As AI workloads move from experimentation into production, storage becomes a core part of the infrastructure rather than simply somewhere to keep data.
The short answer is that AI storage infrastructure usually needs a tiered architecture: high-performance storage for active training, inference and data processing, combined with high-capacity storage for the much larger volume of datasets, outputs, logs and model artefacts that accumulate over time.
That balance matters because AI has two very different storage requirements. GPUs need data quickly, but businesses also need an economical way to retain increasingly large quantities of information.
Recent IDC research sponsored by WD found that 94.7% of surveyed organisations were storing more data because of AI and generative AI adoption. It also found that 74% expected their data volumes to grow by at least 25% over the following three years.
The challenge is therefore straightforward: AI storage must be fast enough to keep compute productive, but scalable and economical enough to accommodate persistent data growth.
Most enterprise AI environments need several storage capabilities working together:
This is important because AI is not a single workload.
Model training, retrieval-augmented generation (RAG), inference, computer vision and data preparation can all generate different I/O patterns. Even one AI application may move information through several stages with very different performance requirements.
The most effective enterprise storage for AI therefore matches storage resources to each part of the data lifecycle rather than attempting to run every workload from one storage tier.
Modern accelerators can process huge quantities of data, but only if information reaches them quickly enough.
When storage cannot supply data at the rate GPUs consume it, expensive compute resources can spend time waiting instead of processing.
That makes high-performance storage for AI particularly important for:
Raw throughput is only part of the equation.
Storage architects also need to consider latency, IOPS, metadata performance, concurrency and network bandwidth.
A training workload reading enormous files sequentially may need very high sustained throughput. An application accessing millions of small files could depend much more heavily on metadata and random-access performance.
The important point is that storage performance needs to be measured against the actual AI workload, not just a headline benchmark.
Storage can influence AI performance throughout the pipeline.
During data preparation, storage determines how quickly information can be ingested, transformed and prepared.
During training, inadequate throughput can leave GPUs waiting for data.
Checkpointing creates another demand. Large distributed models can periodically write substantial amounts of information, and storage needs to absorb those bursts efficiently.
For RAG and inference applications, retrieval performance can affect how quickly supporting data becomes available.
These demands become more significant as AI scales. A system that comfortably feeds four GPUs may behave very differently when dozens of accelerators access the same datasets concurrently.
Storage, compute and networking should therefore be designed as one system.
No single storage technology is ideal for every stage of an AI workload.
| Storage approach | Best suited to | Main strength | Main limitation | Typical AI role |
|---|---|---|---|---|
| NVMe flash | Active training and latency-sensitive data | High throughput and low latency | Higher cost per TB | Performance tier |
| Scale-out file storage | Distributed training and shared datasets | Concurrent access from many nodes | Requires careful metadata planning | Training and checkpoints |
| Object storage | Large unstructured datasets | Very high scalability | Not every workload uses object interfaces natively | Data lakes and RAG sources |
| Enterprise HDD storage | Persistent high-capacity datasets | Density and capacity economics | Lower IOPS and latency than flash | Capacity tier |
| Local storage | Node-specific processing | Data locality | Harder to share and manage at scale | Cache and scratch space |
| Cloud storage | Elastic or variable workloads | Flexible consumption | Recurring storage and data-movement costs | Hybrid and burst workloads |
| Tiered on-prem storage | Production enterprise AI | Balances performance and cost | Requires effective data management | End-to-end AI platforms |
The practical answer is usually a combination.
Flash supports the active working set, while scalable HDD-based capacity provides an economical home for the larger pool of persistent data. Shared file or object storage can provide access across compute nodes, and cloud resources can still be used when elasticity makes sense.
WD's current AI infrastructure guidance similarly emphasises tiered architecture rather than treating HDD and flash as competing technologies.
AI consumes data, but it also creates it.
Inference outputs, synthetic datasets, metadata, embeddings, logs, model versions and checkpoints can all become persistent information.
According to the 2026 IDC research sponsored by WD, 74.3% of surveyed organisations said AI and GenAI had caused them to retain data longer. Historical information is becoming more active too, with organisations increasingly bringing archived data back online for new AI workloads.
Keeping all of that information permanently on premium flash can become expensive.
A tiered architecture solves this by separating the active working set from the much larger persistent data estate.
High-performance storage can contain:
High-capacity storage can contain:
WD's 2026 customer research also found that 87% of respondents prioritised capacity expansion and TCO optimisation when considering AI infrastructure.
This is why the storage conversation should not be framed simply as HDD versus flash.
Flash handles workloads where performance directly creates value. High-capacity HDD infrastructure helps make persistent AI data economical at scale.
Storage limitations often become visible only after an AI environment expands.
Insufficient aggregate throughput can occur when many training nodes request data simultaneously.
Metadata bottlenecks can appear when workloads need to open and manage millions or billions of files.
Checkpoint bursts can place sudden write pressure on shared infrastructure.
Small-file workloads may behave very differently from workloads dominated by large sequential transfers.
Network contention can also look like a storage issue. Faster storage will not help if the network cannot carry the available throughput.
Finally, poor data placement can consume premium storage unnecessarily when inactive data remains on the highest-performance tier.
These issues make workload profiling important before expanding AI infrastructure storage.
Western Digital enterprise storage is particularly relevant to the high-capacity side of a tiered AI architecture.
As AI-generated and retained information grows, enterprises need capacity that can scale without placing the entire data estate on premium flash.
WD's current AI infrastructure strategy focuses on this persistent-data challenge, highlighting capacity, TCO, reliability and tiered architecture as AI systems mature into continuously operating production environments.
The architectural distinction is important.
High-performance technologies should be deployed where latency and throughput directly benefit the workload. High-density enterprise HDD storage can then support the much larger volume of source data, historical information, generated outputs and other persistent datasets.
The two tiers solve different problems, and increasingly need to work together.
For many businesses, the most practical AI storage infrastructure is tiered, scale-out and workload-aware.
A typical architecture includes:
Compute layer
GPU and CPU resources for training, inference and data preparation.
High-speed network fabric
High-bandwidth connectivity between compute and shared storage.
Performance storage tier
Flash-based storage for active datasets, checkpoints and latency-sensitive workloads.
Capacity storage tier
Enterprise HDD infrastructure for data lakes, source datasets, generated information and historical content.
Data management layer
Policies and software that place information on the appropriate tier.
Protection and governance
Replication, snapshots, backup, encryption, authentication and retention controls.
The key is to design for future scale rather than today's dataset alone.
Ask what happens if the active dataset becomes five times larger and retained data becomes twenty times larger. A genuinely scalable storage architecture for AI workloads should allow capacity and performance to grow without rebuilding the entire platform.
On-prem AI storage can be particularly attractive when workloads are predictable and organisations need greater control over performance, data location and infrastructure economics.
Common reasons include:
This does not make on-premises infrastructure universally better than cloud.
Cloud remains valuable for experimentation, managed services and workloads requiring rapid elasticity. Many businesses will therefore use hybrid architectures.
The important question is where each workload and dataset can operate most effectively.
Businesses can control storage costs by avoiding the assumption that every AI dataset needs the fastest storage indefinitely.
Frequently accessed data can remain on performance storage, while colder datasets, logs, outputs and historical model artefacts move to more economical capacity tiers.
This also allows storage to scale independently from GPUs.
If retained data grows faster than compute demand, organisations can add HDD capacity without purchasing unnecessary accelerator resources. If I/O demand increases, the performance tier can be expanded separately.
This approach is particularly useful for RAG, where organisations may want a large body of enterprise information available to AI systems even though only part of it needs to reside on the fastest storage at any moment.
The objective is to size premium storage according to the active working set, rather than according to the growth rate of the entire data estate.
Identify how much information genuinely requires high-performance access.
Model concurrent workloads rather than benchmarking a single compute node.
Include outputs, logs, synthetic information, checkpoints and model artefacts.
This helps determine the appropriate balance between performance and capacity tiers.
Include hardware, rack space, networking, power, software and management as well as initial storage cost.
Storage performs best when it is designed alongside compute, networking, power and cooling.
Hammer Stack takes this infrastructure-level approach to on-prem AI infrastructure, bringing these components together around the requirements of the workload.
That matters because poor GPU utilisation is not necessarily a GPU problem. It may originate in storage throughput, networking, data pipelines or inappropriate placement of data.
Likewise, solving every storage problem with premium performance capacity can create unnecessary cost.
The aim is to build the complete platform around the workload and its expected growth.
AI generally requires high-performance storage for active workloads, high-capacity storage for persistent datasets, high-bandwidth networking and data management capable of moving information between tiers.
GPUs consume data quickly. Storage that cannot provide sufficient throughput can leave compute resources waiting and reduce overall AI infrastructure efficiency.
Storage influences ingestion, training throughput, checkpointing, recovery and information retrieval. Performance should therefore be assessed alongside GPU and network utilisation.
It can be for sustained workloads, large datasets, sovereignty requirements and organisations seeking greater infrastructure control. Cloud remains useful where elasticity or managed services are more important.
For many organisations, a tiered architecture combining flash-based performance storage with scalable capacity storage offers the most practical balance of performance, capacity and cost.
Keep the active working set on high-performance storage and move less active persistent data to economical capacity tiers. Allow storage capacity to scale independently from compute where possible.
AI is not purely a compute challenge.
As workloads scale, businesses need enough performance to keep accelerators productive, enough capacity to retain rapidly accumulating data, and an economic model that continues to work as terabytes become petabytes.
That makes tiered AI storage infrastructure increasingly important.
Use high-performance storage where speed matters. Use high-capacity enterprise storage where density, retention and TCO matter. Then connect those tiers through an architecture designed around how AI data is actually created, accessed and retained.
With Hammer Stack providing the wider on-prem AI infrastructure framework and Western Digital enterprise storage supporting the persistent capacity layer, businesses can plan for both sides of the AI equation: keeping compute productive now while making data growth manageable over the long term.
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