Types of Storage for AI Workloads: What You Need to Know

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 Slow or outdated storage solutions cause I/O bottlenecks, leaving expensive GPUs underutilised and your workloads dragging on for hours longer than necessary.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 provides persistent storage volumes that are attached to virtual machines, functioning much like a “hard drive in the cloud.” Each volume is divided into blocks, which can be formatted and managed independently to make it flexible and reliable for different 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 information into a traditional system of directories and files, making it intuitive and easy to navigate for developers and teams. It allows multiple users or applications to access shared datasets without additional complexity.

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 manages data as discrete objects, each containing both the data itself and rich metadata, identified by a unique key. This structure makes it highly scalable, cost-efficient and suitable for storing massive volumes of unstructured data.

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.

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.

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

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.

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.