7 Steps for Integrating High-Performance Storage (HPS) into AI Environments

To fully realize the benefits of generative AI (GenAI), you need a modern, secure data architecture and infrastructure that supports modern systems and allows for faster processing and larger data sets. A high-performance architecture (HPA) — comprising high-performance computing (HPC), high-performance networking (HPN), high-performance storage (HPS), and AI workflow and orchestration tools — will accelerate AI readiness and deliver faster ROI.

All of these elements are critically important, but organizations under pressure to quickly adopt GenAI may overlook the importance of storage, focusing instead on compute, which can cost five times as much. In comparison, storage is a rounding error. However, if your storage isn't fast enough to keep modern GPUs busy, you won't get maximum value out of your investment. 

When it comes to getting your IT environment AI-ready, storage needs to be part of the initial design discussion. Storage takes planning in terms of space, performance and scalability. If you don't focus on it early enough, it will be much harder and more expensive to change it later and move data around.

HPS is critical because GenAI models consume and generate massive amounts of data. Most traditional storage systems cannot provide the performance or the scale that AI requires today, much less what it will need in the future as GenAI capabilities advance. If you are designing an AI solution using HPA, you will need to consider adopting HPS solutions, like those provided by Dell.

As you prepare for GenAI, it's important to remember that you need not only the right infrastructure but also the right data strategy. 

Seven steps to building a successful GenAI environment with HPS

To build an AI environment that successfully integrates HPS, follow these steps: 

1. Make sure you know what your organization needs.

The first step is to develop a full picture of your enterprise's current and future storage needs, strategies and desired outcomes. It's also crucial to fully understand the organization's appetite for change and to identify and prioritize use cases in terms of value and effort.

2. Take time to plan.

Despite the pressure to get started, you need to make time to develop a thorough plan. Organizations that are scrambling to get GenAI solutions up and running may not yet understand the technology or exactly how their organization might best use it or benefit from it. Decisions made without having planning discussions involving key players will inevitably lead to costly mistakes. The effort and the discipline needed to resist the urge to speed ahead will undoubtedly save you effort, time, resources and more in the long run.

3. Get your data AI-ready.

Before you build your AI use cases or program, you need to understand what data you have and if it is clean. If your source data is a mess, you risk introducing bad insights into your organization.

You need to know your organization's data sources, where data comes from and where it goes. Analysis is required to determine if and how your data structure needs to change. And once you have that figured out, you must make sure you have effective governance to ensure your data will stay clean. And finally, you need to make sure your data is secure and will remain so.

4. Make sure your storage platform is AI-ready (and flexible enough to meet current and future demands).

For storage to be AI-ready, you must consider several elements: performance, scalability and mobility.

Performance

AI is fed by storage, so you need to consider its performance, especially latency and throughput. Performance especially matters for AI model training because faster modeling enables more iterations, which improves the accuracy of outputs. Low latency is critical for storage efficiency, and high throughput is necessary to handle the large data demands of AI.

Scalability

Because AI is complex and data-intensive, you must be able to scale both performance and capacity. As you assess various HPS solutions, you should carefully consider the vendor's scaling approach. HPS systems provide balanced scaling, adding both compute and storage capacity to maintain consistent performance and throughput, yet different vendors offer different architectural designs, such as Flash, HDDs and Storage-Class Memory (SCM). Tiered storage solutions optimize cost. 

Dell offers tiered storage and in-platform automation to move data without administrator interaction, using several node types to allow individual tier optimization as needs change.

Mobility

With GenAI, you need the ability to easily manage and move, cache or replicate your data in and out of a hybrid cloud architecture. Consider a solution like Dell PowerScale, a node-based, scale-out NAS and object storage platform that puts the combined capacity of its nodes into a single file system up to hundreds of petabytes in size. It provides multiprotocol access, including file-to-object, flexible data protection, as well as local and remote replication. It also allows you to easily manage and protect petabytes of data on a single platform with tools such as CloudIQ for high-level monitoring; DataIQ for deep information on stored data; and Superna Eyeglass suite, which automates cluster functions like replication and cybersecurity.

5. Count on experienced partners.

Partners like Dell and WWT work with hundreds of organizations to adopt and integrate AI solutions that deliver tangible business results. Experienced partners like these can offer insight and knowledge to help you move faster, avoid pitfalls and ensure your success. For example, WWT can help you build out a comprehensive AI strategy and story, including software and services. WWT's large data practice includes experts who deeply understand data science and infrastructure. Dell has invested heavily to build out AI systems, as well as a huge storage portfolio that integrates into AI solutions, including Dell PowerScale.

6. Choose the right solutions for your organization.

Product performance can vary by use case so be sure to dedicate adequate time to evaluate your options. Don't just run to the first manufacturer or one you're currently comfortable with. You need to find which AI solution components meet your needs and then compare all the options. For example, WWT maintains long-standing relationships with all the relevant OEMs, ISVs and AI innovators; their experts can help you ascertain which solution components are best for you. 

Then you can leverage WWT's new AI Proving Ground to test various solutions before you buy them. The AI Proving Ground is a state-of-the-art lab environment designed to help you make smart decisions faster when designing your HPAs for AI. You can compare, test and train AI technologies (including HPS solutions) using the latest reference architectures, hardware and software from AI innovators like Dell. 

7. Ensure a painless process for delivery and adoption.

With complex technologies, consider using a third party to streamline your supply chain, logistics and integration processes. WWT's global presence revolves around a network of integration centers in the U.S., the Netherlands, India and Singapore, with facilities spanning more than four million square feet on three continents. Their logistics and supply chain services — from staging and integration to material planning, order management and just-in-time delivery — can play a crucial role in efficiently deploying AI solutions worldwide.

When it comes to readying your IT environment for AI, make sure HPS is part of your greater strategy, planning and design conversations. Doing so will help situate your organization for lasting AI success.