Nutanix Unified Storage Earns NVIDIA Certification to Power Enterprise AI Infrastructure

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Nutanix announced its Unified Storage solution achieved NVIDIA certification at the enterprise level, validating its ability to support large-scale production AI workloads. The certification addresses critical infrastructure challenges as enterprises build AI factories, ensuring full-stack interoperability and linear scalability from 10 GB/s for 32 GPUs to 160 GB/s for 1,024 GPUs.

Nutanix Achieves Enterprise-Level NVIDIA Certification

Nutanix announced that its Nutanix Unified Storage (NUS) solution has achieved NVIDIA certification at the enterprise level, marking a significant milestone for organizations racing to deploy production-ready AI infrastructure

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. This NVIDIA certification validates that the Nutanix storage platform meets the performance, security, and scale requirements necessary for large-scale enterprise AI deployments, addressing a critical bottleneck as companies transform their data centers into AI factories

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The certification comes as enterprises and cloud providers face mounting pressure to build infrastructure that can keep data flowing efficiently to maximize GPU utilization. Without validated storage solutions, organizations risk fragmented infrastructure, siloed data, and inconsistent performance that can limit GPU efficiency and make AI harder to scale reliably

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Addressing Critical Infrastructure Challenges for AI Factories

As Thomas Cornely, executive vice president of Product Management at Nutanix, explained, enterprises must move past fragmented infrastructure and data silos that limit GPU infrastructure efficiency

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. The NVIDIA-powered AI infrastructure certification validates that Nutanix Unified Storage delivers full-stack interoperability, linear scalability, and reliable data velocity that modern AI workloads demand

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Jason Hardy, vice president of Storage Technology at NVIDIA, emphasized that storage is foundational to unlocking full-stack performance, efficiency, and accuracy as enterprises scale AI factory deployments to meet demanding agentic AI workloads

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. The certification gives customers a trusted, interoperable foundation to eliminate data bottlenecks and scale production AI workloads with confidence

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Technical Architecture Delivers Linear Scalability

Built on a 10-node, all-NVMe cluster, the certified configuration leverages enhanced parallel NFS (pNFS) and GPUDirect Storage over NFS with RDMA to establish a low-latency, high-throughput data path directly between GPUs and storage

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. This AI-native storage architecture delivers impressive scalability metrics: from 10 GB/s read and 5 GB/s write for 32 GPUs to 160 GB/s read and 80 GB/s write for 1,024 GPUs

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The solution uses NVIDIA Spectrum-X Ethernet, including NVIDIA Spectrum-4 switches and BlueField-3 DPUs, providing a resilient, zero-downtime architecture for AI workloads

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. This foundation supports training, fine-tuning, inference, and RAG pipelines across compute platforms including x86-based systems with NVIDIA RTX 6000 PRO Blackwell and H200 NVL, NVIDIA HGX servers with B200, H200, or H100 GPUs, and NVIDIA GH200 Grace Hopper Superchip configurations

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Future-Proofing Enterprise AI with Planned BlueField-4 Support

Nutanix is advancing its AI-native storage capabilities with planned support for NVIDIA Vera BlueField-4 STX, reinforcing its focus on faster data access, greater storage efficiency, and simpler AI operations at scale

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. This forward-looking approach positions the Nutanix storage platform to support evolving enterprise AI requirements across hybrid multicloud infrastructure

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Helder Queiros, Senior Director of OEM & Alliances for APJ at Nutanix, noted that the certification empowers customers across Asia-Pacific and Japan to build more reliable and high-performing foundations for enterprise AI from day one

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. The focus remains on helping organizations create unified environments where data and workloads can flow cohesively across data centers, at the edge, and on public clouds to drive maximum value from their AI investments

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