cached.
Buying in Singapore
Hard-drive NAS with room to add cache: the Synology DS1825+ (S$2,148). Higher-performance class: the Synology DS1823xs+ (S$2,844) and QNAP TVS-h874T (S$5,405). SSDs and adapter: Synology SAT5221 enterprise SATA SSD from S$334.80 and the Synology M2D20 dual M.2 NVMe adapter card (S$264), for models that support them. Capacity drives: Seagate Exos X24 from S$835 (listed 12 Nov 2026; prices change; ask for a current quotation). Enterprise NVMe such as the Micron 7600 PRO is price on request. 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 is an SSD cache on a NAS? It is one or more SSDs added to a hard-drive NAS that hold copies of frequently accessed data, so repeated reads, and in read-write mode small writes, are served faster.
Does an SSD cache speed up large file transfers? Not much. A one-off sequential copy of a large file goes to the hard drives. Caching helps repeated and small random access.
Is an all-flash NAS needed for AI? Usually not for a single AI system that copies data to its own NVMe drive. It is justified when several machines need fast random access to shared datasets at once.
Is a read-write SSD cache safe? It carries more risk than read-only, because recent writes may exist only on the cache. Use mirrored SSDs with power-loss protection and a UPS.
