Built to support environments where thousands of compute nodes need simultaneous access to shared data. They enable high-throughput, concurrent reads and writes across large-scale clusters, making them essential for traditional HPC workloads such as simulations, modeling, and large-scale scientific computing. These systems are highly scalable and optimized for environments where performance and concurrency are critical.

This class of storage uses NVMe-based solid-state drives to deliver extremely low latency and high input/output operations per second (IOPS). It's designed for AI and machine learning workloads that involve large datasets and require rapid data access, such as training deep learning models or running inference pipelines. This type of storage is ideal in environments where data must be ingested, processed, and moved at high speed to keep up with compute acceleration.

Object storage provides a highly scalable and cost-effective solution for storing vast amounts of unstructured data. It's ideal for applications such as dataset archiving, video and image storage, and log data retention. Designed for horizontal scalability and built for durability, object storage is often used for data lakes and is well-suited for AI and research workloads that need to store large datasets with flexible metadata tagging and retrieval.

Scale-out NAS systems offer file-level access to data with the ability to scale performance and capacity by adding nodes. These systems are easy to deploy and manage, making them well-suited for collaborative environments such as research labs or AI development teams. They provide high availability and support for POSIX-compliant workflows, which are often required in AI model development, data labeling, and shared analytics environments.

SANs deliver block-level storage over a dedicated high-speed network. These systems are often used in structured environments where reliability, uptime, and low-latency access are crucial, such as with databases, transactional systems, or virtualized environments. Though traditionally associated with enterprise IT, SANs are still used in mixed workloads that combine HPC and business-critical applications.

SDS decouples storage services from the underlying hardware, allowing organizations to build flexible, scalable environments using commodity hardware. SDS platforms often support multiple storage types (block, file, and object) and integrate easily with cloud-native and containerized workflows. This makes them particularly attractive to startups and research institutions looking to maximize agility and reduce infrastructure costs.

Cold storage is designed for data that is infrequently accessed but must be retained for long periods due to compliance, reproducibility, or historical reference. This includes tape-based systems, optical media, or cloud-based archival services. It's commonly used in research disciplines that generate massive datasets, such as genomics, climate modeling, or astrophysics, where data may need to be preserved but not accessed regularly.

Tiered architectures combine multiple storage media—such as NVMe, SSD, HDD, and tape—to optimize both performance and cost. Frequently accessed (hot) data resides on high-speed tiers, while less active (warm or cold) data is migrated to slower, more cost-effective tiers. This approach enables efficient resource allocation and is useful in environments where data access patterns vary over time, such as in AI training and analysis pipelines.

Cloud-based storage solutions tailored for HPC provide elastic, scalable performance and integrate with cloud-native compute environments. These services are ideal for organizations looking to burst into the cloud for temporary workloads or scale quickly without heavy upfront investment in infrastructure. Cloud storage supports AI pipelines, collaborative research, and hybrid deployment models where agility and speed of provisioning are key.

Direct-attached storage refers to storage devices physically connected to a compute node, often using NVMe or traditional SSDs. This configuration offers the fastest possible data access for that node and is frequently used for localized AI inference, rapid testing, container workloads, or edge deployments. While it lacks scalability, DAS provides simplicity and high performance for node-specific workloads.