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Why AI storage is split across different data-access patterns
AI systems handle data through distinct interfaces and access patterns. Teams may need shared file access for unstructured data, S3-compatible object access for large datasets and cloud-native applications, or parallel-file access for demanding workloads. Block storage and unified file/block systems remain relevant to the applications and services surrounding an AI environment.
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That makes the storage decision a question of fit: what form the data takes, how applications access it, and which stage of the AI workflow it serves. Dell’s product roles reflect those distinctions; they do not establish that one product is the best choice for every AI deployment.
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| Product | Storage role | Where Dell positions it |
|---|---|---|
| PowerScale | Scale-out file storage built on OneFS | Unstructured-data workflows including ingestion, preparation, training, and inference; supports NFS, SMB, and HDFS. |
| ObjectScale | S3 object storage | Large unstructured datasets, cloud-native applications, and longer-term retention. |
| Lightning File System | Parallel-file storage | Dell’s option for its most demanding AI workloads; described as available on Dell Exascale. |
| PowerStore | Unified block and file storage | Private-cloud and traditional workloads alongside AI infrastructure. |
These roles reflect Dell’s product and solution descriptions, including its Storage for AI page accessed October 4, 2026. They are a starting point for architecture decisions, not an independent performance comparison.
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PowerScale: a shared file layer across a cluster
Dell describes PowerScale as a distributed file platform powered by OneFS. Its architecture has client access, file presentation, and a compute-and-storage cluster. The file presentation provides a common namespace across cluster nodes, while clients can connect through protocols including NFS, SMB, and HDFS.
In its March 7, 2024 architectural post, Dell also discusses GPUDirect Storage and RDMA technologies for moving data efficiently in GPU-oriented environments. Dell says a cluster can expand and rebalance while maintaining its common file presentation. Those are vendor descriptions of the architecture and its operation, not independently measured conclusions about scaling or simplicity.
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ObjectScale: an S3 layer for object data
Dell positions ObjectScale as enterprise-grade, cloud-scale S3 object storage, with multiprotocol support and a global namespace. Its AI material associates object storage with large unstructured datasets, cloud-native applications, and longer-term retention. Organizations evaluating it should check that their applications’ object-access requirements, data-management policies, and deployment model align with the configuration being considered.
Lightning File System and Dell Exascale: parallel-file access
Dell’s July 15, 2026 article describes Lightning File System as a parallel-file engine for demanding AI workloads. Dell presents Exascale as software-defined storage personalities running on a PowerEdge foundation: file, object, and parallel-file formats are described as available. Dell identifies block support as a roadmap target for the first half of calendar year 2027; that forward-looking target is not a guarantee of delivery.
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PowerStore: storage for the surrounding estate
Dell describes PowerStore as unified block and file storage for private cloud and traditional workloads. It can be relevant to services and enterprise applications around an AI system, but Dell’s AI storage story centers on PowerScale and ObjectScale for its file and object data layer. The appropriate role depends on the applications and data-access needs in the full environment.
What the Dell AI Data Platform adds beyond storage
Dell’s March 16, 2026 platform announcement describes an architecture combining Dell storage systems and modular data engines with NVIDIA accelerated compute, networking, and NVIDIA AI Enterprise software. Dell names retrieval-augmented generation (RAG), multimodal search, agentic workflows, and large-scale data processing as target use cases. It also identifies Iceberg and Delta Lake as supported open table formats.
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The implication for an architecture review is that storage is one layer of a larger system. Teams need to consider how data is prepared and managed, how compute and networking connect to storage, and which software environment supports the intended workflow. Dell also describes Professional Services as assisting with validated designs, deployment practices, and lifecycle management; that is a vendor-described service role, not an independent endorsement.
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How to choose a storage role for an AI workload
Use the workload’s data and access pattern to narrow the options, then validate the deployment against capacity, throughput, integration, and protection requirements. The following questions are a practical decision framework based on Dell’s stated product roles, not a promise that a particular product will meet unprovided requirements.
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- Identify the data interface. Is the workload file-centered, S3-object-centered, parallel-file, or dependent on block storage for an adjacent application?
- Locate the workflow stage. Determine whether storage serves ingestion and preparation, training, inference or RAG, or surrounding enterprise applications.
- Describe access behavior. Establish whether users and applications need broadly shared file access, object access, or parallel high-performance access.
- Set scale and deployment requirements. Specify capacity, throughput, cluster scale, and the deployment model the organization needs. Dell’s general product descriptions alone do not establish sizing for a particular environment.
- Map integrations. Check the required GPU, network, data-engine, and software environment, including the protocols and formats that applications actually use.
- Define resilience and governance. Establish data-protection, security, and lifecycle controls before selecting a design.
For a deployment spanning multiple access patterns, evaluate the storage layers as parts of one architecture rather than assuming a single product must serve every role. Confirm product availability, configuration details, and compatibility for the proposed design with Dell before committing.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.How to interpret Dell’s published performance and energy figures
The figures below are Dell-published claims with different metrics, configurations, and dates. They should not be combined into a single ranking or treated as independent comparative test results.
| Dell-published figure | Stated basis | How to read it |
|---|---|---|
| Up to 8X cluster throughput versus traditional flash-only competitors | Dell Technologies’ 2024 claim, based on PowerScale F710 maximum cluster throughput running NFS 4.2; Dell analysis dated September 2024. Dell says actual results may vary. | A conditional comparison for that product, protocol, metric, and analysis—not a general claim about every PowerScale system or AI workload. |
| Up to 72% less energy use | Dell Technologies’ 2025 claim, based on internal analysis of NVIDIA-validated 64-SU reference designs adhering to the NVIDIA Cloud Platform Reference Architecture specification for high-performance storage; analysis dated August 2025. | A result tied to the stated reference-design analysis, not a guaranteed reduction in another environment. |
| Up to 6 TB/s read performance per rack | Dell Technologies’ 2026 claim for Lightning File System on Exascale. | A vendor-stated per-rack maximum; the cited material does not provide an independent benchmark. |
These numbers describe different measures and test or analysis bases. They do not establish relative performance across PowerScale, ObjectScale, and Lightning File System, nor do they predict results for an organization’s own data, configuration, or workload.
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