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In the Arena: Allyson Klein with Axelera CMO Alexis Crowell on inference-first AI silicon, a customer-driven SDK, and what recent tapeouts reveal about the roadmap.

Infrastructure innovation is rampant in the data center today.

Last week, TechArena dove into the deep end at OCP Summit 2025 to discuss the latest rack and multi-rack designs driving hyperscale implementations. At Cloud Field Day 24 (CFD24) today, Oxide Computer Company took the stage to posit their view on enterprise infrastructure opportunity, inspired by hyperscale innovation.

Steve Tuck, co-founder and CEO at Oxide, started his talk defining the vision that operational efficiency of the cloud needed to be brought to on-premesis (on-prem) implementation. The opportunity? Increased data center efficiency, enhanced compute density, and accelerated infra deployment.

Oxide invested in a proprietary rack technology, top-of-rack switch technology, and unique BIOS to pave a path to this vision. Going under the covers, Oxide has built an Open Oxide System comprised of compute, storage and network services and platform software and firmware coupled with a choice of open integrations from Kubernetes, Tensorflow, Grafana, and more. Oxide claims traction across industries including financial services, life sciences, federal government, AI infrastructure, and energy and manufacturing sectors.  

So what is this and how is it differentiated? Basically, Oxide has combined a custom hypervisor with acute telemetry-based control of systems with a proprietary rack technology that integrates power and network connectivity and compute sleds with a variety of compute configurations. Built-in hardware root of trust? Check. Plug-and-play deployment? Also check. Contrast this with OCP Open Rack v3 (ORV3) designs combined with Open Platform Firmware and integration into a cloud stack of choice, developed by an Oxide home grown tech stack.

The TechArena Take

Oxide is reading the room: bring cloud-class operations to on-premises with strong telemetry, thoughtful rack design, and a clean deployment story. That’s the right problem set, and the innovation suggests a team that sweats details buyers care about—time-to-value, day-2 automation, and security roots that start in hardware.

My caution isn’t a red flag so much as a buyer reality: enterprises often prize standards and multi-vendor flexibility. Oxide’s tight vertical integration is compelling for speed and ultimate efficiency, but some customers will ask for interop, modular buy paths, and lifecycle guarantees that align with existing ORV3 racks and other standards. That’s not a blocker—it’s a roadmap ask.

Meanwhile, the OCP ecosystem is pushing hard into enterprise. That competition can actually help Oxide—clarifying where a couture rack-plus-software stack beats a build-your-own approach on speed, simplicity, and total cost of ownership. Buyers are ready. Secure a handful of marquee deployments, demonstrate compatibility with standards-based components, and document the migration path. That reframes the offer as a low-risk decision.

Data is now the invisible engine running every modern organization. Look inside any business and you will find it quietly driving every decision. Marketing teams analyze engagement to refine campaigns. Operations monitor real-time metrics to improve efficiency. Executives rely on dashboards before making decisions. Data has become the foundation of how companies think, plan, and grow.

The shift did not happen overnight. It started when organizations began collecting information to understand customers better, predict market trends, and reduce risks. Over time, those that learned to connect and act on their data began moving faster than competitors. What used to be a side function of IT has become the central nervous system of the entire business.

Here is why every company today, whether it recognizes it or not, is already a data company.

Data Has Become the New Engine of Growth

For decades, success was defined by brand, product, or location. Today, it is shaped by how effectively a company uses its data. When information from across departments is connected, the organization gains a clearer picture of what works and what does not.

Data-driven companies can predict customer needs, anticipate supply chain issues, and identify new opportunities before others do. They adapt quickly because decisions are based on facts rather than assumptions.

A recent report from Boston Consulting Group found that only a small fraction of organizations see measurable results from artificial intelligence initiatives, and the ones that do have strong data foundations. The lesson is simple: better data, better outcomes.

Every Industry Now Runs on Data

Data has become universal currency. Retailers rely on it to personalize offers. Banks analyze it to detect fraud. Hospitals use it to improve patient outcomes. Even traditional sectors like manufacturing and logistics are now guided by real-time insights.

This widespread adoption shows that data is no longer a luxury or a technical advantage. It is a necessity for survival. The companies that thrive are those that recognize patterns early and use data to make proactive adjustments instead of reacting too late.

A Deloitte study calls data a “strategic asset” that grows in value the more it is used. Organizations that embed analytics into everyday decisions outperform their peers in profitability, efficiency, and customer satisfaction.

Technology Alone Is Not Enough

Many businesses invest heavily in tools but neglect the culture that turns data into value. Software can store, process, and visualize data, but people determine how it is used.

A true data company is one where curiosity and accountability are part of daily work. Teams ask the right questions, share insights openly, and learn from outcomes. When data is everyone’s responsibility, it becomes more reliable and meaningful. MIT Sloan Management Review points out that this cultural shift often starts with leadership. Companies that appoint a chief data officer or equivalent leader signal that data is not just a technical function but a business priority.

Research from Harvard Business Review shows that companies with strong data cultures are several times more likely to improve decision making. The most advanced firms start by building trust in their data before scaling technology.

Data Connects Strategy with Execution

The role of data is no longer limited to reporting what happened. It now guides what should happen next. Modern data ecosystems allow organizations to move from descriptive insights to predictive and prescriptive ones. According to McKinsey’s report “The Data-Driven Enterprise of 2025,” the most effective organizations integrate technology, governance, and talent to make this transition successfully.

In practice, this means creating systems that not only capture information but also learn from it. When customer feedback, operational metrics, and financial data flow together, leaders can make better decisions in real time.

This approach transforms data from a static asset into a living feedback loop. Companies that master it are able to align day-to-day actions with long-term strategy.

How to Prepare

Building a data-first organization is less about scale and more about intent. These steps help companies get started:

• Invest in data literacy. Help every employee understand how to interpret and question information, not just consume it.

• Simplify systems. Focus on clean, consistent data before adding complex tools.

• Establish ownership. Assign clear responsibility for data quality, governance, and access.

• Start small. Choose one area, such as customer engagement or process optimization, and demonstrate measurable results.

Progress grows from these small wins. Over time, the organization learns to trust its data and expand its capabilities.

Conclusion

Every organization today is built on data, whether it admits it or not. Industry leaders are the ones that treat information as a strategic resource rather than a by-product.

Success in the digital era depends on connecting technology, people, and purpose through data. When organizations manage it intentionally, they move faster, innovate smarter, and serve customers better.

Call to Action

Take a closer look at how your company uses data. Is it something you report on after the fact, or is it guiding your next move? Recognizing that difference is the first step toward becoming a truly data-driven organization.

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Srija Reddy Allam builds security where modern infrastructure actually lives—across multi-cloud, APIs, and fast-moving threat surfaces. Starting in network engineering and scaling into cloud and automation, she now architects resilient, zero-trust patterns at Fortinet and explores how AI and ML strengthen web and API defense without slowing teams down. Her throughline: connect networks, cloud, and intelligent security so protections evolve as quickly as the systems they guard.

As one of TechArena’s newest Voices of Innovation, Srija shares how curiosity pulled her from pure networking into cloud security and AI, why constraints—not blank checks—tend to produce the smartest solutions, and how she separates real progress from hype. We also dig into restoring trust in the age of AI, the human-plus-machine partnership, and the habits she relies on to find clarity when problems get complex.

Q1: Can you tell us a bit about your journey in tech?

A1: I began my career in network engineering, working across the full stack, from routing and wireless systems to security design. Over time, I expanded into cloud and automation, learning how to build scalable and secure architectures.

Today, as a cloud security architect, I focus on designing resilient multi-cloud environments and exploring how AI and ML can strengthen web and API defense. My journey has been about connecting networks, cloud, and intelligent security to create solutions that evolve with technology.

Q2: Looking back at your career path, what’s been the most unexpected turn that ended up shaping who you are today?

A2: I never planned to move from pure networking into cloud security and AI, but curiosity took me there. Early in my career, I focused on solving immediate technical problems, getting systems to work and making them faster and more reliable. Over time, I realized the real challenge was designing systems that stay secure and adaptable as technology evolves. That realization changed how I approach my work and how I define success. It is not just about solving problems anymore; it is about anticipating them.

Q3: When you’re evaluating new ideas or technologies, what’s your framework for separating genuine innovation from hype?

A3: When I evaluate new ideas or technologies, I focus on three things: the problem it solves, the real-world usefulness, and the impact it creates. True innovation should address a meaningful problem, improve efficiency, and create measurable benefits. If it doesn’t make something genuinely better, it’s just hype, not progress.

Q4: What’s the biggest misconception you encounter about innovation in the tech industry?

A4: One of the biggest misconceptions about innovation is that it is driven purely by creativity. In reality, innovation often grows out of constraints. It happens when resources are limited, timelines are tight, or systems do not behave as expected. Those boundaries push people to think. Those boundaries push people to simplify, to focus, and to design smarter solutions.

Innovation isn’t about having everything; it’s about creating something exceptional with what you already have.

Q5: How do you see the relationship between AI advancement and human creativity evolving? Are they competitors or collaborators?

A5: I see AI and human creativity as collaborators rather than competitors. AI exists because of human imagination and serves as an extension of our ability to think and create. It can analyze data, detect patterns, and respond to complex challenges much faster than humans ever could.

Creativity, however, reaches beyond logic or algorithms. It’s rooted in empathy, intuition, and our uniquely human ability to find connections between ideas that might seem unrelated. The future belongs to collaboration, where AI enhances our creativity by handling complexity and scale, while humans provide purpose, vision, and meaning.

Q6: If you could solve one major challenge facing the tech industry today, what would it be and why?

A6: If I could solve one challenge in the tech world, it would be restoring trust in the age of AI. The technology has advanced faster than our ability to govern or understand it. Every day, it becomes harder to tell what is real and what is generated.

AI has immense potential to make life better, but its misuse has made people question what they see, read, and believe. The real challenge is not stopping innovation, but ensuring it develops with integrity, responsibility, and human oversight.  

Q7: When you’re facing a particularly complex problem, what’s your go-to method for finding clarity?

A7: When I’m faced with a complex problem, I start by slowing things down. Complexity can create noise, so the first step is to simplify and to break the problem into smaller parts and understand what truly matters. I also believe clarity often comes from stepping away for a moment. A short walk or a quiet break helps the mind make connections that constant focus can block.

Once I have perspective, I shift into hands-on exploration. I test, build, or map out scenarios to see how things behave in reality. Thinking brings understanding, but doing brings insight. That combination of reflection and experimentation helps me move from confusion to clarity.

Q8: Outside of technology, what hobby or interest gives you the most inspiration for your professional work?

A8: Outside of technology, CrossFit has been a huge source of inspiration for me. It keeps me grounded and reminds me what consistency and discipline really mean. Some days I feel strong, other days everything feels heavy, but showing up anyway makes all the difference.

CrossFit has taught me that growth happens in small, uncomfortable moments but when you push yourself a little beyond it feels possible. That lesson carries into my work every day. Whether it’s solving a tough problem or learning something new, I approach it the same way by staying patient, trusting the process, and being consistent.

Q9: What excites you most about joining the TechArena community, and what do you hope our audience will take away from your insights?

A9: What excites me most about joining the TechArena community is being part of a group that thrives on curiosity and collaboration. It’s a space where people don’t just talk about technology but they explore it. That kind of exchange is something I really value.

I hope the audience sees from my insights that innovation is not about perfection, but about progress. I want to inspire others to stay curious, experiment, and keep learning, no matter where they are in their journey. If someone walks away from a conversation or session with a new perspective or idea to try, that’s the best outcome I could hope for.

Q10: If you could have dinner with any innovator from history, who would it be and what would you ask them?

A10: If I could have dinner with any innovator, it would be Whitfield Diffie, one of the pioneers of modern cryptography and co-creator of the Diffie Hellman key exchange algorithm. His work made it possible for two parties to securely share information over an open network. It’s a concept that became the foundation of secure communication and internet privacy.

I would love to ask him how he views the future of security in the age of AI, where data, privacy, and trust are constantly being redefined. It would be fascinating to hear his thoughts on how principles like encryption and trust can evolve as AI systems become more autonomous and interconnected.

No data center component is an island. While the industry conversation around AI infrastructure has focused heavily on graphics processing units (GPUs), a more fundamental truth is emerging: peak performance requires optimizing every component in the stack to work together. In fact, attempting to optimize any single element without considering the broader system inevitably limits what’s possible.

I recently had the opportunity to explore this interconnected reality with Solidigm’s Ace Stryker, product marketing director of AI infrastructure, and Jeniece Wnorowski, director of industry expert programs, to understand how storage requirements are evolving. During our conversation, it became clear that the most significant breakthroughs in AI infrastructure are coming from rethinking how data flows through the entire AI pipeline.

AI’s Expanding Diversity Shapes Storage Requirements

Ace started our conversation by emphasizing that we are still only a few years into AI becoming a prominent cultural force, and that its diverse potential is still yet to be fully uncovered. As AI-enabled workloads diversify with new models, tools, and solution stacks, the requirements for hardware—including storage—are diversifying as well. Truly understanding these requirements, however, means looking beyond storage specifications alone. “From a storage perspective, we’re really concerned about how the storage in an AI cluster interacts with the memory to deliver optimized outcomes,” Ace said. “Storage does not do the job on its own.”

The challenge extends beyond simple read-write speeds. Modern AI systems require careful orchestration between storage layers, host dynamic random-access memory (DRAM), and high-bandwidth memory on GPUs. Understanding how data moves between these memory tiers has become essential for IT architects planning next-generation infrastructure. As Ace noted, attempting to optimize storage in isolation limits the ability to understand what’s actually happening in the AI pipeline.

Challenging Conventional Wisdom

This focus on the interaction between memory and storage has led to research with surprising outcomes. Solidigm recently worked with Metrum AI to examine what happens when significant amounts of AI data are strategically moved from memory onto solid state drives (SSDs) in ways that weren’t typically considered.  

The companies used video from a busy traffic intersection and fed it into an analysis pipeline that generated embeddings and created a RAG database, then created a report about what happened in the video with suggestions for safety improvements. By offloading RAG data and inactive model weights from memory to SSDs, they achieved a 57% reduction in DRAM usage for a 100 million vector dataset. More surprisingly, queries per second actually increased by 50% compared to keeping data in memory, thanks to more efficient indexing algorithms in the SSD offload approach.

The implications extend beyond cost savings. The research demonstrated running the Llama 3.3 70 billion parameter model on an NVIDIA L40S GPU, a combination that normally exceeds the GPU’s memory constraints. For organizations looking to repurpose legacy hardware or deploy AI capabilities in edge environments with power limitations, this represents new possibilities for using hardware previously considered inadequate for modern AI-enhanced workloads.

The Density Revolution

While performance optimization captures headlines, capacity evolution tells an equally compelling story. Solidigm’s 122 terabyte (TB) drives, roughly the size of a deck of cards, represent just one milestone in a rapid progression that’s seen capacities jump from 30TB to 60TB to 122TB in a single year. The company has announced plans for 256TB drives, and as Ace said, “It’s not too long before you’re going to see Solidigm and others aiming at a petabyte in a single device, which was unfathomable even five years ago.”

These density improvements deliver practical benefits across the infrastructure stack. Higher capacity per drive means fewer physical devices required, reducing rack space requirements, power consumption, and cooling costs while maintaining the throughput AI-enabled workloads demand.

The Partnership Imperative

Throughout the conversation, Ace returned repeatedly to collaboration as Solidigm’s core operating principle. The company’s logo, an interlocking “S” design, symbolizes partnerships fitting together to solve complex problems. It’s reflected in their approach across the ecosystem, from working with NVIDIA on thermal solutions to collaborating with software orchestration leaders and cloud service providers.

This partnership focus acknowledges a fundamental reality: storage optimization happens within a broader system context involving networking, software orchestration, and compute resources. Solutions that work in isolation rarely deliver optimal outcomes at scale.

The TechArena Take

As AI-enhanced workloads continue their exponential growth, the organizations that understand storage as a strategic enabler rather than a commodity component will gain sustainable advantages. Solidigm’s research demonstrates that intelligent storage strategies can unlock performance improvements while simultaneously reducing costs and expanding deployment possibilities. For IT architects planning next-generation AI infrastructure, the message is clear: look beyond the GPU specifications and examine how data moves through your entire system. The gains not only in efficiency, but overall capability, may surprise you.

Learn more about Solidigm’s AI-focused storage innovations at solidigm.com or connect with Ace Stryker on LinkedIn.

In this episode of Data Insights, host Allyson Klein and co-host Jeniece Wnorowski sit down with Dr. Rohith Vangalla of Optum to discuss the future of AI in healthcare.

From hyperscale direct-to-chip to micron-level realities: Darren Burgess (Castrol) explains dielectric fluids, additive packs, particle risks, and how OCP standards keep large deployments on track.

A 2025 field guide for architects: why Arm’s software gravity and hyperscaler adoption make it the low-friction path today, where RISC-V is gaining ground, and the curveballs that could reshape both.

From OCP in San Jose, PEAK:AIO’s Roger Cummings explains how workload-aware file systems, richer memory tiers, and capturing intelligence at the edge reduce cost and complexity.

Once defined by monolithic architectures and predictable workloads, today’s enterprise data center strategies are shaped by the explosive rise of AI, the realities of hybrid multicloud, and the mounting pressure of regulatory and efficiency demands. I recently spoke with Glenn Dekhayser, global principal technologist at Equinix, and Scott Shadley, leadership marketing director at Solidigm, who shared their perspectives on how enterprises are adapting, and what it will take to succeed in the years ahead.

The conversation began with an important insight on data center infrastructure from Glenn, who noted that AI has “10x’d” hybrid multicloud architectures. As he explained, organizations are grappling with where to deploy AI workloads—cloud, GPU-as-a-service, on-premises, or edge. As those workloads move to production, they’re driving a fundamental shift toward dense power solutions and liquid cooling as enterprises seek to control costs and performance.  

But the real transformation is in how organizations think about data itself, with “data-centric” strategy, which while complex in execution, comes down to a simple idea. “Whatever you’re doing, creating value starts with your data,” said Glenn. For enterprises trying to extract new value streams out of data, that means now workloads come to that data, rather than the reverse. Enterprises are creating entire data marts to reflect the change that data no longer has one-to-one relationships with applications, and instead, multiple applications access shared datasets.  

This centralized data approach addresses the reality that while workloads are relatively easy to deploy and orchestrate, datasets carry constraints: they’re slow to move, require governance, and face compliance and sovereignty requirements. In response to these challenges, Glenn said he counsels customers to create an “authoritative core,” one copy of active datasets on equipment you control in locations you can access.  

This core, of course, must be balanced with the ability to project data where it needs to be for optimal governance, compliance, cost, and performance. That could be in public clouds, at the edge, or in the core data center. Many enterprises, however, “Are starting to realize they’re not necessarily set up to take advantage of all those places,” Glenn said. “And if their data architecture...[isn’t] ready to accommodate this mobility, they’re going to find themselves at a competitive disadvantage.”  

As organizations work to upgrade their data architecture to avoid such a disadvantage, data center infrastructure technology advancements are keeping pace. Scott explained how storage technology in particular is evolving to meet the needs of new data center strategies. Large capacity drives help enterprises keep data close and store more while using less power, while performance drives enable fast work with the data at hand, so storage doesn’t become a bottleneck. As he summarized, “A modern architecture of flash tiering, flash plus hard drives, is becoming even more and more valuable.”  

When asked about the one question enterprise data center managers should consider that they weren’t thinking about five years ago, both experts converged on a theme: architectural flexibility for unknown future requirements. Scott emphasized not just focusing on capital expenses, but considering operational expenses and building systems with five-year operational efficiency in mind. Glenn emphasized the unknown, saying he often asks organizations how they are “architected for change” in a world where the next transformational service provider could emerge from completely unexpected origins.  

The conversation culminated in an analogy: if the best time to plant a tree was 50 years ago, the second-best time is now. For enterprises sitting on large datasets in public cloud environments, the cost and complexity of data mobility only increase with time. For organizations that aren’t yet “architected for change,” the time to remedy that is now, not in one to two years when their data lake will be even larger, and therefore more difficult and more expensive to move.  

The TechArena Take  

The enterprise data center conversation has evolved from optimizing known workloads to architecting for unknowable futures. Equinix and Solidigm’s insights reveal that success increasingly depends on maintaining data sovereignty while preserving access to innovation. Organizations that establish authoritative data cores with agile connectivity to diverse service ecosystems today will be positioned to capitalize on tomorrow’s transformational opportunities, whatever form they may take.

Innovative power delivery unlocks a shift in data-center design. CelLink's PowerPlane routes thousands of amps in a flat, flexible circuit—cutting cabling and accelerating AI factory builds.

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CEO Carl Schlachte joins TechArena at OCP Summit to share how Ventiva’s solid-state cooling—proven in dense laptops—scales to servers, cutting noise, complexity and power while speeding deployment.

From OCP Summit San Jose, Allyson Klein and co-host Jeniece Wnorowski interview Dr. Andrew Chien (UChicago & Argonne) on grid interconnects, rack-scale standards, and how openness speeds innovation.

From OCP Summit, Metrum AI CEO Steen Graham unpacks multi-agent infrastructure, SSD-accelerated RAG, and the memory-to-storage shift—plus a 2026 roadmap to boost GPU utilization, uptime, and time-to-value.

Recorded live at OCP in San Jose, Allyson Klein talks with CESQ’s Lesya Dymyd about hybrid quantum-classical computing, the new Maison du Quantique, and how real-world use cases may emerge over the next 5–7 years.

The unit of design for data centers and AI factories has pivoted from system and rack to rack and data hall. It’s how this industry will reconcile physics (power and heat), timelines (time-to-online), and diversity (multi-vendor systems) in the AI era.

Perhaps nowhere is this shift more evident than on the show floor of the Open Compute Project Foundation’s (OCP’s) 2025 global summit in San Jose this week. OCP’s latest news, along with a litany of exhibitor and partner announcements, land squarely in this rack-first world. The OCP community is leaning into an “Open Data Center for AI” framework—codifying common physical and operational baselines so racks, pods, and clusters can be assembled and operated with far less bespoke engineering.

In this OCP 2025 industry spotlight, I’m going to talk about the innovations that stood out to me and share my take on the next steps I believe this industry needs to take in lockstep.

Silicon Steps Up: Governance and Chiplets

With AMD, Arm, and NVIDIA now on the OCP Board—represented by Robert Hormuth (AMD), Mohamed Awad (Arm), and Rob Ober (NVIDIA)—we know that the future of hyperscale is heterogeneous. For Arm, that governance role pairs with its Foundation Chiplet System Architecture (FCSA) effort aimed at a vendor-neutral spec for interoperable chiplets. In a heterogeneous era, a common chiplet language becomes the on-ramp to modular silicon roadmaps—and ultimately to more fungible racks and faster deployment.

Intel’s Rack-Scale Vision Shows Up on the Floor

A decade ago, Intel’s Rack Scale Design (RSD) pitched a composable, disaggregated rack where compute, storage, and accelerators are pooled and composed by software using open, Redfish-based APIs. The goal: upgrade resources independently, raise utilization, and manage at the pod level—not the node. That picture read like science fiction in 2015; in 2025, it looks a lot like the OCP show floor – open firmware: check; rack and multi-rack design points: check; integration with power...are you kidding me?...check. Read on!

Helios: AMD’s Rack Is the Product

AMD used OCP to stop talking parts and start selling a rack: Helios. Built on Meta’s Open Rack Wide (ORW) spec, Helios aligns EPYC head-nodes, Instinct accelerators, ROCm, and networking into a serviceable, buyable rack system—72 MI450s per rack with an HBM footprint that outmuscles Vera Rubin-class designs on total memory. Oracle leaning in (50K MI450s slated for Helios-based clusters) is the market tell: rack-as-a-SKU is no longer hypothetical.

This is the cleanest proof yet that the hyperscale playbook—standardized mechanics, consistent service windows, and software-first ops—has crossed into open, multi-vendor racks. Let’s go team Instinct!

Flex: Pre-Engineered Building Blocks for the AI Factory

Flex’s OCP-timed platform is an emblem of the shift. The company is productizing what operators actually fight with at scale: prefabricated power pods and skids, 1 MW rack designs, rack-level coolant distribution, and capacitive energy buffering to tame synchronized AI load spikes. Flex positions it as a faster path from design to energized racks—an argument that resonates with any operator trying to compress site schedules without inviting risk. The bigger point: when the rack becomes the product, integration, commissioning, and serviceability must be first-class features.

GIGAPOD: Turnkey NVIDIA Racks at Scale

Giga Computing (GIGABYTE) is shipping GIGAPOD clusters—multi-rack, turnkey NVIDIA HGX (Hopper/Blackwell) systems offered in air-cooled and liquid-cooled topologies. The point isn’t one server; it’s a pre-integrated pod where nine air-cooled racks (or five liquid racks) behave like a single AI system with known thermals, service flows, and performance envelopes—exactly the "rack as product" pattern operators keep asking for.  

MiTAC: You Want to Rack it? We can Stack It.

Just when you think we are done, enter MiTAC. This confluence of Tyan and the Intel systems businesses has the heritage and technical chops to deliver what operators want, and their OCP 21” rack, sidled up with a standard 19” rack spoke volumes about delivering infrastructure to customer requirements. What’s more, they offer CPU and GPU configurations to serve every palatte, blended with a leading open firmware solution to make integration easy.

Unique innovation in the rack complemented these rack scale innovations. Here are a few of the highlights.

Celestica’s 1.6-TbE Class Switches Underline “Rack as Product”

Celestica’s new switch family advances bandwidth and thermal design for AI/ML clusters with open-rack friendly mechanics and cooling options. It’s the kind of network gear built to live as a rack component—aligned with power, airflow/liquid paths, and service models—rather than a freestanding device. When operators talk about standardizing deployment at the rack/pod level, this is what they mean.

ASRock Rack + ZutaCore: Waterless Two-Phase DTC for AI Factory Racks

ASRock Rack is showcasing an HGX B300 platform that integrates ZutaCore’s waterless, two-phase direct-to-chip cooling—dielectric fluid, direct heat removal, serviceable design. The aim is simple: enable higher rack densities without committing the building to a single facility-water strategy. For brownfield deployments or sites with water constraints, that optionality matters. ASRock also has an air-cooled B300 variant to meet operators where their rooms are today.

Accelsius: Multi-Rack Two-Phase, Packaged for Speed

Accelsius’ latest CDU solution targets multi-rack direct-to-chip deployments, turning two-phase cooling into something operators can drop in alongside existing plant gear. The value prop is commissioning predictability and density headroom—two things in short supply on accelerated build schedules.

Cold-Plate & Process Innovations

We’re also seeing upstream manufacturing advances that change the thermal math—new plate geometries and additive processes that increase heat flux at lower flow rates. For facilities teams, that can translate into smaller pumps, simpler loops, and more room for the rest of the rack kit.

Why This Matters Now

Focusing on what actually moves projects: deployability, serviceability, and schedule certainty, these announcements matter because they turn “open” from a philosophy into a repeatable path—standardized racks, power and liquid interfaces, network gear designed as rack components, and thermal advances that reduce the number of custom steps between CAD and capacity.

Deployment velocity, not theory. Flex’s pre-engineered power/cooling pods, 1 MW racks, and rack-level CDUs turn “integration” into a repeatable process—shortening the path from CAD to energized capacity.

Density without a rebuild. ASRock Rack + ZutaCore (waterless two-phase) and Accelsius (multi-rack two-phase CDU) give operators liquid options that fit existing plants and brownfield realities—freeing space/flow for what matters inside the rack.

Rack-native networking. Celestica’s 1.6 TbE/102.4T-class switches are engineered as components of the rack, aligning thermals, power domains, and service windows so pods stay installable and maintainable.

Platforms > parts. AMD’s Helios shows vendors shipping serviceable rack systems, not just nodes—reducing onsite glue work and clarifying ownership across CPU, accelerator, interconnect, and service.

Upstream thermal gains. New cold-plate geometries and additive processes raise heat-flux at lower flow rates—translating to smaller pumps, simpler loops, and more payload room per rack.

Circular racks, not just circular parts. Not every AI program needs bleeding-edge silicon Day 1. Rack Renew (a Sims Lifecycle Services subsidiary) is packaging recommissioned OCP gear—think Tioga Pass sleds back into ORV2 racks with busbar power—for rapid, budget-sane deployments. Their process adds firmware baselines, multi-day burn-in, and BOM documentation; customers see 40–60% capex relief and reuse racks with up to ~15% power-delivery efficiency gains from the OCP busbar versus traditional PDUs. It’s a pragmatic on-ramp that complements new AI halls with affordable general compute, storage tiers, and edge fleets.

TechArena Take

Open only matters if it reduces steps and risk. What stood out on Days 3 and 4 was how many vendors are productizing the gaps that slow AI halls: Flex’s platformization of power/cooling, Accelsius’ multi-rack two-phase, ASRock Rack + ZutaCore’s waterless DLC, AMD’s rack-scale Helios, and upstream cold-plate advances that ease the facility side.

The next mile: the industry needs integration, not aspiration. Publish reference rack BOMs, commissioning checklists (power, liquid, network), and baseline telemetry/firmware schemas that travel across vendors. If we align on rack mechanics, service clearances, power rails, coolant interfaces, and observability, multi-vendor pods become selectable products—not projects.

With all this innovation, one question tops my list: how do the companies embracing open compute integrate their designs—cleanly, repeatedly, and at speed? That’s my biggest takeaway from OCP 2025, and it’s what I’ll be pressing in upcoming TechArena podcasts.

At OCP’s 2025 global summit, Momenthesis founder Matty Bakkeren joins Allyson Klein to explore why open standards and interoperability are vital to sustaining AI innovation at datacenter scale.

From OCP Summit 2025, Kelley Mullick joins Allyson Klein and co-host Jeniece Wnorowski for a Data Insights episode on rack-scale design, hybrid cooling (incl. immersion heat recapture), and open standards.

From the OCP Global Summit in San Jose, Allyson Klein sits down with Chris Butler of Flex to unpack how the company is collapsing the gap between IT and power—literally and figuratively.

Graid Technology, the creators of SupremeRAID, the GPU-accelerated redundant array of independent disks (RAID) solution that has emerged as a breakout player in the NVIDIA ecosphere, has announced an agreement with Intel to take on Intel Virtual Raid on CPU (VROC) licensing for data center and workstation customers. The move advances Graid Technology’s traction with enterprise as Intel VROC has a loyal following across industries and gives the company a complement to the SupremeRAID portfolio addressing a broader landscape of deployment scenarios. It also signals a continued evolution of Intel’s business as it focuses its investment on areas tied to foundry and core processor development.

The deal, which is expected to be finalized by end of year, will move all licensing of Intel VROC keys and customer management to Graid Technology while Intel will maintain key generation based on Intel Xeon processors. VROC enables the Intel Xeon processor to act as a RAID controller, eliminating the need for an add-on card to manage RAID environments. It has been well received in the market since its introduction in 2017 as part of the Intel Xeon “Purley” platform. The technology is utilized by many original equipment manufacturers as the default RAID software control for NVMe configurations and is seen as foundational for Intel RAID environments.

The TechArena Take

So, how do we view this deal? Intel realized how sticky VROC was with Xeon customers a couple of years ago when the team tried to cancel support for the feature and received urgent customer feedback to change directions. The Graid Technology deal gives Intel an elegant solution, placing the business in the hands of a company that is very much invested in RAID solution delivery. Graid Technology benefits from an expanded solution portfolio to bring to customers, delivering Intel VROC for less-performance-sensitive arrays and SupremeRAID for those environments that require the performance boost that SurpremeRAID provides. Ultimately, customers win as their trusted VROC keys persist in the market with a new trusted partner.

Walking the halls of the Open Compute Project Foundation’s 2025 summit in San Jose this week, you can feel it: open collaboration has graduated from “nice-to-have” to the de facto playbook for AI-era buildouts.

With more than 11,000 people converging on OCP Global Summit 2025, the conversation has moved well past server SKUs and into rack- and data-center-level design—how we standardize power, cooling, networking, and fleet operations so the whole industry can build AI capacity at the pace of demand. As one keynote put it, we’re at the precipice of an intelligence revolution, but rockets don’t launch without a ground crew. And OCP community is Mission Control.

OCP’s news cadence underscores the shift from parts to platforms. The Foundation unveiled its “Open Data Center for AI” strategic push—a unifying umbrella to define the common physical and operational substrate of AI facilities so racks, pods, and clusters are fungible across operators and geographies. The explicit aim: speed time-to-deploy by reducing fragmentation in how we bring power and liquid cooling into the hall and how we certify the facility for high-density AI in the first place. Think of it as OCP moving from open boxes to an open blueprint for the whole building.  

Education and talent are getting the same open treatment. OCP Academy is live, packaging community know-how into courses for everyone from newcomers to seasoned operators. For an industry racing to retrain on liquid loops, 800 VDC distribution, and AI-centric fleet ops, this is oxygen.

Data movement is foundational; networking is the lifeblood of AI clusters. OCP introduced ESUN (Ethernet for Scale-Up Networking), a new workstream backed by hyperscalers and silicon providers to advance Ethernet as a scale-up option (the single-hop, ultra-low-latency links inside accelerators and tightly coupled racks). It complements the existing SUE-Transport track and coordinates with UEC and IEEE—ideally reducing bespoke glue and improving multi-vendor interoperability. That said, we aren’t sold that Ethernet is the only solution; purpose-built scale-up fabrics and emerging interconnect approaches will continue to have a place depending on workload, topology, and time-to-value.

On the facilities side, OCP is stitching together the standards bodies that determine whether a design can leave the whiteboard. An alliance with OIX harmonizes OIX-2 with OCP Ready™ for metro-edge interconnection—useful for anyone pushing AI nearer to data sources and end users.

Governance matters, too. OCP added AMD, Arm, and NVIDIA to its Board—a signal that the silicon leaders want to shape, not just ship, the standards that will define AI factories. It’s hard to overstate how important that is as the ecosystem navigates CPU/XPU diversity, link-layer choices, and the migration to higher-voltage DC power.  

From the keynotes and hallway conversations, a few themes stand out:

1. Fungibility beats fragility. Hyperscalers described designing for “agility and fungibility” from chip to rack to region. That’s driving common mechanicals (e.g., Open Rack variants), standardized firmware/telemetry interfaces, and power architectures that can swing from air-augmented deployments to fully liquid-cooled pods without re-architecting the building. The OCP umbrella initiative is explicitly about codifying that common ground so more of us can buy, build, and operate at hyperscale tempo.  
2. Power and cooling graduate to first-class citizens. The big numbers are sobering: multi-MW racks, 15× power-density growth, and liquid-first designs. The industry is coalescing around higher-voltage distribution and rack-level energy buffering to tame synchronized AI load spikes. This is open standards work with direct capex/opex impact.  
3. Networking at scale—Ethernet plays a growing role, but not the only one. ESUN’s formation is a pragmatic move to tap Ethernet’s broad supply chain and toolchain while defining what scale-up links need (losslessness, error handling, deterministic latency). Paired with ongoing OCP work on universal link layers, it points toward more multi-vendor interoperability. That said, we don’t view Ethernet as the only destination—purpose-built fabrics and alternative transports will still make sense depending on workload, topology, and time-to-value.

The TechArena Take

OCP’s superpower has always been translating hyperscale breakthroughs into reusable playbooks. What’s different this year is the scope: open standards are now spanning the entire stack, from accelerator fabrics and firmware to 800 VDC buses, CDUs, and interconnection-ready metro-edge sites. Add formal education (OCP Academy) and cross-org alliances (OIX), and you get a faster flywheel: publish spec → validate in the open → train the market → scale across operators.

If you’re an enterprise, colo, or regional cloud eyeing AI expansion, your path to “AI-ready” increasingly starts with OCP checklists. If you’re a vendor, aligning roadmaps to ESUN, OCP Ready™ v2, and the Open Data Center for AI guidelines will shorten sales cycles because you’ll be speaking the same language as your customers’ facilities and networking teams.  

The community is also growing up in governance. Seeing AMD, Arm, and NVIDIA take board seats alongside the traditional hyperscalar leadership matters; the next three years will be defined by choices about link layers, liquid classes, telemetry standards, and power domains. Having the architects at the table can keep us on a path where silicon diversity is a feature, not a headache.  

At the scale AI now demands, you can’t buy your way out of physics, and you can’t vendor-lock your way to speed. OCP is where the industry is deciding, together, how we wire the next decade of compute. Open is no longer the alternative—it’s the plan.

Today, Arm took a trailblazing step toward its bold vision to innovate the future of converged AI data centers.

During the Open Compute Project (OCP) Foundation’s 2025 Global Summit, the OCP Foundation announced the company as the latest board of directors member, underscoring the relative importance of Arm processors to the future of hyperscale and AI data centers.

Arm also made a splash, announcing a new contribution to the OCP ecosystem: a vendor-neutral Foundation Chiplet System Architecture (FCSA), with plans to drive further innovation up the compute stack.

“Every AI data center today is chasing densification—packing as much compute as possible into every rack,” said Eddie Ramirez, vice president of go-to-market for infrastructure at Arm. “That means we’re talking about racks that consume the equivalent power of 100 homes. Efficiency isn’t just an advantage anymore; it’s a survival requirement.”

With its new FCSA spec, Arm is doubling down on openness, efficiency, and co-design across the semiconductor supply chain, driving toward establishing vendor-neutral chiplet interoperability. The company also unveiled new growth in its Arm Total Design ecosystem, which has tripled in size since its 2023 launch. Together, these moves underscore Arm's strategy to drive innovation as the data center industry shifts from commodity servers to “rack-level systems and large-scale clusters designed specifically for AI.”

“We’ve reached a point where we’re not just bringing our standards into OCP,” Ramirez added. “We’re bringing the entire Arm Total Design ecosystem.”

From Efficient Cores to Efficient Racks

Arm’s contribution to OCP comes at a pivotal moment. Across the industry, power-hungry AI workloads are reshaping data center design—from servers to racks to entire campuses. The latest AI systems push rack-level power densities to previously unthinkable levels, multiplying power consumption tenfold, and redefining the physics of deployment.

That message plays directly to Arm’s long-standing strength: energy-efficient computing. Arm’s low-power architecture has already enabled hyperscalers like AWS, Google, and Microsoft to deliver significant total cost of ownership (TCO) advantages in the cloud. Now, as AI demand accelerates, those same principles are being applied to massive, heterogeneous data center systems where every watt counts.

Redefining Integration: From Boards to Packages

While OCP has historically focused on modularity at the server level, Arm sees a shift happening inside processor composition itself. AI accelerators now combine compute, networking, and memory into highly integrated System-on-Chips (SoCs) composed of multiple chiplets—discrete dies that can be mixed and matched to optimize performance, cost, and power.

This evolution demands a new kind of open standard, Ramirez said.

“The integration point is moving from the server board to the silicon package,” he said. “AI SoCs now use chiplets for HBM memory, compute, IO, NPUs, and more—all married together. The Foundation Chiplet System Architecture enables that same modularity and interoperability at the silicon level.”

By contributing FCSA to OCP, Arm aims to enable companies to develop interoperable chiplets that can be reused across multiple products—expanding opportunities for smaller design houses and accelerating the overall pace of semiconductor innovation.

Arm Total Design: Scaling the Ecosystem

FCSA builds on momentum from Arm Total Design, a global collaboration launched two years ago to bring foundries, design houses, IP vendors, and manufacturers together to shorten design cycles and reduce development costs.

The ecosystem now includes 36 members, with 10 new additions debuting at OCP—among them Astera Labs, Credo, Eliyan, Marvell, Alchip, ASE, CoAsia, Insyde Software, Rebellions, and VIA NEXT. These companies span the AI SoC value chain, from IO chiplets and interconnect technology to advanced 3D packaging and die-to-die communication.

That infusion of collaboration is designed to create what Ramirez called a “mix-and-match” future where companies can specialize in a single chiplet and integrate it seamlessly into others’ designs through common frameworks and interfaces.

Driving Open, Sustainable AI Infrastructure

Beyond silicon innovation, Arm’s engagement with OCP reflects a broader sustainability and openness agenda. With AI driving exponential energy consumption, efficiency has become inseparable from environmental responsibility.

“The old approach of multiplying power per rack by ten is simply not sustainable,” Ramirez said. “We need to deliver performance gains through efficiency, not escalation.”

He emphasized that Arm’s OCP participation will prioritize vendor-neutral, open ecosystems and collaboration, including among traditional competitors from the x86 ecosystem. The company’s leadership role in OCP’s chiplet work group aims to expand participation from both large and small players, strengthening the global chiplet supply chain.

Looking Ahead: Beyond Training to Inference

While much of the AI infrastructure discussion centers on massive training clusters, Arm is also looking toward the next frontier: inference. Ramirez noted that future OCP efforts will increasingly focus on inference workloads closer to the edge, where latency, efficiency, and scalability drive different architectural requirements than the mega-racks built for model training.

These dual tracks—AI training and inference—mirror the broader compute evolution from cloud to edge, and from monolithic design to modular intelligence.

The TechArena Take

Even five years ago, thinking of Arm as an OCP board member was beyond believable. Arm’s ascendency as a critical player in hyperscale and AI infrastructure has been swift and impressive. The FCSA contribution to OCP could mark a pivotal shift in how the semiconductor industry collaborates moving forward, further underscoring Arm’s relative influence. By opening chiplet design to vendor-neutral standards, Arm is moving the ecosystem closer to the “plug-and-play” era of heterogeneous computing—one where silicon innovation can scale as fluidly as software.

As rack-level AI architectures push power and complexity to their limits, Arm’s strategy—anchored in efficiency, interoperability, and co-design—positions it at the heart of the industry’s most urgent transformation.

Check out Arm’s post for more information.

AMI CEO Sanjoy Maity joins In the Arena to unpack the company's shift to open source firmware, OCP contributions, OpenBMC hardening, and the rack-scale future—cooling, power, telemetry, and RAS built for AI.

As AI spreads into every corner of the technology ecosystem, the industry is under mounting pressure to measure real performance consistently and transparently. Whether it's hyperscale training runs, edge deployments, or domain-specific AI applications like automotive, the need for shared benchmarks is coming into sharper focus.

At the AI Infra Conference in Santa Clara, Jeniece Wnorowski and I sat down with David Kanter, Founder of MLCommons and Head of MLPerf, for a Data Insights conversation that captured a pivotal moment for the benchmarking community. 2025 has been, as Kanter described it, “the summer of MLPerf.”

Benchmarking Matures as AI Expands  

“This has actually been a really breakout year for us,” Kanter said. “...In the last couple months, I was jotting an e-mail down to the team and I was describing it as the summer of MLPerf.”  

The momentum is tangible. He went on to describe new initiatives that reflect how AI adoption is spreading into new domains.

“We talked about MLPerf Storage. We also have MLPerf Automotive, which came out very recently, [and] MLPerf Client. And so, part of this is, as we’re seeing AI being adopted in more and more places, we have to come in and help fill those gaps. Storage was sort of us working with some storage folks to spot some coming challenges and...automotive was sort of in our response to the automotive folks saying, ‘You know, OK, we are going to be using more AI, we’re making more intelligent vehicles, we need to get our hands around this.’”

These expansions reflect the growing role of benchmarking beyond traditional training and inference in hyperscale environments. Storage and automotive, in particular, highlight the diversification of AI workloads across industries.  

Still in “Benchmarking Grade School”  

Kanter offered a candid perspective on MLPerf’s age compared to other well-established benchmarks.

“Some of the most established benchmarking organizations that we look up to are 30 years old and they’ve been honing their craft,” he said. “We’re seven years old...We’re still in grade school. The babies of benchmarks.”

That self-awareness underscores the pace at which MLPerf is evolving. Unlike existing benchmarks that matured slowly over decades, MLPerf operates in a landscape where new accelerators, models, and deployment modalities emerge every quarter.

“Just keeping a pace of things is both exciting, a little stressful, and a bit of a challenge,” he said.  

Building Community Through Standards  

Kanter emphasized that MLCommons isn’t just about benchmarking—it’s a volunteer-driven community.  

“I always say to anyone...if any of these resonate with you, please show up. We are a community of volunteers...It came together with a bunch of folks who just saw a problem and said we all want to solve it together.”  

Beyond MLPerf, he stressed that MLCommons works on a ton of projects around data including AI risk and reliability, research, and foundational datasets and schemas.

“How do we standardize making data accessible to AI?” he said. “How do we make AI more reliable and responsive to what humans want?”  

That openness has been key to MLPerf’s rise as the de facto performance yardstick across the AI ecosystem. Submissions from vendors large and small now shape how the industry evaluates real-world performance.  

Why Benchmarks Matter Now  

As enterprises wrestle with where to deploy AI—in centralized facilities, at the edge, or in hybrid environments—benchmarks like MLPerf give them objective tools to evaluate trade-offs. They’re also increasingly relevant for sustainability strategies, allowing organizations to understand performance per watt or per dollar across platforms.  

In an era of rapid infrastructure buildout and diversification, shared benchmarks provide a common language for vendors, operators, and developers alike.  

TechArena Take  

Benchmarking is becoming a strategic force in AI infrastructure. What started as a niche performance measurement initiative has grown into a foundational layer that shapes how chips are designed, systems are built, and deployments are optimized.

David Kanter’s reflections highlight both the rapid maturity of MLPerf and the youthful dynamism of a community racing to keep up with AI’s evolution. As AI spreads into storage systems, automotive environments, and edge devices, the role of shared, open benchmarks will only deepen.  

The bottom line: In the AI era, you can’t scale what you can’t measure. And MLCommons is ensuring the industry has the tools—and the community—to do just that.

Watch the full podcast

To keep up with AI’s relentless pace, companies are innovating on every aspect of technology inside the data center, from silicon, storage, network, and compute systems to the power and cooling technologies that support data center facilities. This innovation extends to system power delivery, where major efforts are being made to re-architect foundational power delivery technologies from the ground up.

The Problem: Power Constraints in the AI Era

Hyperscale operators are now planning gigawatt-class data centers designed to consume more than 1 billion watts of power. At the rack level, individual GPUs frequently draw more than 1,000 watts, compared to CPUs that average a third of that, and multi-GPU servers can easily top 8,000 watts per node. As a result, racks that once operated comfortably at 10–20 kilowatts are being pushed toward megawatt-class racks.

Legacy facilities, sized for lower-density server racks, struggle to manage the heat and stable power delivery for GPU-dense configurations that can surge from idle to peak in milliseconds. Distribution losses remain stubborn: roughly 10% of total data center power is lost in delivery and conversion. The net effect: power delivery is becoming a critical limiting factor to scaling AI compute.

The AI Factory Owner’s Pain Points

Data Center operators know the struggle:

• Cable trays hit limits fast. Bulky copper cables consume space and block airflow.
• Rear-of-rack congestion slows liquid-cooling retrofits. Bend radius and routing constraints make every change a headache.
• Commissioning drags. Crews battle heavy conductors, torque bulky terminations, and chase faults in overcrowded terminations.
• Losses remain stubborn. Roughly 10% of data center power vanishes in distribution and conversion.
• Safety and sustainability risks escalate. Every kilogram of copper is embodied carbon today and represents potential e-waste tomorrow.

In short, today’s power delivery methods are colliding with tomorrow’s density—and that friction shows up as schedule risk, safety risk, and escalating OpEx. This isn’t a problem of inefficiency; it’s a problem of scalability. The rack-level power model built for CPU loads has collapsed under GPU surges.

This surge is stressing infrastructure in several ways:

• Distribution loss: Every extra power conversion stage bleeds watts; long copper runs add resistive power loss, forcing overprovisioning and increasing cooling requirements.
• Physical bulk: Heavy gauge round wire conductors are hard to route, consume white space, constrict airflow, and cap rack density.
• Installation risk: Bulky terminations increase touchpoints and invite human error in assembly.

Rethinking Power Delivery in Alignment with OCP’s Core Tenets

Meeting AI demand isn’t about adding thicker cables or more copper layers in a printed circuit board—it requires a fundamentally new power delivery model. We need solutions that are lighter, denser, more efficient, and built for automation, so operators can deploy faster, pack more compute per unit footprint, and cut distribution losses.

CelLink’s solutions advance the Open Compute Project Foundation’s core tenets of Efficiency, Impact, Scalability, Openness, and Sustainability.

CelLink’s Breakthrough Solution

CelLink already has a track record of driving power delivery innovations in the automotive industry through the deployment of over three million flex harnesses into electric vehicles on the road, and we also have a presence in industrial, aerospace, and power storage applications. We’re now turning our attention to the largest power delivery challenge in the world today: Flex harness technology that redefines what’s possible in data center power delivery. Our solutions aim to address present-day core challenges of power delivery, while also unlocking robotic assembly for systems and removing manual wire terminations and the errors and delays that come with them.

Across the industry, designs are moving toward 1 megawatt IT racks supported by liquid cooling. CelLink’s flex harnesses, already capable of carrying 1 megawatt per harness in the automotive industry, align with that trajectory by freeing rear-of-rack space for cooling hardware, simplifying tight cable routing in high-density racks, and enabling automation-first builds that keep pace with liquid-cooled deployments. Our POC demonstration at the OCP Summit will showcase the integration of liquid cooling directly into power delivery.

Potential benefits for AI factory owners include:

• Greater Compute Density: Thinner, patterned-to-shape cabling means more room for compute hardware per rack.
• Lower material use: Less conductor mass translates to a reduced environmental footprint.
• Compatible with automation: Robotic assembly and a software-defined, reproducible conductor footprint streamlines buildouts, eliminates manual error, and improves field reliability.
• Faster construction: Lighter, patterned-to-shape harnesses and robotic terminations compress install, QA, and rework time cycles.
• Efficiency gains: Backside liquid cooling of vertical power VRMs keeps VRMs operating at high efficiency, reducing waste heat by tens to hundreds of watts per server.
• Future-proofs infrastructure: Positions operators to handle the surge toward gigawatt campuses and megawatt-class racks without being choked by legacy cabling constraints.

What’s Next  

With energy consumption projected to double by 2030, solving power delivery is not optional. CelLink’s innovation represents more than a clever engineering tweak; it signals a revolution in power delivery. By replacing bulky round wire cabling with a flat alternative, server manufacturers gain a clear path to building AI factories sustainably and at speed.

The industry has reimagined compute, networking, and cooling. Now it’s time to reimagine power delivery. Check out this tech brief for more details on CelLink’s solutions and connect with CelLink in the Innovation Village at the OCP Summit in San Jose, October 13-16, to learn more.