What Types of Storage Are Best for AI Workloads?

If you’re trying to understand what AI storage is and why it matters, here’s the short answer: AI storage is the specialised infrastructure built to handle the massive, high-velocity data pipelines required for training and running AI models. Unlike traditional systems, it’s optimised for rapid throughput, low latency and continuous data access so your GPUs never sit idle.

Typical enterprise storage simply can’t keep up with the scale and speed of AI. That’s why platforms like Hyperstack offer AI-optimised NVMe and shared storage built for high-performance training, fine-tuning and inference, ensuring your datasets, checkpoints and workloads move at GPU speed.

Let’s dive deeper into what AI storage means, the challenges it solves and the storage types you should consider for your AI workloads.

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 workloads.

Unlike traditional enterprise storage, AI storage must deal with:

• High throughput requirements to keep GPUs fed with data.
• Low latency needs for faster training and inference.
• Scalability to handle growing datasets without compromising performance.
• Persistence so that data survives across VM restarts and workload interruptions.

What are the Challenges of Managing Massive AI Data

If you’re deploying AI workloads, you are likely to run into specific storage challenges that directly affect performance and costs. Here’s what you need to look out for:

1. Data Scalability

AI models live on data and as your datasets grow into terabytes or even petabytes, traditional storage can crumble. Without scalable storage, your experiments get stuck at a small scale and your business risks missing out on deeper insights.

2. Performance Bottlenecks

GPUs are fast. But they’re only as effective as the data feeding them. Slow or outdated storage solutions cause I/O bottlenecks, leaving expensive GPUs underutilised and your workloads dragging on for hours longer than necessary.

3. Latency Issues 

In multi-node training or distributed inference, even small latency spikes add up. Without optimised storage, your AI workloads can face delays. And this breaks the flow of real-time or near real-time processing.

4. Cost Management

Storage is not free and inefficient systems rack up unnecessary costs. The challenge is finding a solution that balances high performance with cost, especially when scaling globally.

Types of AI Data Storage to Consider

When planning your AI workload, you should carefully check which storage type aligns with your project needs. Here are the five main types of AI storage:

Block Storage

Block storage attaches directly to your virtual machine, functioning like a high-performance cloud SSD or NVMe drive. It operates at the block level, meaning data is split into fixed-size chunks that can be read or written independently.

In AI workloads, this matters more than you think.

Training loops constantly read batches of data and write checkpoints. If storage can’t keep up with those read/write cycles, your GPUs stall. Block storage delivers high IOPS, predictable latency and strong throughput, making it ideal for performance-sensitive workloads.

Here’s why it matters:

• Ensures data persistence even when virtual machines are powered off or restarted.
• Balances performance with reliability for steady workflow execution.
• Supports flexible allocation and scalability for evolving projects.

Block Storage Use Cases

Block storage is ideal for:

• AI experiments that require consistent data retention across multiple runs.
• Workflows where reliability and continuity are critical.
• Projects needing predictable performance with persistent data availability.

File Storage

File storage organises data into directories and files, making it intuitive and collaborative. It is typically network-attached (like NFS-based systems) and allows multiple virtual machines to mount and access the same dataset simultaneously.

This becomes critical in multi-node AI training or Kubernetes environments where multiple instances need shared access to the same training data.

Unlike block storage, which is attached to a single VM, file storage enables distributed collaboration.

Here’s why it matters:

• Provides a simple, user-friendly structure for data organisation.
• Makes collaboration easier across teams and workflows.
• Integrates seamlessly with existing development tools and environments.

File Storage Use Cases

File storage is ideal for:

• Saving model checkpoints for reuse and recovery.
• Hosting datasets for fine-tuning or iterative training.
• Enabling collaborative AI development across teams.

Object Storage

Object storage works differently. Instead of organising data into blocks or hierarchical file systems, it stores data as discrete objects, each with its own metadata and unique identifier.

This design makes object storage extremely scalable and cost-efficient, especially for unstructured data like images, videos, logs and raw datasets.

For AI teams dealing with terabytes or petabytes of training data, object storage becomes the backbone of long-term storage.

Here’s why it matters:

• Scales efficiently to accommodate growing AI datasets.
• Provides cost-effective storage for large, unstructured workloads.
• Ensures the durability and accessibility of archived data.

Object Storage Use Cases

Object storage is ideal for:

• Storing raw or archived datasets in AI pipelines.
• Managing large collections of images, videos, or logs.
• Handling long-term data storage needs at scale.

How to Choose the Right Storage for Your AI Workload

The smartest AI infrastructure setups don’t rely on just one storage type. They combine them strategically:

• Use NVMe block storage for active training and checkpointing.
• Use shared file storage for collaborative or distributed workloads.
• Use object storage for large-scale datasets, logs and archives.

Available Storage Types on Hyperstack for AI Workloads

At Hyperstack, we understand that not all AI workloads are created equal. That’s why we provide storage solutions for different workloads. Our goal is to deliver scalable, high-performance and cost-effective storage that integrates seamlessly with your GPU virtual machines.

Here’s what we offer:

1. NVMe Block Storage

Our NVMe Block Storage is designed for workloads that demand lightning-fast performance and persistence.

• Built as the default storage product with up to 3 configurable options, depending on your VM.
• High-speed data transfer ensures your GPUs are never starved for data.
• Located directly within GPU nodes for ultra-low latency.
• Persistent across VM shutdowns, so you don’t lose your progress.

NVMe Block Storage is ideal for AI training, data science and any workload requiring fast data access and retention across reboots.

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

2. Shared Storage Volumes (SSVs)

For teams and workloads that require collaboration and replication, Hyperstack offers Shared Storage Volumes:

• Network-based SSD storage ensures high availability.
• Data is replicated across multiple servers for resilience.
• Persistent across multiple VMs, making it ideal for shared environments.

Shared Storage Volumes (SSVs) are ideal for Kubernetes clusters, multi-VM workloads, and scenarios where teams need simultaneous access to data.

Pricing: $0.000096774 per GB per hour, giving you enterprise-grade storage without breaking your budget.

3. Object Storage

For teams managing and scaling unstructured data, Hyperstack offers Object Storage.  It is built for AI/ML datasets, logs, backups and media. 

• Designed for high-volume usage, Hyperstack Object Storage uses a pay-as-you-go model, letting you use high-volume storage while keeping costs predictable.
• Connect instantly with existing tools and SDKs such as S3cmd, Boto3 Python SDK, MinIO. Client (mc) and more.
• Store and manage unstructured data like logs, datasets and media at scale with Hyperstack Object Storage designed to handle massive workloads.
• Add custom metadata to every object, making it easier to search, categorise and retrieve exactly what you need, when you need it.
• Hyperstack Object Storage supports multipart uploads, enabling faster and more reliable transfers for large files through parallel uploads and automatic retry handling.

Pricing: $0.000019397/hr per GB.

Conclusion

AI is only as powerful as the infrastructure supporting it and the right storage can determine how fast and efficiently your workloads run. Even the most advanced GPUs cannot deliver their true potential without high-performance, reliable storage to keep them fed with data. That’s why Hyperstack offers AI-optimised storage solutions to match the pace of your most demanding workloads. 

New to Hyperstack? Sign up today and get started with high-performance GPUs and storage built for AI workloads

Related Blogs

• Use Cases of Object Storage

• What Is Object Storage? A Complete Beginner’s Guide

• Leveraging High‑Speed Networking for High‑Performance Cloud Applications

• RAG vs Fine‑Tuning: Choosing the Right Approach for Improving AI Models

FAQs

What is AI storage?

AI storage is designed to handle massive datasets with low latency and high throughput, unlike traditional storage which slows under heavy workloads.

What are the three main types of data storage?

The three main types are block storage, file storage, and object storage—each suited for different AI workload needs.

What is the primary storage requirement for AI workloads?

AI workloads require high-throughput, low-latency, and scalable storage to continuously feed data to GPUs for optimal performance.

Why use object storage for AI projects?

Object storage efficiently manages massive unstructured datasets like images or videos, offering scalability, durability and cost-effective long-term retention.

How does storage performance impact GPU utilisation?

Faster storage ensures GPUs receive data without delay, preventing bottlenecks and maximising training, inference and overall workload performance.

Which storage option works best for training AI models?

Local NVMe storage delivers ultra-low latency and high throughput, making it ideal for training large models that require rapid dataset access.

How does object storage help in AI workflows?

Object storage manages unstructured data like images and videos at scale, offering durability, flexibility and cost efficiency.

How does Hyperstack charge for storage?

Pricing is flexible and billed per GB per hour, so you only pay for what you use, whether short-term or long-term.