Storage Solutions for AI Datasets

A high-performance GPU can process data at an incredible rate, but its efficiency is entirely dependent on how quickly that data can be delivered. When the storage system cannot keep up with the compute engine's demand, it creates an input/output (I/O) bottleneck, leaving your expensive accelerators idle. For machine learning, where datasets can easily span from gigabytes to petabytes, selecting the right storage solution is a significant part of infrastructure design.

The ideal storage system for AI must balance three attributes: throughput, latency, and capacity.

• Throughput is the data transfer rate, usually measured in megabytes or gigabytes per second (MB/s or GB/s). High throughput is essential for loading large batches of data, like images or video frames, into memory quickly.
• Latency is the time delay to retrieve the first piece of data, often measured in microseconds ($\mu$s) or milliseconds (ms). Low latency is important when training on datasets composed of millions of small files, where the time spent seeking each file can add up.
• Capacity is the total amount of data the system can hold. This is a straightforward requirement dictated by the size of your datasets.

Storage solutions can be broadly categorized into local, network-attached, and object storage, each offering a different mix of these attributes.

Local Storage: Speed at Your Fingertips

Local storage is directly attached to the machine performing the computation. This physical proximity provides the lowest possible latency and, with the right technology, the highest throughput.

• Hard Disk Drives (HDDs): These are traditional spinning magnetic disks. While they offer the lowest cost per gigabyte and enormous capacities, their mechanical nature results in high latency and slow transfer speeds. HDDs are generally too slow for the primary storage of an active training dataset and are better suited for long-term archival or cold storage.
• SATA Solid-State Drives (SSDs): SSDs use flash memory and have no moving parts, giving them dramatically lower latency and higher throughput than HDDs. A SATA SSD is a good baseline for a workstation handling small to medium-sized datasets that fit on a single drive.
• NVMe Solid-State Drives (SSDs): Non-Volatile Memory Express (NVMe) is a protocol designed specifically for SSDs to communicate directly with the CPU over the high-speed PCIe bus. This bypasses the older, slower SATA interface, enabling breathtaking speeds. A modern NVMe drive can deliver sequential read speeds over 7,000 MB/s, making it the preferred choice for single-node training where maximum I/O performance is needed to feed a hungry GPU.

Performance characteristics of common local storage types. Note the logarithmic scale for latency, which highlights the orders-of-magnitude difference between device types.

Network and Distributed Storage

When datasets are too large to fit on a single machine or need to be accessed by multiple compute nodes simultaneously for distributed training, you must turn to network-based solutions.

• Network-Attached Storage (NAS): A NAS is a dedicated file storage server that makes its storage available to other machines over a local area network (LAN), typically using a protocol like NFS (Network File System). A high-performance NAS with fast networking (10GbE or faster) can serve data effectively to a small cluster of machines. However, the NAS device itself can become a single point of failure and a performance bottleneck if many clients make requests at once.

• Distributed File Systems: For large-scale operations, a distributed file system is often the answer. These systems pool the storage from multiple servers (nodes) into a single, unified namespace. Data is spread across the nodes, and a file can be read in parallel from multiple disks at once, providing extremely high aggregate throughput. Systems like Ceph or Lustre are examples used in high-performance computing (HPC) and large AI clusters. They are complex to set up and manage but offer scalability and fault tolerance that a single NAS cannot.

Cloud Object Storage

Cloud providers offer a highly scalable and durable storage approach known as object storage. Services like Amazon S3, Google Cloud Storage (GCS), and Azure Blob Storage are the foundations of data storage in the cloud.

Instead of a hierarchical filesystem of folders and files, object storage manages data as "objects" in a flat address space. Each object consists of the data itself, some metadata, and a unique ID.

• Pros: Object storage offers virtually unlimited capacity, high durability (data is replicated across multiple locations), and a relatively low cost for storing massive amounts of data. This makes it the perfect place to build a "data lake" to house all of your raw and processed datasets.
• Cons: Accessing data from object storage over the internet introduces higher latency compared to local or even LAN-based storage. Because of this, it is not typically used for direct access during high-performance training. The common pattern is to use object storage as the persistent source of truth and copy the required subset of data to faster, local NVMe storage on the compute instance before a training job begins.

The hierarchy of storage solutions for AI. Performance generally decreases as data moves further away from the compute core, trading speed for increased scale and shareability.

The choice of a storage solution involves trade-offs. For a data scientist working on a laptop, a large internal NVMe drive might be sufficient. For a startup building its first dedicated AI server, a RAID array of NVMe drives is a powerful starting point. For a large enterprise running distributed training across dozens of GPUs, a distributed file system or a hybrid approach using cloud object storage with high-performance local caches becomes necessary. Understanding these trade-offs is fundamental to designing an infrastructure that can handle your data, not just your models.