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NetApp announced Novus on September 29, 2026, as a storage architecture for large AI factories that separates metadata management from the data path. The goal is to scale metadata, capacity, concurrency, and performance more independently while presenting data through one NFS namespace. NetApp described the initial configuration and headline performance figures, but did not publish independent results from a named customer deployment.
What NetApp announced
At NetApp INSIGHT, NetApp introduced Novus as a next-generation file-system and storage architecture aimed particularly at neocloud operators and GPU-as-a-service providers. NetApp says the design is intended for multi-tenant, cloud-scale environments. Its central architectural choice is to separate metadata management from the data path, rather than requiring both to scale together as a conventional storage system grows. NetApp’s September 29 announcement describes a single NFS namespace across the architecture.
Why separate metadata from the data path?
AI factories can involve large GPU populations accessing data in parallel. In that setting, storage must do more than provide aggregate capacity: it also has to locate files and directories, handle many concurrent requests, and supply data at the pace of the workload. NetApp’s design premise is that metadata activity and bulk data movement can become distinct scaling pressures. Separating the services is meant to let operators add resources for those pressures more independently, while retaining a unified namespace.
That is an architectural aim, not proof that metadata is the bottleneck in every AI deployment. Workload behavior varies: training data reads, checkpoint writes, model serving, and other AI tasks place different demands on storage. NetApp Chief Product Officer Syam Nair framed the vendor’s concern this way: “AI factories struggle and GPU economics collapse when data can’t keep up.” That is his characterization of the problem Novus is intended to address, not a universal measurement.
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What is in the initial configuration?
NetApp’s announced starting design brings together three elements. The company said this initial architecture was orderable at launch; it did not publish pricing or a detailed capacity and ordering configuration in the cited announcement. The launch release and NetApp’s INSIGHT materials identify the components:
- NetApp Novus Data Director: metadata software running on qualified Supermicro infrastructure.
- NetApp ONTAP data services: delivered through NetApp AFF A90 systems.
- One NFS namespace: the shared namespace presented across the architecture.
Those details define the announced initial configuration, not every possible future deployment option. The release does not establish how a particular customer’s capacity, fault domains, tenant boundaries, or service levels would be configured.
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How to interpret the 100 TB/s and scale claims
NetApp says Novus is designed for up to 100 TB/s of aggregate throughput, can support hundreds of thousands of GPUs, and can scale toward zettabyte-scale file systems. These are vendor descriptions of design targets and intended scale—not reported results from a named Novus installation. NetApp’s announcement also cites an Omdia projection of up to 100 TB/s of sequential read throughput with dozens of exabytes of effective capacity. The release describes that as a projection based on observed tests and modeling, not a customer result.
NetApp also says GPU utilization can fall below 30% when traditional storage cannot feed an AI factory’s GPUs. That figure is a company characterization of a potential problem; it should not be read as a typical or independently established utilization rate. A storage throughput target alone cannot show how a real environment will perform: results depend on workload, data layout, concurrency, network and compute configuration, and the exact system deployed.
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At the conference, NetApp CEO George Kurian described GPUs as not being fed at the rates they need and said their parallelism and concurrency challenge traditional storage architectures. ITPro reported his remarks; they are a conference statement, not an independent benchmark of Novus. ITPro also reported Kurian’s comparison that data-center capacity shipped the prior year amounted to 2,000 exabytes and his description of Novus as the “first zettabyte-scale file system.” Those, too, are statements attributed to him, rather than measurements independently established in that report.
Where NVIDIA fits—and where it does not
The September Novus announcement is NetApp’s. NVIDIA’s role is broader collaboration and event context, not a co-announcement of Novus in the sources cited here. NVIDIA’s INSIGHT 2026 page discusses validated storage, governed data, AI-ready context for agentic workflows, metadata handling, a unified namespace, and massively parallel data access. It lists a September 30 keynote titled “Feed Every GPU. One Namespace. No Compromise.” with NVIDIA storage technology vice president Jason Hardy and NetApp leaders.
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That event framing follows earlier work announced in March 2026. NetApp described its AI Data Engine (AIDE) as a secure, unified AI data platform co-engineered with NVIDIA and integrated with the NVIDIA AI Data Platform reference design. NetApp said AIDE creates a global metadata catalog and analyzes content in place for discovery and governance. The same release discussed planned support for NVIDIA STX, a modular rack-scale storage reference architecture with a specialized KV-cache tier. These are separate roadmap statements, not components established as part of Novus’s September initial configuration. See NetApp’s March 16, 2026 release and NVIDIA’s AI Data Platform overview.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.What infrastructure teams should evaluate
Novus’s design addresses a recognizable scaling question, but the available launch information is not enough to determine how it will perform against another architecture for a specific environment. An evaluation should use workloads and service expectations that resemble the planned deployment, rather than relying on a peak aggregate-throughput figure alone.
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- Metadata scaling: Test operations under the expected file counts, directory patterns, and metadata request rates.
- Parallel reads and writes: Measure the workload’s actual access patterns, including the effects of concurrent GPU clients.
- Tail latency and checkpoints: Check latency during synchronized activity, such as many jobs writing checkpoints at once, not only average throughput.
- Concurrency: Validate behavior at the expected GPU and tenant counts, including contention between workloads.
- Scaling behavior: Ask how capacity, bandwidth, and metadata resources can grow, and what limits or dependencies apply at each step.
- Namespace, isolation, and resilience: Establish how the shared namespace maps to tenant boundaries, failure handling, recovery, and operational controls.
- Governance and deployment fit: Confirm which data services are included and whether the qualified infrastructure and data-center design fit the organization’s environment.
- Comparable evidence: Request independently measured results on representative workloads and compare test conditions, configuration, and metrics across alternatives.
The cited sources provide no independent head-to-head test of Novus, named customer workload results, product pricing, or detailed ordering configurations. That leaves procurement teams needing deployment-specific evidence before translating the architecture’s stated targets into expected operational outcomes.
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