As cyber threats grow more sophisticated and perimeter defenses become obsolete, organizations are embracing Zero Trust security—a model that assumes no user or device can be inherently trusted. The CISA Zero Trust Maturity Model Whitepaper by Zscal...

Voyager Technologies and Infleqtion have formed a strategic partnership aimed at accelerating the development of neutral atom-based quantum systems in space, signaling a growing alignment between the aerospace and quantum computing sectors. The collaboration comes as Infleqtion prepares to enter the public markets through a merger with Churchill Capital Corp X, positioning the company to scale its quantum offerings for commercial and government customers. The partnership centers on demonstrating advanced quantum capabilities in low-Earth orbit, beginning with the deployment of Infleqtion’s Tiqker Quantum atomic clock on the International Space Station. The system is designed to serve as an ultra-stable, alternative timing source to support secure communications, precision navigation and autonomous spacecraft operations. After the ISS mission, the companies intend to transition their work to Starlab, the commercial space station set to replace the current platform later this decade. Voyager Technologies Chairman and CEO Dylan Taylor said the agreement represents a shift from laboratory experimentation to real-world deployment, describing the effort as the introduction of “a completely new class of dual-use capabilities” intended to bolster mission resilience in contested space environments. Infleqtion CEO Matthew Kinsella emphasized that quantum systems gain performance advantages in microgravity, where improved stability and reduced environmental noise can benefit timing, sensing and computing applications. Neutral Atom-Based Quantum Technologies Infleqtion has spent more than a decade developing neutral atom-based quantum technologies, including work on NASA’s Cold Atom Lab, which continues to operate aboard the ISS. Its collaboration with Voyager is intended to push quantum capabilities into operational use, supporting applications ranging from resilient spaceborne data networks to next-generation navigation architectures. For both companies, the effort marks a significant step toward establishing quantum hardware as a core layer of future orbital infrastructure. As global demand grows for more secure communications, autonomous satellite coordination and hardened space systems, the companies see quantum devices as a strategically important foundation. Their first on-orbit demonstrations are expected to help validate how quantum timing and sensing could support commercial and national security missions in the years ahead. Executive Insights FAQ: Neutral Atom-Based Quantum Technology How do neutral atom systems differ from other quantum approaches? They use individual atoms held in optical traps as qubits, offering long coherence times and highly scalable architectures. Why are neutral atom platforms suited for space? Microgravity reduces environmental interference, enabling improved stability in precision timing and sensing tasks. What advantages does quantum timing bring to spacecraft? It offers more accurate and tamper-resistant time signals, supporting secure communications and autonomous coordination. How could quantum sensors enhance orbital infrastructure? They provide extremely sensitive measurements of motion, fields and gravitational changes, improving navigation and situational awareness. What makes neutral atom systems attractive for dual-use missions? Their scalability and precision support both commercial services and defense needs, from resilient networks to high-assurance navigation.

Google Cloud CTO Will Grannis joins TechCrunch Disrupt 2025’s AI Stage to discuss how he is steering the company toward an agent-driven future. In this in-depth conversation, Grannis explains the fundamental building blocks behind cloud-based AI age...

Kristen Pressner has been appointed by Nokia as the Chief People Officer and a member of the Group Leadership Team, with effect from May 1, 2026. Pressner has worked in HR, talent management, and organizational transformation for more than 30 years ...

Cloud storage costs have risen quickly due to the growing use of cloud object storage from data-intensive and AI applications, such as big training datasets, model artifacts, and inference logs. For operations and cloud efficiency teams, effectively...

For more than two decades, memory performance has quietly determined the success or failure of enterprise servers and hosting infrastructure. As workloads evolve - from web hosting to AI, analytics, and high-performance computing - the type of RAM (Random Access Memory) you choose has become one of the most critical architectural decisions in IT planning. From legacy DDR3 modules to the new high-speed DDR5 generation, the right configuration can make or break a server’s ability to handle resource-intensive workloads, maintain uptime, and control operating costs. The leap in performance between each generation isn’t just incremental - it’s reshaping how data centers, hosting providers, and businesses approach scalability. The Evolution of Server Memory Memory technology has advanced steadily since DDR (Double Data Rate) first emerged in the early 2000s. Today, most servers use one of three generations: DDR3, DDR4, or DDR5. Each reflects a step-change in speed, density, and energy efficiency - factors that directly impact application performance and overall infrastructure ROI. * DDR3, launched in 2007, was once the mainstay of data centers. Operating between 800 and 2133 MHz, it offered solid reliability at a low cost, consuming less power than its predecessor, DDR2. Despite its age, DDR3 remains in use in older systems and budget-conscious environments. * DDR4, introduced in 2014, quickly became the industry standard. Its speeds range from 1600 to 3200 MHz, and it operates at a lower voltage of 1.2V, improving both bandwidth and energy efficiency. The balance between cost and performance has made DDR4 the go-to memory for most enterprise and hosting workloads. * DDR5, released in 2020, represents a true generational leap. Designed for the most demanding applications - AI, big data, and high-concurrency hosting - DDR5 starts at 4800 MHz and scales beyond 8400 MHz, doubling the bandwidth of DDR4 while drawing just 1.1V. This new memory class supports larger capacities per module, advanced power management, and on-die ECC (Error-Correcting Code), improving both performance and reliability. For businesses running compute-intensive operations or high-traffic sites, DDR5 is becoming the new benchmark for speed and efficiency. The Performance Equation: Why Memory Matters A server’s memory determines how fast it can process simultaneous requests, manage caching, and handle user concurrency. For hosting providers, milliseconds of latency can mean the difference between a seamless user experience and performance bottlenecks. * Bandwidth and Speed: DDR5’s ultra-high transfer rates allow servers to handle exponentially more data per cycle, reducing page load times and improving database responsiveness. Its increased bandwidth also supports parallel processing across multiple workloads, which is crucial in containerized or multi-tenant hosting environments. * Latency and Efficiency: Newer DDR generations offer lower latency - meaning data is retrieved and processed faster. For workloads such as scientific modeling, real-time analytics, and large-scale virtualization, this reduction in memory delay can yield substantial productivity gains. * Energy and Thermal Efficiency: Every DDR generation has lowered voltage requirements. DDR3 ran at 1.5V, DDR4 at 1.2V, and DDR5 drops that again to 1.1V. Lower voltage translates to reduced heat output and operational power costs—a vital consideration for energy-conscious data centers. * Capacity and Density: Memory density has also increased dramatically. DDR3 tops out at 16 GB per module, DDR4 supports up to 64 GB, and DDR5 reaches 128 GB. These larger modules allow for fewer servers to handle more workloads, supporting both performance scaling and cost consolidation. * Channel Architecture: DDR5 introduces a dual 32-bit channel per module, effectively doubling the communication lanes between CPU and memory. This design delivers higher throughput and enables faster task execution without additional latency penalties. Compatibility, Market Trends, and Adoption Upgrading memory isn’t as simple as swapping modules. Each DDR generation requires compatible motherboards and processors. For example, CPUs built for DDR3 cannot natively support DDR4 or DDR5 modules. Data center architects must verify compatibility between CPU, motherboard, and memory before implementing upgrades. In terms of market adoption, DDR3 remains available but is gradually being phased out. DDR4 continues to dominate due to its wide support and cost efficiency. However, DDR5 is quickly gaining ground, particularly in high-performance computing (HPC), AI workloads, and next-generation web hosting platforms. As production scales and costs decline, DDR5 is expected to become the default standard for enterprise deployments by the late 2020s. The Business Case for DDR5 * Efficiency and Consolidation - DDR5’s combination of bandwidth, density, and low power usage allows servers to handle more workloads with fewer physical machines. This reduces rack space, power draw, and cooling requirements - key benefits for data centers pursuing greener, more sustainable operations. * Reliability and Uptime - The integrated on-die ECC function in DDR5 helps automatically detect and correct memory errors before they impact applications. This is particularly critical in mission-critical environments such as financial systems, e-commerce platforms, and SaaS providers where uptime directly affects revenue. * Scalability for Modern Hosting - In shared or virtualized hosting models, memory performance directly influences how many clients can be served from a single server. DDR5’s higher bandwidth enables faster handling of concurrent workloads, improving resource allocation and end-user response times. * Future-Proofing Infrastructure - Investing in DDR5 positions businesses for the next decade of workloads. As AI, data analytics, and immersive applications become standard components of enterprise IT, DDR5 ensures systems can scale without hitting hardware limits. When DDR3 or DDR4 Still Makes Sense While DDR5 leads in performance, DDR4 remains the most practical choice for many small to medium enterprises (SMEs) and managed hosting providers. It offers strong performance at lower cost, with broad hardware support and proven reliability. DDR3, meanwhile, still serves a purpose in legacy systems or cost-controlled environments - such as test labs, archival servers, or internal tools where maximum performance is not a priority. However, given its aging architecture and limited future support, organizations using DDR3 should plan upgrades to DDR4 or DDR5 soon. Strategic Recommendations * For shared hosting or VPS providers: DDR4 remains a cost-effective balance of performance and stability * For dedicated and high-performance servers: DDR5 offers substantial long-term ROI through higher throughput, energy savings, and reliability * For legacy systems: DDR3 is viable for short-term use, but upgrade cycles should be planned before compatibility and supply constraints emerge Selecting the right RAM generation is not just a technical decision - it’s a strategic one that affects performance, scalability, and operational costs across your infrastructure lifecycle. FAQ: Managing Memory Performance in Server Environments How does DDR5 improve security and reliability in data centers?  DDR5 integrates on-die error correction (ECC) to detect and fix bit errors in real time. This reduces data corruption, improves uptime, and enhances overall system stability. What is the biggest challenge when upgrading from DDR4 to DDR5?  Compatibility. DDR5 requires newer motherboards and CPUs built to support its higher clock speeds and voltage specifications. Existing DDR4 systems cannot be upgraded directly. How does RAM speed impact hosting performance?  Faster RAM improves data transfer rates, allowing web applications and databases to respond more quickly under heavy traffic. This leads to faster page loads and better user experiences. Does DDR5 significantly reduce power consumption?  Yes. DDR5 operates at just 1.1 volts, making it more energy-efficient than DDR4 or DDR3. This lowers operating costs and supports sustainability goals. Which memory generation offers the best value today?  DDR4 remains the best balance between performance, price, and compatibility. However, for AI-driven workloads or enterprises planning future scalability, DDR5 is the clear long-term choice.

74% of IT leaders anticipate budget increases in 2026, according to statistics from the ‘2026 Data Infrastructure Survey Report’ released by DataStrike, a pioneer in data infrastructure managed services. More than half, however, claim they still lack the internal resources necessary to spur innovation or swiftly resolve problems. However, 60% of businesses now use MSPs to handle their data infrastructure, which is more than twice as many as DataStrike's ‘2025 Data Infrastructure Survey Report,’ highlighting an increasing reliance on outside knowledge as teams deal with technical debt and modernization. While cloud adoption has become commonplace, many organizations are struggling with what to do next and are focusing on optimizing current systems, bolstering data strategies, and preparing infrastructure for the growing influence of artificial intelligence, according to the second annual survey, which collected insights from nearly 280 IT leaders across industries. Rob Brown, President and COO of DataStrike, stated, "It's evident that IT teams have progressed past the debate over whether or not to embrace the cloud. They are currently concentrating on modernization, cost control, and developing a data strategy that fosters the upcoming wave of innovation. In order to simplify operations and lessen dependency on pricey proprietary systems, they are also reevaluating their resources, hiring MSPs to handle data infrastructure, and implementing open-source databases like PostgreSQL.” Updating Outdated Systems Internal database teams, however, continue to be modest. Despite handling workloads across several platforms, including Oracle, SQL Server, PostgreSQL, and cloud-native databases, only one-third of respondents have dedicated database administrators (DBAs), and over half of those organizations only have one or two DBAs. About 25% of them claim that in order to stay competitive, they want five or more DBAs, which is an expensive prospect considering that the average DBA compensation is more than $120,000 annually. In place of last year's worries about tool sprawl and sluggish technology adoption, respondents' top problems for 2026 were updating outdated systems (46%) and controlling technical debt (33%). The growing understanding that a solid data foundation is necessary to realize benefit from AI investments is highlighted by the fact that 61% of respondents said that creating a data strategy was their top priority. IT departments are depicted in the poll as striking a balance between change and stability. As businesses go from cloud migration to modernization, the best strategy for maintaining performance and fostering long-term growth is the combination of managed services, fractional assistance, and internal expertise. To fill skills shortages and cut expenses, 83% would look at alternative providers, and nearly three-quarters are interested in outsourcing database infrastructure administration.

At the Equinix Co-Innovation Facility (CIF) in Ashburn, Virginia, global manufacturing and technology solutions leader Flex has unveiled a next-generation rack-level liquid cooling system designed to address the surging power and thermal demands of AI and high-performance computing (HPC). The installation marks a major step in Flex’s collaboration with Equinix to develop and validate advanced cooling technologies for high-density workloads. The new system integrates JetCool’s direct liquid cooling (DLC) technology - a Flex subsidiary known for its precision-engineered microconvective cooling systems - with Open Compute Project (OCP) ORv3 rack architecture. Flex manufactures every component of the solution, including the rack, cooling, and power infrastructure, offering a fully vertically integrated system for data centers striving to achieve higher energy efficiency and scalability in the face of AI-driven heat loads. Cooling at the Rack Level: SmartPlate and SmartSense Innovations At the heart of the Equinix CIF deployment is JetCool’s SmartPlate System for Dell PowerEdge R760 servers, a self-contained, closed-loop DLC solution that requires no facility plumbing or external coolant distribution units (CDUs). This enables data centers to migrate from air to liquid cooling with minimal operational disruption. The SmartPlate System can cut total IT power usage by up to 15%, a key advantage for operators facing energy constraints. Complementing it are two Dell PowerEdge R660 systems equipped with JetCool’s DLC cold plates, designed to handle extremely high inlet coolant temperatures - up to 70°C. Operating at these warmer temperatures not only supports higher compute density but also reduces water usage by up to 90% and cooling energy consumption by 50% compared to traditional methods. The installation also includes Flex’s SmartSense 6U Liquid-to-Liquid (L2L) CDU, a rackmount unit capable of delivering 300kW of cooling for high-density workloads. Integrated manifolds, busbars, and quick disconnects consolidate all liquid cooling capabilities into a single ORv3 rack unit - directly connecting with Equinix’s thermal control system (TCS) for full facility integration. Industrial Partnership and Global Scalability “Flex’s end-to-end portfolio will help data center operators launch equipment more quickly as the need for AI and HPC workloads keeps growing,” said Rob Campbell, President of Flex’s Communications, Enterprise, and Cloud business. “We are proud to expand our collaboration with Equinix by bringing our high-performance, energy-efficient liquid cooling solutions into real-world deployment environments.” Flex’s integrated approach - spanning manufacturing, design, and lifecycle support - positions the company uniquely in the cooling market. As one of the world’s largest producers of server hardware, Flex manufactures millions of systems per year and offers global-scale production and supply chain resilience. Its comprehensive service model covers every layer of liquid cooling infrastructure - from quick disconnects and CDUs to cold plates and manifolds - with full maintenance and warranty support. Equinix’s Role in Testing Next-Gen Infrastructure According to Pawel Wlodarczak, Innovation Director at Equinix, the Co-Innovation Facility provides “a collaborative environment where emerging infrastructure technologies can be tested and refined for real-world, high-density compute environments.” He added that JetCool’s system “can cool up to 4kW per processor socket with additional headroom for future thermal loads,” underscoring its potential for scalability. The Ashburn deployment forms part of a global demonstration network, with additional Flex and JetCool showcases planned at Telehouse facilities, Flex’s Milpitas, California factory, Dell Customer Solution Centers, and multiple Asia-Pacific and European locations. Visitors to the Equinix CIF can view the system’s real-time thermal performance metrics and discuss deployment strategies with technical experts Executive Insights FAQ: Liquid Cooling in Data Centers What makes liquid cooling essential for AI and HPC workloads? AI and HPC generate extreme thermal densities that air-based systems cannot efficiently manage. Liquid cooling provides direct heat removal at the chip level, allowing sustained performance and higher rack densities. How does direct liquid cooling differ from immersion cooling? Direct liquid cooling (DLC) circulates coolant through cold plates attached to processors, whereas immersion cooling submerges entire servers in dielectric fluids. DLC offers modular deployment with less retrofitting required. Why are higher coolant inlet temperatures important? Running coolant at warmer temperatures - such as JetCool’s 70°C - reduces energy spent on chilling water, enabling free cooling and improving overall data center power efficiency (PUE). What is the role of a CDU in liquid cooling systems? A Coolant Distribution Unit (CDU) transfers heat between liquid loops, maintaining safe temperatures, pressure balance, and fluid flow while isolating IT equipment from facility water systems. How does liquid cooling advance sustainability goals?  Liquid systems drastically reduce both water and energy consumption, enable reuse of waste heat, and lower total carbon footprint - helping operators meet aggressive ESG and efficiency targets.

AI research company Anthropic has announced a sweeping $50 billion investment to build a new generation of AI data centers across the United States, beginning with large-scale facilities in New York and Texas. The project, developed in partnership with cloud infrastructure provider Fluidstack, marks one of the largest private infrastructure commitments in the emerging AI economy. The facilities are designed to align with the U.S. AI Action Plan, a federal initiative aimed at bolstering domestic innovation, securing AI supply chains, and maintaining U.S. leadership in advanced computing. The significant investment by Anthropic comes amid a surge of large-scale data center spending by global tech players, including OpenAI and SoftBank’s $500 billion Stargate Project and Meta’s $600 billion U.S. infrastructure program. Anthropic, known for its Claude AI assistant, said the new data centers are critical to meeting exploding demand for high-performance AI workloads and ensuring its models remain at the frontier of research and enterprise deployment. According to CEO and co-founder Dario Amodei, the initiative will also contribute to local economic growth, creating approximately 2,400 construction jobs and 800 permanent technical roles once operational. “These sites will create American jobs while helping us develop more capable AI systems that can accelerate breakthroughs in science, technology, and beyond,” said Dari Amodei. Rapid Gigawatt Power Deployment Anthropic selected Fluidstack as a partner for its ability to move with exceptional agility, enabling rapid delivery of gigawatts of power. “Fluidstack was built for this moment,” said Gary Wu, co-founder and CEO of Fluidstack. “We’re proud to partner with frontier AI leaders like Anthropic to accelerate and deploy the infrastructure necessary to realize their vision.” The first data centers are expected to go live in 2026, supporting both Anthropic’s research operations and its growing commercial user base - now exceeding 300,000 customers worldwide. Over the past year, Anthropic has seen its major enterprise accounts grow nearly sevenfold, reflecting the rapid adoption of AI-powered business tools. Executive Insights FAQ: About AI Data Centers Why are AI data centers different from traditional data centers? AI data centers are built for high-density GPU and accelerator workloads, requiring advanced cooling, power, and networking systems optimized for training and inference tasks. Why are companies investing so heavily in AI infrastructure now? The rapid scaling of generative AI models demands unprecedented compute power, prompting tech firms to secure long-term capacity and energy resources domestically. What challenges do AI data centers face in the U.S.? Key issues include power availability, environmental sustainability, and supply chain constraints for chips and cooling systems. How do AI data centers support national competitiveness? They enable faster AI innovation, reduce dependence on foreign infrastructure, and contribute to workforce development in advanced manufacturing and digital engineering. Will this investment trend continue? Yes. As AI becomes foundational to sectors like healthcare, energy, and finance, sustained infrastructure investment will be essential to meet global demand.

In this CNBC Squawk Box interview, AMD CEO Lisa Su discusses the company’s latest earnings, its bullish long-term growth projections, and the surging global demand for AI chips. Speaking after AMD’s first Analyst Day in three years, Su outlines the ...

ABB has deepened its collaboration with Applied Digital, a U.S.-based developer of high-performance data centers, to supply the power infrastructure for the company’s second large-scale AI Factory campus in North Dakota. The expanded partnership underscores the rapidly accelerating demand for electrical systems capable of supporting the extreme power densities associated with artificial intelligence (AI) and GPU-intensive workloads. The new order - booked in Q4 2025 - covers medium-voltage electrical architecture for Applied Digital’s Polaris Forge 2 development near Harwood, North Dakota. The 300-megawatt campus is being built in phases across two 150-MW buildings, scheduled to enter operation in 2026 and 2027, with additional expansion capacity planned. As the industry shifts toward AI factories - large, power-intensive facilities designed specifically for AI training clusters and high-performance compute - operators increasingly require more efficient, scalable, and resilient power systems. ABB’s technologies will serve as the electrical backbone of the Polaris Forge 2 site, enabling high energy efficiency and supporting what Applied Digital projects to be an industry-leading low PUE (Power Usage Effectiveness). Building the power foundation for AI-intensive workloads Applied Digital says the new campus represents the next evolution of its AI Factory model, which aims to combine high power density with optimized energy efficiency and operational flexibility. “Our partnership with ABB reflects Applied Digital’s commitment to redefining what is possible in data center scale and performance,” said Todd Gale, Chief Development Officer at Applied Digital. “Polaris Forge 2 begins with two 150-megawatt buildings that can scale further, reinforcing our leadership in delivering high-performance, energy-efficient AI infrastructure.” ABB will supply both low- and medium-voltage systems, continuing its focus on raising power density while reducing overall energy waste and deployment complexity. The collaboration builds on ABB’s Smart Power division’s ongoing efforts to modernize electrical infrastructure for AI workloads, which in many cases exceed the power demands of earlier cloud and enterprise data centers by multiples. “As AI reshapes data centers, ABB is working with leading digital infrastructure innovators to introduce a new generation of power system solutions,” said ABB Smart Power President Massimiliano Cifalitti. “Developing medium-voltage architecture with Applied Digital marks a significant step forward for large-scale AI facilities, enabling higher efficiency, performance, and reliability at lower cost.” Medium-voltage UPS at hyperscale Both Polaris Forge 1 and Polaris Forge 2 use ABB’s HiPerGuard medium-voltage UPS technology and advanced medium-voltage switchgear. By shifting from traditional low-voltage to medium-voltage architecture, operators gain greater power density, reduced cabling complexity, improved reliability, and the ability to deploy capacity in larger 25-MW blocks - an increasingly critical feature for hyperscale AI deployments. Applied Digital recently reported that the first 200 MW of the new Polaris Forge 2 site will be leased to a U.S.-based hyperscaler, reflecting strong market demand for AI-optimized facilities. Executive Insights FAQ: Power Infrastructure for AI Factories Why do AI factories require fundamentally different power architectures? AI training clusters operate at extreme, rapidly scaling power densities - often 2–5× those of cloud data centers - requiring medium-voltage distribution, advanced UPS systems, and higher-efficiency conversion. What role does medium-voltage UPS technology play? Medium-voltage UPS reduces energy loss, increases reliability, and enables data centers to scale power delivery in much larger increments, critical for AI clusters exceeding 100 MW. How does power infrastructure impact PUE? More efficient electrical distribution and cooling systems reduce overhead power consumption, allowing operators to achieve lower PUE values even at very high rack densities. Why is scalability so important for AI data centers? AI training workloads expand rapidly as models grow. Scalable power blocks (e.g., 25 MW units) allow operators to add capacity without re-engineering electrical systems. How is power reliability maintained at such large megawatt levels? Technologies like medium-voltage switchgear, redundant UPS systems, and grid-interactive controls ensure continuous operation even during fluctuations or outages. What is driving hyperscaler demand for AI factories? The rise of generative AI, LLM training, and GPU-intensive workloads is pushing hyperscalers to secure multi-hundred-megawatt campuses purpose-built for AI. How do AI factory power systems support sustainability goals? Higher-efficiency electrical architecture, reduced cabling losses, and optimized cooling lower total energy consumption and help operators integrate renewable energy more effectively.

As enterprises accelerate digital transformation, the convergence of security and networking has become mission-critical. Versa Networks’ “SSE Adoption Pathway” white paper offers a detailed, actionable roadmap for adopting Security Service Edge (SS...

Eviden, an Atos Group product brand and long-standing player in the high-performance computing (HPC) market, has introduced the BullSequana XH3500, a next-generation supercomputer engineered to meet the escalating demands of converged AI and HPC workloads. Positioned as the successor to the BullSequana XH3000, the new platform is designed to scale beyond exascale, offering higher density, efficiency, and flexibility to support scientific research, industrial R&D, and large-scale AI model training. The launch comes at a pivotal moment for the industry. As organizations race to build AI factories and HPC systems capable of supporting large language models, physics simulations, climate modeling, and biomedical workloads, power and cooling constraints have become central challenges. Eviden’s new system aims to address these constraints with a blend of hardware innovation, advanced cooling, and an open, modular architecture. Higher Density, Lower Environmental Footprint Compared to its predecessor, the BullSequana XH3500 delivers substantial gains in power and cooling capacity. Eviden reports more than 80% additional electrical power per square meter and 30% more cooling capacity per kilowatt, achieved without increasing rack footprint. This translates into higher performance density - allowing data centers to accommodate AI and HPC growth without expanding physical space. Energy efficiency is another key pillar. The system features 100% fan-less fifth-generation Direct Liquid Cooling (DLC), using warm-water cooling at up to 40°C. By eliminating fans and supporting heat-reuse strategies, the design significantly reduces total cost of ownership while aligning with sustainability goals for next-generation AI infrastructure. Modular and Vendor-Neutral Eviden maintains a long-standing strategy of avoiding vendor lock-in, and the XH3500 continues that approach. Its open, modular chassis allows customers to mix CPUs, GPUs, AI accelerators, and networking technologies from multiple suppliers, adapting the supercomputer’s configuration to specific applications - from classical simulations to generative AI and deep learning. This flexibility, Eviden emphasizes, also prepares customers for future technologies, including emerging AI accelerators and quantum components, which can be integrated as they mature. Built as Part of a Complete Ecosystem The BullSequana XH3500 is part of a broader environment comprising software optimization tools, engineering support, and lifecycle services. Power-aware scheduling, intelligent resource management, and application tuning capabilities are offered to help organizations reduce time-to-solution - a longtime priority for HPC and AI operators facing massive data volumes and complex workflows. “The convergence of HPC and AI is redefining how we solve the world’s most complex challenges,” said Bruno Lecointe, VP and global head of HPC, AI and Quantum Computing at Eviden. “With a modular architecture able to integrate diverse CPU, GPU, and AI accelerators, the BullSequana XH3500 will support customers in meeting the demands of today’s AI factories and future hybrid computing environments.” Industry analysts say Eviden’s strategy aligns strongly with user needs. “The BullSequana XH3500 addresses the top needs cited in our surveys: bringing AI capabilities to scientific applications while staying rooted in high-performance computing,” said Addison Snell, CEO of Intersect360 Research. He noted that Eviden’s vendor-neutral, modular approach stands out at a time when many organizations seek to avoid dependence on single-supplier roadmaps. The BullSequana XH3500 is now positioned as a flagship platform for the AI-HPC convergence wave, targeting universities, research labs, national supercomputing centers, and enterprises deploying large-scale AI workloads. Executive Insights FAQ: Supercomputers and AI/HPC Use Cases Why are modern supercomputers increasingly built for both AI and HPC? AI workloads (like large language model training) and traditional simulations both require massive parallelism and accelerator-heavy architectures. Converged systems allow organizations to run scientific modeling and AI training on the same infrastructure, improving efficiency and lowering cost. What types of AI workloads typically run on supercomputers?  Common use cases include LLM training, scientific machine learning, predictive analytics, drug discovery, fusion energy simulations, and digital twin environments that blend physics-based modeling with neural networks. How do supercomputers support industrial innovation? Supercomputers accelerate R&D in sectors such as automotive (autonomous driving models), aerospace (flight physics), pharmaceuticals (molecule simulation), finance (risk modeling), and energy (reservoir simulation, grid optimization). Why is liquid cooling essential for next-generation AI systems? AI accelerators produce heat densities far surpassing traditional CPUs. Direct liquid cooling ensures stable thermal performance, reduces energy consumption, and enables operators to maintain reliability as rack power exceeds 100 kW and beyond. How will quantum computing interact with HPC and AI systems? Quantum systems will not replace HPC or AI but will work alongside them. Future supercomputers will integrate quantum accelerators for specific tasks, using classical AI/HPC hardware for orchestration, pre-processing, and simulation workloads. If you’d like, I can also create a shorter summary version, a headline, SEO tags, or a LinkedIn-ready excerpt.

IQM Quantum Computers has introduced IQM Halocene, a fully modular, on-premises quantum computing product line engineered specifically for quantum error correction (QEC) research - a foundational requirement for building fault-tolerant quantum computers. The launch represents a strategic pivot for the Finnish-German company as it moves from Noisy Intermediate-Scale Quantum (NISQ) machines toward systems capable of supporting large-scale, error-mitigated and ultimately error-corrected quantum workloads. The first system in the Halocene lineup is a 150-qubit superconducting quantum computer, expected to be commercially available by the end of 2026. It features advanced error-correction functionality, including support for logical qubits, modular decoding architectures, and system-level transparency across the full QEC stack. Future releases will scale the architecture to beyond 1,000 qubits, following IQM’s roadmap toward demonstrating fault-tolerant quantum computing by 2030. Platform Built for Error Processing and Logical Qubit Research Quantum error correction remains one of the largest technical hurdles on the path to practical quantum computing. While today's quantum processors can execute NISQ-class algorithms, they remain too error-prone for long-running, high-fidelity workloads in chemistry, finance, optimization, or cryptography. With Halocene, IQM aims to give researchers and industry partners access to: * logical qubit creation and characterization * full-stack error correction experiments * error-mitigation strategies * NISQ algorithm exploration on larger qubit arrays The system integrates IQM’s new Crystal QPU, targeting 99.7% physical two-qubit gate fidelity, a key threshold for stabilizing error-corrected operations. The processor is paired with hardware-accelerated QEC features, support for NVIDIA NVQLink, and a transparent control and decoding environment that allows researchers to design, swap, and test different QEC schemes. “Halocene is the result of co-developing our technology stack with our partners and customers as we build a thriving quantum ecosystem together,” said Jan Goetz, Co-CEO of IQM. “We are shaping the next frontier in error-corrected quantum computing, transforming research into technologies that will drive industrial innovation and economic growth.” Designed for Global On-Premises Deployments Unlike cloud-only quantum offerings, IQM focuses on on-site systems—a model that has resonated with national labs, universities, and enterprises developing sensitive intellectual property or requiring full-stack control over their quantum workflows. Halocene continues that strategy: modular tanks, rack-level organization, and serviceable components are designed to enable easier installation and long-term upgradeability. The initial Halocene system will support research on up to five logical qubits and implementation of Clifford gates, offering organizations a foundation to test real-world QEC implementations rather than simulated models. IQM Co-CEO Mikko Välimäki, who oversees business operations, expects the new product line to accelerate market leadership: “IQM Halocene is our answer to the rising demand for large, error-corrected next-generation quantum computers. We are ready to build and ship systems worldwide, with the first installations starting at the end of 2026.” Intellectual Property IQM’s collaborative R&D model - delivering systems directly to customers and building regional quantum ecosystems - remains a differentiator as countries invest heavily in sovereign quantum computing capabilities. With Halocene, the company seeks to give users not only advanced hardware but also the ability to generate their own intellectual property in logical qubit control, decoding strategies, and scalable QEC architectures. As governments, academia, and industry increasingly shift their focus from NISQ experimentation to pre-fault-tolerant architectures, Halocene positions IQM as a supplier ready for the next phase of global quantum commercialization. Executive Insights FAQ: Quantum Error Correction Why is quantum error correction essential? Quantum states are extremely fragile. Noise from the environment and imperfect gates introduce errors at rates far too high for meaningful large-scale computation. QEC encodes information across many physical qubits so that logical qubits can operate reliably over long algorithms. What makes QEC research challenging today? Most current quantum hardware lacks the qubit counts, gate fidelities, and control stack transparency needed to test full error-corrected pipelines. Access to configurable, on-premises systems like Halocene enables experimentation that cloud systems often restrict. What industries will benefit most from fault-tolerant quantum computing? Advanced materials, pharmaceuticals, logistics optimization, financial modeling, energy grid optimization, and national security applications will see the earliest impact. How many physical qubits are typically needed for one logical qubit? Depending on the error-correction code, ranges vary from ~20–100+ physical qubits per logical qubit. Achieving practical fault tolerance may require millions of physical qubits in the long term. How does quantum error correction relate to the shift beyond NISQ? While NISQ systems can demonstrate quantum advantage in narrow cases, QEC is required for reliable, scalable quantum computing. Halocene-like platforms bridge the gap by allowing hybrid workloads - running NISQ algorithms while advancing error-corrected research.

Vantage Data Centers has announced a $2 billion investment to build a 192-megawatt hyperscale data center campus in Fredericksburg, Virginia. The development, known as VA4, marks the company’s fourth campus in the Commonwealth and expands its total Virginia capacity to 782MW - a combined investment now nearing $8 billion across the state. Situated in Stafford County, roughly an hour south of Vantage’s existing campuses in Loudoun County’s Data Center Alley, the new VA4 site will span 82 acres and feature three data centers totaling nearly 929,000 square feet. The project reflects the growing trend of hyperscale operators moving beyond Northern Virginia’s dense, power-constrained corridors to nearby regions offering both land availability and robust connectivity. “Virginia has been a cornerstone of Vantage’s North American growth since 2017,” said Dana Adams, President of North America at Vantage Data Centers. “The Fredericksburg region gives us a unique opportunity to serve customers who want the capacity and performance of Data Center Alley - without the constraints of operating within it.” Sustainability and Cooling Efficiency Built under Vantage’s “sustainable by design” framework, VA4 will target LEED Silver certification and feature advanced liquid-to-liquid cooling systems using CDU-based (Coolant Distribution Unit) technology capable of handling 100% of IT workloads. This approach replaces traditional air-cooling systems with direct liquid cooling at the rack level, significantly improving energy efficiency while minimizing water consumption through a closed-loop chilled water system. As data centers worldwide confront increasing scrutiny over their energy and water use, Vantage’s strategy underscores a broader industry shift toward liquid cooling as AI and high-performance computing workloads drive new density and heat-management challenges. Economic and Workforce Impact “Virginia has been a cornerstone of Vantage’s North American growth since 2017,” said Dana Adams, President of North America at Vantage Data Centers.According to local officials, the Fredericksburg project is poised to have a major economic impact on the region. Construction of VA4 is expected to generate around 1,100 jobs, with at least 50 permanent positions once operational. The company also plans to collaborate with local schools, contractors, and workforce development programs to prepare residents for long-term careers in skilled trades and data center operations. “Vantage Data Centers will create hundreds of jobs and help establish Stafford as a growing technology hub across the Commonwealth,” said Darrell English, Hartwood District Supervisor, Stafford County Board. “This project strengthens our economy while powering the technologies businesses and communities increasingly depend on.” The groundbreaking ceremony, held earlier this week, featured Luis Lopez Stipes, Deputy Secretary of Commerce and Trade for Virginia, and local representatives including Meg Bohmke, Falmouth Supervisor, and Don Slaiman from IBEW Local 26, highlighting the partnership between public and private stakeholders in bringing digital infrastructure investment to the region. With the first of three data center buildings slated to open by late 2027, Vantage’s Fredericksburg expansion reinforces Virginia’s continued dominance in North American data infrastructure while signaling a shift toward regional diversification, sustainability, and liquid-cooled hyperscale design. Executive Insights FAQ: Liquid-to-Liquid Cooling and CDU Technology What is liquid-to-liquid cooling, and how does it differ from traditional methods? Liquid-to-liquid cooling uses a closed system where coolant absorbs heat directly from IT equipment and transfers it to a secondary liquid loop for heat rejection. Unlike air cooling, it eliminates fans and minimizes energy waste, allowing much higher rack densities. What role does a CDU (Coolant Distribution Unit) play in this system? The CDU acts as an intermediary between the facility’s chilled water loop and the server cooling circuits, ensuring precise temperature regulation, leak detection, and pressure balancing for efficient, safe operation. Why is this technology important for AI and high-density workloads? AI clusters and GPU-based systems generate significantly more heat per rack than traditional servers. CDU-based liquid cooling supports full utilization of compute resources without thermal throttling, maximizing performance. How does liquid-to-liquid cooling support sustainability goals? By operating as a closed-loop system, it reduces or eliminates evaporative water loss and lowers total energy use - helping operators achieve aggressive PUE (Power Usage Effectiveness) and WUE (Water Usage Effectiveness) targets. Can CDU-based systems handle 100% of IT workloads reliably? Yes. Modern CDU configurations are engineered for redundancy and continuous operation, capable of cooling entire IT loads - including AI and HPC clusters - while maintaining uptime and thermal stability at hyperscale.

Vertiv has expanded its global liquid cooling portfolio with the launch of the Vertiv CoolCenter Immersion cooling system, a fully engineered solution designed to meet the extreme thermal requirements of AI and high-performance computing (HPC) environments. The new immersion cooling system is now available across Europe, the Middle East, and Africa (EMEA), marking a strategic step in Vertiv’s efforts to support customers facing unprecedented rack densities and soaring heat loads driven by next-generation compute. Immersion cooling works by submerging servers directly into a dielectric liquid - one that does not conduct electricity - allowing heat to be removed evenly and far more efficiently than traditional air-based methods. This approach has gained momentum as GPUs and accelerators push thermal envelopes beyond what legacy cooling designs can accommodate. The Vertiv CoolCenter Immersion system offers a complete, integrated architecture engineered for high-density compute, with cooling capacities ranging from 25 kW to 240 kW per system. Designed for reliability, the solution incorporates internal or external liquid tanks, a coolant distribution unit (CDU), temperature and pressure sensors, variable-speed pumps, and fluid piping to ensure stable and precise thermal performance. AI and HPC Deployments “Immersion cooling is playing an increasingly important role as AI and HPC deployments push thermal limits far beyond what conventional systems can handle,” said Sam Bainborough, EMEA vice president of thermal business at Vertiv. “With the Vertiv CoolCenter Immersion, we’re applying decades of liquid-cooling expertise to deliver fully engineered systems that handle extreme heat densities safely and efficiently, giving operators a practical path to scale AI infrastructure without compromising reliability or serviceability.” Redundancy features include dual power supplies and redundant pumps to maintain high cooling availability - an essential requirement for AI clusters that run continuously. The system also supports heat-reuse strategies, enabling operators to capture and redistribute waste heat for facility heating or other reuse initiatives, aligning with broader energy-efficiency and sustainability goals. Operational management is streamlined through integrated monitoring sensors, a 9-inch touchscreen interface, and building management system (BMS) support, giving operators granular visibility over coolant temperature, flow rates, and overall system health. Vertiv is pairing the new solution with its Liquid Cooling Services, offering design, installation, tuning, maintenance, and lifecycle optimisation to help enterprises adopt the right liquid-cooling architecture. The company now provides a full suite of options, from rear-door heat exchangers to direct-to-chip (D2C) cooling and immersion systems, reflecting increasing market demand for scalable, low-energy thermal solutions as AI workloads accelerate worldwide. Executive Insights FAQ: Immersion Cooling for AI and HPC Why is immersion cooling becoming essential for AI and HPC workloads? AI and HPC clusters generate extremely high and sustained thermal loads that exceed the capabilities of air cooling. Immersion cooling removes heat directly at the source, enabling stable performance for dense GPU and accelerator systems. How does immersion cooling impact data center energy efficiency? Because immersion removes the need for large air-handling systems, it significantly lowers cooling energy consumption. Operators can achieve better PUE scores and reduce operational costs, especially in high-density environments. Can immersion cooling help data centers scale AI infrastructure faster? Yes. Immersion tanks allow operators to deploy higher compute densities per rack, reducing physical footprint constraints and simplifying thermal design. This accelerates the rollout of new AI/HPC clusters. Does immersion cooling improve hardware reliability? Immersion creates a controlled thermal environment with uniform heat dissipation, reducing thermal cycling stress on components. Many operators report longer hardware lifespan and fewer thermal-related failures. How does immersion cooling support sustainability goals? Immersion systems use less energy, enable heat reuse, and eliminate water consumption typical in evaporative cooling. This makes them attractive for operators targeting carbon reduction and long-term sustainability targets.

Netskope, a global provider of modern network and cloud security technology, has announced a new partnership with managed security service provider NEVERHACK to deliver a fully managed Security Service Edge (SSE) solution. The jointly developed service integrates the Netskope One platform with NEVERHACK’s 24/7 security operations expertise. This would give organizations a unified, turnkey approach to securing data, users, and cloud environments. The partnership comes at a time when enterprises face rapidly expanding attack surfaces, driven by hybrid work, cloud adoption, and AI-driven threats. Cybersecurity leaders increasingly struggle with fragmented toolsets, limited internal resources, and the rising complexity of managing cloud security policies at scale. Netskope and NEVERHACK aim to address these challenges with a managed offering that pairs best-in-class SSE capabilities with continuous monitoring and expert threat response. A Fully Managed SSE Approach At the core of the collaboration is the Netskope One platform, a tightly integrated cloud-native security stack that consolidates Secure Web Gateway (SWG), Cloud Access Security Broker (CASB), Zero Trust Network Access (ZTNA), Remote Browser Isolation (RBI), unified data security, SD-WAN, FWaaS, and digital experience monitoring into a single ecosystem. NEVERHACK’s global MSSP operations layer 24/7 threat detection, response, and intelligence onto the platform. Their SOC analysts continuously monitor customer environments, correlate cross-domain telemetry -including from firewalls and endpoint detection tools - and take immediate action on malicious activity. Key service capabilities include the following: * 24/7 Managed Threat Detection & Response: NEVERHACK SOC teams use Netskope’s analytics to detect threats in real time and perform proactive threat hunting * Unified Threat Intelligence: Correlates signals from disparate security tools into a contextual threat picture for faster triage and reduced alert fatigue * Automated Remediation: New indicators of compromise feed directly into Netskope One, updating global security controls to block emerging threats * End-to-End Data Protection: Safeguards sensitive data across SaaS, web, private applications, and remote endpoints * Zero Trust Access Enforcement: Delivers secure, segmented connectivity to private apps without exposing network infrastructure * Compliance and Operational Efficiency: Aligns policies to regulatory requirements while reducing internal workload and tool sprawl Strengthening Zero Trust for Cloud and AI Netskope emphasizes that the cloud and AI era requires a dynamic, context-aware security model built on Zero Trust principles. As organizations face increasingly autonomous attacks and cloud-native exploitation, SSE has become a foundational component of modern enterprise security architectures. “The cloud and AI era demands a security model that can keep pace,” said Julien Fournier, Vice President for Southern Europe at Netskope. “By combining NEVERHACK’s expert security operations with the real-time capabilities of Netskope One, we’re giving customers a truly differentiated, turnkey SSE solution.” NEVERHACK’s leadership says the partnership reduces complexity for organizations overwhelmed by cloud security management. “This joint offering is not just a tool deployment - it’s a managed outcome of continuous cyber resilience,” said Michael Berdugo, Managing Partner at NEVERHACK. “Our SOC is now leveraging Netskope’s granular visibility to enrich alerts, reduce false positives, and drive immediate incident response.” Executive Insights FAQ: Zero Trust and SSE What is Security Service Edge (SSE)? SSE consolidates cloud-delivered security - SWG, CASB, ZTNA, and data protection - into a unified platform designed to secure users, devices, and data anywhere. How does SSE support a Zero Trust strategy? SSE enforces identity-, device-, and context-based access controls, ensuring no user or workload is trusted by default - critical for hybrid work and cloud environments. Why is Zero Trust important for AI-era threats? AI-driven attacks automate lateral movement and privilege escalation. Zero Trust reduces attack surfaces and limits blast radius through segmentation and continuous validation. How does managed SSE differ from traditional MSSP services? Managed SSE integrates cloud-native controls with SOC operations, enabling real-time policy enforcement and automated response across SaaS, web, and private apps. What benefits does unified threat intelligence bring? It correlates signals across security domains, reducing false positives and enabling faster detection of multi-vector cloud attacks. How does SSE improve incident response speed? Automated IOC ingestion and policy updates block threats globally within seconds - removing manual bottlenecks typical in legacy security environments. What role does ZTNA play in securing hybrid work? ZTNA replaces VPNs by granting application-specific access rather than network access, minimizing exposure and preventing lateral movement. Is SSE suitable for organizations with limited internal security staff? Yes. Managed SSE offloads policy management, monitoring, compliance alignment, and threat response - making enterprise-grade cloud security accessible to lean teams.

As a global leader in cybersecurity and network convergence, Fortinet’s new system delivers advanced AI threat prevention with ultra-low latency while reducing power consumption by up to 69% compared to traditional approaches. The platform is engine...

For more than two decades, memory performance has quietly determined the success or failure of enterprise servers and hosting infrastructure. As workloads evolve - from web hosting to AI, analytics, and high-performance computing - the type of RAM (Random Access Memory) you choose has become one of the most critical architectural decisions in IT planning. From legacy DDR3 modules to the new high-speed DDR5 generation, the right configuration can make or break a server’s ability to handle resource-intensive workloads, maintain uptime, and control operating costs. The leap in performance between each generation isn’t just incremental - it’s reshaping how data centers, hosting providers, and businesses approach scalability. The Evolution of Server Memory Memory technology has advanced steadily since DDR (Double Data Rate) first emerged in the early 2000s. Today, most servers use one of three generations: DDR3, DDR4, or DDR5. Each reflects a step-change in speed, density, and energy efficiency - factors that directly impact application performance and overall infrastructure ROI. * DDR3, launched in 2007, was once the mainstay of data centers. Operating between 800 and 2133 MHz, it offered solid reliability at a low cost, consuming less power than its predecessor, DDR2. Despite its age, DDR3 remains in use in older systems and budget-conscious environments. * DDR4, introduced in 2014, quickly became the industry standard. Its speeds range from 1600 to 3200 MHz, and it operates at a lower voltage of 1.2V, improving both bandwidth and energy efficiency. The balance between cost and performance has made DDR4 the go-to memory for most enterprise and hosting workloads. * DDR5, released in 2020, represents a true generational leap. Designed for the most demanding applications - AI, big data, and high-concurrency hosting - DDR5 starts at 4800 MHz and scales beyond 8400 MHz, doubling the bandwidth of DDR4 while drawing just 1.1V. This new memory class supports larger capacities per module, advanced power management, and on-die ECC (Error-Correcting Code), improving both performance and reliability. For businesses running compute-intensive operations or high-traffic sites, DDR5 is becoming the new benchmark for speed and efficiency. The Performance Equation: Why Memory Matters A server’s memory determines how fast it can process simultaneous requests, manage caching, and handle user concurrency. For hosting providers, milliseconds of latency can mean the difference between a seamless user experience and performance bottlenecks. * Bandwidth and Speed: DDR5’s ultra-high transfer rates allow servers to handle exponentially more data per cycle, reducing page load times and improving database responsiveness. Its increased bandwidth also supports parallel processing across multiple workloads, which is crucial in containerized or multi-tenant hosting environments. * Latency and Efficiency: Newer DDR generations offer lower latency - meaning data is retrieved and processed faster. For workloads such as scientific modeling, real-time analytics, and large-scale virtualization, this reduction in memory delay can yield substantial productivity gains. * Energy and Thermal Efficiency: Every DDR generation has lowered voltage requirements. DDR3 ran at 1.5V, DDR4 at 1.2V, and DDR5 drops that again to 1.1V. Lower voltage translates to reduced heat output and operational power costs—a vital consideration for energy-conscious data centers. * Capacity and Density: Memory density has also increased dramatically. DDR3 tops out at 16 GB per module, DDR4 supports up to 64 GB, and DDR5 reaches 128 GB. These larger modules allow for fewer servers to handle more workloads, supporting both performance scaling and cost consolidation. * Channel Architecture: DDR5 introduces a dual 32-bit channel per module, effectively doubling the communication lanes between CPU and memory. This design delivers higher throughput and enables faster task execution without additional latency penalties. Compatibility, Market Trends, and Adoption Upgrading memory isn’t as simple as swapping modules. Each DDR generation requires compatible motherboards and processors. For example, CPUs built for DDR3 cannot natively support DDR4 or DDR5 modules. Data center architects must verify compatibility between CPU, motherboard, and memory before implementing upgrades. In terms of market adoption, DDR3 remains available but is gradually being phased out. DDR4 continues to dominate due to its wide support and cost efficiency. However, DDR5 is quickly gaining ground, particularly in high-performance computing (HPC), AI workloads, and next-generation web hosting platforms. As production scales and costs decline, DDR5 is expected to become the default standard for enterprise deployments by the late 2020s. The Business Case for DDR5 * Efficiency and Consolidation - DDR5’s combination of bandwidth, density, and low power usage allows servers to handle more workloads with fewer physical machines. This reduces rack space, power draw, and cooling requirements - key benefits for data centers pursuing greener, more sustainable operations. * Reliability and Uptime - The integrated on-die ECC function in DDR5 helps automatically detect and correct memory errors before they impact applications. This is particularly critical in mission-critical environments such as financial systems, e-commerce platforms, and SaaS providers where uptime directly affects revenue. * Scalability for Modern Hosting - In shared or virtualized hosting models, memory performance directly influences how many clients can be served from a single server. DDR5’s higher bandwidth enables faster handling of concurrent workloads, improving resource allocation and end-user response times. * Future-Proofing Infrastructure - Investing in DDR5 positions businesses for the next decade of workloads. As AI, data analytics, and immersive applications become standard components of enterprise IT, DDR5 ensures systems can scale without hitting hardware limits. When DDR3 or DDR4 Still Makes Sense While DDR5 leads in performance, DDR4 remains the most practical choice for many small to medium enterprises (SMEs) and managed hosting providers. It offers strong performance at lower cost, with broad hardware support and proven reliability. DDR3, meanwhile, still serves a purpose in legacy systems or cost-controlled environments - such as test labs, archival servers, or internal tools where maximum performance is not a priority. However, given its aging architecture and limited future support, organizations using DDR3 should plan upgrades to DDR4 or DDR5 soon. Strategic Recommendations * For shared hosting or VPS providers: DDR4 remains a cost-effective balance of performance and stability * For dedicated and high-performance servers: DDR5 offers substantial long-term ROI through higher throughput, energy savings, and reliability * For legacy systems: DDR3 is viable for short-term use, but upgrade cycles should be planned before compatibility and supply constraints emerge Selecting the right RAM generation is not just a technical decision - it’s a strategic one that affects performance, scalability, and operational costs across your infrastructure lifecycle. FAQ: Managing Memory Performance in Server Environments How does DDR5 improve security and reliability in data centers?  DDR5 integrates on-die error correction (ECC) to detect and fix bit errors in real time. This reduces data corruption, improves uptime, and enhances overall system stability. What is the biggest challenge when upgrading from DDR4 to DDR5?  Compatibility. DDR5 requires newer motherboards and CPUs built to support its higher clock speeds and voltage specifications. Existing DDR4 systems cannot be upgraded directly. How does RAM speed impact hosting performance?  Faster RAM improves data transfer rates, allowing web applications and databases to respond more quickly under heavy traffic. This leads to faster page loads and better user experiences. Does DDR5 significantly reduce power consumption?  Yes. DDR5 operates at just 1.1 volts, making it more energy-efficient than DDR4 or DDR3. This lowers operating costs and supports sustainability goals. Which memory generation offers the best value today?  DDR4 remains the best balance between performance, price, and compatibility. However, for AI-driven workloads or enterprises planning future scalability, DDR5 is the clear long-term choice.

74% of IT leaders anticipate budget increases in 2026, according to statistics from the ‘2026 Data Infrastructure Survey Report’ released by DataStrike, a pioneer in data infrastructure managed services. More than half, however, claim they still lack the internal resources necessary to spur innovation or swiftly resolve problems. However, 60% of businesses now use MSPs to handle their data infrastructure, which is more than twice as many as DataStrike's ‘2025 Data Infrastructure Survey Report,’ highlighting an increasing reliance on outside knowledge as teams deal with technical debt and modernization. While cloud adoption has become commonplace, many organizations are struggling with what to do next and are focusing on optimizing current systems, bolstering data strategies, and preparing infrastructure for the growing influence of artificial intelligence, according to the second annual survey, which collected insights from nearly 280 IT leaders across industries. Rob Brown, President and COO of DataStrike, stated, "It's evident that IT teams have progressed past the debate over whether or not to embrace the cloud. They are currently concentrating on modernization, cost control, and developing a data strategy that fosters the upcoming wave of innovation. In order to simplify operations and lessen dependency on pricey proprietary systems, they are also reevaluating their resources, hiring MSPs to handle data infrastructure, and implementing open-source databases like PostgreSQL.” Updating Outdated Systems Internal database teams, however, continue to be modest. Despite handling workloads across several platforms, including Oracle, SQL Server, PostgreSQL, and cloud-native databases, only one-third of respondents have dedicated database administrators (DBAs), and over half of those organizations only have one or two DBAs. About 25% of them claim that in order to stay competitive, they want five or more DBAs, which is an expensive prospect considering that the average DBA compensation is more than $120,000 annually. In place of last year's worries about tool sprawl and sluggish technology adoption, respondents' top problems for 2026 were updating outdated systems (46%) and controlling technical debt (33%). The growing understanding that a solid data foundation is necessary to realize benefit from AI investments is highlighted by the fact that 61% of respondents said that creating a data strategy was their top priority. IT departments are depicted in the poll as striking a balance between change and stability. As businesses go from cloud migration to modernization, the best strategy for maintaining performance and fostering long-term growth is the combination of managed services, fractional assistance, and internal expertise. To fill skills shortages and cut expenses, 83% would look at alternative providers, and nearly three-quarters are interested in outsourcing database infrastructure administration.

At the Equinix Co-Innovation Facility (CIF) in Ashburn, Virginia, global manufacturing and technology solutions leader Flex has unveiled a next-generation rack-level liquid cooling system designed to address the surging power and thermal demands of AI and high-performance computing (HPC). The installation marks a major step in Flex’s collaboration with Equinix to develop and validate advanced cooling technologies for high-density workloads. The new system integrates JetCool’s direct liquid cooling (DLC) technology - a Flex subsidiary known for its precision-engineered microconvective cooling systems - with Open Compute Project (OCP) ORv3 rack architecture. Flex manufactures every component of the solution, including the rack, cooling, and power infrastructure, offering a fully vertically integrated system for data centers striving to achieve higher energy efficiency and scalability in the face of AI-driven heat loads. Cooling at the Rack Level: SmartPlate and SmartSense Innovations At the heart of the Equinix CIF deployment is JetCool’s SmartPlate System for Dell PowerEdge R760 servers, a self-contained, closed-loop DLC solution that requires no facility plumbing or external coolant distribution units (CDUs). This enables data centers to migrate from air to liquid cooling with minimal operational disruption. The SmartPlate System can cut total IT power usage by up to 15%, a key advantage for operators facing energy constraints. Complementing it are two Dell PowerEdge R660 systems equipped with JetCool’s DLC cold plates, designed to handle extremely high inlet coolant temperatures - up to 70°C. Operating at these warmer temperatures not only supports higher compute density but also reduces water usage by up to 90% and cooling energy consumption by 50% compared to traditional methods. The installation also includes Flex’s SmartSense 6U Liquid-to-Liquid (L2L) CDU, a rackmount unit capable of delivering 300kW of cooling for high-density workloads. Integrated manifolds, busbars, and quick disconnects consolidate all liquid cooling capabilities into a single ORv3 rack unit - directly connecting with Equinix’s thermal control system (TCS) for full facility integration. Industrial Partnership and Global Scalability “Flex’s end-to-end portfolio will help data center operators launch equipment more quickly as the need for AI and HPC workloads keeps growing,” said Rob Campbell, President of Flex’s Communications, Enterprise, and Cloud business. “We are proud to expand our collaboration with Equinix by bringing our high-performance, energy-efficient liquid cooling solutions into real-world deployment environments.” Flex’s integrated approach - spanning manufacturing, design, and lifecycle support - positions the company uniquely in the cooling market. As one of the world’s largest producers of server hardware, Flex manufactures millions of systems per year and offers global-scale production and supply chain resilience. Its comprehensive service model covers every layer of liquid cooling infrastructure - from quick disconnects and CDUs to cold plates and manifolds - with full maintenance and warranty support. Equinix’s Role in Testing Next-Gen Infrastructure According to Pawel Wlodarczak, Innovation Director at Equinix, the Co-Innovation Facility provides “a collaborative environment where emerging infrastructure technologies can be tested and refined for real-world, high-density compute environments.” He added that JetCool’s system “can cool up to 4kW per processor socket with additional headroom for future thermal loads,” underscoring its potential for scalability. The Ashburn deployment forms part of a global demonstration network, with additional Flex and JetCool showcases planned at Telehouse facilities, Flex’s Milpitas, California factory, Dell Customer Solution Centers, and multiple Asia-Pacific and European locations. Visitors to the Equinix CIF can view the system’s real-time thermal performance metrics and discuss deployment strategies with technical experts Executive Insights FAQ: Liquid Cooling in Data Centers What makes liquid cooling essential for AI and HPC workloads? AI and HPC generate extreme thermal densities that air-based systems cannot efficiently manage. Liquid cooling provides direct heat removal at the chip level, allowing sustained performance and higher rack densities. How does direct liquid cooling differ from immersion cooling? Direct liquid cooling (DLC) circulates coolant through cold plates attached to processors, whereas immersion cooling submerges entire servers in dielectric fluids. DLC offers modular deployment with less retrofitting required. Why are higher coolant inlet temperatures important? Running coolant at warmer temperatures - such as JetCool’s 70°C - reduces energy spent on chilling water, enabling free cooling and improving overall data center power efficiency (PUE). What is the role of a CDU in liquid cooling systems? A Coolant Distribution Unit (CDU) transfers heat between liquid loops, maintaining safe temperatures, pressure balance, and fluid flow while isolating IT equipment from facility water systems. How does liquid cooling advance sustainability goals?  Liquid systems drastically reduce both water and energy consumption, enable reuse of waste heat, and lower total carbon footprint - helping operators meet aggressive ESG and efficiency targets.

AI research company Anthropic has announced a sweeping $50 billion investment to build a new generation of AI data centers across the United States, beginning with large-scale facilities in New York and Texas. The project, developed in partnership with cloud infrastructure provider Fluidstack, marks one of the largest private infrastructure commitments in the emerging AI economy. The facilities are designed to align with the U.S. AI Action Plan, a federal initiative aimed at bolstering domestic innovation, securing AI supply chains, and maintaining U.S. leadership in advanced computing. The significant investment by Anthropic comes amid a surge of large-scale data center spending by global tech players, including OpenAI and SoftBank’s $500 billion Stargate Project and Meta’s $600 billion U.S. infrastructure program. Anthropic, known for its Claude AI assistant, said the new data centers are critical to meeting exploding demand for high-performance AI workloads and ensuring its models remain at the frontier of research and enterprise deployment. According to CEO and co-founder Dario Amodei, the initiative will also contribute to local economic growth, creating approximately 2,400 construction jobs and 800 permanent technical roles once operational. “These sites will create American jobs while helping us develop more capable AI systems that can accelerate breakthroughs in science, technology, and beyond,” said Dari Amodei. Rapid Gigawatt Power Deployment Anthropic selected Fluidstack as a partner for its ability to move with exceptional agility, enabling rapid delivery of gigawatts of power. “Fluidstack was built for this moment,” said Gary Wu, co-founder and CEO of Fluidstack. “We’re proud to partner with frontier AI leaders like Anthropic to accelerate and deploy the infrastructure necessary to realize their vision.” The first data centers are expected to go live in 2026, supporting both Anthropic’s research operations and its growing commercial user base - now exceeding 300,000 customers worldwide. Over the past year, Anthropic has seen its major enterprise accounts grow nearly sevenfold, reflecting the rapid adoption of AI-powered business tools. Executive Insights FAQ: About AI Data Centers Why are AI data centers different from traditional data centers? AI data centers are built for high-density GPU and accelerator workloads, requiring advanced cooling, power, and networking systems optimized for training and inference tasks. Why are companies investing so heavily in AI infrastructure now? The rapid scaling of generative AI models demands unprecedented compute power, prompting tech firms to secure long-term capacity and energy resources domestically. What challenges do AI data centers face in the U.S.? Key issues include power availability, environmental sustainability, and supply chain constraints for chips and cooling systems. How do AI data centers support national competitiveness? They enable faster AI innovation, reduce dependence on foreign infrastructure, and contribute to workforce development in advanced manufacturing and digital engineering. Will this investment trend continue? Yes. As AI becomes foundational to sectors like healthcare, energy, and finance, sustained infrastructure investment will be essential to meet global demand.

In this CNBC Squawk Box interview, AMD CEO Lisa Su discusses the company’s latest earnings, its bullish long-term growth projections, and the surging global demand for AI chips. Speaking after AMD’s first Analyst Day in three years, Su outlines the ...

The accolade positions Gcore as a leading supplier of developer-friendly, high-performance, and sovereign AI cloud services, putting it at the forefront of the AI sector. 84 providers were assessed using ClusterMAXTM 2.0 based on ten important factors, such as networking, storage, orchestration, and security. The quality of Gcore's AI-ready cloud architecture and high-performance GPU network across edge, hybrid, and sovereign deployments was validated by its Silver rating. "Gcore's platform is robust and among the best purely self-service offerings for Kubernetes," SemiAnalysis noted. With PCI DSS certification and a global datacenter footprint, the console has all the enterprise perks. They point out "a balance of usability and strong underlying hardware performance." Seva Vayner, Gcore's Product Director for Edge Cloud and AI, stated: "ClusterMAXTM benchmarks validate what our clients encounter on a daily basis: Gcore provides high-performance GPU infrastructure with significant operational economy. The advancements we've achieved in creating a truly global AI cloud that blends edge proximity, high-performance GPU resources, and sovereign deployment options are reflected in our Silver assessment. In order to enable our clients to train and implement AI workloads effectively, safely, and wherever they are required, we are dedicated to ongoing optimization. Gcore provides a full range of AI products, including both public and private AI solutions. For both training and inference, the Public Gcore AI Cloud supports everything from metal to models. Additionally, the firm offers proprietary software and services including Everywhere AI, which enables 3-click AI training and inference in any setting, and AI Cloud Stack, which offers enterprise-grade AI cloud services by converting raw GPU resources. Telcos and newcomers to the market can use Gcore's strong independently verified performance to provide hyperscaler-grade AI infrastructure under their own name as they grow into the cloud. 

In this episode of Gartner ThinkCast, experts Alicia Mullery and Daryl Plummer dive into one of today’s most pressing CIO challenges - how to balance ambition and readiness in the era of accelerating AI. Building on insights from their Opening Keyno...