Power your AI workloads and data at the speed of innovation

When your next competitive edge depends on training models faster and scaling HPC without breaking the budget, data infrastructure stops being just a part of the infrastructure and becomes the lifeblood of the business. Organizations are using GPU-accelerated AI infrastructure to train and fine-tune foundation and domain models that then power applications like customer support automation, code assistance, and document intelligence to name a few. Driven by the need to transform their businesses, organizations are using their data and AI infrastructure to reduce time-to-market, deliver differentiated user experiences, and improve productivity all while keeping IP in-house. At the same time, organizations are expanding HPC for large-scale simulations (e.g., CFD, genomics, risk modeling) and training-data pipelines to shorten design cycles and improve forecast accuracy, while tightly managing GPU utilization, energy consumption, rack-scale performance, and infrastructure cost at scale.

The real problem is that when storage can’t keep up, GPU utilization plummets—idle GPUs still burn budget and waste significant power, turning expensive accelerators into costly space heaters. Modern training workflows punish slow filesystems with dataset shuffles, small-file metadata storms, and checkpoint bursts that can stall pipelines for minutes at a time. In HPC, checkpoint/restart cycles are equally unforgiving: if scratch throughput lags, every save and recovery stretches runtimes and amplifies the penalty of failure. High-throughput storage and parallel file systems remove these bottlenecks and reduce the amount of power consumed, so GPUs stay fed, and jobs keep moving efficiently.

AI infrastructure pressure has shifted because datasets are now larger and multimodal, and teams fine-tune more often turning “one-time training” into a continuous cycle. Distributed training adds dozens to thousands of workers hammering shared data concurrently, while higher checkpoint frequency boosts resilience and shortens iteration loops but creates intense burst I/O. At the same time, pipelines have grown more complex: preprocessing and ETL must stream clean, transformed data fast enough to keep training fully utilized.

As AI becomes a core driver of innovation, organizations must reevaluate their infrastructure and identify opportunities to solve business challenges and realize value. However, AI performance is only as good as the data pipeline that feeds it. It’s not enough to quote peak speeds on an idle system—focus on GB/s per job, metadata ops/sec during shuffle or ingest, and tail latency (p95/p99) under real contention. Achieving this at scale calls for storage that provides consistent performance at both the array and rack scale – maintaining sustained throughput and low latency across connected racks in large-scale workloads.

Power is a finite constraint for AI all around the world. It’s not just about reducing cost – organizations have limited access to power above a certain envelope. So, a power efficient system becomes a critical need and competitive advantage - by minimizing watts per GB/s to deliver high performance sustainably, reducing operational costs. With fast, reliable data access, teams can process, analyze, and act on insights more quickly, unlocking new value and sharpening competitive advantage.

General purpose storage systems often fall short of meeting the requirements of AI and HPC workloads, where predictable throughput, microsecond latency, and optimized integration with parallel file systems are essential. NetApp EF-Series is purpose-built to meet these extreme performance needs by providing high-performance block storage behind the parallel file system.

With EF-Series, organizations can eliminate storage bottlenecks and deliver consistent, reliable performance for the most demanding workloads. This enables customers to:

• Scale efficiently with an architecture that optimizes both your infrastructure and power usage without sacrificing efficiency or latency
• Balance budget with high-performance needs and sub-millisecond response times that support faster, more informed decision-making
• Simplify operational complexity with proven reliability through streamlined deployment, intuitive management, and expert NetApp support

EF-Series is an ideal fit for specialized workloads such as HPC and AI, as well as transactional databases, media and entertainment, backup and recovery, and video surveillance deployments.

The new NetApp EF-Series – EF50 and EF80 - is purpose-built for the modern AI-driven enterprise. With maximum bandwidths exceeding 110GB/s, the EF80 powers workloads that demand extreme performance such as AI and HPC, while the EF50 is designed for mixed workload environments such as databases. Delivering up to 5M IOPs and ultralow latency, EF-Series provides the high performance needed to support your most performance-intensive workloads.

When scaling up to rack-level performance, EF-Series delivers over 100M IOPS and 2.35TB/s of read throughput at under 37KW per rack, making it easier to scale without expanding your data center footprint. With the proven reliability and scalable architecture NetApp customers expect, these systems offer the consistent performance, energy efficiency, and cost-effective storage required to scale sustainably and accelerate innovation.

Optimized for parallel file systems such as Lustre and BeeGFS, EF-Series delivers consistent throughput and predictable performance at scale. Whether you are running HPC simulations, training large-scale AI models, or running high-bandwidth applications, EF-Series provides exceptional efficiency and results.

• Built-in observability to prove “no more GPU stalls”: EF50/EF80 expose rich performance telemetry (IOPS/GB/s, latency, cache behavior) so teams can correlate storage behavior with training slowdowns and verify improvements via before/after runs and p95/p99 latency under load.
• Operational safety for always-on clusters: Designed for non-disruptive lifecycle operations—rolling firmware/software updates, online component replacement, and clear support workflows—reducing downtime risk during critical training windows.
• Supportability that simplifies on-call: Predictive health checks, call-home/support bundles, and straightforward sparing practices help teams move from reactive firefighting to proactive maintenance.
• Easy integration into what you already run: Fits into common monitoring stacks (e.g., Prometheus/Grafana via exporters, Splunk/ELK via syslog/API) and supports automation through REST/Ansible-style workflows for consistent provisioning and change control.
• Plays well with HPC tooling: Works cleanly behind Lustre/BeeGFS OSS/MDS nodes with standard multipathing and established runbooks, minimizing integration friction and speeding time-to-production.

Whether you are training AI models, powering AI inference, or running HPC simulations, NetApp EF-Series gives you the consistent speed and reliability to move at the pace of innovation.

Learn how EF-Series can help power your AI and HPC workloads. 

Download the datasheet to explore the technical specifications for EF50 and EF80.