Store the AI models you are using on the workstation's local NVMe SSD, and store everything else on the NAS. Local NVMe is several times faster at loading a model, and it keeps the AI service independent of the network. A NAS is slower to read from, but it offers many times the capacity at a much lower cost per terabyte, survives a failed drive, keeps snapshots and can be shared by every machine in the office. The two are not competitors. The useful question is which copy of which file lives where.
How do local SSD and NAS compare?
| Local NVMe SSD | NAS with hard drives | NAS with SSDs | |
|---|---|---|---|
| Read speed | Thousands of MB/s | Hundreds of MB/s, capped by the network | Up to the network limit |
| Capacity | 1 to 4TB typical in AI desktops | Tens to hundreds of TB | Lower than hard drives for the same budget |
| Cost per TB | High | Lowest | High |
| Survives a drive failure | No, usually a single drive | Yes, with RAID | Yes, with RAID |
| Snapshots and versioning | Not by default | Yes | Yes |
| Shared between machines | No | Yes | Yes |
| Depends on the network | No | Yes | Yes |
| Best for | Models in use, active index | Model library, datasets, backups | Many small files, several users at once |
How much faster is local storage for loading a model?
CALC. Loading a 60GB model file into memory:
| Source | Working speed | Time |
|---|---|---|
| Local NVMe SSD | about 3,000 MB/s | 60,000 / 3,000 = 20 seconds |
| NAS over 10GbE | about 600 MB/s | 60,000 / 600 = 100 seconds |
| NAS over 2.5GbE | about 280 MB/s | 60,000 / 280 = 214 seconds |
| NAS over 1GbE | about 110 MB/s | 60,000 / 110 = 545 seconds |
These are typical working speeds, not benchmarks of a particular product; real figures depend on the drives, the file system and what else is using the link. The gap matters each time a model is loaded: after a restart, when switching models, and when a serving engine unloads an idle model and reloads it on demand. Once the model is in memory, storage speed no longer affects how fast it answers. See why data locality matters.
Why not keep everything on the local SSD?
- It fills up. A handful of large models, a dataset and a few training checkpoints can consume a 2TB or 4TB drive. See 1TB vs 2TB vs 4TB.
- It is a single point of failure. Most AI desktops have one internal drive with no redundancy. SSDs can fail without the gradual warning signs hard drives often give; see your SSD could lose data without warning.
- It is not shared. A second AI machine needs its own copy of every model.
- It has no history. Overwrite a fine-tuned adapter and the previous version is gone.
Why not keep everything on the NAS?
- Slower loads, as the table shows.
- Network dependence. A switch reboot or cable fault stops the AI service.
- Contention. A backup job or another user's large copy slows model loading for everyone.
- Random access. Vector indexes and datasets of many small files perform poorly on hard drives over a network.
Speed belongs next to the GPU. Safety belongs on the NAS. A model file should exist in both places, and the one on the NAS is the one you cannot afford to lose.
What is the tiering rule?
| Data | Local SSD | NAS |
|---|---|---|
| Models served today | Working copy | Master copy |
| Models used occasionally | No | Yes; copy across when needed |
| Fine-tuned adapters and your own models | Working copy | Every version, with snapshots |
| Vector index | Yes | Scheduled backup |
| Training data for the current run | Yes, during the run | Master copy |
| Raw datasets and source documents | No | Yes |
| Checkpoints | Latest | Retained set |
A scheduled sync keeps the two in step. Publicly downloadable models are replaceable, though re-downloading takes time and the exact version may no longer be available; anything you created or fine-tuned is not replaceable at all. That distinction, not file size, decides how carefully a file is protected. See how to back up AI models, datasets and checkpoints.
When does an SSD-based NAS make sense?
When several AI machines work from shared data at once, when datasets consist of millions of small files, or when models are loaded directly from shared storage by design. For a single AI desktop that copies models locally, hard drives in the NAS with an optional SSD cache are the economical choice, and the money saved is better spent on capacity and a second backup copy.
Buying in Singapore
NAS for the model library and datasets: the Synology DS925+ (S$1,260), DS1525+ (S$1,800) or DS1825+ (S$2,148). Drives: Seagate IronWolf Pro from S$390 and Seagate Exos X24 from S$835; see which drive belongs in a business NAS. AI systems with fast internal NVMe: the NVIDIA DGX Spark 4TB (S$9,450) and HP ZGX Nano G1n 4TB (S$11,499) (listed 11 Nov 2026; prices change; ask for a current quotation). 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
Should AI models be stored on a NAS or a local SSD? Both. Keep the models in use on the local NVMe SSD for fast loading, and keep the full model library and your own fine-tuned models on the NAS, where they are protected and shared.
Is a NAS fast enough to load AI models from? It works but is slower. A large model that loads in seconds from local NVMe can take minutes over 1GbE or 2.5GbE. Copying models to local storage avoids the delay.
Does storage speed affect AI response speed? Not once the model is loaded into memory. Storage speed affects loading, model switching and training data throughput, not token generation.
Do I need an all-flash NAS for AI? Usually not for a single AI system. It becomes worthwhile when several machines read shared data at once or when datasets contain very large numbers of small files.
