Nine storage suppliers lined up in a carefully choreographed parade at Nvidia GTC 2025 to help Jensen Huang sell more GPUs, NICs, switches, and software to businesses buying AI stack systems, anticipating an agentic AI boom.
We have Cloudian, Cohesity, DDN, Dell, HPE, Hitachi Vantara, NetApp, VAST Data, and WEKA, but not Pure – it made its big FlashBlade//EXA announcement last week – nor IBM. The nine suppliers are tying their announcements to Nvidia’s news about its Blackwell Ultra GPUs and AI Data Platform, a reference design integrating Nvidia’s GPUs, BlueField NICs, Spectrum-X switches, and AI software with storage systems holding the block, file, and object data to be sent via the NICs and switches to the GPUs, there to be processed with Nvidia’s AI software.
Object storage vendor Cloudian said its HyperStore object storage platform supporting GPUDirect for objects can supply both data lake capacity and HPC-class high-performance data access. It can compete directly with HPC file products for even the most strenuous AI training and inferencing use cases, at a third of the cost of systems that do not support GPUDirect. Cloudian announced Nvidia-based reference architectures (RA) on Lenovo and Supermicro server platforms with all-flash HyperStore supporting GPUDirect and RDMA networking. The Lenovo/Nvidia RA delivers throughput of 20 GBps per node with linear scaling capabilities and supporting large language model (LLM) training, inference operations, and checkpointing functions.
Access the Lenovo RA document, with its ThinkSystem SR635 and SR655 V3 server options here, and the Supermicro RA with its Hyper A+ Storage Server here.
Cohesity has updated its Gaia GenAI search assistant “to deliver one of the industry’s first AI search capabilities for backup data stored on-premises.” Sanjay Poonen, Cohesity president and CEO, stated that “by deploying Cohesity Gaia on-premises, customers can harness powerful data intelligence directly within their environment and not worry about any of that data leaving their infrastructure.”
The updated Gaia relies on Nvidia GPUs, NIM microservices, and NeMo Retriever, and can search petabyte-scale datasets. It supports multi-lingual indexing and querying, and comes with reference architectures and pre-packaged on-premises LLMs. Cohesity and HPE will validate and deploy Gaia on HPE Private Cloud AI, a turnkey, cloud-based offering co-developed with Nvidia. Cisco and Nutanix will also offer Gaia with their full stack systems.
DDN said it’s integrating the Nvidia AI Data Platform reference design with its EXAScaler and Infinia 2.0 storage products, both part of its own AI Data Intelligence Platform, and announcing formal support for Nvidia Blackwell-based systems, including its DGX and HGX systems. The AI Data Intelligence Platform, hooked up to Nvidia’s GPU server HW and SW provides BlueField-3 DPU and Spectrum-X network switch integration, access to NIM and NeMo Retriever microservices, and reference architectures.
DDN’s AI400X2 and AI400X2 QLC storage arrays have achieved Nvidia-certified Storage status. They are fully validated with latest DGX SuperPOD with DGX GB200 systems and the GB200 NVL72 and optimized for Nvidia’s Spectrum-X networking. In general, they deliver sub-millisecond latency and 1 TBps bandwidth.
In testing, a single DDN AI400X2-Turbo achieved 10x the usual minimum requirement of 1 GBps/GPU for read and write operations, paired with a Nvidia DGX B200. Multiple DDN AI400X2-Turbo appliances deliver up to 96 percent network utilization per DGX B200, saturating nearly 100 GBps (800 Gbps) of bandwidth in both read and write operations. More benchmark testing details can be inspected here.
The vendor launched DDN Inferno, claiming it’s “a game-changing inference acceleration appliance” integrating DDN’s Infinia storage with Nvidia’s Spectrum-X AI-optimized networking. Early testing showed Inferno outperforms AWS S3-based inference stacks by 12x and can “provide 99 percent GPU utilization,” but there are no workload, configuration or storage capacity details available.
Omar Orqueda, SVP, Infinia Engineering at DDN, stated: “Inferno delivers the industry’s most advanced inference acceleration, making instant AI a reality while slashing costs at enterprise scale.”
The company is also combining its ExaScaler Lustre parallel file system storage with its Infinia object storage in a hybrid xFusionAI offering. Infinia has S3 support now, and coming block, file, and SQL access protocols, so ExaScaler provides a parallel file system complementing Infinia’s current S3-only storage. There are no details available of how AI processing workloads are spread across the component ExaScaler and Infinia systems.
DDN says Supermicro reports 15x faster AI data center workflows, “unlocking unprecedented efficiency in model training and deployment.” Also, enterprise customers achieve radical improvements in multimodal AI, from high-speed RAG pipelines to autonomous decision-making systems. There is “seamless AI scaling across environments, including on-premises, cloud, and air-gapped systems.”
DDN CTO Sven Oehme stated: “xFusionAI is the convergence of AI’s past, present, and future. It brings together the raw performance of ExaScaler with the intelligent scalability of Infinia, delivering a true ‘best of both worlds’ platform that revolutionizes AI infrastructure.”
The company will provide fully validated Nvidia-Certified Storage reference architectures in the near future.
Dell is announcing new infrastructure, software, and services as part of its Dell AI Factory with Nvidia products, which combine Dell storage with Nvidia GPUs, networking, and AI software, integrated with Nvidia’s AI Data Platform reference design.
CEO Michael Dell stated: “We are celebrating the one-year anniversary of the Dell AI Factory with Nvidia by doubling down on our mission to simplify AI for the enterprise … We are breaking down barriers to AI adoption, speeding up deployments, and helping enterprises integrate AI into their operations.”
The storage product is the PowerScale scale-out file system now validated for Nvidia’s Cloud Partner Program and with Nvidia’s Certified Storage designation for enterprise AI factory deployment. Recent PowerScale updates deliver 220 percent faster data ingestion – streaming writes – and 99 percent quicker data retrieval than previous generation systems.
Dell is adding an open source RAG Connector for LangChain and Nvidia NIM microservices to PowerScale. It will integrate the Nvidia RAPIDS Accelerator for Apache Spark with Dell Data Lakehouse software to speed data prep. Dell also supports Nvidia Dynamo, which frees up GPU memory by offloading key-value cache data from GPU server nodes to PowerScale or other external storage.
There are new Dell professional services for data management strategy and data cleansing to optimize Dell’s AI Data Platform with the Nvidia features, with a systematic approach to data discovery, integration, automation, and quality. A blog discusses how customers can improve RAG data ingestion with PowerScale’s RAG connector.
HPE storage staked its claim to a little bit of Nvidia’s GTC limelight this week, touting a new “unified data layer” it claims will help drive the “agentic AI era.” The unified data layer “brings together both structured and unstructured data” to speed up the AI data lifecycle and spans its high-performance data fabric and “enterprise storage infrastructure with sophisticated data intelligence.”
“As we integrate our Alletra storage MP [platform], our private cloud AI assets and our cross multi cloud environments, we create a truly unified data layer that moves AI closer to the data, and that’s absolutely critical,” SVP GM HPE storage Jim O’Dorisio said ahead of the conference.
“This ability to provide a single name space from edge to cloud across a heterogeneous set of data sources, to deliver universal access, multi-protocol support, automated tiering, and security is absolutely foundational to enabling efficient data-ready, AI use cases.”
Asked to explain where the fabric ended and the layer begins, O’Dorisio said: “The global name space is really that unified access layer within the data fabric. So today it’s kind of ubiquitous. And from that perspective, we’re going to be continuing to invest here and evolve things over time.”
The company announced new software features across its Alletra range. The MP B10000 gets unified file access, as well as enhanced ransomware protection with HPE’s Zerto service. HPE also announced Alletra Block Storage for Azure, in addition to pre-existing support for AWS. The vendor said this would simplify data management and workload placement across hybrid cloud setups.
The object storage focused MP X10000 platform gets automated inline metadata tagging, which HPE said meant enterprises can “infuse their object data – as it is stored – with intelligence that accelerates ingestion by downstream AI applications.”
O’Dorisio said this “allows customers to literally chat with their data almost immediately upon ingestion.”
HPE “expects to further accelerate” the platform’s performance through GPUDirect for object support, in collaboration with Nvidia, to “enable a direct data path for remote direct memory access transfers between GPU memory, system memory and the X10000. This will be rolled out in the coming release,” O’Dorisio said.
The storage enhancements were just one element in HPE’s GTC tie-in. The firm also highlighted an “instant AI development environment” to its Private Cloud AI portfolio – Nvidia-fueled, naturally. It includes an integrated control node, end-to-end AI software and 32 TB of integrated storage. Private Cloud AI now supports rapid deployment of pre-validated Nvidia blueprints, including the Multimodal PDF Data Extraction Blueprint and Digital Twins Blueprint, “enabling instant productivity from Nvidia’s extensive library of agentic and physical AI applications.”
There are new HPE professional services for agentic AI. Deloitte’s Zora AI for Finance on Private Cloud AI is a new joint offering that will be available to customers worldwide, plus CrewAI combined with Private Cloud AI can deploy, and scale agent-driven automation tailored for specific business needs.
And HPE unveiled the AI Mod POD, a performance optimized datacenter in a physical container. This supports up to 1.5 MW per module and can be “delivered with speed.” HPE’s Trish Damkroger, SVP GM HPC Computing and AI, said this meant companies that don’t have datacenter capacity or can’t install liquid cooling in existing datacenter space could be up and running in months rather than years.
“We have examples of our customers siting these in parking lots where they used to have employees,” she said. “But with the work from home from COVID, they have the space.”
HPE Data Fabric with support for HPE Private Cloud AI and HPE Alletra Storage MP X10000 will be available in summer 2025. The updates to the Alletra Storage MP B10000 and the X10000 will be available in May 2025.
Hitachi Vantara has added an M Series to its iQ AI infrastructure product portfolio, combining Nvidia GPUs, Virtual Storage Platform One (VSP One) storage, integrated file system choices, and optional Nvidia AI Enterprise software, meaning NIM microservices, Riva, NeMo retriever, RAPIDS, and other software libraries and tools. The result, Hitachi Vantara says, is “a scalable, adaptable, cost-effective AI infrastructure” offering with separate compute and storage scaling.
The file system choices are either a high-performance file system or a global namespace file system; Hitachi Vantara is now reselling Hammerspace’s Global Data Environment data orchestration software, integrated with the VSP One storage product, and building on a November 2024 partnership agreement. This “ensures distributed data is easily and transparently accessible from anywhere for GenAI workloads.”
There is also an optional object storage repository in the Hitachi iQ portfolio. Lastly, Hitachi iQ is integrating the Nvidia AI Data Platform reference design with its storage with Nvidia’s GPU, networking, and AI software to “enable AI agents with near real-time business insights.” A blog discusses the Nvidia Hitachi Vantara offerings.
NetApp ONTAP storage now has Nvidia validation for SuperPOD, Cloud Partners, and Nvidia-certified systems. Specifically, the AFF A90 product gets DGX SuperPOD validation and is certified as High-Performance Storage for Nvidia Cloud Partners with HGX B200 and H200 Systems. NetApp’s AIPod has achieved the new Nvidia-Certified Storage designation to support Nvidia Enterprise Reference Architectures with high-performance storage. NetApp is releasing a new version of its AIPod with Lenovo, including the Nvidia AI Enterprise software platform, which it says provides more flexibility for customers deploying AI infrastructure for inferencing and fine-tuning
NetApp says these certifications “ready NetApp to tap the Nvidia AI Data Platform reference design” and use ONTAP storage with AI agents for reasoning model inference workloads. This reference design includes Blackwell GPUs, Nvidia networking, and the Nvidia Dynamo open source inference library.
It means NetApp customers will be able to connect their data with agents using Nvidia AI Enterprise software, including the AI-Q Blueprints, NIM microservices for Nvidia Llama Nemotron Reason, and other models.
NetApp says it is developing features to accelerate end-to-end AI processing:
- Global Metadata Namespace in which customers can discover, manage, and analyze all their data across the hybrid multicloud to enable feature extraction and data classification for AI.
- Integrated AI Data Pipeline so customers can more automatically prepare their unstructured data to use in AI applications by tracking incremental changes, leveraging “incredibly efficient replication with NetApp SnapMirror,” classifying data, and creating highly compressed vector embeddings to enable semantic searches on data for retrieval augmented generation (RAG) inferencing.
- Disaggregated Storage Architecture to enable customers to optimize their network and flash speeds and infrastructure costs to achieve high performance with minimal space and power requirements for compute-intensive AI workloads.
This, we understand, is part of NetApp’s ONTAP Data Platform for AI project announced last October.
VAST Data, now saying it’s a $9.1 billion AI Platform company, is calling its Nvidia GTC 2025 news its “biggest launch of the year.” This involves an enterprise-ready AI Stack combining VAST’s InsightEngine with Nvidia DGX products, BlueField-3 DPUs, and Spectrum-X networking. It converges “instant automated data ingestion, exabyte-scale vector search, event-driven orchestration, and GPU-optimized inferencing into a single system with unified global enterprise-grade security.”
The InsightEngine incorporates Nvidia’s AI Data Platform reference design, including AI agents that use Nvidia AI-Q Blueprint, video search and summarization (VSS) blueprint, and Llama Nemotron Reason model NIM microservices. It will be available with Nvidia-certified systems from other server providers in the future.
The key features are:
- Vector Search & Retrieval: AI-powered similarity search in VAST DataBase for real-time analytics and discovery.
- Serverless Triggers & Functions: Event-driven automation for AI workflows and real-time data enrichment in VAST DataEngine.
- Fine-Grained Access Control & AI-Ready Security: Advanced row and column-level permissions, ensuring compliance and governance for analytics and AI workloads, while unifying permissions for raw data and vector representations. Plus, there is built-in encryption and real-time monitoring that spans unstructured, structured, vector and stream data.
VAST InsightEngine for Nvidia DGX is available now. Read more in a VAST blog.
WEKA, the high-speed parallel file system data supplier, supporting up to 32,000 Nvidia GPUs in a single namespace, has achieved data store certification for Nvidia GB200 deployments, supporting Nvidia Cloud Partners (NCP). Specifically, WEKApod Nitro Data Platform Appliances have been certified for Nvidia Cloud Partner (NCP) deployments with HGX H200, B200, and GB200 NVL72 products.
The company says its WEKApod appliances deliver “incredible performance density and power efficiency to Nvidia Cloud Partner deployments.” Each WEKApod node achieves 70 GBps read (560 GBpsec per minimum configuration) and 40 GBps write throughput (320 GBps per minimum configuration).
WEKA says its zero-tuning architecture optimizes dynamically for any workload, delivering sub-millisecond latency and millions of IOPS. A single 8U entry configuration meets the I/O demands of a GB200 Blackwell scalable unit (1,152 GPUs). WEKA’s S3 interface delivers ultra-low latency and high throughput, speeding small object access for AI, ML, and analytics workloads.
A new Augmented Memory Grid feature enables AI models to extend memory for large model inferencing to the WEKA Data Platform. It’s a software-defined extension, which provides exascale cache at microsecond latencies with multi-terabyte-per-second bandwidth, delivering near-memory speed performance. This provides additional petabytes of capacity, 1,000x more “than today’s fixed DRAM increments of single terabytes.”
It integrates with the Nvidia Triton Inference Server and caches or offloads prefixes or key value (KV) pairs from a GPU server’s high-bandwidth memory (HBM). The company says that, when processing 105,000 tokens, the Augmented Memory Grid reduced time to first token by 41x compared to recalculating the prefill context and “dramatically changing the response time to an end user’s query from 23.97 seconds to just 0.58 seconds.” It avoids the need to add more GPUs so as to get more memory.
The Augmented Memory Grid and WEKA’s Data Platform software is integrated with Nvidia GPUs, networking, and enterprise software to accelerate AI inference, “maximize the number of tokens processed per second, and dramatically increase token efficiency.”
WEKA’s NCP reference architecture for Nvidia Blackwell systems will be available later this month. The WEKA Augmented Memory Grid capability will be generally available for WEKA Data Platform customers in spring 2025.
