AMD and Rackspace Technology have signed a definitive agreement to deploy an initial 30MW footprint of AMD-based compute capacity across Rackspace’s global data center portfolio. The phased rollout is scheduled to begin in late 2026 and continue through 2028, advancing the memorandum of understanding the companies announced in May.

Under the agreement, AMD becomes a strategic silicon partner within Rackspace’s enterprise AI infrastructure strategy. The deployment will combine AMD Instinct accelerators, including the MI355X, MI350P, and future generations, with AMD EPYC processors to support AI training, inference, and enterprise workloads in regulated industries.

At full deployment, the 30MW environment is expected to provide substantial AI compute capacity for enterprise customers, including organizations in healthcare and other regulated sectors. Rackspace said the infrastructure is designed to support large-scale clinical AI initiatives, inference services, and other workloads that require governance, accountability, and operational oversight.

The companies plan to integrate the AMD hardware stack into Rackspace’s Enterprise AI Cloud architecture. The platform is intended to match workloads with the appropriate compute resources while providing centralized management and operational accountability across the infrastructure stack.

Rackspace CEO Gajen Kandiah said regulated industries require AI infrastructure that is governed end-to-end rather than assembled from multiple independent providers. He positioned the collaboration as an effort to combine compute infrastructure and operating services into a single managed framework with accountability extending from the hardware layer through business outcomes.

AMD Senior Vice President and General Manager of Compute and Enterprise AI Dan McNamara said enterprise AI deployments increasingly require a mix of accelerated and general-purpose computing resources optimized for different workload requirements. He noted that the combination of AMD’s AI compute portfolio and Rackspace’s managed cloud operating model is intended to provide enterprises with scalable and accountable infrastructure for production AI environments.

The agreement also includes joint go-to-market activities. Both companies will dedicate sales and marketing resources and commit personnel to jointly develop and pursue customer opportunities across regulated industries built on AMD-powered infrastructure.

The deployment is expected to accelerate the delivery of the four services outlined in the earlier memorandum of understanding:

• Enterprise AI Cloud
• Enterprise Inference Engine
• Inference as a Service
• Bare Metal AMD Instinct

Together, these offerings are designed to provide a managed AI infrastructure stack spanning bare-metal compute through fully operated inference services. The two companies position the initiative as an alternative to traditional self-managed bare-metal AI deployments, targeting enterprises moving beyond pilot projects and into production AI and agentic workflow implementations within core business systems.

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At HPE Discover 2026, HPE announced a broad set of GreenLake enhancements to help enterprises modernize hybrid infrastructure, manage AI operations, and reduce virtualization complexity. The updates span agentic AIOps, private cloud platforms, virtualization alternatives, and software for AI infrastructure management.

The announcements reflect HPE’s continued push to position GreenLake as a unified operating model for hybrid cloud and AI environments, providing centralized operations, governance, and automation across infrastructure, applications, and AI workloads.

“As enterprises scale AI, they need a simpler way to govern AI infrastructure and modernize operations across hybrid environments without fragmentation or unpredictable costs,” said Fidelma Russo, executive vice president, Hybrid Cloud and CTO at HPE. “The latest advancements in GreenLake give enterprises a proven, unified path for agentic hybrid operations today and foundation for future autonomous operations.”

GreenLake Intelligence Adds Agentic AI Operations

At the center of the updates is GreenLake Intelligence, HPE’s agentic AI framework for hybrid cloud and AI operations.

The platform introduces centralized agent management through an agent registry, orchestration capabilities, and governance controls designed to coordinate AI agents across infrastructure, applications, and operational workflows.

HPE is also expanding HPE OpsRamp with a new Operations Copilot that provides visibility into AI agents and large language models. The platform enables organizations to monitor AI utilization, track token consumption, and understand operational costs across AI factories and hybrid infrastructure environments.

Using telemetry correlation and AI-driven root cause analysis, OpsRamp Operations Copilot can proactively identify operational issues and accelerate troubleshooting workflows.

HPE also announced a partnership with ServiceNow to integrate GreenLake Intelligence and OpsRamp observability capabilities into ServiceNow’s AI-driven service management platform. The integration is intended to create a common operational framework spanning infrastructure monitoring and autonomous service delivery.

Morpheus Gains AI-Driven Automation and Centralized Management

HPE continues to position Morpheus as a virtualization and private cloud platform for organizations seeking alternatives to traditional virtualization environments.

The latest release adds HPE Morpheus Orchestration Copilot, a GreenLake Intelligence capability that automates infrastructure and workload provisioning through AI-assisted workflows. The platform supports a bring-your-own-model approach while applying governance and security controls to orchestration processes.

HPE also introduced HPE Morpheus Central, providing centralized governance and management across multiple Morpheus deployments through a single interface.

Several previously announced capabilities are now generally available. Software-defined networking support brings multitenancy, zero-trust security controls, policy enforcement, and VXLAN overlay networking into Morpheus environments while reducing provisioning time by up to 60%.

The platform also now supports intent-based network automation through integration with HPE Juniper Apstra. The capability continuously validates network configurations, detects drift, and automates policy enforcement.

In addition, stretched cluster functionality is now generally available, enabling active-active deployments across two sites with synchronous replication and automated failover for higher availability.

HPE Zerto integration further supports virtualization modernization efforts by enabling live workload migration from VMware environments to HPE virtual machines while maintaining continuous data protection.

New Programs Target Virtualization Migrations

Following announcements at the HPE Partner Growth Summit 2026, HPE is introducing additional programs to accelerate virtualization migrations.

A new platform migration program lets new HPE Morpheus VM Essentials customers receive up to one free year of VM Essentials licenses and a year of HPE Zerto for $1 to support non-disruptive migration to HPE virtual machines, with 0% interest on software financing through HPE Financial Services. The program is intended to reduce migration costs and help organizations avoid double-paying for overlapping virtualization licenses during transitions.

For service providers, HPE introduced HPE CloudOps Software, a platform designed to support the delivery of private cloud services. The software includes multitenancy, self-service provisioning, software-defined networking, policy-based governance, and cost management capabilities.

The offering is paired with HPE’s Cloud Commit model, which provides pricing and service benefits tied to committed spending levels.

Private Cloud Portfolio Gains Air-Gapped Enhancements

HPE also announced updates across its Private Cloud portfolio focused on operational consistency from edge environments through core data centers.

HPE Private Cloud PC3000 now supports standardized air-gapped deployments for disconnected and regulated environments. The platform also adds validation for VMware vSphere 9, enabling customers to stay current with VMware infrastructure while using Morpheus to manage virtual machines and containers through a common control plane.

HPE Private Cloud PC7000 receives similar VMware vSphere 9 validation while incorporating the latest Morpheus capabilities, including Terraform support, infrastructure-as-code workflows, and automated private cloud operations.

For government and highly regulated deployments, the air-gapped version of PC7000 now supports Department of Defense Impact Level 4 (IL4) certification requirements. HPE said the enhancements address secure design, configuration hardening, vulnerability management, and compliance objectives commonly required in sovereign and regulated environments.

GreenLake Flex Expands Hybrid Infrastructure Management

HPE is also updating GreenLake Flex Solutions with additional operational and procurement capabilities.

A new integrated management interface combines infrastructure observability, sustainability metrics, and consumption analytics into a single operational view. The goal is to simplify hybrid infrastructure management while providing better visibility into resource utilization and costs.

HPE also announced that customers can now purchase selected third-party software offerings directly through the GreenLake Marketplace, extending the platform’s ecosystem capabilities.

Combined with GreenLake’s consumption-based pricing model, the updates are intended to give organizations greater flexibility in managing infrastructure investments while supporting modernization initiatives across hybrid cloud and AI environments.

Availability

HPE OpsRamp Operations Copilot within GreenLake Intelligence is available today, as are HPE CloudOps Software for cloud service providers and the GreenLake Marketplace, which supports direct customer-to-ISV transactions.

The latest HPE Morpheus Software updates are rolling out across the second and third quarters of 2026. Air-gapped deployments of HPE Private Cloud PC3000 and PC7000, along with additional Private Cloud capabilities, are expected in the third quarter of 2026.

GreenLake Intelligence and ServiceNow integrations will roll out across 2026 and 2027.

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Everpure has announced the availability of Everpure Data Stream, a new platform component based on the NVIDIA AI Data Platform reference design. It brings AI processing closer to enterprise data while addressing common challenges related to data preparation, governance, and scalability. The release expands the company’s broader strategy of delivering AI-ready data infrastructure for enterprise environments.

As organizations move from AI experimentation to production deployments, many face obstacles related to ingesting and preparing enterprise data, enforcing security and governance policies, and scaling infrastructure to support growing AI workloads. Everpure says Data Stream reduces data preparation timelines from months to minutes while maintaining stream-level access controls that keep data within enterprise boundaries. Its scale-out architecture also allows storage and compute resources to scale independently as AI requirements evolve.

According to Everpure CTO Robert Lee, organizations building AI platforms require flexible architectures that can support both rapid deployment and long-term scaling. He noted that enterprises need secure, high-performance data pipelines that accelerate data processing and reduce time-to-results.

Connecting Data Readiness to Production AI

Everpure positions Data Stream as part of a broader end-to-end AI data platform focused on preparing enterprise information for AI use. The company argues that AI-ready data requires classification, contextualization, governance, security, and scalable access before it can be effectively used for training, inference, or agentic AI applications.

A key component of this strategy is Everpure Data Intelligence, formerly known as 1touch. The platform discovers, classifies, and contextualizes enterprise data across SaaS applications, cloud services, on-premises infrastructure, and mainframe environments. It maps relationships between datasets into a data relationship graph, creating a metadata layer accessible via APIs and the Model Context Protocol (MCP).

The platform also applies attribute-based access controls and governance policies, enabling enterprises to maintain security and compliance requirements as AI models and agents interact directly with business data.

GPU-Accelerated Data Processing

Data Stream is built on the NVIDIA AI Data Platform reference architecture and is designed to simplify the conversion of unstructured enterprise data into AI-ready information. Rather than relying on manual ingestion and data preparation processes, the platform uses a GPU-accelerated pipeline spanning data ingestion through inference.

The goal is to reduce operational complexity while improving the speed at which organizations can deploy AI services and generate actionable results.

NVIDIA Vice President of Storage Technology Jason Hardy said modern AI infrastructure requires architectures that connect secure, governed enterprise data with accelerated computing resources. He noted that Everpure’s integration with the NVIDIA AI Data Platform is intended to help organizations move AI initiatives from proof-of-concept stages into production deployments.

Everpure also disclosed ongoing work on next-generation AI-native storage technologies based on NVIDIA Vera and the NVIDIA BlueField-4 STX storage processor. The effort is focused on bringing acceleration, security, and intelligent data services closer to enterprise datasets as agentic AI deployments continue to expand.

Scaling AI Infrastructure

To address storage bottlenecks that can limit AI training and inference performance, Everpure highlighted FlashBlade as the storage foundation for Data Stream deployments. The platform delivers low-latency data access and incorporates KV Cache Accelerator technology to improve memory efficiency during inference workloads.

Everpure’s Evergreen architecture allows organizations to scale from FlashBlade//S systems to FlashBlade//EXA deployments without disruptive migrations, supporting growth from smaller AI projects to large-scale AI factory environments. Portworx provides the container platform layer for deploying and managing AI pipelines across edge, core, and data center environments.

By combining data intelligence, data streaming, storage, and container orchestration within a unified architecture, Everpure aims to reduce infrastructure fragmentation and eliminate the need for separate AI data silos.

The announcement aligns with findings from a recent IDC Global AI Readiness Survey commissioned by Everpure, which reported that 94% of IT leaders view data quality as the primary factor influencing AI success. Everpure positions its integrated approach as a way for enterprises to maintain flexibility while adapting to rapidly changing AI requirements.

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Everpure has announced new capabilities to help organizations accelerate enterprise AI initiatives while maintaining visibility, governance, and control over their data. The announcement centers on the introduction of Everpure Data Intelligence, formerly known as 1touch.io, alongside several updates to the company’s Enterprise Data Cloud (EDC) architecture.

The company positions the release as a response to the growing challenges created by application-centric IT environments, where critical business information is often isolated within individual applications. As AI adoption increases, these data silos can lead to data sprawl, governance challenges, and costly duplication of information, making it more difficult to establish trusted data sources for AI models and agents.

Everpure Chairman and CEO Charles Giancarlo said enterprises must transition from application-centric architectures to data-centric strategies as AI reshapes IT operations. He emphasized that governance, context, and semantic understanding need to be embedded directly within the data layer so organizations can create trusted, real-time intelligence repositories that support both traditional applications and AI-driven workflows.

Building a Data-Centric Foundation for AI

Everpure’s approach centers on what it calls a data-primacy model, in which data exists independently of the applications that consume it. In this model, information becomes a shared system of record that carries its own context, meaning, governance policies, and lifecycle controls.

Applications and AI agents can read from and contribute to the data environment, but ownership remains with the data platform itself. This approach is intended to improve consistency while reducing the fragmentation often associated with modern SaaS and AI ecosystems.

Everpure Data Intelligence

Available immediately, Everpure Data Intelligence is designed to discover, classify, and contextualize enterprise data at its source. The platform operates across on-premises infrastructure, public cloud environments, SaaS applications, third-party storage systems, and the broader Everpure platform.

The software provides visibility into both structured and unstructured datasets, including information stored in databases such as Microsoft SQL Server and Oracle. By identifying where critical business data resides, organizations can gain a clearer view of enterprise-wide data assets without relying on application-specific silos.

Data Intelligence also automates governance functions by scanning environments for sensitive information, including personally identifiable information (PII) and protected health information (PHI). The platform tracks data lineage and relationships, helping organizations maintain compliance and governance requirements across distributed environments.

A key component is its semantic knowledge graph, which maps enterprise data to business context and relationships. According to Everpure, this allows AI agents to better understand enterprise information, improving response accuracy while reducing context window requirements and associated token consumption.

Enterprise Data Cloud Enhancements

The company also introduced several additions to its Enterprise Data Cloud architecture, beginning with updates to its Unified Data Plane. The goal is to provide a common operational foundation across enterprise infrastructure while reducing storage and performance silos.

One of the more notable announcements is Evergreen//One Overdrive, scheduled for availability in Q3 2026. The capability is designed to provide temporary performance expansion for on-premises storage environments, allowing organizations to absorb workload spikes of up to 25% above baseline levels without permanently increasing subscribed capacity.

Intelligent Control Plane Updates

Everpure is also expanding its Intelligent Control Plane with AI-driven operational capabilities intended to simplify infrastructure management.

Workload Rebalance & Mobility, expected in Q4 2026, will automatically move active workloads across storage resources without downtime to optimize capacity utilization and maintain application performance.

Copilot Workflow Execution, planned for Q2 2026, introduces natural-language orchestration for storage operations, enabling administrators to plan, validate, and execute infrastructure tasks across distributed environments.

Enhanced Cyber Anomaly Detection, also expected in Q2 2026, analyzes telemetry across the entire infrastructure footprint to identify suspicious behavior patterns and coordinated login activity that may not be visible at the individual system level.

Fusion Compliance & Agentic Triage, scheduled for Q4 2026, will monitor hardware and software configurations for policy drift while using AI-assisted analysis to identify potential root causes and remediation options.

Enterprise Data Cloud Roadmap

To support organizations adopting a data-centric architecture, Everpure also introduced its EDC Success Blueprint. The framework provides a structured roadmap for assessing infrastructure readiness, identifying security and operational risks, and progressing toward a more automated enterprise data environment.

The broader strategy is focused on creating a unified data fabric that provides visibility into where data resides, how it is connected, and the business context associated with it. By integrating governance, semantic understanding, and AI-driven operations directly into the data layer, Everpure aims to provide enterprises with a foundation for managing both traditional workloads and emerging AI applications at scale.

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At HPE Discover 2026, HPE announced a broad set of networking enhancements to support AI infrastructure, autonomous operations, and zero-trust security. The updates span AI data center networking, AIOps, routing, and secure access, extending the company’s self-driving networking strategy across AI factories, enterprise data centers, and edge deployments.

The announcements build on HPE’s agentic enterprise vision, where networking serves as a foundational layer for autonomous IT operations. The latest additions include expanded AI-driven automation, new AI-optimized switching platforms, deeper integration between HPE Aruba Networking and HPE Juniper Networking technologies, and a unified AI-native Secure Access Service Edge (SASE) platform.

“The success of agentic AI in the enterprise depends on a modern networking foundation built for autonomous workflows, where network performance, reliability, and intelligence determine the effectiveness of the entire AI architecture,” said Rami Rahim, executive vice president, president and general manager, Networking, HPE. “HPE is delivering that foundation, enabling enterprises to deploy agentic AI with greater control, confidence, security, and operational simplicity.”

New Networking Platforms Target AI Infrastructure

HPE is expanding its AI Data Center Solution by integrating HPE Juniper Networking QFX switches, managed via HPE Networking Data Center Director. The addition extends HPE’s full-stack AI infrastructure portfolio across compute, storage, networking, software, and services while providing a validated architecture designed to accelerate AI data center deployments.

The company said the enhanced design improves interoperability and provides a scalable foundation for AI training and inference environments, including emerging rack-scale platforms such as AMD Helios.

Two new switching products were introduced as part of the expansion.

The HPE Juniper Networking QFX5140 switch is designed for AI inference clusters and edge AI deployments, addressing the growing demand for distributed inference infrastructure. HPE positions the platform as a key component in extending AI Factory architectures beyond centralized data centers.

HPE also introduced the HPE Juniper Networking QFX5252 switch tray for AMD Helios. Designed as a scale-up networking module for rack-scale AI systems, the platform provides low-latency, high-bandwidth connectivity intended to maximize GPU utilization and reduce network bottlenecks in large-scale AI environments.

HPE said the new switching platforms are designed to improve infrastructure efficiency by reducing idle GPU time caused by network limitations, helping organizations move AI workloads from proof of concept to production deployments.

Expanded AIOps Across Aruba and Juniper Platforms

HPE continues to merge operational capabilities across its Aruba Networking and Juniper Networking portfolios as part of its broader networking integration strategy.

A key announcement is support for HPE Networking CX wired access switches on the HPE Mist AIOps platform. The integration gives CX customers access to Mist’s AI-driven operational capabilities, including AI-native visibility, zero-touch provisioning, wired assurance, dynamic packet capture, service-level insights, and HPE Marvis-driven automation.

HPE also announced that Marvis AI-powered self-driving networking capabilities are now available within HPE Aruba Central. New automated remediation functions include wired-port troubleshooting and corrective actions designed to reduce manual operational tasks further.

New Agentic AI Capabilities for Data Center Operations

The HPE Mist platform is also gaining additional AI-driven operational capabilities focused on data center environments.

One new capability uses predictive analytics to identify potential hardware and optical failures before service disruption occurs. HPE said the system combines AI and machine learning with multidimensional visualization techniques to improve resiliency and reduce unplanned outages.

A second enhancement introduces an advanced reasoning agent designed to automate root cause analysis and remediation workflows. The platform continuously analyzes operational telemetry, historical support data, and contextual information from HPE Networking Data Center Director to identify issues and recommend corrective actions.

The goal is to accelerate troubleshooting while reducing operational complexity in increasingly large and distributed AI infrastructure environments.

Tighter Integration Across Networking, Compute, and Hybrid Cloud

HPE is also extending integrations across its infrastructure management portfolio following the integration of HPE OpsRamp Software and HPE Morpheus Software.

HPE Mist Networking Data Center Assurance is now integrated with HPE Compute Ops Management, providing cross-domain visibility across networking and compute infrastructure. HPE said the integration reduces management tool sprawl while enabling operations teams to scale infrastructure more efficiently.

The same networking assurance capabilities are now integrated into the HPE GreenLake platform, creating a more unified management experience across hybrid cloud and data center environments.

These integrations support HPE’s longer-term objective of building a self-driving data center that operates through coordinated automation across networking, compute, and cloud infrastructure domains.

Unified SASE Platform Combines Networking and Security

HPE also introduced a new unified SASE platform built on HPE Networking EdgeConnect technology.

The platform combines SD-WAN and Security Service Edge (SSE) capabilities within a single AI-native management framework, allowing organizations to manage networking and security policies from a common console.

Among the key features is an embedded SSE connector that accelerates zero-trust deployments without requiring a separate Zero Trust Network Access infrastructure. A dedicated Secure Web Gateway tunnel extends web protection capabilities to a broader range of devices, including IoT endpoints.

HPE said the platform also supports sovereign SASE architectures by keeping traffic within enterprise-controlled boundaries rather than routing through external cloud security points of presence.

Operationally, the platform incorporates AI-assisted analytics and natural-language interactions via SASE Copilot, helping administrators identify security gaps and resolve issues more quickly.

Financing Program Targets AI Network Upgrades

To support infrastructure modernization efforts, HPE Financial Services announced a new Network Migration Program to help organizations transition to AI-ready networking environments.

The program brings together better-than-cash hardware financing, 0% software financing, and a new IT Asset Program that unlocks value from existing gear to help fund new networking deployments.

HPE said the initiative is intended to reduce the financial and operational barriers associated with upgrading networks to support AI-driven workloads and autonomous operations.

The post HPE Expands Self-Driving Networks Across Edge, Campus, Data Center, and AI Factories appeared first on StorageReview.com.

At HPE Discover 2026, HPE announced a series of enhancements to its AI infrastructure portfolio to help enterprises operationalize agentic AI with greater governance, security, and scalability. The updates expand HPE AI Factory offerings within the NVIDIA AI Computing by HPE portfolio and focus on moving AI initiatives from pilot projects into production environments.

The announcements center on HPE Private Cloud AI and larger-scale HPE AI Factory deployments, adding new capabilities for agent governance, data preparation, inference efficiency, and confidential computing.

“As AI becomes more autonomous, organizations need a new architecture to run it securely, govern it responsibly, and scale it economically,” said Antonio Neri, president and CEO of HPE. “Across networking, servers, storage and software, HPE is delivering full-stack AI solutions with NVIDIA that build the foundation for agentic enterprises, helping customers move from experimentation to production with control and confidence.”

“Every layer of the computing stack is being reinvented for the age of AI agents,” said Jensen Huang, founder and CEO of NVIDIA. “Together with HPE, we are building AI factories for this new era of computing, powered by NVIDIA Vera CPUs, accelerated infrastructure, and secure AI software, to help enterprises transform their data into intelligent action.”

HPE Private Cloud AI Adds Agentic AI Capabilities

HPE Private Cloud AI, the company’s turnkey AI platform co-engineered with NVIDIA, is receiving several enhancements focused on enterprise deployment of AI agents.

A key addition is support for the NVIDIA Agent Toolkit, which includes NVIDIA Nemotron open models, NVIDIA NemoClaw, and the NVIDIA OpenShell runtime environment. Together, these technologies provide a framework for agent reasoning, policy enforcement, behavioral monitoring, and operational governance.

HPE is also introducing the HPE ProLiant Compute DL394 Gen12 with NVIDIA Vera CPU support as a compute platform optimized for agentic AI workloads and high-performance data processing.

 

To improve operational resilience, HPE is extending HPE Zerto capabilities to monitor agent actions and identify potentially harmful or unauthorized behavior. Continuous data protection features will allow organizations to restore environments to known-good states when necessary.

The platform also supports local agent registration, enabling enterprises to approve AI models, tools, and skills through centralized governance and security policies.

Addressing Data Preparation and Inference Efficiency

Data readiness remains a significant challenge for enterprise AI deployments, and HPE is targeting this area with several updates.

Built-in intelligence in the HPE Alletra Storage MP X10000 enables automatic metadata tagging and enforcement of governance policies for unstructured data. HPE says the approach helps organizations prepare AI-ready datasets more quickly while significantly improving inference performance.

The company reports that token response times can be reduced by up to 20x, while prompt-processing efficiency and overall token throughput can improve by up to 20%.

HPE Data Fabric Software is also expanding support for agentic AI workflows. New capabilities include Model Context Protocol (MCP) support for Apache Airflow and an enterprise AI inventory that enriches distributed datasets with metadata, improving discoverability and governance.

For organizations seeking simpler deployment models, HPE will also offer a standalone HPE Data Fabric appliance running on HPE ProLiant servers.

Scaling AI Infrastructure and Managing Costs

HPE is introducing several capabilities designed to improve resource utilization and control operational costs in large AI environments.

A new unified model gateway provides governed access to multiple AI models through a centralized interface. Additional features include workload prioritization and multi-node inference support that can scale across up to 256 GPUs.

The platform also supports fine-tuning pre-trained models, including NVIDIA Nemotron models, using enterprise data through NVIDIA NeMo integration.

These enhancements are intended to help organizations maximize GPU utilization, manage token consumption costs, and support the long-term growth of AI infrastructure.

Confidential Computing Comes to HPE AI Factory

For large-scale and sovereign AI deployments, HPE is integrating NVIDIA Confidential Computing technologies across its AI Factory portfolio.

The technology protects AI models and sensitive data during runtime through hardware-backed security, encryption, and cryptographic attestation. HPE said the capability is designed to help organizations meet regulatory, industry, and sovereignty requirements while maintaining operational performance.

Additional security capabilities leverage NVIDIA BlueField DPUs and NVIDIA DOCA software to provide zero-trust enforcement, runtime threat detection, and encrypted networking across AI infrastructure environments.

The enhancements will be available across HPE AI Factory at Scale and HPE Sovereign AI Factory deployments.

Expanded NVIDIA Hardware Integration

HPE also announced broader support for NVIDIA’s latest AI infrastructure technologies across its AI Factory portfolio.

HPE AI Factory solutions now support NVIDIA RTX PRO 6000 Blackwell Server Edition GPUs, NVIDIA Spectrum-X Ethernet networking, NVIDIA BlueField-3 DPUs, and NVIDIA ConnectX-8 SuperNICs.

Based on NVIDIA reference architectures, the solutions are designed to support a range of AI use cases from model development and training through production-scale deployment. The platforms also integrate NVIDIA AI Enterprise software and ecosystem offerings from HPE’s Unleash AI partner program.

Availability

HPE said the new unified model gateway and additional HPE Private Cloud AI capabilities will be available in July 2026.

HPE Data Fabric Software updates are scheduled for October 2026.

Additional Private Cloud AI capabilities, including agentic observability, data intelligence services, HPE Alletra Storage MP X10000 integration, NVIDIA Agent Toolkit support, and NVIDIA NemoClaw support, are expected in the fourth quarter of 2026.

HPE Zerto support for agent monitoring and recovery workflows is planned for the fourth quarter of 2026, alongside the availability of NVIDIA Confidential Computing across HPE AI Factory solutions.

Support for NVIDIA RTX PRO 6000 Blackwell Server Edition GPUs, Spectrum-X Ethernet, BlueField-3 DPUs, and ConnectX-8 SuperNICs is available immediately.

The post HPE Expands AI Factory Portfolio for Agentic AI Deployments appeared first on StorageReview.com.

HPE has announced that it has expanded its collaborations with eight quantum technology companies as it works to develop hybrid computing environments that combine high-performance computing (HPC) and quantum systems. The effort is designed to accelerate the development of practical classical-quantum workflows and support future large-scale scientific and industrial applications.

Building on its position as a global leader in HPC, where it is the builder of the three fastest exascale supercomputers in the world, as verified by the November 2025 TOP500 list, HPE is focusing on the infrastructure required to integrate emerging quantum technologies into existing supercomputing environments. The company is working with partners spanning quantum processing, quantum control, and quantum error correction to create hybrid architectures that leverage both classical and quantum resources.

 

The effort aims to move quantum computing beyond research environments and toward operational use cases by coupling quantum technologies with established HPC infrastructure. “By bringing supercomputing and quantum technologies together in a hybrid platform, we will accelerate the transition from research to real-world application,” said Trish Damkroger, senior vice president and general manager, HPC & AI Infrastructure Solutions at HPE. “Our new strategic collaborations will extend world-class HPC infrastructure to make quantum accessible, scalable, and operational.”

Building a Full-Stack Hybrid Quantum Platform

HPE’s expanded ecosystem includes Intel, IQM, Qblox, Quantinuum, QuEra Computing, Quantum Machines, Rigetti, and Riverlane. Together, the companies are developing a full-stack hybrid quantum supercomputing platform that supports multiple quantum hardware approaches and enables closer integration with HPC and AI environments.

The collaborations are expected to focus on integrated testbeds for hybrid algorithm co-design, software interoperability, and system-level performance benchmarking across HPC and AI environments. These environments will allow researchers and developers to evaluate how quantum systems can work alongside traditional supercomputers and AI infrastructure while measuring application-level performance across different architectures.

Supporting Multiple Quantum Modalities

A key aspect of the initiative is support for several quantum computing modalities rather than a single hardware approach. HPE is working across neutral-atom, ion-trap, superconducting, and silicon-spin qubit technologies, while also incorporating quantum control and error-correction platforms.

By supporting a diverse set of quantum architectures, HPE aims to help researchers evaluate technology trade-offs and identify the most effective approaches for specific workloads. The integrated environments will also support the development, testing, and benchmarking of hybrid quantum applications running alongside HPC and AI systems.

Expanding Hybrid Computing Capabilities

The expanded partnerships reflect a broader industry effort to connect quantum technologies with established supercomputing infrastructure rather than treating them as standalone systems. HPE’s strategy centers on integrating quantum resources into existing HPC environments, enabling hybrid workflows that leverage both classical and quantum processing capabilities.

As quantum technologies continue to mature, hybrid architectures are expected to play a significant role in scientific research, national security applications, and industrial computing workloads that require capabilities beyond those available from classical systems alone.

See It at HPE Discover

Attendees can explore hybrid classical-quantum computing at HPE Discover demo #629 on the show floor, or attend either of two quantum sessions during the week.

“The future of AI and quantum in the public sector” runs June 16 from 1:00 to 1:45 p.m. PT in Titan 2303 (session PNL P1604). “The next leap: Innovating with quantum, agentic AI, and HPC” runs June 17 from 12:15 to 1:15 p.m. PT in Titan 2201a (session TB1394).

The post HPE Expands Quantum Ecosystem to Advance Hybrid HPC and Quantum Computing appeared first on StorageReview.com.

At HPE Discover 2026 in Las Vegas, HPE announced plans to consolidate its HPE and Juniper Networks partner programs into a single global framework under HPE Partner Ready Vantage. Effective November 1, 2026, partners will operate under a single program structure spanning networking, hybrid cloud, AI, and services, with aligned incentives, common competencies, and a unified engagement model.

The move is part of HPE’s broader strategy to deliver what it describes as one portfolio, one program, and one experience across the combined HPE and Juniper ecosystem. HPE said existing partner investments and certifications will be protected during the transition.

Simon Ewington, senior vice president of Worldwide Channel and Partner Ecosystem at HPE, said the company is simplifying engagement while expanding opportunities for partners across networking, cloud, and AI.

Expanding Channel-Only Opportunities

HPE is also broadening its channel-only sales strategy beyond HPE Morpheus VM Essentials Software. Additional products moving to a channel-only route to market include HPE Private Cloud PC3000, HPE SimpliVity PC1000, and HPE Zerto Software.

The expansion gives partners exclusive opportunities around private cloud infrastructure, virtualization, and data protection deployments. These areas continue to see demand as organizations modernize virtualized environments and evaluate alternatives to traditional virtualization platforms.

To support partner adoption, HPE introduced VM Essentials for Partner IT, enabling partners to run the software in their own IT environments and build hands-on expertise. For the 600 partners that achieve the Private Cloud with Virtualization competency by year’s end, HPE will provide VM Essentials software licenses free of charge for three years, with partners paying only support costs.

New Virtualization Migration Programs

HPE also announced a platform migration program for virtualization designed to reduce financial risk for customers transitioning their environments. New VM Essentials customers can receive up to one free year of VM Essentials licenses, HPE Zerto for $1 to support non-disruptive migration into HPE virtual machines, and 0% interest on software financing through HPE Financial Services.

CloudOps Software Targets Service Providers

For cloud service providers, HPE introduced HPE CloudOps Software as a platform for building and operating private cloud services.

The software includes multi-tenancy, self-service provisioning, software-defined networking, policy-based governance, and cost management capabilities. HPE said the platform is intended to help service providers create differentiated service offerings while improving operational efficiency.

The offering is supported through HPE Partner Ready Vantage and HPE’s Cloud Commit consumption model, which provides pricing and services tied to committed spending levels.

One Program Across Build, Sell, and Service

Under the unified Partner Ready Vantage framework, HPE will consolidate membership tiers, competencies, and compensation models across HPE and Juniper.

Within the Build track, HPE is consolidating its Technology Partner Program into an expanded Technology Validation Center and aligning it more closely with the HPE Unleash AI ISV partner program. A subset of Unleash AI partners have been validated on NVIDIA accelerated computing and integrated with HPE AI Factory with NVIDIA, including HPE Private Cloud AI, the company’s turnkey AI factory. The initiative is intended to streamline solution validation, interoperability testing, and readiness certification across the broader HPE portfolio.

HPE is also launching a Customer Use Case Hub where partners can showcase validated solutions across AI, hybrid cloud, networking, and edge environments.

For sales-focused partners, HPE is aligning incentives across the portfolio and extending rebates tied to technical competencies, solution specialization, and new-logo wins. The company is also standardizing new-business incentives for storage, giving sellers upfront margin for winning new business. Combined with rebates for focus areas such as block storage and competencies such as virtualization and GreenLake Flex Solutions, HPE said the total margin stack can reach up to 24 percent on competitive storage CAPEX takeouts.

On the services side, HPE plans to introduce partner-branded services on selected platforms. Qualified partners will be able to deliver support under their own brand while leveraging HPE escalation, logistics, and engineering resources through HPE Partner Services. HPE is also expanding migration and lifecycle service opportunities designed to increase recurring revenue streams for partners.

Competency-Based Rewards and Sustainability Focus

HPE is extending financial rewards tied to technical competencies beyond its traditional medallion partner status structure. Eligible partners will be able to earn rebates based on investments in targeted solution competencies.

Working with HPE Financial Services (HPEFS), the company is also expanding sustainability-focused tools, dashboards, and competencies intended to help partners address power consumption and efficiency challenges associated with AI infrastructure deployments.

Streamlining the Partner Experience

Beginning in November 2026, HPE will also consolidate partner-facing systems into a single experience that includes a unified portal, onboarding process, contracting framework, development funds program, and deal registration system.

HPEFS is adding new capabilities to its partner portal, including annual payment structures, promotional pricing programs, and relationship-based financing options. The company said it is also reviewing and, where possible, expanding available customer credit capacity in response to increasing demand for financing flexibility.

Expanding AI Validation Resources

As part of its AI strategy, HPE is expanding its global network of Private Cloud AI testing facilities and associated services. The initiative is designed to help customers evaluate, validate, and operationalize AI solutions before production deployment.

HPE said the program will be supported through partners, distributors, and global solution providers, creating additional opportunities for channel partners to participate in enterprise AI adoption projects while helping customers accelerate deployment timelines.

Availability

The channel-only transition for HPE SimpliVity PC1000, HPE Private Cloud PC3000, and HPE Zerto Software takes effect July 1, 2026, when those products become available for new business exclusively through the channel.

HPE CloudOps Software for cloud service providers is now available to partners with an HPE Service Provider contract and to partners in the HPE Partner Ready Vantage Managed Services Center.

The unified HPE Partner Ready Vantage program and integrated partner experience will become available beginning November 1, 2026. Partner-branded services are currently available to Triple Platinum Plus partners and will expand to additional medallion partners by November 1, 2026.

HPEFS partner portal enhancements will be available by July 1, 2026, in the United States, Mexico, Canada, France, Germany, the United Kingdom, Spain, and Italy, with rollout in remaining supported countries continuing through the rest of the year.

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Valvoline Global Operations has introduced Beyond by Valvoline, a new fluid platform focused on advanced thermal management applications, including AI infrastructure, high-performance computing (HPC), and utility-scale battery energy storage systems. To get up to speed on the science behind Valvoline’s work, check out our podcast interview.

The announcement builds on work Valvoline has been pursuing in liquid cooling for several years. StorageReview previously visited the company’s Kentucky facility, where its engineering teams discussed thermal management technologies and showcased developments in immersion cooling fluids. Additionally, StorageReview has covered several immersion cooling innovations, including solutions from Hypertec and other ecosystem partners working to address growing AI infrastructure power densities.

As AI clusters continue to scale and rack power requirements increase, thermal management has become a critical component of data center design. Traditional air-cooling approaches are increasingly challenged by next-generation GPU deployments, driving broader adoption of direct liquid and immersion cooling technologies. The launch of Beyond by Valvoline reflects the growing opportunity for fluid specialists to support these emerging infrastructure requirements.

According to Valvoline, the Beyond platform was developed to address the performance, efficiency, and reliability challenges associated with heat-intensive computing and energy systems. The company positions the offering as an extension of its expertise in fluid engineering, applying knowledge developed across automotive and industrial markets to new technology-focused applications. Valvoline says its fluids in this space have already earned the trust of some of the world’s leading providers of high-performance cooling solutions.

Valvoline notes that the relationship between performance and heat remains consistent across mechanical, electrical, and computational systems. As processing density increases, heat becomes a limiting factor affecting system performance, energy efficiency, and equipment longevity. The company says the Beyond platform is designed to help operators maintain optimal operating conditions and sustain performance in demanding environments.

Roger England, Chief Technical Officer at Valvoline Global, emphasized that effective heat management is essential for maintaining reliability and efficiency across modern technology platforms. He noted that increasing performance typically results in higher thermal loads, creating challenges that must be addressed to preserve system output and service life. The Beyond platform was developed to help mitigate those issues while supporting the next generation of high-performance systems.

For the data center market, the announcement highlights the continued maturation of the immersion cooling ecosystem. As StorageReview has observed across numerous deployments and technology evaluations, successful immersion cooling implementations depend not only on servers and tank designs but also on the performance, stability, and long-term reliability of the cooling fluid itself. Vendors with deep expertise in fluid chemistry are increasingly becoming important participants in the AI infrastructure supply chain.

With Beyond by Valvoline, the company is extending its 160-year history in lubricants and fluid technology into emerging markets where thermal management is becoming a foundational requirement for future computing and energy platforms.

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Veeam Software has introduced new agentic AI capabilities within its Veeam DataAI Command Platform, addressing enterprise challenges related to privacy, compliance, and AI governance. The update adds three AI-driven agents designed to automate policy enforcement and provide continuous, evidence-based validation of compliance across complex data environments.

The announcement reflects a shift from manual, point-in-time compliance processes toward real-time governance aligned with the operational pace of AI systems. Veeam positions these agents as a way to address gaps in traditional privacy programs, which often rely on spreadsheets and disconnected workflows that cannot scale with modern AI-driven data usage.

Addressing Regulatory Complexity in the AI Era

Organizations are facing expanding regulatory requirements that extend beyond data protection into AI model behavior, consent management, and cross-border data flows. Frameworks such as GDPR, the EU AI Act, ePrivacy, and DORA pose significant financial and operational risks, with potential penalties of up to 7% of annual global revenue.

Cassandra Maldini, Head of Product Strategy for Privacy and AI Governance at Veeam, said compliance is no longer a point-in-time exercise and has to be continuous, evidence-based, and built directly into how organizations operate. The company added that AI agents now act on enterprise data at machine speed, generating compliance events faster than any human-operated program can track, which is why it is pushing automation and direct integration into operational workflows.

PrivacyOps Agents for Automated Governance

The new PrivacyOps agents are designed to reduce operational overhead and standardize governance processes across hybrid environments. Built on the DataAI Command Platform’s agent framework, the three agents target key areas where privacy teams typically face bottlenecks.

The Consent Agent serves as a full-stack consent compliance and remediation agent that manages the entire consent lifecycle, from banner creation and automated testing through continuous monitoring and auto-remediation. It captures user consent signals such as cookie preferences, marketing opt-outs, and revoked permissions for AI personalization, then propagates and enforces those signals across downstream systems. This includes analytics platforms, AI pipelines, advertising technologies, SaaS applications, and third-party ecosystems. Powered by Veeam’s regulatory database, it applies automated remediation when policies are violated and generates audit-ready evidence with jurisdiction-aware risk scoring.

The Data Subject Request Agent focuses on automating the intake and management of data subject rights requests. It generates compliant web forms tailored to an organization’s regulatory footprint and keeps them up to date as requirements change. This reduces the need for repeated legal and development cycles and is expected to cut deployment time for these forms by approximately half.

The Assessment Agent targets compliance documentation and reporting. It analyzes available evidence and generates responses for common regulatory requirements such as Data Protection Impact Assessments, EU AI Act conformity assessments, and vendor risk questionnaires. This reduces manual effort while improving consistency and accuracy in compliance reporting.

Unified Data and AI Trust Infrastructure

These agents are delivered through the Veeam DataAI Command Platform, which integrates data security, governance, compliance, privacy, and resilience into a single control plane. The platform is built on the DataAI Command Graph, an intelligence layer that connects to hundreds of data sources across cloud, SaaS, and on-premises environments.

A key component is the People Data Graph, which Veeam describes as the industry’s most advanced identity intelligence graph, unifying structured and unstructured personal data across hybrid multi-cloud environments. This enables real-time, jurisdiction-aware policy enforcement and produces audit-ready evidence of how intent and policy are applied. By operating on live, continuously updated context rather than point-in-time snapshots, the platform supports governance that can keep pace with the agentic era.

Availability

The Consent Agent is available immediately as part of the Veeam DataAI Command Platform. The Data Subject Request Agent and Assessment Agent are expected to be released in the third quarter of 2026.

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10ZiG Technology and Liquidware have expanded their partnership to deepen the integration between 10ZiG OS and Liquidware FlexApp, aiming to deliver applications more efficiently in virtual desktop and cloud-hosted environments.

The joint effort combines 10ZiG’s thin- and zero-client endpoint solutions and management platform with Liquidware’s FlexApp dynamic application attachment technology. The integration is designed to help IT teams simplify the deployment, update, and maintenance of applications across VDI, DaaS, and other hosted desktop environments. For organizations managing distributed users and mixed-endpoint strategies, the partnership focuses on reducing the operational burden associated with traditional desktop image management.

Application delivery remains a persistent problem in end-user computing environments, particularly where administrators maintain multiple desktop images to support diverse user groups or software requirements. That model increases management overhead, slows change cycles, and can complicate patching and version control. FlexApp addresses that issue by decoupling applications from the base operating system, allowing them to be attached dynamically rather than baked into the core image.

Reducing Image Sprawl

With the expanded integration, 10ZiG and Liquidware are positioning the combined stack to reduce image sprawl and improve deployment flexibility. Tom Dodds, Global Strategic Alliances Manager at 10ZiG Technology, said customers want practical ways to simplify application management without sacrificing performance or control, and that expanding the partnership helps IT teams move toward a more flexible model that reduces image management overhead and accelerates application delivery. Jason E. Smith, VP of Marketing & Alliance at Liquidware, framed FlexApp as designed to eliminate the constraints of traditional application packaging and OS image modification, with the 10ZiG integration extending those benefits across a broader range of endpoint strategies.

From a technical standpoint, the partnership centers on four operational gains. First, dynamic application attachment allows IT teams to modernize delivery workflows by separating apps from the base image. Second, updates and new applications can be rolled out faster because administrators no longer need to rebuild and redistribute full images for every change. Third, the approach lowers ongoing overhead by reducing the number of images that must be maintained across environments. Finally, it supports a broader endpoint mix, including thin clients, repurposed devices, and cloud desktops, while preserving a more consistent user experience.

Expanding Virtualized and Cloud Workspace Adoption

Beyond the product integration, the companies said they will expand joint go-to-market work around virtualized and cloud workspace adoption. That includes tighter coordination within the 10ZiG Ready partner ecosystem, as well as broader field enablement and customer engagement efforts.

More information on the 10ZiG Ready ecosystem is available at 10ZiG Ready Program.

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At COMPUTEX 2026, Synology unveiled updates across its storage, data protection, collaboration, surveillance, and private cloud portfolios. The announcements centered on AI integration, hybrid cloud management, and expanded data governance capabilities for both enterprise and consumer environments. Synology also unveiled ActiveProtect Manager 2.0.

Synology executives emphasized the company’s focus on data ownership, security, and privacy, positioning its platforms as infrastructure that gives organizations and individuals greater control over their data while enabling the adoption of AI-driven technologies. What’s clear now more than ever is how Synology continues to push the brand into the enterprise space with a cohesive message across multiple product lines and offerings.

APM 1.2 Expands ActiveProtect Capabilities, APM 2.0 Unveiled

Synology has released ActiveProtect Manager (APM) 1.2, the latest software update for its ActiveProtect data protection appliance portfolio. The update focuses on increasing the scale of centralized management, expanding platform compatibility, and enhancing backup copy workflows to support modern data protection strategies.

Increased Centralized Management Scale

A key enhancement in APM 1.2 is expanded centralized management capacity. Synology has doubled the management scale of its DP7400 and DP7200 appliances, allowing organizations to manage and monitor up to 300,000 workloads from a single management interface.

The update also introduces support for DP320 and DP340 appliances as management servers. This provides enterprises and managed service providers (MSPs) with additional deployment options, enabling centralized oversight of distributed backup environments without requiring larger management platforms.

Specification	DP7400	DP7200
Rack
Suggested Backup Source*	83.5 TB	56 TB
Suggested Built-in Restored VM	9*	4*
Cluster Management Capability	Supports up to 2,500 servers or 300,000 workloads	Supports up to 2,500 servers or 300,000 workloads
Form Factor	2U (RU)	2U (RU)
CPU	AMD EPYC 7272	AMD EPYC 7272
Memory	64 GB (Max. 512 GB)	32 GB (Max. 512 GB)
Storage Configuration	2 x 3840 GB 2.5″ SSD (RAID 1) 10 x 20 TB 3.5″ HDD (RAID 6 + 1 Spare)	2 x 1920 GB 2.5″ SSD (RAID 1) 10 x 12 TB 3.5″ HDD (RAID 6 + 1 Spare)
Network Interface	1 x 1GbE RJ-45 (Management) 2 x 10GbE RJ-45 (Data transfer)	1 x 1GbE RJ-45 (Management) 2 x 10GbE RJ-45 (Data transfer)

Specification	DP340	DP320
Tower
Suggested Backup Source*	14.5 TB	5 TB
Suggested Built-in Restored VM	2	1
Cluster Management Capability	Supports up to 30 servers or 1,500 workloads	Supports up to 10 servers or 500 workloads
Form Factor	Desktop	Desktop
CPU	AMD Ryzen R1600	AMD Ryzen R1600
Memory	16 GB	8 GB
Storage Configuration	2 x 400 GB M.2 SSD (RAID 1) 4 x 8 TB 3.5″ HDD (RAID 5)	2 x 8 TB 3.5″ HDD (RAID 1)
Network Interface	1 x 1GbE RJ-45 (Management) 1 x 10GbE RJ-45 (Data transfer)	1 x 1GbE RJ-45 (Management) 1 x 1GbE RJ-45 (Data transfer)

Expanded Platform Compatibility

APM 1.2 extends support for a broader range of operating systems, virtualization platforms, and database environments.

New platform support includes macOS 26, major Linux distributions, and Microsoft Hyper-V running on Windows Server 2025. Synology also added enhancements to protect critical database workloads, reflecting continued demand for broader workload coverage across hybrid and mixed infrastructure environments.

Enhanced 3-2-1-1-0 Data Protection Workflows

The release introduces improvements to backup copy management designed to strengthen adherence to the 3-2-1-1-0 data protection framework. New capabilities include retroactive copy enablement and extended retention options, allowing organizations to adjust protection policies while maintaining compliance with retention and recovery objectives.

Administrators also gain real-time visibility into backup copy operations through enhanced progress tracking. The added monitoring capabilities help identify issues earlier and provide operational insight into backup replication and retention activities.

Synology Broadens ActiveProtect with APM 2.0

Synology also unveiled APM 2.0. This is a significant product expansion rather than a simple product update. APM 2.0 expands the scope of the ActiveProtect platform, positioning ActiveProtect Manager 2.0 as a broader backup and cyber resilience layer for hybrid infrastructure. According to Synology, APM 2.0 adds protection for AWS EC2, Azure VM, Proxmox, Nutanix AHV, and Google Workspace, along with cross-platform recovery and AI-powered anomaly and malware detection. Synology also paired the software update with a new DP5200 appliance, signaling that the ActiveProtect roadmap remains tightly coupled to dedicated hardware.

With this update, ActiveProtect moves beyond a narrower appliance-first backup story and toward hybrid protection. Support for cloud VMs, Google Workspace, and alternative virtualization stacks such as Proxmox and Nutanix AHV gives Synology a more relevant message for organizations that no longer want separate tools for branch infrastructure, core virtualization, SaaS data, and cloud workloads. APM 2.0 is Synology’s clearest effort yet to turn ActiveProtect into a centralized management and recovery plane rather than a point product.

Competition

Synology’s competitors, such as Veeam, Rubrik, and Cohesity, already frame backup as a cyber resilience platform, with broad workload coverage, ransomware posture, and centralized policy management. Synology is now aligning its messaging to that same set of requirements. However, Synology’s historical advantage has been simplicity, vertical integration, and appliance economics, not feature breadth or ecosystem depth at the very high end.

Synology appears more appliance-focused and integrated, appealing to midmarket buyers seeking a pre-configured stack. Veeam, on the other hand, benefits from broader platform familiarity, extensive channel reach, and a more mature, software-led approach across diverse environments. Compared to Rubrik and Cohesity, Synology likely competes on operational simplicity and lower costs. Still, those vendors are perceived as stronger in enterprise features like large-scale policy management, security workflows, and platform extensibility.

Not Just Another Protected Workload

The most important addition is not just another protected workload. It is the combination of hybrid workload support and cross-platform recovery. If Synology executes well, that could make ActiveProtect more attractive to distributed enterprises and upper-midmarket accounts with mixed estates that want a consolidated backup experience without adopting a larger, more complex platform stack.

Another notable point is the inclusion of AI-based anomaly and malware detection. That is now table stakes in backup marketing. However, it still matters strategically because buyers increasingly expect backup platforms to contribute to threat detection and clean recovery workflows, not just data retention. At this stage, Synology has announced the capability. Still, public technical details remain limited on how deep the analytics go, how alerts are operationalized, and how mature the recovery workflow is under real-world incident conditions.

APM 2.0 is a meaningful product expansion, not a cosmetic refresh. Synology is pushing ActiveProtect into more competitive territory by broadening workload coverage and strengthening its cyber-resilience positioning. For SMB, distributed enterprise, and cost-sensitive midmarket buyers, that could materially improve Synology’s standing. For larger enterprise deals, the key question will be execution: scale, policy depth, reporting, immutability options, and how well cross-platform recovery performs beyond launch messaging.

Next-Generation DSM Targets Enterprise AI Deployments

Synology previewed the next generation of DiskStation Manager (DSM), which introduces capabilities designed for AI-enabled infrastructure and large-scale enterprise deployments.

The updated platform supports GPU-equipped NAS systems and dedicated AI appliances, enabling organizations to run AI inference workloads on-premises while maintaining governance and control over sensitive data. Synology also introduced DSM Agent, a new orchestration layer intended to automate and coordinate intelligent workflows across the environment.

For larger deployments, Cluster Manager provides centralized administration across multiple Synology systems through a single management interface. The platform supports workload migration, quality-of-service controls, and centralized protection policies. Active Insight gains a Mass Deployment feature for provisioning systems across distributed locations. At the same time, an updated Log Center adds expanded observability and auditing capabilities aimed at security and compliance use cases.

Surveillance Portfolio Adds Access Control and AI Analytics

Synology expanded its surveillance ecosystem with new access control hardware, cameras, analytics platforms, and cloud monitoring services.

New products include the AC100 door controller, AR Series access readers, and DC Series dome cameras. The company also introduced updated Deep Video Analytics (DVA) appliances that leverage AI for semantic event search and re-identification path tracking, enabling faster investigation and analysis of surveillance footage.

The surveillance portfolio is further complemented by Surveillance365, a video surveillance-as-a-service (VSaaS) platform that integrates with on-premises Surveillance Station deployments. The hybrid architecture provides centralized monitoring and management across remote and multi-site environments.

Synology Office Adds Collaboration Tools

Synology expanded its Office Suite with the introduction of ChatPlus and Meet, adding enterprise collaboration and communications capabilities to the platform.

Both applications include administrative controls and permission management features designed for business environments. AI-powered transcription and translation capabilities are also integrated, while keeping data processing and storage under organizational control through on-premises deployment.

Bee Series Gains New Private Cloud Capabilities

For consumers and small office users, Synology expanded its Bee Series private cloud ecosystem with new BeeStation and BeeStation Plus offerings.

The company also introduced BeeCamera, which enables home monitoring through BeeStation Plus when paired with Synology cameras. In addition, Synology Deep Search adds AI-powered local search capabilities across content stored on Windows and macOS systems, allowing users to locate files and information while keeping data private and under local control.

The announcements collectively reflect Synology’s broader strategy of integrating AI capabilities across its product portfolio while maintaining an emphasis on on-premises deployment, data ownership, and centralized management.

Availability

ActiveProtect Manager 1.2 is available now as an update for existing ActiveProtect deployments, and Synology ActiveProtect appliances remain available globally through the company’s distributor and channel partner network. ActiveProtect Manager 2.0 and the new DP5200 appliance were showcased at COMPUTEX 2026 and are not yet generally available; Synology has positioned them as forthcoming additions to the ActiveProtect lineup rather than shipping products at announcement.

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Broadcom has released its Private Cloud Outlook 2026 report, highlighting a significant shift in enterprise AI deployment strategies. According to the survey, organizations are increasingly moving production AI workloads to private cloud environments as concerns about cost, governance, security, and data sovereignty reshape infrastructure planning.

The report suggests the enterprise AI market has moved beyond experimentation, with production deployments driving infrastructure decisions. While the public cloud remains important for AI development and testing, many organizations now prefer the private cloud for large-scale AI inference workloads.

A key finding shows that 56% of enterprises are either running or planning to run production AI inference on private cloud infrastructure. By comparison, only 41% expect to run those workloads in public cloud environments, down from 56% a year earlier. The 15-point decline is among the most significant year-over-year shifts identified in the study.

Broadcom notes that enterprises increasingly associate production AI with higher infrastructure costs, stricter governance requirements, and greater operational complexity. Survey respondents identified data protection and privacy (37%) and security and control (36%) as the top new demands AI is placing on IT organizations.

Cost Pressures Drive Reassessment of Public Cloud

The report also points to growing dissatisfaction with public cloud economics. For the first time since Broadcom began tracking the data, cost has overtaken security as the leading concern associated with public cloud adoption. Thirty-one percent of respondents cited cost as their primary concern, up from 26% in the previous year’s survey.

Nearly all surveyed IT leaders (97%) reported that some portion of their public cloud spending is wasted. More notably, 52% estimated that more than one-quarter of their public cloud budget fails to deliver expected value.

These concerns are driving workload repatriation initiatives. Eighty-three percent of enterprises are evaluating moving workloads from public cloud environments back to private cloud infrastructure, while half of respondents have already completed some level of repatriation.

Security and compliance remain the primary drivers of repatriation efforts, cited by 51% of respondents. Cost predictability and performance followed closely at 39% each, underscoring the growing importance of predictable operating expenses as AI deployments scale.

The report also found that private cloud investment plans continue to outpace public cloud spending. Over a three-year outlook, private cloud spending intent increased by 21 percentage points, compared with a 10-point increase for public cloud spending intent. Additionally, 58% of respondents identified building new workloads on private cloud as a strategic priority, up from 53% a year ago.

Data Sovereignty Becomes a Strategic Priority

Beyond economics, the survey found that geopolitical considerations are increasingly shaping enterprise infrastructure decisions.

Four out of five IT leaders reported that geopolitical developments are shaping IT strategy and operations. For the first time, data sovereignty and residency requirements emerged as the top geopolitical concern, cited by 54% of respondents, surpassing jurisdiction-specific compliance requirements, cited by 51%.

The trend is especially pronounced in highly regulated industries, including financial services, healthcare, life sciences, and the public sector. Organizations in these sectors face mounting pressure to maintain control over sensitive data while complying with increasingly complex cross-border governance requirements.

Broadcom argues that the convergence of AI-driven data growth, regulatory pressures, and cloud cost concerns is strengthening the business case for private cloud infrastructure, enabling organizations to maintain greater control over data location and governance.

Prashanth Shenoy, vice president of marketing for Broadcom’s VMware Cloud Foundation Division, tied the shift directly to the move from pilots to production. “As enterprises move from pilots to running AI at production scale, infrastructure and operational costs spike, security gaps surface, and complexity compounds,” he said. “The research is clear: enterprises increasingly prefer private cloud for production AI.”

Survey Details

The Private Cloud Outlook 2026 report is based on research conducted by Radius Tech in partnership with Broadcom. The survey was fielded between February and March 2026 and included 1,800 senior IT decision-makers from enterprise organizations with at least 1,000 employees across eight countries in North America, Europe, and Asia-Pacific. The report was published in June 2026.

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At Cisco Live, Cisco introduced Cisco Cloud Control, a unified management platform intended to bring networking, security, compute, observability, and collaboration into a single operational plane. The company positions the platform as the foundation of its broader AgenticOps model, in which human operators and AI agents work from the same data, telemetry, and policy context while maintaining human oversight.

Cloud Control is designed around a single-login experience that gives IT teams a consolidated view of Cisco infrastructure and services. Rather than splitting operations across separate consoles, the platform provides a unified system for monitoring, management, and response. Cisco said customers will also be able to build their own apps and AI agents inside the platform using natural-language workflows. The surrounding ecosystem includes integrations with AWS, Microsoft, Google Cloud, ServiceNow, PagerDuty, Slack, Linear, and Wiz.

Cisco President and Chief Product Officer Jeetu Patel framed the launch around the operational impact of AI agents on enterprise infrastructure, describing Cloud Control as “a command center for agentic AI: a platform where your team and your AI agents work together, in the same environment, with the same information, and with humans in control.” The company’s view is that continuously operating agents will change how environments are scaled, monitored, and defended, but only if those agents operate within a unified and governed platform. Cloud Control is intended to serve as that shared command environment for both human teams and automated agents.

One Control Plane Across Domains

At the platform level, Cloud Control is meant to unify cross-domain telemetry from networking, security, observability, and collaboration systems. Cisco’s argument is straightforward: if operators and agents are acting on the same telemetry and context, they can respond more quickly and with less fragmentation across teams and tools. That cross-domain visibility is positioned as especially important for uptime, AI agent behavior, and cost control tied to token usage.

Cisco also said Cloud Control uses a mix of purpose-built and frontier AI models, including its Deep Network Model, which the company says is grounded in 40 years of Cisco operational networking data. The emphasis here is on model specialization rather than relying on a single large general-purpose model, with Cisco describing the result as system intelligence that scales with the complexity of the problem rather than the size of the model alone. In practice, Cisco is presenting this as a way to improve problem-solving across infrastructure operations without sacrificing domain-specific accuracy.

A key part of the announcement is Cisco’s plan to introduce trusted agents within Cloud Control. These agents are expected to move through a structured workflow: detect an issue, determine the likely cause, recommend or apply remediation, test the proposed change, and verify that service quality has recovered. Cisco said these capabilities will draw on telemetry, digital twin technology, expanded experience metrics, deep reasoning, and agentic workflows. The larger goal is closed-loop automation that remains visible and governed rather than opaque.

AI Canvas and Studio Extend Customization

Cisco also introduced Cisco AI Canvas, described as a collaborative workspace where operators and agents can investigate and resolve issues together using shared live context. The feature is built to preserve operational history across handoffs, shifts, and escalations so teams do not have to reconstruct the same troubleshooting trail multiple times.

On the customization side, Cloud Control Studio adds two development environments: Agent Builder and App Builder. Agent Builder is intended for customers who want to create agents aligned to internal workflows, policies, and toolchains. Cisco said it supports connections to more than 50 third-party platforms through native connectors or the Model Context Protocol. App Builder is designed to let customers build applications and workflows from natural-language prompts, with OpenAI Codex integrated into the experience. Cisco said customer-built apps and agents, along with ecosystem-developed extensions, can be published through a Cloud Control Marketplace.

Cisco said Cisco Cloud Control is entering Controlled Availability in the United States as of June 2, with global availability to follow.

Security Posture Shifts Toward Runtime Protection

Security was a second major pillar of the announcement. Cisco’s premise is that the time between vulnerability disclosure and exploitation has compressed to the point where traditional reactive defenses are no longer sufficient. The company said it is a charter member of Anthropic’s Project Glasswing and OpenAI’s Daybreak, using the latest frontier AI models to stress-test its own products and uncover weaknesses before adversaries can. It also highlighted the open-sourced Foundry Security Spec as a way to make that evaluation rigor more broadly available.

At the infrastructure layer, Cisco is expanding Live Protect, which it describes as a runtime protection capability that shields supported platforms from newly discovered vulnerabilities without requiring reboots, software upgrades, or maintenance windows. Live Protect is now available on Cisco Nexus 9000 series switches and included with Nexus One entitlement. Cisco said it plans to extend the technology to campus and branch smart switches next, followed by secure routers later this year.

The company also highlighted the Hybrid Mesh Firewall, which extends policy enforcement across networks and applications, as well as Cisco and third-party firewalls. The goal is to reduce blast radius and maintain consistent protection as infrastructure becomes more distributed and hybrid.

More Controls for AI Agents

Cisco used the event to further expand on the security framework around enterprise AI agents. Building on prior announcements at RSAC, the company said it is adding new capabilities spanning AI Defense, Zero Trust for agents, and an Agentic SOC model. The broader message is that organizations will need to secure both sides of the AI equation: protecting agents from external manipulation and constraining what agents themselves can do inside production environments.

Quantum-safe Roadmap Becomes More Explicit

Cisco outlined a more concrete quantum-safe infrastructure roadmap. The company said it plans to enable quantum-safe communications capabilities across most of its core portfolio by December 2026, extending post-quantum protections to platforms carrying sensitive enterprise traffic.

Starting immediately, Cisco said any newly introduced campus, branch, and data center routers, switches, and firewall series will ship with quantum-safe secure boot enabled by default. This extends earlier work already in production on campus smart switches.

Cisco is also adding two assessment and planning tools. Quantum Ready Assessments, available through Cisco IQ, are designed to identify assets most exposed to harvest-now-decrypt-later risk and help organizations prioritize remediation. Global availability is planned for July 2026. Cisco also introduced a Quantum Resilience Framework, which organizes enterprise preparation into two broad areas: quantum-safe communications and quantum-safe products.

Cisco Services and IQ Add Resilience Focus

To support the operational model behind these announcements, Cisco is also rolling out Resilient Infrastructure Services through Support and Professional Services. The services framework includes three stages: exposure assessment, infrastructure modernization, and defense resiliency. Cisco is positioning this as a structured path for customers who need to evaluate the risks posed by AI-era threats and modernize their infrastructure in parallel.

Cisco IQ, now integrated with Cloud Control, acts as the company’s AI-driven delivery interface for support and professional services. Cisco said it is intended to help customers build a longer-term resilience plan using AI-driven insights and Zero Trust principles. For organizations with sovereignty and residency requirements, Cisco will also offer on-premises deployment options for Cisco IQ.

Another addition is Peer Benchmarking in Cisco IQ, which uses anonymized data to show how an organization compares with similar environments in areas such as last-day-of-support exposure and vulnerability rates. Cisco said that the feature is planned for global availability in July 2026.

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Intel announced a set of data center updates at Computex 2026 in Taipei spanning compute, networking, and its AI accelerator roadmap. The headline item is the introduction of Intel Xeon 6+ processors, paired with an expanded 800 Series Ethernet lineup based on Intel Ethernet E835 controllers and adapters. Intel also provided an update on its next data center GPU, code-named Crescent Island. Collectively, the announcements reflect Intel’s positioning around agentic AI, in which the CPU increasingly serves as the control plane for orchestration, concurrency, and data movement across clusters.

Intel data center leadership framed AI scaling as a coordinated systems problem rather than a component upgrade cycle. As AI agents become more autonomous and workloads become more distributed, Intel is emphasizing tight coupling between CPU, memory, and networking to reduce bottlenecks and improve efficiency under real power and rack constraints.

Xeon 6+ Extends the Xeon 6 Family with Density, Efficiency, and Scale-Out Focus

Xeon 6+ processors extend the Xeon 6 family, emphasizing performance density, power efficiency, and operational scale for cloud-native, network-intensive, and agentic AI-driven workloads. Intel said Xeon 6+ is built on Intel 18A, marking the company’s first use of that process node in a data center CPU. The platform’s focus is sustained throughput in environments where watts per rack, throughput per core, and predictable latency are primary constraints.

Intel’s stated feature set targets scale-out infrastructure that needs to add new AI-adjacent services without requiring a disruptive data center redesign. Configuration highlights include up to 288 efficient cores, which Intel says deliver up to 2.5 times more performance than the previous generation and up to 45 percent better per-thread performance per watt versus the competition, along with 12-channel DDR5 and 96 lanes of PCIe Gen5 with CXL support to move data across heterogeneous infrastructure. Intel also claims up to 9:1 server consolidation versus 2nd Gen Xeon systems. In addition, Intel is introducing Intel Application Energy Telemetry on Xeon 6+, providing real-time, workload-level telemetry of CPU energy and activity to improve visibility into consumption and utilization.

On security, Intel highlighted silicon-level protections, including Intel SGX and Intel TDX, aimed at confidential computing and multi-tenant deployments. Intel also said Xeon 6+ platforms are already being tested in telecom network infrastructures and configured into data center systems across the ecosystem, with servers, networking, and integrated solutions from ASUS, Dell Technologies, Ericsson, GIGABYTE, HPE, Lenovo, Supermicro, and others developing on Xeon 6+ today.

Intel Ethernet E835 Targets Power-Efficient 10GbE to 200GbE for AI and Virtualized Data Centers

Intel expanded its 800 Series Ethernet portfolio with Intel Ethernet E835 controllers and network adapters, positioning networking as a key limiter for modern AI, cloud, and distributed workloads. The E835 line targets dense, virtualized deployments where bandwidth and latency consistency must be delivered within tight power envelopes.

Intel said the E835 family supports up to 200GbE and a range of port configurations, including 2x25GbE, 4x25GbE, 2x100GbE, and 1x200GbE, with additional configurations enabled through Intel’s Ethernet Port Configuration Tool. For efficiency, Intel said the E835-CQDA2 adapter delivers up to 1.9 times higher performance per watt than the comparable NVIDIA ConnectX-6 Dx and 1.4 times higher than the Broadcom BCM957508-P2100G, positioning the product line as reducing energy consumption without sacrificing throughput.

On offloads and data path efficiency, E835 supports RDMA via RoCEv2 and iWARP, along with Dynamic Device Personalization to streamline packet processing. For security and manageability, Intel highlighted a hardware root of trust, signed SPDM, DMTF-based manageability, and OS support for Linux, VMware ESXi, and Windows. Intel also called out a 10+ year lifecycle target for long-term fleet standardization. Recommended pricing varies by configuration and is listed at intel.com/ethernet.

Xeon 6300 Adds a 12-Core Entry Option for SMB Servers

Intel also announced general availability of a new 12-core option in the Xeon 6300 family for entry servers, extending the platform beyond 8 cores. The key message is a drop-in upgrade path for existing entry-level server designs, allowing SMB environments to increase compute capacity without a platform change, and with availability through major OEMs.

Crescent Island: Intel Updates Next Data Center GPU for Inference and Token-Heavy Workloads

Intel provided an update on Crescent Island, its next-generation data center GPU built on the Xe 3P architecture. Intel is positioning the part around memory capacity, bandwidth, and efficiency as differentiators for agentic AI inference, particularly as token-intensive workloads grow.

According to Intel, Crescent Island pairs LPDDR5X with up to 480GB of capacity. It targets a 350W air-cooled PCIe form factor for scale-out deployments where rack-level thermals and power delivery are limiting factors. Intel also highlighted broad data-type support, ranging from native FP4 and MXFP4 to FP64, as well as expanded support for AI operations and scalability features. On software, Intel reiterated its commitment to an open, programmable stack designed to reduce friction in heterogeneous environments, with Arc Pro cited as a development platform aligned with the same Xe foundation for forward and backward compatibility.

Related: Supermicro Launches 12 Xeon 6+ Optimized Platforms, Expands X14 and DCBBS Offerings

In related news, Super Micro Computer announced 12 new server platforms optimized for Intel Xeon 6+ processors, spanning its Hyper, SuperBlade, FlexTwin, and GrandTwin families. Supermicro is emphasizing high core density and performance-per-watt for high-density cloud, virtualization, 5G analytics, and other throughput-intensive workloads. The new systems expand the company’s X14 lineup, with Supermicro positioning its DCBBS platform, a modular infrastructure approach built from validated components and subsystems, as the integration layer across the portfolio.

Supermicro’s updated portfolio spans multiple server families tuned for different densities, cooling, and deployment models, giving operators a clearer path to standardize on a common platform while still matching hardware to workload requirements. The Hyper series targets mainstream rack deployments with single- and dual-socket 1U and 2U systems that prioritize configurability. In practice, that means flexible CPU options, high-memory configurations for virtualization and data services, and the ability to pair the platform with advanced networking where east-west traffic and storage connectivity become performance limiters.

For environments where rack efficiency is the primary constraint, Supermicro’s SuperBlade and multi-node designs push density higher while maintaining serviceability. SuperBlade packages up to 10 compute nodes into a compact 6U chassis, using shared infrastructure to improve utilization at scale, a fit for large fleet deployments that benefit from simplified power and management domains. FlexTwin and GrandTwin take a multi-node approach in rack form factors, with FlexTwin emphasizing liquid-cooled, dual-socket nodes that operate independently while sharing power and cooling resources, and GrandTwin focusing on single-socket density optimized for high-core-count, E-core-heavy workloads where throughput per rack and thermal efficiency matter.

Supermicro said the new X14 platforms can scale up to 576 efficient cores per server in dual-socket configurations, improving deployment efficiency and energy consumption for large-scale data centers.

At the rack and data center level, Supermicro positions DCBBS as the integration layer that ties these systems together into a modular AI infrastructure. Built from validated components and subsystems, DCBBS is designed to reduce deployment friction by offering a repeatable building-block approach that scales from individual servers and networking to full rack-scale solutions, with supporting software and services for operators building out larger cloud and AI footprints.

Supermicro also cited architectural gains it attributes to Xeon 6+ systems, including double the core count, up to 17 percent higher IPC, five times more last-level cache, and 25 percent faster memory support compared to previous generations.

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NetApp and Cisco have introduced an expanded set of FlexPod-validated solutions to simplify the deployment of secure, scalable AI infrastructure. The announcement builds on the long-standing FlexPod partnership, positioning the platform as a pre-validated foundation for organizations looking to address the performance, data management, and security demands of modern AI workloads.

The companies are targeting enterprises that need predictable infrastructure outcomes without the integration overhead typically associated with AI environments. FlexPod continues to serve as a converged architecture that combines compute, networking, and storage, now extended with capabilities aligned with AI training and inference pipelines.

NetApp highlighted that AI workloads are placing increasing demands on data infrastructure as IT teams are tasked with delivering reliable, consistent performance across environments. Dallas Olson, Chief Commercial Officer at NetApp, noted that the FlexPod partnership has already saved customers up to 20% of their time in infrastructure management and maintenance, and that the companies are now applying their combined expertise to accelerate AI adoption while reducing risk with built-in security. Cisco reinforced the need to build security into AI infrastructure from the start, with Jeremy Foster, GM and SVP at Cisco, pointing to AI-specific risks such as data exposure, governance gaps, and compliance challenges.

Validated Architectures for AI Workloads

The updated FlexPod solutions are delivered as pre-tested reference architectures designed to support organizations at different stages of AI adoption. These configurations integrate NetApp data services, Cisco networking, and NVIDIA AI technologies to provide a consistent and scalable foundation.

For enterprise AI deployments, the architecture supports use cases such as retrieval-augmented generation and semantic search. The design reduces integration complexity by allowing AI capabilities to run directly where the data resides, with built-in, end-to-end security. NetApp AFX, the company’s disaggregated all-flash storage system, allows independent scaling of performance and capacity, which is critical for AI pipelines with uneven resource demands.

The solution also incorporates NetApp’s AI Data Engine, which is being developed to address data discovery, preparation, and governance challenges. This integrates with the NVIDIA AI Data Platform reference design, providing a structured approach to managing large enterprise datasets for AI use. Security is implemented across the stack through Zero Trust-aligned controls, with Cisco Secure AI Factory with NVIDIA providing policy-driven protections throughout the AI lifecycle.

On the networking side, Cisco AI networking infrastructure with Nexus One transforms the network into a deterministic, high-performance fabric that maximizes XPU utilization, reduces job completion times, and delivers predictable AI outcomes at scale. NetApp and Cisco also collaborated with NVIDIA to build FlexPod solutions based on NVIDIA Enterprise Reference Architectures, enabling organizations to design, deploy, and scale high-performance AI factories using a validated full-stack approach.

Simplified AI Inferencing and Departmental Adoption

In addition to full-scale deployments, NetApp and Cisco are targeting smaller teams and departmental use cases with pre-integrated solutions for AI inferencing and RAG workflows. These configurations are designed to reduce both cost and operational complexity, enabling organizations to leverage existing datasets without requiring specialized AI infrastructure expertise.

By standardizing deployment models, the companies aim to lower barriers to entry for AI adoption while maintaining enterprise-grade data management and security controls.

Extending AI to the Edge

The FlexPod expansion also includes validated architectures for edge environments, where AI inference and data processing must occur close to data sources. These solutions combine Cisco Unified Edge platforms with NetApp storage to support containerized and virtualized workloads in distributed locations.

The approach emphasizes centralized management and automation, allowing IT teams to deploy and operate AI infrastructure consistently across multiple sites. Policy-based configuration and orchestration enable repeatable deployments, reducing the operational overhead associated with managing isolated edge stacks.

Data Foundation and Ecosystem Integration

NVIDIA’s involvement centers on aligning data infrastructure with AI processing requirements. The companies are integrating NetApp’s data management capabilities with NVIDIA’s AI Data Platform to provide a unified, AI-ready data foundation. This includes support for data preparation, governance, and secure access, which remain key challenges for enterprises scaling AI initiatives.

The combined solution is validated within the Cisco Secure AI Factory framework, enabling organizations to deploy scalable AI environments on FlexPod with integrated security and data services. The goal is to provide a consistent architecture that supports both current AI workloads and future expansion without requiring significant redesign.

Expanded Cyber Resilience Collaboration with Splunk SOAR Integration

At the same time, NetApp and Cisco announced an expansion of their collaboration focused on cyber resilience and operational visibility. The expanded collaboration introduces deeper integration between NetApp storage and Splunk analytics and orchestration, strengthening defense-in-depth strategies at the data layer.

The companies are positioning intelligent data infrastructure as a core component of enterprise security, particularly as AI-driven threats increase in speed and sophistication. By combining observability, automation, and storage-level controls, the joint solution aims to reduce response times and limit the impact of cyber incidents.

Storage-Level Security Automation with Splunk SOAR

A key component of the announcement is the introduction of a NetApp Splunk Security Orchestration, Automation, and Response playbook. This integration extends Splunk’s existing visibility into NetApp environments by enabling automated response actions directly on ONTAP storage systems.

Splunk Enterprise Security is already integrated with NetApp Ransomware Resilience to collect analytics from the data layer, enhancing incident triage and prioritization. The new SOAR playbook builds on that foundation by allowing security teams to operationalize those insights. Automated actions can now be triggered by signals from NetApp Ransomware Resilience and other solutions in the environment, including blocking a suspicious user, taking snapshots of data, and taking data volumes offline to protect against further infection.

This approach shifts part of the incident response process closer to the data itself, reducing reliance on manual intervention and enabling faster containment. NetApp emphasized that integrating storage systems into security workflows helps reduce the blast radius of ransomware attacks, improves recovery times, and lowers overall remediation costs.

Enhancing Defense-in-Depth Strategies

NetApp and Cisco are aligning this integration with broader defense-in-depth strategies by connecting storage infrastructure into the security operations ecosystem. The solution combines NetApp’s data management and ransomware resilience features with Cisco’s secure AI infrastructure and Splunk’s analytics and orchestration capabilities.

NetApp noted that the rapid evolution of AI-enabled cyberattacks requires faster and more automated responses. Extending SOAR workflows to ONTAP enables organizations to take direct action on enterprise data during incident response, rather than treating storage as a passive layer. This enables more effective containment and reduces the window of exposure during an attack.

Cisco highlighted the importance of end-to-end visibility across the entire technology stack, including the data layer. By integrating NetApp storage into Splunk SOAR workflows, companies enable coordinated responses spanning networking, compute, and storage. This integration is intended to improve collaboration between security and storage teams while increasing confidence in automated response actions.

Operational Impact and Enterprise Readiness

The automation introduced by the NetApp Splunk SOAR playbook is expected to improve key security metrics, such as mean time to contain incidents, while reducing the manual effort and skills required to protect data. By embedding response capabilities directly into storage systems, organizations can respond more quickly without requiring specialized storage intervention during an incident. The playbook is available now for download from SplunkBase.

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Nutanix announced that its Nutanix Unified Storage (NUS) solution is now NVIDIA-Certified at the enterprise level, validating the platform for enterprise and cloud provider deployments running large-scale production AI workloads. The certification provides a validated configuration intended to reduce integration risk and help customers scale AI infrastructure with more predictable storage behavior.

The company also disclosed plans to extend its AI-native storage roadmap with support for NVIDIA Vera BlueField-4 STX. Nutanix framed this as an effort to improve data access and storage efficiency while simplifying operations as AI environments grow.

Addressing Storage as a GPU Utilization Constraint

As organizations build out “AI factory”-style infrastructure, Nutanix is targeting a recurring problem in production environments: GPU capacity is often limited by the ability to consistently feed data to accelerators. Fragmented infrastructure, siloed datasets, and inconsistent I/O can introduce bottlenecks that slow deployments and reduce effective GPU utilization. Nutanix is positioning NUS, along with its NVIDIA certification, to deliver a more consistent data path and reduce deployment variability across the stack.

Thomas Cornely, EVP of Product Management at Nutanix, emphasized that the goal is to remove infrastructure fragmentation and data silos so AI pipelines can sustain reliable throughput at scale. Jason Hardy, VP of Storage Technology at NVIDIA, similarly highlighted storage as a gating factor for enterprise AI, noting that certification provides customers with a more interoperable platform, reducing bottlenecks and improving GPU efficiency. Both sets of comments centered on interoperability, predictable scaling, and validated configurations rather than point performance claims.

Reference Architecture

Nutanix described the NVIDIA-Certified NUS reference architecture as being built on a 10-node all-NVMe cluster. On the protocol side, it uses enhanced parallel NFS (pNFS) and GPUDirect Storage over NFS with RDMA. The objective is a low-latency, high-throughput data path between GPU hosts and storage, designed to maintain resilience and minimize downtime as environments scale.

For the network fabric, Nutanix stated the design uses NVIDIA Spectrum-X Ethernet, including Spectrum-4 switches and BlueField-3 DPUs. Nutanix also provided scaling figures, claiming linear performance growth from 10GB/s read and 5GB/s write at 32 GPUs up to 160GB/s read and 80GB/s write at 1,024 GPUs.

Workload Coverage and Supported GPU Platforms

Nutanix positioned the architecture as a foundation for a range of AI workflows, including training, fine-tuning, inference, and RAG pipelines. The company also described broad compute compatibility, citing support for x86-based systems and multiple NVIDIA GPU configurations, including NVIDIA RTX 6000 PRO Blackwell, NVIDIA H200 NVL, NVIDIA HGX platforms with B200, H200, or H100 GPUs, and NVIDIA GH200 Grace Hopper Superchip configurations.

Availability

Nutanix said the NVIDIA-Certified Nutanix Unified Storage reference architecture is available now. Planned support for NVIDIA BlueField-4 STX is expected in the second half of 2026.

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ZutaCore has secured $100 million in Series C funding, with participation from Mitsubishi Electric, Carrier Ventures, Samsung Electronics, and others through its corporate venture arm, Samsung Ventures. The investment is aimed at accelerating global commercialization, expanding deployments, and advancing research and development as demand for high-density AI and HPC infrastructure continues to rise.

The funding comes as data center operators face increasing thermal challenges driven by next-generation processors that are pushing well beyond traditional power envelopes. Liquid cooling adoption has accelerated across hyperscale and enterprise environments, a trend StorageReview has covered extensively as operators shift from air to direct liquid cooling to manage higher rack densities and improve efficiency.

Two-Phase Cooling Targets Next-Gen Power Levels

ZutaCore’s platform focuses on waterless two-phase direct-to-chip cooling, designed to support processors exceeding 4,000W. This approach uses phase-change heat transfer at the chip level to remove heat more efficiently than traditional air or single-phase liquid cooling.

The company is positioning its technology to integrate alongside existing air and single-phase liquid systems, enabling incremental deployment within current data center designs. This hybrid compatibility is increasingly important as operators adopt liquid cooling in stages rather than full facility retrofits.

ZutaCore reports more than 75 deployments across the Americas, Europe, and Asia, reflecting growing production adoption of two-phase cooling in AI and HPC environments.

Investment Supports Scale and Product Development

The Series C funding will be used to expand global operations and address increasing customer demand. It will also support ongoing R&D focused on in-package thermal management and system-level integration for megawatt-scale deployments.

As AI clusters scale into multi-megawatt configurations, cooling infrastructure must evolve to maintain performance and reliability. ZutaCore is targeting these requirements with thermal management designs that extend from the chip package itself to full megawatt-class system deployments.

The company also highlighted continued collaboration with ecosystem partners to align cooling solutions with emerging chip roadmaps and accelerate deployment timelines.

Validation at Megawatt Scale

To support scaling efforts, ZutaCore has expanded its executive team with four key hires: Yaniv Reinhold as Chief Financial Officer, Sharon Shafran as Chief Operating Officer, Yoni Nir as Chief Research and Development Officer, and Sarah Warshavsky Oberman as Chief People Officer. The additions bring experience in global finance, semiconductor technologies, and large-scale system deployment, aligning with the company’s focus on hyperscalers, neoclouds, and demanding enterprise compute environments.

This type of pre-deployment validation is becoming more critical as liquid-cooled AI infrastructure increases in complexity and cost.

Expanding Product Portfolio

ZutaCore continues to extend its product portfolio, including the OmniTherm cold plate designed for NVIDIA RTX PRO 6000 Blackwell Server Edition GPUs. The design enables waterless two-phase cooling within a single-slot PCIe form factor, supporting full-power operation in standard enterprise and AI server configurations.

This reflects a broader industry shift toward component-level liquid cooling solutions that can be deployed in conventional server architectures while still delivering the thermal performance required for modern accelerators.

Leadership Expansion to Support Growth

To support scaling efforts, ZutaCore has expanded its executive team with hires across finance, operations, R&D, and human resources. The additions bring experience in global operations, semiconductor technologies, and large-scale system deployment, aligning with the company’s focus on expanding into hyperscale, neocloud, and enterprise markets.

Industry Context

The funding round underscores growing momentum behind liquid cooling technologies as AI workloads reshape data center design. StorageReview has observed increasing adoption of both single-phase and two-phase cooling approaches, with vendors aligning solutions to support higher power densities, warm-water operation, and improved energy efficiency.

ZutaCore’s latest funding and deployment activity reflect the next phase of this transition, where cooling is no longer a supporting function but a primary enabler of compute scalability.

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CoolIT Systems has announced the development of what it describes as the first 15kW direct liquid cooling (DLC) coldplate design, positioning single-phase liquid cooling as a viable path for AI infrastructure well beyond 2030. The company reports that the design delivers nearly four times the performance of earlier single-phase coldplates, indicating that the architecture can scale alongside increasing GPU and accelerator power levels.

Single-phase DLC is already widely deployed across AI data centers, particularly in hyperscale environments. This latest development focuses on extending that model to support significantly higher thermal design power targets without requiring a transition to more complex cooling approaches.

Scaling Thermal Capacity for AI Accelerators

The 15kW coldplate represents a substantial increase in cooling capacity compared to prior designs. CoolIT states that the new coldplate delivers nearly 4x the capacity of the 4kW design it announced in March 2025 and more than 10x the cooling required for current-generation AI GPUs.

This level of thermal headroom is increasingly relevant as next-generation AI accelerators push beyond traditional power envelopes. Higher per-device wattage combined with dense system packaging is driving the need for more efficient heat removal at the component level.

Microchannel Design and Warm-Water Operation

The coldplate is based on CoolIT’s Split-Flow microchannel architecture, designed to optimize heat transfer across high-power silicon. Validation was performed using a standard water-glycol coolant at a flow rate of 1.2 L/min/kW.

The system is designed to operate in 45°C warm-water environments, aligning with broader industry trends toward higher coolant temperatures to improve overall data center efficiency. Warm-water cooling reduces reliance on mechanical chillers and enables more efficient heat reuse strategies.

Alignment with Industry Direction

The announcement reflects broader momentum behind single-phase DLC as a standard approach for AI infrastructure. NVIDIA has indicated support for single-phase liquid cooling operating at elevated supply temperatures in its platform roadmap, reinforcing the relevance of warm-water-compatible coldplate designs.

By demonstrating performance at 15kW, CoolIT is positioning single-phase DLC as capable of supporting both current deployments and future accelerator generations without requiring architectural changes to cooling systems.

Expanding Cooling Beyond the GPU

In parallel with the coldplate development, CoolIT is working to extend liquid-cooling coverage to additional server components. This includes targeting peripheral devices and addressing localized hot spots within advanced AI processors.

These efforts aim to increase total heat capture at the system level and improve thermal consistency across increasingly complex AI server designs. As power densities rise, comprehensive cooling strategies that go beyond the primary compute die are becoming necessary to maintain performance and reliability.

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