What is AI Storage? 5 Types and How to Choose the Right One

GPU time is expensive. When storage cannot keep up with your training pipeline, those GPUs stall -- waiting for data instead of computing. For teams running large-scale training or distributed inference, storage is often the bottleneck that does not show up until it costs you hours of wasted compute.

This guide covers what AI storage is, the four core challenges it solves, the five storage types you should know, and a decision framework for choosing the right one for your workload.

What is AI Storage?

AI storage refers to the infrastructure designed to handle the massive amounts of data generated, processed, and stored during AI workflows. AI workloads are not like typical business applications. They require continuous data pipelines, rapid read/write cycles, and the ability to support different workload types simultaneously.

Unlike traditional enterprise storage, AI storage must deal with:

• High throughput requirements to keep GPUs fed with data continuously
• Low latency needs for faster training iterations and inference responses
• Scalability to handle datasets growing from gigabytes to petabytes without performance degradation
• Persistence so that checkpoints and datasets survive VM restarts and workload interruptions

What are the Challenges of Managing Massive AI Data?

If you are deploying AI workloads at scale, these are the storage challenges most likely to affect your performance and costs. According to IDC's 2023 Global StorageSphere report, AI and analytics workloads are among the fastest-growing contributors to enterprise storage demand, with data volumes doubling roughly every two years.

1. Data Scalability

AI models require data, and as datasets grow into terabytes or petabytes, traditional storage architectures hit hard limits. Without scalable storage, experiments stay small and models train on incomplete data, limiting the quality of outputs.

2. Performance Bottlenecks

GPUs can process data far faster than most storage systems can deliver it. When storage throughput falls below what your training pipeline demands, GPUs sit idle waiting for the next batch. This I/O bottleneck extends training runs and increases compute cost without producing better results.

3. Latency Issues

In multi-node training or distributed inference, even small latency spikes compound across nodes. A 50ms storage delay hitting every data fetch across a 64-GPU cluster adds up to meaningful wasted time per epoch, breaking the flow of near real-time processing pipelines.

4. Cost Management

High-performance storage carries a cost, and inefficient architectures -- such as using premium NVMe for cold archival data -- waste budget without improving performance. The challenge is matching storage tier to workload type so you pay for performance only where it is actually needed.

How to Choose the Right Storage for Your AI Workload

The smartest AI infrastructure setups do not rely on a single storage type. They combine storage tiers strategically based on access patterns, performance requirements, and cost tolerance. Use this comparison to guide the decision:

As a general rule:

• Use NVMe block storage for active training jobs and checkpoint writes
• Use shared file storage for distributed or multi-VM workloads needing simultaneous data access
• Use object storage for large-scale datasets, logs, and long-term archives
• Layer in-memory caching on top of NVMe when your training loop repeatedly reads the same hot dataset

5 Types of AI Data Storage

Here are the five storage types to understand when planning your AI infrastructure:

1. Block Storage

Block storage attaches directly to a virtual machine, functioning like a high-performance SSD or NVMe drive. Data is split into fixed-size blocks that can be read or written independently, which is what makes it fast for the random I/O patterns common in training loops.

Training jobs constantly read data batches and write checkpoint files. Block storage delivers the predictable latency and strong throughput those operations need. It is persistent across VM restarts, so checkpoints are not lost when a VM is stopped or rebooted.

Block storage is ideal for:

• AI training jobs that require consistent, fast data access across many iterations
• Checkpoint storage where write latency directly affects how often you can save progress
• Any single-VM workload where performance and persistence are both required

2. File Storage

File storage organises data into directories and files and is typically network-attached (NFS-based). Multiple virtual machines can mount and read from the same storage volume simultaneously, which makes it the natural choice for distributed training or Kubernetes-based inference pipelines where multiple nodes need access to the same dataset.

Unlike block storage, which is tied to a single VM, file storage enables shared access across a cluster without duplicating data across nodes.

File storage is ideal for:

• Multi-node training where all nodes need to read the same training data
• Saving and loading model checkpoints in a shared environment
• Collaborative development where multiple team members or processes access the same files

3. Object Storage

Object storage stores data as discrete objects, each with its own metadata and a unique identifier, rather than as blocks or files in a hierarchy. This design makes it extremely scalable and cost-efficient for large volumes of unstructured data: images, video, audio, logs, and raw datasets.

For AI teams managing terabytes or petabytes of training data, object storage works as the long-term backbone of the data pipeline. It integrates directly with standard SDKs like Boto3, S3cmd, and MinIO, so it slots into existing tooling without custom integration work.

Object storage is ideal for:

• Storing raw or preprocessed training datasets before loading into active storage
• Archiving completed run artifacts and logs for later audit or reuse
• Managing large collections of images, video, or sensor data at scale

4. In-Memory Storage

In-memory storage keeps frequently accessed data in RAM or a tmpfs filesystem, eliminating disk I/O entirely for hot data. This is the fastest storage tier available and is particularly effective when a training loop reads the same dataset repeatedly -- caching it in memory means each epoch reads from RAM rather than from disk.

The trade-off is cost and volatility: in-memory storage is expensive per GB and data is lost when the instance stops. It works best as a caching layer on top of persistent NVMe, not as a primary storage solution.

In-memory storage is ideal for:

• Datasets small enough to fit entirely in RAM where training speed matters most
• Hot caching layers that pre-load the next data batch while the GPU processes the current one
• Low-latency inference serving where model weights are kept resident in memory

5. Tape and Cold Archive Storage

Tape and cold archive storage sits at the opposite end of the latency spectrum. Access times are slow -- minutes to hours -- but the cost per GB is the lowest of any storage tier. Cold archive is appropriate for completed run artifacts, regulatory retention data, and historical datasets that are unlikely to be accessed again but must be preserved.

Cold archive is not suitable for active training pipelines, but it plays an important role in keeping long-term storage costs manageable as your data estate grows.

Tape and cold archive storage are ideal for:

• Long-term retention of completed training run artifacts
• Regulatory or compliance data that must be kept but is rarely accessed
• Historical datasets preserved for future retraining or audit purposes

Available Storage Options on Hyperstack

Hyperstack provides three storage types designed to cover the full range of AI workload requirements, from active training to long-term archival.

1. NVMe Block Storage

Hyperstack's NVMe Block Storage is the default storage product for GPU virtual machines. It is located directly within GPU nodes to minimise the distance data travels between storage and compute.

NVMe Block Storage is best for AI training, data science workflows, and any workload requiring fast data access with reliable persistence across reboots.

Pricing: Billed per GB per hour, offering flexibility for short-term experiments and long-term projects alike.

2. Shared Storage Volumes (SSVs)

Shared Storage Volumes are network-attached SSD volumes that can be mounted across multiple VMs simultaneously. Data is replicated across multiple servers for resilience, making SSVs appropriate for teams and distributed workloads where availability matters.

SSVs are best for Kubernetes clusters, multi-VM distributed training, and scenarios where multiple processes or team members need simultaneous access to the same data.

Pricing: $0.000096774 per GB per hour.

3. Object Storage

Hyperstack Object Storage is built for AI and ML datasets, logs, backups, and media. It uses a pay-as-you-go model and connects with standard tools and SDKs including S3cmd, Boto3, and the MinIO client.

Object Storage is best for managing large unstructured datasets, archiving completed run artifacts, and storing logs or media at scale.

Pricing: $0.000019397 per GB per hour.

Conclusion

Storage is not a secondary concern in AI infrastructure -- it is what determines whether your GPUs spend their time computing or waiting. Matching storage type to workload type is one of the highest-leverage decisions in AI system design.

For active training, NVMe block storage provides the throughput and persistence training loops require. For distributed or collaborative workloads, shared file storage removes the need to duplicate data across nodes. For datasets and archives at scale, object storage keeps costs predictable as data volumes grow.

Hyperstack provides all three storage types alongside its GPU VM fleet, so storage and compute can be provisioned together without managing separate infrastructure. Sign up on Hyperstack to get started.

FAQs

What is AI storage?

AI storage is infrastructure designed to handle the high throughput, low latency, and massive scale requirements of AI training and inference workloads. Unlike traditional enterprise storage, it is optimised to keep GPUs continuously fed with data.

What are the five main types of AI data storage?

The five main types are block storage, file storage, object storage, in-memory storage, and tape/cold archive storage. Each serves a different role in the AI data pipeline based on performance, access patterns, and cost.

What is the primary storage requirement for AI workloads?

AI workloads require high-throughput, low-latency, and scalable storage to continuously deliver data to GPUs. Any gap in data delivery causes GPU idle time, which wastes expensive compute.

Why use object storage for AI projects?

Object storage efficiently manages large unstructured datasets like images, video, and logs, offering horizontal scalability, durability, and cost-effective long-term retention. It integrates with standard SDKs and handles petabyte-scale data without performance degradation.

How does storage performance impact GPU utilisation?

When storage throughput falls below what a training pipeline demands, GPUs stall waiting for the next data batch. Faster storage ensures data is ready before the GPU needs it, keeping utilisation high and training times short.

Which storage option works best for training AI models?

NVMe block storage delivers the lowest latency and highest sustained throughput for training workloads. For distributed training across multiple nodes, shared file storage allows all nodes to access the same dataset simultaneously without data duplication.

How does Hyperstack charge for storage?

Storage is billed per GB per hour. NVMe block storage pricing varies by VM flavour. Shared Storage Volumes are priced at $0.000096774 per GB per hour. Object Storage is priced at $0.000019397 per GB per hour.