Empowering Innovation
with Solidigm

Anusha Nerella joins hosts Allyson Klein and Jeniece Wnorowski to explore responsible AI in financial services, emphasizing compliance,collaboration, and ROI-driven adoption strategies.

Scality CMO Paul Speciale joins Data Insights to discuss the future of storage—AI-driven resilience, the rise of all-flash deployments, and why object storage is becoming central to enterprise strategy.

From racing oils to data center immersion cooling, Valvoline is reimagining thermal management for AI-scale workloads. Learn how they’re driving density, efficiency, and sustainability forward.

This Data Insights episode unpacks how Xinnor’s software-defined RAID for NVMe and Solidigm’s QLC SSDs tackle AI infrastructure challenges—reducing rebuild times, improving reliability, and maximizing GPU efficiency.

In this episode, Allyson Klein, Scott Shadley, and Jeneice Wnorowski (Solidigm) talk with Val Bercovici (WEKA) about aligning hardware and software, scaling AI productivity, and building next-gen data centers.

From AI Infra Summit, Celestica’s Matt Roman unpacks the shift to hybrid and on-prem AI, why sovereignty/security matter, and how silicon, power, cooling, and racks come together to deliver scalable AI infrastructure.

Discover how JetCool’s proprietary liquid cooling is solving AI’s toughest heat challenges—keeping data centers efficient as workloads and power densities skyrocket.

Solidigm’s Ace Stryker joins Allyson Klein and Jeniece Wnorowski on Data Insights to explore how partnerships and innovation are reshaping storage for the AI era.

From storage to automotive, MLPerf is evolving with industry needs. Hear David Kanter explain how community-driven benchmarking is enabling reliable and scalable AI deployment.

Dell outlines how flash-first design, unified namespaces, and validated architectures are reshaping storage into a strategic enabler of enterprise AI success.

Haseeb Budhani, Co-Founder of Rafay, shares how his team is helping enterprises scale AI infrastructure across the globe, and why he believes we’re still in the early innings of adoption.

Direct from AI Infra 2025, AI Expert & Author Daniel Wu shares how organizations build trustworthy systems—bridging academia and industry with governance and security for lasting impact.

Equinix’s Glenn Dekhayser and Solidigm’sScott Shadley discuss how power, cooling, and cost considerations are causingenterprises to embrace co-location among their AI infrastructure strategies.

Explore myths, metrics, and strategies shaping the future of energy-efficient data centers with Solidigm’s Scott Shadley, from smarter drives to sustainability-ready architectures.

Equinix’s Glenn Dekhayser and Solidigm’s Scott Shadley join TechArena to unpack hybrid multicloud, AI-driven workloads, and what defines a resilient, data-centric data center strategy.

Industry leader Scott Shadley reveals how Solidigm’s innovations in SSDs, partnerships, and architecture are reshaping data centers to meet the rising demands of AI, edge, and enterprise workloads.

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In highly collaborative industries like media and entertainment, time isn’t just money—it’s opportunities. Giving your animators, designers, and visual effects artists more time means they have more space to coordinate and develop better creative outcomes. And when you have hundreds of collaborators, saving each one just a few minutes every hour can exponentially increase the amount of time spent on creative endeavors instead of, for example, waiting for software to load.  

I recently had the opportunity to explore how storage innovation is enhancing collaborative workflows in the media and entertainment industry with Alex Timbs, Senior Business Development Manager of Media and Entertainment at Dell Technologies, and Scott Shadley, Leadership Marketing Director at Solidigm. During our Data Insights episode, it became clear that changes in content production workflows from pre-production to final edits are causing a fundamental shift in how storage supports content creation, moving flash storage from “nice to have” to essential for modern production pipelines.

Alex brought a unique perspective to our conversation, having spent 15 and a half years at Animal Logic (now Netflix Animation) before joining Dell. His experience as the company scaled from 80 to over 1,000 people globally provided compelling real-world context for understanding storage evolution in creative environments.  

Alex saw firsthand the “serendipitous performance improvements” that emerge when organizations transition to flash storage and save minutes that add up to hours of freed-up creative time, witnessing gains that went far beyond what traditional metrics might predict. At Dell, he’s worked with customers who achieved this as well. He cited a recent Dell film studio customer who achieved 100x performance improvements—not 100% gains, but literally 100 times faster workflows.  

The need for faster storage has been recently accelerated by AI and real-time workloads, which demand rapid filling and flushing of video random access memory (VRAM) on graphics processing units (GPUs). Where 24GB VRAM used to be sufficient, today’s workloads often demand 96GB or more. To keep these GPUs fed, VRAM must be filled and flushed at extreme speeds, making high-performance flash storage no longer a luxury, but an absolute necessity.

Scott emphasized how storage has transformed from an afterthought to a critical performance enabler. The concurrent access patterns required by modern workflows—where multiple users need simultaneous access to large files alongside their associated metadata—can only be efficiently handled by flash technology. Existing spinning HDD storage simply cannot deliver the random access performance required for today’s collaborative, high-resolution content creation environments.

Dell’s AI Factory serves as a robust foundation for media and entertainment organizations striving to lead amid surging data growth, new content formats, and adoption of AI-powered workflows. The platform uniquely combines validated, full-stack solutions, enabling companies to start small and scale incrementally, directly addressing the sector’s dual mandates of technological advancement and financial discipline.

At its core, Dell AI Factory leverages the PowerScale family: from the cost-effective F210, optimized for studio or departmental use, to the high-density, high-performance F910 designed for the most demanding enterprise-scale operations. This architecture empowers customers to only pay for what they need today, with the confidence they can scale both performance and capacity linearly as their needs evolve, eliminating the risks of overprovisioning or stranded investment.

The result is a unified platform that streamlines collaborative workflows (including editing, visual effects, and broadcast), consolidates data silos, and supports both on-premises and multi-cloud deployment, all with high security and efficiency. Multiple industry-leading media organizations already rely on PowerScale for everything from 4K/8K post-production to real-time virtual production and generative AI–driven analytics. Dell’s integrated data reduction, metadata solutions, and cyber protection further drive down operational costs, while the modular “grow as you go” model enables ongoing financial prudence. This makes the AI Factory a trusted partner: future-ready, validated by top global brands, backed by deep ISV partnerships, and proven to accelerate creative delivery while protecting the bottom line.

The edge computing dimension adds another layer of complexity and opportunity. A modern film production might have 10 cameras that are capable of capturing resolutions up to 17K, and the crew will want to start working with that immediately. Alex described in-camera visual effects (ICVFX) scenarios where directors give real-time creative feedback, viewing final-quality visual effects directly on on-set monitors. This surge in edge computing for ICVFX pushes the need for high-performance storage that can operate in demanding production environments, all while delivering the rock-solid reliability that tight shooting schedules require.

Interestingly, Alex compared today’s transformation to the shift from analog film to digital photography. Just as digital cameras delivered instant feedback and removed the high cost of mistakes tied to film processing, modern workflows in content production combine real-time creative feedback with minimal risk. This immediacy allows teams to iterate more often, experiment more boldly, and ultimately achieve stronger creative outcomes by removing traditional bottlenecks.

Solidigm’s collaborative approach resonates strongly with this philosophy. Rather than pushing customers toward the highest-performance solutions regardless of need, Scott described how their solutions lab and upcoming AI lab allow customers to test workloads before making commitments. This “try-before-you-buy” model helps organizations right-size their storage investments while ensuring they can achieve their performance objectives.

Looking ahead, both experts see storage demands continuing to accelerate. Organizations working in 4K today need to prepare for native 8K workflows tomorrow, requiring storage architectures that can scale both performance and capacity over multi-year timeframes.

The TechArena Take

The convergence of AI, real-time workflows, and edge computing is fundamentally reshaping storage requirements across industries, with media and entertainment serving as the proving ground for technologies that will eventually transform other verticals. As Alex noted, the future belongs to organizations that can make the most informed real-time decisions possible, and that capability fundamentally depends on having the right storage foundation in place. Dell and Solidigm’s partnership demonstrates how thoughtful collaboration can deliver solutions that scale from individual creators to global production companies.

For more insights on Dell’s storage solutions for media and entertainment, visit their website www.delltechnologies.com/powerscale or connect with Alex Timbs on LinkedIn. Learn more about Solidigm’s AI-focused storage solutions at solidigm.com/ai or reach out via LinkedIn to Scott Shadley.

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In the not-so-distant past, data center storage was somewhat of an afterthought. You needed a place to gather data; you needed it to be reliable; and you needed it to be economical. And that’s pretty much where the conversation ended. Now in the era of AI workloads, storage is taking center stage for the critical role it plays in data activation. Having the right storage solutions in the right place provides the flexibility, efficiency, and security to feed AI at scale.

I recently had the opportunity to explore this transformation with Saif Aly, senior product marketing manager at Dell, and Scott Shadley, leadership marketing director at Solidigm, to explore how enterprise storage requirements are evolving in response to AI-driven workloads and data-intensive applications. During our TechArena Data Insights episode, it became clear that storage has evolved to the critical foundation enabling AI success.

The AI workload revolution has created unprecedented demands on storage infrastructure. As Saif explained, these workloads require sustained throughput, low latency, and massive scale simultaneously. The challenge extends beyond simple performance. Enterprises face data fragmentation across edge, core, and cloud environments, creating operational complexity that can lead to vendor lock-ins and underutilized graphics processing unit (GPU) resources.

Dell’s response centers on their AI Data Platform, built on the principle that modern storage must support the entire data lifecycle. The PowerScale platform serves as the foundation, delivering what Saif described as unmatched performance improvements: 220% faster data ingestion and 99% faster data retrieval compared to previous generations. The introduction of MetadataIQ further accelerates search and querying capabilities, directly supporting AI workload requirements.

Scott emphasized how customer conversations have evolved beyond traditional capacity discussions to focus on “time to first data”—how quickly organizations can access information when they need it. In AI application workloads, different data types require varying levels of accessibility and performance characteristics. The challenge lies in understanding what data needs to sit directly adjacent to GPUs versus what can be retrieved from more distant storage tiers.

The discussion revealed how inference workloads, particularly retrieval-augmented generation (RAG) architectures, create unique storage demands. These systems require large datasets to be readily accessible for real-time referencing while simultaneously managing active data processing next to compute resources. Success depends on optimizing the balance between high-performance local storage and efficient data movement from archive locations.

While flash storage dominates high-performance applications, both experts acknowledged that hard disk drives (HDDs) retain value for cold and warm datasets. The key insight: not all data is equal, and successful architectures blend flash-based solid-state drives (SSDs) and HDD storage within unified namespaces to balance performance and cost considerations.

The conversation highlighted remarkable capacity evolution, with Saif recounting his amazement at holding Solidigm’s 122 TB drive, a device containing massive data volumes in a small form factor. This density revolution, progressing from 30 TB to 60 TB to 122 TB drives just in the last year, enables dramatic improvements in rack space efficiency, power consumption, and cooling costs while maintaining the throughput AI workloads demand.

Scott connected this capacity evolution to practical customer needs, explaining how optimization now focuses on the right bandwidth, density, and time-to-data characteristics rather than simply maximum speed. As storage capacity per device increases, the focus shifts to infrastructure optimization that delivers customer value through improved total cost of ownership and operational efficiency.

Real-world impact emerged through customer examples Saif shared. Kennedy Miller Mitchell, the studio behind the Mad Max franchise, used PowerScale to enable pre-visualization of entire scenes before filming. That capability allows directors to iterate creatively and make real-time decisions. Subaru leveraged the platform to manage exponentially growing data volumes, handling 1,000 times more files than previously possible and directly improving their AI-driven driver-assistance technology accuracy.

Looking ahead, both experts see storage demands continuing to accelerate, driven by AI’s exponential data growth and evolving workload requirements. As Saif noted, “the data explosion is not going to stop,” with AI both consuming and creating massive amounts of data. The distributed nature of modern computing—spanning edge, core, and cloud environments—requires storage solutions that provide consistent experiences and seamless data mobility across all locations.

The TechArena Take

The convergence of AI workloads, massive data growth, and distributed computing architectures is fundamentally reshaping enterprise storage from a cost center to a strategic enabler. Dell and Solidigm’s partnership demonstrates how thoughtful collaboration can deliver solutions that scale from individual creators to global enterprises while addressing the critical balance between performance, capacity, and cost efficiency. As storage continues to assert its place as a foundation of modern workloads, organizations that invest in flexible, high-performance architectures today will be best positioned to capitalize on tomorrow’s AI-driven opportunities.

For more insights on Dell’s enterprise storage solutions, visit Dell.com/PowerScale or connect with Saif Aly on LinkedIn. Learn more about Solidigm’s AI-focused storage innovations at solidigm.com/AI or reach out via LinkedIn to Scott Shadley.

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The enterprise AI landscape is undergoing a fundamental transformation. While organizations have focused heavily on graphics processing unit (GPU) compute power and model sophistication, a critical infrastructure component has emerged as the new performance differentiator: storage. The Supermicro Open Storage Summit, running from August 12 to 28 with online sessions from leading solutions providers, promises to reveal how innovative storage strategies are delivering breakthrough performance improvements that could reshape your AI deployment economics.

The Hidden Performance Multiplier in AI Infrastructure

As organizations scale from AI experimentation to production deployment, they’re discovering that inference workloads demand different storage characteristics than training pipelines. The data tells a compelling story: enterprises deploying solid state drive (SSD) storage solutions are seeing 10x to 20x throughput improvements, 4,000x input-output per second (IOPS) scaling improvements, and up to 40% total cost of ownership (TCO) reductions compared with traditional storage solutions.

These aren’t theoretical gains. Real-world implementations for retrieval-augmented generation (RAG) workloads have demonstrated that storage optimization with SSDs can deliver 70% increases in queries per second while simultaneously reducing memory footprint by 50%. For enterprises struggling with the economics of AI deployment, these performance multipliers represent an opportunity to maximize return on investment.

Two Critical Sessions You Can’t Afford to Miss

The Supermicro Open Storage Summit expands on these opportunities with two must-attend sessions that tackle the most pressing storage considerations facing enterprise AI deployments today.

Storage to Enable Inference at Scale (August 19, 10:00 AM PT) brings together industry leaders from Solidigm, Supermicro, NVIDIA, Cloudian, and Hammerspace to explore how new storage protocols and distributed inference frameworks are enabling large-scale inference processing. This session will reveal how organizations are moving beyond traditional storage approaches to deploy validated infrastructure optimized for GPUs that unlocks real-time performance at scale.

Enterprise AI Using RAG (August 27, 10:00 AM PT) dives deep into RAG, one of the most critical enterprise AI use cases. With experts from Solidigm, Supermicro, NVIDIA, VAST Data, Graid Technology, and Voltage Park, this session addresses how enterprises can operationalize generative AI securely and efficiently while maintaining proximity to their most valuable data assets.

The SSD Revolution: Beyond Traditional Storage Thinking

One of the most compelling insights emerging from enterprise AI deployments challenges conventional storage wisdom. Solidigm’s recent breakthrough work, which will be discussed in the upcoming sessions, demonstrates that strategically offloading data from memory to high-performance SSDs doesn’t just reduce costs: it actually improves performance in many scenarios.

The company’s innovative approach involves moving model weights and RAG database components from expensive distributed random-access memory (DRAM) to optimized SSDs, achieving better performance at lower cost. In one demonstration involving a 100 million vector dataset, this approach delivered 57% less DRAM usage while maintaining or even improving query performance. The economic implications are huge as enterprises can run complex models on GPUs that would otherwise lack sufficient onboard memory.

From Data Center Footprint to Power Efficiency: The Complete TCO Story

The storage optimization story extends far beyond raw performance metrics. In the upcoming sessions, Solidigm will also discuss how cutting-edge storage solutions are demonstrating dramatic improvements in TCO across the entire infrastructure stack.

Take a practical example, a 50-petabyte dataset deployment with 12 NVIDIA H100 systems. Traditional HDD-based approaches require nine racks consuming 54 kilowatts. Deploy high-density 122TB SSDs, and that footprint shrinks to a single rack with up to 90% power reduction and 50% increase in available GPU footprint.

These efficiency gains matter more than ever as enterprises grapple with data center space constraints, cooling challenges, and escalating power costs.

The TechArena Take: The Storage Revolution Is Here

Organizations that leverage cutting-edge storage optimization strategies are positioning themselves for sustainable competitive advantage. While competitors struggle with infrastructure costs and performance limitations, early adopters are achieving superior AI outcomes at lower total cost of ownership.

The ability to deploy more sophisticated models, process larger datasets, and deliver faster inference responses directly translates to better customer experiences and operational efficiency.

The window for competitive advantage is narrowing rapidly. As these storage optimization techniques become mainstream, the organizations that implement them first will establish performance and cost advantages that become increasingly difficult for competitors to match.

Register Now

The Supermicro Open Storage Summit provides an opportunity to learn directly from teams of industry leaders who are defining the future of AI infrastructure. With sessions featuring experts representing all layers of the stack, you’ll gain access to the collective expertise of the companies driving AI infrastructure innovation. The summit’s focus on real-world implementations, demonstrated performance improvements, and practical deployment strategies makes it essential viewing for any organization serious about scaling AI effectively.

Don’t let storage bottlenecks limit your AI ambitions. Register below today and discover how strategic storage optimization can transform your enterprise AI performance while dramatically improving your deployment economics.

Storage to Enable Inference at Scale | August 19, 10:00 AM PT

Enterprise AI Using RAG | August 27, 10:00 AM PT

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Dell and Solidigm leaders explore how modern storage—flash, SSDs, and flexible architectures—enables AI, accelerates performance, and helps enterprises manage data across edge to cloud.

Fragmented approaches to security and IT solutions have frustrated the private and public sector for decades, creating a need for costly integrations while still leaving vulnerabilities. My recent Data Insights interview with Jeniece Wnorowski, director of industry expert programs at Solidigm, and Bora Güzey, senior IT consultant at sayTEC, revealed how organizations are finally solving this problem as they demand unified, security-first solutions that eliminate the complexity of these legacy approaches to IT architectures.

During our conversation, Bora provided an in-depth look at how sayTEC is pioneering sovereign IT infrastructure by fundamentally reimagining how security, access, and storage work together. This transformation begins with recognizing that traditional IT models treat these critical components as separate, siloed systems — an approach that increases risk, cost, and administrative overhead while leaving organizations vulnerable to evolving cyber threats.

The Unified Security-First Approach

Bora highlighted a key differentiator that sets sayTEC apart from conventional solutions: their holistic approach to IT security. sayTEC has built a unified platform where access control, data protection, and system performance are integrated from the ground up. This security-first architecture is based on zero trust and includes built-in regulatory compliance. The combination ensures that protection isn’t an add-on but is embedded across every layer of the system, delivering what Bora described as “military-grade security without compromising performance or cost efficiency.”

The company’s hyperconverged infrastructure (HCI) platform combines compute, S3 object storage, backup, and secure remote access into a single integrated system. Thanks to partnerships with companies like Solidigm and Virtuozzo, sayTEC can deliver impressive performance metrics — S3 storage speeds of up to 150 gigabytes per second and seamless scaling up to 200 petabytes, all with zero downtime.

Zero Trust and Sovereign Data Handling

As enterprises grapple with increasingly sophisticated cyber threats, Bora addressed how sayTEC’s zero trust architecture goes beyond basic implementations. Their sayTRUST VPSC (Virtual Private Secure Communication) technology actively monitors the full communication path, blocking unauthorized traffic before it even enters the tunnel. The system deploys pre-tunnel verification, token-based access control, and layered encryption, including perfect forward secrecy, to create what they call a “darknet environment” for secure communications.

For sovereign data handling — a critical concern for government and enterprise customers dealing with sensitive information — sayTEC’s systems ensure full control over where and how data is stored and accessed. This resonates particularly strongly with organizations dealing with critical infrastructure or sensitive personal data, where sovereignty and adaptability are paramount.

Scalability Without Compromise

One of the most impressive aspects of sayTEC’s solution is their promise of dynamic scaling without system downtime. Bora explained how their modular architecture allows customers to start with as few as three nodes and scale up to hundreds without interrupting operations. This is achieved through distributed workloads, erasure coding for redundancy, and live data migration capabilities.

For organizations facing rapid growth or stringent regulatory demands, this means no painful transitions or migrations. They can grow their infrastructure in real time while maintaining full compliance, business continuity, and budget predictability.

Strategic Technology Partnerships

Bora also emphasized the importance of strategic partnerships in delivering exceptional value to customers. The company’s research and development collaboration with Solidigm enables them to leverage high-performance NVMe drives that dramatically reduce latency while optimizing energy efficiency. These partnerships have allowed sayTEC to reduce infrastructure costs by over 50%, accelerate deployment times, and offer return on investment often within just 12 months.

Market Traction and Future Roadmap

sayTEC’s solutions are particularly well-suited for sectors where security, compliance, and scalability are non-negotiable. The company has seen strong demand in finance, public sector, defense, and health care — industries that deal with sensitive data and face constant regulatory scrutiny. In addition, their simplified deployment model and competitive cost structure are increasingly attracting medium-sized enterprises looking for secure, future-proof IT systems without requiring large in-house expertise.

Looking ahead, Bora outlined an ambitious roadmap that includes hyperconverged infrastructure with GPU computing for AI and machine learning workloads, enhanced zero trust for mobile environments, privileged access management integration, and plans to double S3 storage acceleration to 300 gigabytes per second. The company also plans to expand compute power support to 256 cores per node and scaling up to one petabyte per node.

The TechArena Take

In the rapidly evolving landscape of enterprise IT security, sayTEC’s approach represents a significant departure from traditional fragmented architectures. By delivering a truly unified, security-first platform that combines infrastructure, access, and storage into a single system, they’re addressing fundamental challenges that have plagued enterprise IT for decades.

The company’s focus on plug-and-play systems that simplify complexity while delivering military-grade security positions them well for the growing demand for sovereign IT solutions, particularly in Europe, where data sovereignty regulations are becoming increasingly stringent.

Check out sayTEC’s full range of solutions at www.saytec.eu. To connect with Bora and learn more about their sovereign IT infrastructure approach, you can reach out via LinkedIn or email for direct inquiries and demo opportunities.

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Dell and Solidigm explore how flash storage is transforming creative pipelines—from real-time rendering to AI-enhanced production—enabling faster workflows and better business outcomes.

I recently sat down with Solidigm’s Jeniece Wnorowski and Mohan Potheri, principal solutions architect at Hypertec, to unpack how immersion cooling is reshaping data-center economics for AI and high-performance computing (HPC). During our discussion, it became clear that the biggest constraint on AI progress isn’t silicon — it’s keeping that silicon cool. Hypertec, founded in 1984 and now shipping over 100,000 servers a year to customers in more than 80 countries, has spent four decades learning how to squeeze more compute into less space without breaking the power budget, an experience that set the stage for our conversation.  

Mohan painted a sobering picture of an industry straining under the weight of its own momentum. AI, HPC, and edge-computing workloads have pushed power and cooling demand to record highs just as sustainability-focused goals demand lower energy footprints. Operators face a conflicting mandate: deploy clusters faster than ever, but do so with tighter efficiency targets and, in many sites, within real-estate footprints that can’t grow any further. Space-constrained facilities must find ways to condense more compute while still meeting aggressive thermal budgets, all without blowing out capital or operating expenses. These pressures, he said, turn traditional air-cooled data centers into bottlenecks the moment racks tip into multi-kilowatt territory.

Hypertec’s answer is to start with liquid rather than retrofit for it. The company’s single-phase "immersion-born" servers live permanently in dielectric fluid, eliminating fans and chillers and cutting cooling power by roughly 50% while driving site-level power usage effectiveness (PUE) down to about 1.03.

Because every component is designed for submersion from day one, the servers avoid material-compatibility problems that plague air-cooled hardware dipped into tanks after the fact, and they let central processing units (CPUs) and graphics processing units (GPUs) sustain 90-95% of peak clocks instead of throttling under heat. A 10-megawatt deployment that would normally sprawl across 100,000 square feet collapses into roughly a tenth of that footprint, and Hypertec’s field data shows hardware lasting up to 60% longer thanks to the vibration-free, contaminant-free bath.

Tanks roll in pre-assembled, set up in under 10 minutes, and fill with fluid in less than half an hour, giving operators a shortcut from loading dock to AI production. Add immersion-ready storage nodes that put as much as two petabytes beside the compute they feed, plus 800 Gigabit-per-second networking, and Hypertec delivers a dense, sustainable, and rapidly deployable platform that sidesteps the very constraints throttling its air-cooled peers.

Before we wrapped, Mohan shifted the spotlight to storage—the quiet partner that can still slow an otherwise cutting-edge system. He explained that if data can’t reach the processors quickly, even the fastest GPUs and CPUs end up waiting. To avoid that pinch-point, Hypertec extends its immersion approach to storage as well, placing dense drive enclosures in the same fluid bath and on the same high-throughput fabric as the compute nodes. By treating cooling, compute, and data as one integrated stack, the company keeps every component working in sync and lays a cleaner path to future scale.

The TechArena Take

What’s the TechArena take? Together, these solutions make a compelling argument: immersion isn’t a niche experiment but a practical response to AI’s insatiable appetite for watts, racks, and real estate. Hypertec’s immersion-born solutions show how vendors can rethink server design to meet that challenge head-on—reducing energy, shrinking footprints, extending equipment life, and freeing budgets to buy more compute instead of more chillers.  

Listen to the full conversation here, to learn how immersion cooling is quickly moving from “interesting” to inevitable.  

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Allyson Klein and Jeniece Wnorowski welcome Mohan Potheri of Hypertec to explore how immersion cooling slashes energy use, shrinks data-center footprints, and powers sustainable, high-density AI, HPC, and edge solutions on this Data Insights episode. Find the audio-only podcast here.

SayTEC redefines IT with a zero trust, hyper-convergedplatform delivering sovereign cloud, seamless scalability, and military-gradesecurity for critical industries.

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