There is no universal winner among NAS, SAN, and object storage for AI. Choose the interface your software can use—shared files, block volumes, or an object API—then benchmark that complete data path with your workload, network, cache, and GPU setup. A fast storage label or a vendor peak figure alone cannot tell you which system will perform best.
What separates NAS, SAN, and object storage?
The main difference is how an application addresses data. NAS provides files and directories, typically over NFS or SMB. SAN presents block devices or volumes to hosts; the host or cluster manages the filesystem or other structures on those blocks. Object storage exposes objects through an API, commonly an S3-compatible one, using identifiers and metadata rather than ordinary filesystem paths.
These are different access models, not interchangeable product names. Some systems expose more than one protocol, and different front ends can sit on the same underlying devices. What matters for an AI workload is the interface it can use, how that interface behaves under the workload, and what the complete system can sustain.
| Architecture | Application-facing model | Typical fit to examine | Questions to validate |
|---|---|---|---|
| NAS / file storage | Hierarchical files and directories accessed through NFS or SMB | Shared datasets and workflows already built around filesystem paths and permissions | Shared-access behavior, metadata load, client count, file sizes, network, and filesystem performance |
| SAN / block storage | Block devices or volumes presented to hosts; a host or cluster manages the filesystem and data layout | Workloads needing block-level control or an application stack built over volumes | Volume access model, host or cluster coordination, filesystem choice, I/O size, queue behavior, and operational complexity |
| Object storage | Objects addressed by identifiers and metadata through an API | Large repositories and application workflows designed to read and write through object APIs | API compatibility, object layout, request pattern, parallelism, metadata, network, and support for direct GPU/object interfaces |
These are architectural tendencies, not performance guarantees. A system’s actual behavior depends on its implementation and configuration.
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- Entry-level NAS Personal Storage：UGREEN NAS DH2300 is your first and best NAS made easy. It is designed for beginners who want a simple, private way to store videos, photos and personal files, which is intuitive for users moving from cloud storage or external drives and move away from scattered date across devices. This entry-level NAS 2-bay perfect for personal entertainment, photo storage, and easy data backup (doesn't support Docker or virtual machines).
- Set Your Devices Free, Expand Your Digital World: This unified storage hub supports massive capacity up to 64TB.*Storage drives not included. Stop Deleting, Start Storing. You can store 22 million 3MB images, or 2 million 30MB songs, or 43K 1.5GB movies or 67 million 1MB documents! UGREEN NAS is a better way to free up storage across all your devices such as phones, computers, tablets and also does automatic backups across devices regardless of the operating system—Window, iOS, Android or macOS.
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- Blazing Speed, Minimal Power: Equipped with a high-performance processor, 1GbE port, and 4GB RAM on Board, this NAS handles multiple tasks with ease. File transfers reach up to 125MB/s—a 1GB file takes only 8 seconds. Don't let slow clouds hold you back; they often need over 100 seconds for the same task. The difference is clear.
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How do training and inference workloads change the choice?
Start with the application’s data contract, then describe the workload rather than assuming that “AI” has one storage pattern. Training and inference can differ in read/write mix, I/O size, concurrency, latency sensitivity, and how often data is reused. Those differences affect what to test; they do not automatically make one storage category the right answer.
Training
For training, establish how data is laid out and read, how many clients or workers access it at once, and whether the working dataset fits in cache. Include writes such as checkpoints in the workload where they matter. NVIDIA’s DGX SuperPOD reference architecture notes that data exceeding local cache can make it difficult to sustain high training performance. Treat that as workload-specific guidance for its H100 SuperPOD context, not as a general ranking of storage products.
Rank #2
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- 【Built for Creators, Media Servers & Advanced Apps】Powered by the Intel N100 Quad-Core CPU, 8GB DDR5 RAM, 2.5GbE networking, and dual M.2 NVMe slots, DXP2800 handles large files and heavier workloads with ease. Run Docker, virtual machines, and media server applications compatible with Plex—ideal for content creators, tech enthusiasts, and advanced home users managing 4K videos, RAW photos, personal media libraries, and multiple NAS apps.
- 【Up to 80TB for Growing Digital Libraries】 Supports up to 80TB of storage using two HDD bays and two M.2 NVMe SSD slots for family photos, movies, RAW photos, 4K videos, work files, and device backups. AI photo management supports recognition of people, objects, scenes, and locations, album organization, and duplicate photo detection. HDDs and SSDs are not included.
- 【AI-powered Home Surveillance】Turn DXP2800 into a centralized home surveillance hub by connecting compatible network cameras and storing recordings locally on your NAS. AI-powered features include Face Recognition, People Detection, and Pet Detection, helping advanced home users review important events more efficiently while managing home surveillance and personal data in one place.
- 【One data Center Across Your Devices】Keep files from desktops, laptops, phones, tablets, and other devices together instead of scattered across cloud accounts and external drives. Access, back up, organize, and share data across Windows, macOS, Android, iOS, web browsers, and compatible smart TVs—ideal for creators and advanced home users working across multiple devices.
Inference
For inference, measure the request pattern the serving application actually creates, including concurrency and latency sensitivity. Check whether it expects filesystem paths, block-level control, or API requests for objects. An object platform can suit an application designed around object APIs; an application expecting ordinary filesystem semantics may require adaptation rather than working unchanged against an object endpoint.
In either case, include the complete access path in the evaluation: application behavior, storage interface, network, cache, backend, client count, and GPU topology. A result from one part of that path does not establish how the whole system performs.
Rank #3
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- Smart Home Surveillance - Support up to 30 IP cameras with AI detection, instant alerts and secure remote monitoring
What does GPU-direct storage change?
NVIDIA describes GPUDirect Storage (GDS) as a direct DMA data path between storage and GPU memory that can avoid a CPU bounce buffer. Its documentation distinguishes filesystem-based cuFile access from cuObject interfaces for supported S3-compatible object-storage solutions. These are specific supported paths, not a feature to assume is present in every NAS, SAN, or object deployment.
Availability and usefulness depend on the platform, software, I/O mode, hardware, and topology. Verify the exact configuration and requirements, then measure whether the path improves the workload you care about. The presence of a GDS-capable interface by itself does not establish end-to-end performance.
Rank #4
- Entry-level NAS Home Storage: The UGREEN NAS DH4300 Plus is an entry-level 4-bay NAS that's ideal for home media and vast private storage you can access from anywhere and also supports Docker but not virtual machines. You can record, store, share happy moment with your families and friends, which is intuitive for users moving from cloud storage, or external drives to create your own private cloud, access files from any device.
- Smart Photo Backup & AI Album: Automatically back up photos and videos from your phone in real time and keep growing family memories organized with AI-powered photo albums. Semantic search, custom learning, and recognition of people, objects, pets, and similar photos help you quickly find the moments you want. Duplicate photo removal also helps keep your library organized—ideal for families and users with large photo collections.
- User-Friendly App & Easy Setup: Connect quickly via NFC, set up simply and share files fast on Windows, macOS, Android, iOS, web browsers, and smart TVs. You can access data remotely from any of your mixed devices. What's more, UGREEN NAS enclosure comes with beginner-friendly user manual and video instructions to ensure you can easily take full advantage of its features.
- More Cost-effective Storage Solution: Unlike cloud storage with recurring monthly fees, A UGREEN NAS enclosure requires only a one-time purchase for long-term use. For example, you only need to pay $629.99 for a NAS, while for cloud storage, you need to pay $719.88 per year, $1,439.76 for 2 years, $2,159.64 for 3 years, $7,198.80 for 10 years. You will save $6,568.81 over 10 years with UGREEN NAS! *NAS cost based on DH4300 Plus + 12TB HDD; cloud cost based on 12TB plan (e.g. $59.99/month).
- Your Data, You Control：No third-party clouds, no hidden access, UGREEN NAS provides a more secure and private data storage solution. It stores data locally on your private hard drives and does automatic backups. Thus, you can keep full control over it. The advanced encryption is TRUSTe certified in the United States and is awarded the first (and only) ETSI EN 303 645 certification mark for NAS products by TÜV SÜD Group.
How should you compare performance claims?
Performance figures are tied to their test configuration. NVIDIA’s NFS/GDS guide reports 12.76 GiB/sec sustained throughput and 4,896.86 microseconds average latency for one documented setup; the guide page does not state a publication date. Those are results for that configuration, not a general NAS rating or a fair direct comparison with a different system tested another way.
NVIDIA’s DGX SuperPOD reference architecture also gives “Good,” “Better,” and “Best” throughput tiers for single-node and aggregate read/write scenarios. The visible reference page does not state a publication year. Those tiers are relevant to the specified H100 SuperPOD context, not a cross-vendor rule for selecting storage.
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Best Value
- Value NAS with RAID for centralized storage and backup for all your devices. Check out the LS 700 for enhanced features, cloud capabilities, macOS 26, and up to 7x faster performance than the LS 200.
- Connect the LinkStation to your router and enjoy shared network storage for your devices. The NAS is compatible with Windows and macOS*, and Buffalo's US-based support is on-hand 24/7 for installation walkthroughs. *Only for macOS 15 (Sequoia) and earlier. For macOS 26, check out our LS 700 series.
- Subscription-Free Personal Cloud – Store, back up, and manage all your videos, music, and photos and access them anytime without paying any monthly fees.
- Storage Purpose-Built for Data Security – A NAS designed to keep your data safe, the LS200 features a closed system to reduce vulnerabilities from 3rd party apps and SSL encryption for secure file transfers.
- Back Up Multiple Computers & Devices – NAS Navigator management utility and PC backup software included. NAS Navigator 2 for macOS 15 and earlier. You can set up automated backups of data on your computers.
No universal NAS-versus-SAN-versus-object performance statistic is established here. Compare systems only with representative workloads and equivalent test conditions, rather than treating category labels or isolated vendor peaks as an apples-to-apples result.
How can you benchmark the storage path you intend to deploy?
- Confirm the application contract. Identify whether the software needs shared file paths, block devices, or object API operations. Check protocol and API compatibility before comparing throughput.
- Describe the workload. Record read/write ratio, I/O sizes, concurrency, dataset size, cache-hit behavior, and latency sensitivity. Include the relevant mix of training reads, inference requests, and writes such as checkpoints.
- Test the complete intended path. Use representative data, the expected client count, and the intended network, cache, backend, and GPU topology. Avoid extrapolating a component-level result to the full deployment.
- Record more than throughput. Measure throughput, IOPS, latency, GPU utilization, and CPU load under the same workload. These results help show whether a data path is keeping up and where resource use is occurring.
- Verify direct-I/O support precisely. Check the hardware, software, I/O mode, platform requirements, and topology for any proposed GPU-direct path. Benchmark it against the ordinary path rather than assuming it will help.
- Include operational criteria. Assess scale-out behavior, reliability, quality of service, multi-tenancy, security, and data services alongside speed. A performance result alone does not describe the operational fit.
Which architecture should you shortlist?
- Shortlist NAS when the application is already designed around shared files and directories, then test metadata behavior, client scaling, file sizes, and filesystem performance.
- Shortlist SAN when block-level control or a filesystem/application stack built over volumes is important, then validate coordination, filesystem choice, queue behavior, and operational overhead.
- Shortlist object storage when the application is built for object API operations, then test API compatibility, request patterns, object layout, parallelism, and any supported direct GPU/object interface.
These are starting points for evaluation, not recommendations based on a category’s name. Select the system that meets the application contract and workload requirements in a representative end-to-end test.
Quick Recap
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