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A Guide to Storage for AI Workloads
Learn how AI-optimized storage solutions can enhance performance, reliability, and cost-efficiency for AI workloads by providing the right mix of scalability, speed, and data flexibility.
4 Min Read
Alamy
You may know that AI workloads can benefit from specialized types of compute resources, like GPUs or FPGAs. But did you know that AI-optimized storage, too, is a major consideration for deploying cost-effective, high-performing AI workloads?
If not, this article's for you. Read on for guidance on the role that storage plays in AI workloads, and which types of storage options can optimize AI performance, reliability, and costs.
AI and Storage: The Basics
Like all types of workloads that store data persistently, AI needs storage — although the way AI uses storage can vary between different types of AI workloads. Common examples of storage needs for AI include:
Generative AI workloads need to store training data while models are being trained.
Once trained, generative AI models may need to store data temporarily while performing inference (which means the process through which models generate content based on novel input).
Descriptive and predictive AI models rely on storage to house the data that they analyze — such as log files or documents — when looking for patterns.
On balance, it's important to note that not all AI workloads require significant quantities of persistent storage. For example, you could deploy a descriptive AI tool that ingests real-time streaming data, analyzes it in real time, and then writes the results to volatile memory (i.e., RAM). In this case, the AI wouldn't need to store any information persistently, so it wouldn't need storage.
Most AI workloads, however, are likely to require substantial storage resources.
CREDIT: ALAMY
Optimizing Storage for AI
In general, any type of storage can support AI workloads. To perform optimally, however, AI tools and services benefit from storage that delivers the following capabilities:
High I/O rates, which allow AI workloads to process data faster.
Scalability, so that AI workloads don't stop running due to lack of storage space.
Support for diverse types of data (such as structured as well as unstructured data), since some AI workloads will need to access multiple types of information.
The ability to compress data where feasible to reduce storage costs.
Support for data backup and recovery, to mitigate the risk of data loss.
Here again, specific storage requirements will vary between different types of AI workloads. For example, high I/O is less important for generative AI model training than it is for real-time AI analytics because delays are typically acceptable when training models.
But the point is that, in general, AI workloads work best when they can access specialized types of storage.
AI Storage Options
Options for provisioning AI workloads with storage vary widely. Here's a look at common approaches.
Cloud storage
Cloud storage, like the object storage services provided by all the major public clouds, offer the benefit of massive scalability. They are also low in cost, and in the case of object storage, they can accommodate virtually any type of data. (File-based cloud storage services are less flexible in this respect, as noted below.)
However, reading and writing data may take longer due to network latency and bandwidth limitations — so cloud storage is not ideal for AI workloads that require the highest possible I/O rates.
On-prem commodity storage
An on-prem storage array that consists of conventional hard disks is relatively inexpensive to build and maintain. It's also relatively high-performing because it provides AI workloads with direct access to storage resources. The major downside is that scalability is limited because you can't quickly add more storage resources.
Solid-state storage
Storage arrays built using solid-state disks offer better performance than conventional hard drives, but they're more expensive. They make sense for AI workloads where I/O is a top priority.
Choosing Object vs. File-Based Storage for AI
In addition to selecting storage infrastructure, organizations that deploy AI need to choose which type of storage system to use. Object storage — which is available in the cloud but can also be configured on-prem using solutions like OpenStack Swift — is typically the most flexible because it lets you store any type of data. Object storage is ideal when you don't know which types of data you'll be exposing to AI services. For instance, if you are training a generative AI model using a vast trove of information of varying types, object storage is likely your best option.
However, for AI workloads that need to store data in a structured or ordered way, file storage typically makes more sense. For example, file storage may be most appropriate if you are deploying a predictive AI tool whose main job is to analyze structured log files and write the results to other files.
Conclusion: The Future of AI Storage
To date, storage optimization for AI has not been a major focus of storage vendors. But as AI deployments continue to grow, expect to see more investment in storage solutions designed specifically for AI. The diverse storage needs of AI workloads mean that there is no one-size-fits-all solution, but storage options that cater to the needs of AI will make it easier to deploy AI workloads without letting storage become the weakest link.
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