NetApp announced Novus on September 29, 2026, as a storage architecture aimed at large AI factories, particularly neoclouds and GPU-as-a-service providers. Its central design change is to separate metadata management from the data path, so metadata, capacity, concurrency and performance can scale more independently beneath one NFS namespace. NetApp announced Novus; NVIDIA’s role is broader collaboration and event context, not a co-announcement of this product.
Why AI factories are rethinking storage
Large GPU clusters can read data in parallel at a scale that exposes limits in conventional storage designs. If storage cannot keep many GPUs supplied, compute may sit idle—but the extent of that problem depends on the workload, data layout and system configuration. NetApp Chief Product Officer Syam Nair framed the risk this way: “AI factories struggle and GPU economics collapse when data can’t keep up.” That is a vendor characterization, not a universal measured outcome.
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NetApp CEO George Kurian similarly told ITPro that GPUs may not be fed at the rates they need, citing their parallelism and concurrency as a challenge for traditional storage architectures. These statements describe the design problem Novus is intended to address; they do not establish that all AI workloads are storage-bound.
What Novus changes in the storage architecture
NetApp describes Novus as a next-generation file-system and storage architecture. Metadata management is separated from the data path, with the aim of allowing metadata, bandwidth, capacity and concurrency to scale more independently while data remains accessible through a single NFS namespace. This is intended to address environments where file and metadata operations, high aggregate throughput and large numbers of concurrent GPU clients must coexist.
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The single namespace is part of the architecture’s scaling proposition: it is meant to present a unified view even as underlying resources expand. NetApp says the design is intended for hundreds of thousands of GPUs and file systems approaching zettabyte scale. Those are intended scale descriptions, not evidence that a named installation has reached those figures.
What the initial Novus configuration includes
NetApp’s announcement describes an initial configuration made up of three components:
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- NetApp Novus Data Director metadata software.
- Qualified Supermicro infrastructure on which Data Director runs.
- NetApp ONTAP data services delivered through AFF A90 systems.
NetApp said this initial architecture was orderable at the September 29, 2026 announcement and designed for multi-tenant, cloud-scale environments. The announcement does not specify pricing, detailed capacity configurations or customer workload results.
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NetApp’s September announcement gives up to 100 TB/s aggregate throughput as an architecture design target. It is not a reported result from a particular Novus deployment or an independently verified benchmark. The same release quotes an Omdia projection of up to 100 TB/s sequential read throughput with dozens of exabytes of effective capacity; Omdia’s figure is described as a projection based on observed tests and modeling, not a Novus customer result.
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| Figure | What it represents | How to interpret it |
|---|---|---|
| Up to 100 TB/s aggregate throughput | NetApp’s stated design target for the architecture in its September 2026 announcement. | Not proof that an installed system has delivered this throughput. |
| Up to 100 TB/s sequential read throughput, with dozens of exabytes of effective capacity | Omdia projection quoted in NetApp’s announcement, based on observed tests and modeling. | An analyst projection, not a measured Novus customer deployment. |
| GPU utilization can fall below 30% | NetApp’s characterization of a possible consequence when traditional storage cannot feed AI factory GPUs. | Not a universal utilization measurement. |
NetApp’s announcement also says Novus is designed to support hundreds of thousands of GPUs and scale toward zettabyte-scale file systems. Those descriptions communicate intended scale rather than independently established deployment capacity. ITPro reported Kurian’s conference comparison that data-center capacity shipped “last year” amounted to 2,000 exabytes and his description of Novus as the “first zettabyte-scale file system”; these were statements made at the conference, not independently verified measurements in that report.
Where NVIDIA fits—and where it does not
NVIDIA’s NetApp INSIGHT 2026 page frames the companies’ wider work around validated storage, governed data, AI-ready context for agentic workflows, metadata and massively parallel data access. It lists a September 30 keynote, “Feed Every GPU. One Namespace. No Compromise.” featuring NVIDIA storage technology vice president Jason Hardy and NetApp leaders. That context helps explain the event’s emphasis on feeding GPUs, but it does not make Novus a joint NVIDIA–NetApp announcement.
The companies’ earlier roadmap is a separate development. On March 16, 2026, NetApp announced AI Data Engine (AIDE), described as a secure, unified AI data platform co-engineered with NVIDIA and integrated with the NVIDIA AI Data Platform reference design. NetApp said AIDE would build a global metadata catalog and analyze content in place to support discovery and governance. That release also described planned support for NVIDIA STX, a modular rack-scale storage reference architecture with a specialized KV-cache tier. Those roadmap statements should not be treated as components or confirmed capabilities of the September Novus configuration.
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A headline throughput target alone cannot establish whether a storage system fits a particular GPU cluster. Compare architectures against the actual mix of training, inference, checkpointing and data-preparation work, and ask for results measured under comparable conditions.
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- Metadata scaling: Ask how performance behaves as file counts and metadata operations rise, not only as total capacity grows.
- Parallel reads and writes: Check whether the system sustains the access patterns and concurrency expected from the target GPU count.
- Tail latency and checkpoints: Examine latency under synchronized checkpoint activity, when many clients may issue large operations at once.
- Capacity and bandwidth scaling: Establish how each grows as the system expands, and whether the reported figures describe sequential, aggregate, read or mixed-workload performance.
- Namespace and tenancy: Understand how a unified namespace is maintained and how tenant isolation and resilience work in a multi-tenant deployment.
- Data services and governance: Confirm which data-management capabilities are included and how they fit the organization’s governance requirements.
- Deployment compatibility: Verify infrastructure qualifications, integration requirements and operational responsibilities for the proposed configuration.
- Independent workload evidence: Request reproducible results on representative workloads, with the configuration, concurrency and measurement method stated.
As of October 3, 2026, the reviewed announcements and reporting do not provide an independent head-to-head test of Novus against competing architectures or an independently validated customer benchmark. NetApp’s launch establishes the design and the initial configuration; it does not establish how Novus performs for a particular operator’s workload.
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