Empowering Innovation
with Solidigm

At NVIDIA GTC 2025, Cloudflare shared an exciting vision for the future of AI, automation, and developer tools. During a conversation with Scott Shadley, Director of Leadership Narrative and Evangelist at Solidigm, Aly Cabral, Cloudflare VP of Developer GTM, explained how they are becoming a critical player in the rapidly evolving tech landscape. As industries shift and change, Cloudflare’s focus is clear: empowering developers to navigate these transformations with the right tools and ample support.

One of the main topics of discussion was agentic AI, and how to define the loosely used term that’s been all the rage for next-gen AI predictions. Simply put, agentic AI goes beyond traditional automation by enabling systems to make decisions and manage more complex, dynamic tasks autonomously. While automation improves efficiency, agentic AI adds intelligent oversight, making it easier to both monitor and manage automated systems. Aly emphasized that automation alone isn’t sufficient — it’s about creating systems that are not only efficient, but also transparent and easy to troubleshoot. Cloudflare’s Workflows product addresses this by giving developers visibility into complex systems, helping them quickly identify and resolve issues in multi-step processes. This capability is becoming even more essential as automation plays a larger role in development.

In addition to automation, CodeGen tools are emerging as valuable resources for developers. These AI-powered tools simplify the coding process, allowing developers to generate code faster and with less effort. However, as Aly pointed out, the real challenge lies not in creating applications, but in managing and maintaining them over time. Cloudflare’s platform is built to support developers throughout the entire lifecycle of an application — from creation to long-term management — ensuring that systems remain scalable, secure, and efficient as they evolve.

Looking forward, Cloudflare is doubling down on AI and developer tools. As Aly mentioned, the company is preparing for Developer Week in April, where they’ll unveil new launches and innovations aimed at improving the developer experience. With new features and tools focused on simplifying the development process and harnessing the power of AI, Cloudflare is working to ensure developers have everything they need to create smarter, more scalable applications.

So, what’s the TechArena take? Cloudflare’s approach to partnerships sets them apart. Rather than locking customers into proprietary ecosystems, Cloudflare prides itself on being a connector, offering a globally distributed network and an open ecosystem that integrates well with a wide variety of third-party services – without an aggressive egress tax. This flexibility allows developers to use the best tools for their needs without being restricted to a specific platform. It’s this open approach that makes Cloudflare an ideal partner for companies like Solidigm, who offer unique solutions that complement Cloudflare’s services.

Watch the full video here. Learn more about Solidigm’s data storage solutions for the AI era here.

For those interested in staying connected with Cloudflare’s latest developments, the company maintains an active Discord community, YouTube channel, and X presence, providing ample opportunities for engagement and learning. Or visit their website.

I recently had the opportunity to sit down with Jeniece Wnorowski of Solidigm, and George Crump, Chief Marketing Officer at Verge.io, to discuss how Verge.io is taking a fresh approach to IT solutions.

The Verge.io solution is game-changing for enterprises looking to optimize their IT infrastructure. Instead of relying on the traditional method of integrating different components through a GUI (Graphical User Interface), Verge.io has gone a step further by combining all aspects of IT infrastructure—networking, storage, and virtualization—into a single, unified code base. This results in a seamless user experience coupled with a dramatic improvement in performance - all while lowering hardware requirements.

By integrating these components, Verge.io improves efficiency and increases hardware flexibility. George shared that Verge.io supports hardware up to seven years old, making it highly adaptable for organizations with legacy systems. This innovation stems from Verge.io’s founding story — Greg Campbell, Verge.io’s CTO, was initially frustrated by the amount of time he spent managing infrastructure while developing a search engine to compete with Google and Amazon – so he decided to build his own solution. Today, this approach has resulted in a product that runs on less than 300,000 lines of code, ensuring fewer bugs and greater reliability compared to traditional solutions that often operate on millions of lines of code.  

The solution also addresses one of the most pressing concerns for IT professionals today—cost predictability. Verge.io’s simple licensing model charges per server rather than by core or capacity. This straightforward pricing model appeals to a wide range of IT professionals. By supporting multi-tenancy, Verge.io makes it easier to manage resources across various clients, delivering efficiency and flexibility in a shared environment.

As AI continues to shape the future of IT infrastructure, Verge.io’s platform is designed to be AI-ready. George highlighted the growing importance of AI in enterprise workloads, particularly as organizations explore the use of private AI models. Verge.io is addressing the challenge by ensuring its platform can easily integrate GPUs for AI workloads.

One of the key factors that sets Verge.io apart from others in the market is its approach to migration. As George pointed out, migration is essential when transitioning to a new infrastructure. He also shared that Verge.io’s approach ensures minimal downtime, enabling businesses to shift their data and settings with greater efficiency. This streamlined migration process is key to Verge.io’s ability to deliver a seamless experience for their customers, no matter the scale.

In addition to seamless migration, Verge.io focuses heavily on reliability through its integrated platform. George pointed out that the system continuously monitors the environment, ensuring operations run smoothly even during network disruptions. For example, he described how Verge.io's platform maintained data integrity during network failures when traditional infrastructure would have struggled to do the same.

For enterprises considering storage solutions, Verge.io is leveraging Solidigm’s storage technology to optimize performance and lifespan. George shared how the platform supports various classes of SSDs, including QLC and TLC drives, and integrates them seamlessly into the infrastructure to meet workload demands. This approach ensures that organizations can optimize performance while managing their storage needs efficiently.

So, what’s the TechArena take? Verge.io’s unified infrastructure solution is reshaping the way organizations manage their IT environments. With a focus on cost predictability, AI-readiness, and seamless migration, Verge.io presents a compelling option for businesses that are looking to simplify their infrastructure and improve efficiency.

Listen to our full discussion here.

In this Data Insights episode, Andrew De La Torre discusses how Oracle is leveraging AIOps to enable automation and optimize operations, transforming the future of telecom.

During GTC, Solidigm’s Scott Shadley and Dell’s Rob Hunsaker, director of engineering technologists, discussed how Dell is tackling the challenges of AI data infrastructure with cutting-edge solutions.

Tune in to our latest episode of In the Arena to discover how Verge.io’s unified infrastructure platform simplifies IT management, boosts efficiency, & prepares data centers for the AI-driven future.

Join us on Data Insights as Mark Klarzynski from PEAK:AIO explores how high-performance AI storage is driving innovation in conservation, health care, and edge computing for a sustainable future.

When you think of Gigabyte, gaming hardware probably comes to mind. But this Taiwan-based computer hardware manufacturer develops much more than motherboards and graphics cards – they provide a spectrum of computer hardware as well as liquid and immersion cooling and have a long history of contributing to open standards and advancing server technologies.

I recently had the pleasure of chatting with Chen Lee, VP of Sales, HPC, Data Center and Enterprise for Giga Computing and learned a fascinating tidbit about how the company became involved in the Open Compute Project Foundation (OCP).

“Around 2004, this very little-known company came to us and said, ‘We’ve got a search engine, and we want to build this motherboard and this thing called OpenRack,’” Chen explained.

The little-known company was Google, he said.

“So that's how we got into OCP,” Chen said. “(Gigabyte was) actually the first company to help Google develop open compute.”

Gigabyte’s collaboration with Google on OpenRack marked the company’s entry into the open infrastructure movement, making them one of the initial contributors to OCP standards.

Today, Gigabyte’s portfolio extends beyond Intel and AMD servers — they also produce Arm-based solutions using Ampere technology and specialize in advanced cooling systems like immersion and direct-to-chip liquid cooling. With this holistic approach, they continue to drive efficiency and performance in the data center space, reflecting their adaptability and forward-thinking approach.

Embracing AI: Gigabyte's Focus on GPU Servers

Artificial Intelligence (AI) has reshaped the demands on data centers, particularly in terms of computing power and infrastructure. Chen discussed how Gigabyte has been positioning itself in the AI hardware game, particularly through high-density GPU servers. He shared a pivotal moment for Gigabyte in 2010 when they introduced a 2U server that could support eight double-wide, dual-link GPUs, which at the time was the highest density on the market.

Today, Gigabyte’s expertise in GPU servers continues to be an asset, providing systems for AI model training and inferencing, using cutting-edge GPUs like Nvidia’s H100 and soon, Blackwell. As AI shifts towards edge deployments, Gigabyte is also preparing for the growing importance of edge inferencing, which Chen predicts will be a significant area of growth in the near future. Industries such as medical, finance, and retail are moving fast to adopt AI solutions at the edge, from convenience store smart shelving to real-time customer analytics. Gigabyte is ready to meet these needs with high-performance, scalable server technology that suits the unique challenges of edge computing.

Liquid Cooling and Efficiency in Data Centers

The demand for powerful servers to support AI training and inferencing has pushed energy consumption to unprecedented levels, making cooling a top priority. Chen highlighted how immersion and direct liquid cooling are allowing Gigabyte to manage energy efficiency better while meeting the needs of customers working on advanced AI projects. It’s a testament to the company’s adaptability and focus on sustainable solutions—aligning well with the OCP’s values of open innovation and energy efficiency.

AI Beyond the Data Center: The Future of Inference

Chen and I also discussed moving from centralized data center training to inferencing at the edge. Today, most inferencing still happens within large data centers, using high-power systems designed for training. But Chen believes that as AI technologies mature, edge inferencing will become critical—allowing smaller, more efficient hardware to perform tasks where the data is generated, such as in retail stores, hospitals, and banks.

Chen shared an interesting example involving a convenience store, where AI systems can detect customer behavior in real-time and use edge servers tucked away in the back to provide analytics directly to the headquarters. The potential for rapid, on-site AI-driven insights will push industries to adopt smaller-scale AI inferencing solutions—a market that Gigabyte is well-positioned to serve.

This shift to the edge will transform how AI is implemented across industries, bringing smarter technology closer to users and changing how data centers interact with local environments. Chen also shared that, in his view, AI isn’t just a passing trend—it’s a new wave that’s here to stay.

So what’s the TechArena take? As AI evolves and the infrastructure to support it becomes more advanced, hats off to Gigabyte for doubling down on its strengths—high-performance GPU servers, innovative cooling technologies, and partnerships with leading hardware and storage vendors.

Thanks to Solidigm for sponsoring this delightful Data Insights discussion. In case you missed it, check out the full episode here. As AI and edge computing continue to advance, the innovations coming from companies like Gigabyte are paving the way for the data centers of tomorrow.

While AMD has been consistent in recognizing the new demands of AI-enabled applications, the company remains steadfast in ensuring that AMD EPYC^TM  processors continue to offer leading performance for traditional compute workloads, such as HPC, database, cloud native applications, collaboration systems, finance, and more.

I recently caught up with Ravi Kuppuswamy, AMD Senior Vice President of Server Product & Engineering, to explore the company’s approach to the evolving landscape of enterprise workloads, hyperscale innovation, and the growing influence of AI.

Traditional compute applications are also adapting and adding elements of AI into their application environment, he said.

“In a wide array of apps from Microsoft, Oracle, SAP, we see them adding AI-enhanced tools such as recommendation engines, chatbots, into their application,” he said. “While massive AI models are indeed a significant step…the vast majority of real world applications still are more evolutionary and focused on general compute.”

This dual focus allows AMD to serve diverse customer needs, ensuring that cutting-edge AI capabilities don’t overshadow the ongoing importance of reliable, efficient traditional computing.

A Portfolio Built for Versatility

AMD's diverse portfolio spans CPUs, GPUs, AI NICs, and more, offering flexibility for a wide range of customer requirements. Kuppuswamy described this strategy as “letting customer needs guide the discussion,” highlighting how AMD supports everything from cost-effective solutions to high-performance configurations.

For workloads requiring heavy training or models exceeding 13 billion parameters, AMD’s CPU-GPU combinations, such as the recently launched MI300 series, provide the scalability and efficiency necessary for advanced AI applications. This approach ensures that customers can select solutions tailored to their specific operational goals and budgets.

Hyperscale Design and Energy Efficiency

During the OCP Summit, hyperscale configurations took center stage. Kuppuswamy explained how AMD collaborates with customers to design systems optimized for evolving data center demands. The focus on energy-efficient design is critical, as global technology-related energy consumption rises in tandem with increasing data generation.

AMD’s commitment to open standards plays a significant role in these efforts. By embracing interoperability, AMD fosters innovation that benefits hyperscalers as well as enterprises looking to leverage cutting-edge technology without proprietary limitations.

The Enterprise and Cloud Continuum

Enterprises are increasingly adopting hybrid models that combine on-premises and cloud computing. Kuppuswamy highlighted how AMD technologies enable customers to build robust on-premises infrastructures while seamlessly scaling to the cloud when demand spikes.

This flexibility is especially valuable for enterprises that lack the resources of hyperscalers.

Impact and Future Vision

AMD’s leadership in the data center market has grown significantly, with a remarkable rise in market share from less than 1% to 34% in recent years. This growth underscores the appeal of AMD’s energy-efficient solutions and customer-first approach.

Looking ahead to 2025, Kuppuswamy anticipates a wave of IT infrastructure upgrades driven by outdated systems nearing the end of their lifecycles. He highlighted the dramatic efficiency improvements offered by the latest generation of AMD EPYC processors: replacing 1,000 four-year-old CPUs with just 131 new-generation processors delivers the same workload performance, with significantly reduced power and space requirements.

Collaboration and Open Standards

One of the most surprising announcements at the OCP Summit was the launch of the x86 Ecosystem Advisory Group, a collaboration between AMD and its key competitors. The initiative aims to establish common standards for compatibility and interoperability, reflecting the company’s commitment to open ecosystems.

So what’s the TechArena take? As data becomes increasingly distributed across edge and cloud environments, AMD solutions empower customers to extract value from this continuum. From the high-performance EPYC 9000 series for data centers to Ryzen-powered endpoints, AMD offers a comprehensive portfolio designed for efficiency and scalability. This adaptability is critical in a world where businesses and consumers demand instant access to data and services. 

Tune in to our Data Insights podcast with Kuppuswamy. For those seeking more insights into AMD data center technologies, Kuppuswamy encouraged audiences to explore resources on their website and social media platforms.

Ocient is disrupting the data warehousing space by offering a unified data platform that optimizes for always-on, compute-intensive data and AI workloads. This hyperscale enterprise data warehouse platform enables swift transformation and analysis of petabyte-scale data at speeds 10- to 50 times faster than competitor solutions - at a disruptively better price.

I recently had the pleasure of learning more about Ocient from Vice President of Marketing Jenna Boller Chorn during the Open Compute Project (OCP) Summit. Jenna joined Jeniece Wnorowski of Solidigm and me to discuss Ocient’s pioneering data warehousing technology and its impact on the industry. 

Ocient’s platform consolidates various capabilities into a single system, eliminating the need for data movement between disparate platforms.

“By bringing more capabilities directly to their data in one platform, our customers typically realize a 50 to 90% savings in cost, system footprint, and energy consumption,” Jenna said.

Ocient’s unique architecture centers on a concept known as Compute Adjacent Storage Architecture (CASA), which tightly integrates compute and storage layers. Ocient’s technology leverages Solidigm’s NVMe SSDs instead of traditional hard drives, providing the speed and performance essential for real-time data management and analysis.

Jenna emphasized Ocient’s commitment to innovation at the software level for higher performance and lower operational costs.

“We’re really focused on maximizing that efficiency benefit, having a really tight data layer at the foundation,” Jenna said.

As companies prepare for increasingly data-driven and AI-intensive workloads, Ocient’s technology is built to help manage costs and performance at scale. Jenna noted that traditional data warehousing models have often focused on elasticity and convenience, allowing users to spin up new environments quickly. This approach can lead to unpredictable costs.

“We’re starting to see with customers that operate on a very always-on basis, the cost quickly gets out of control and actually becomes very unpredictable for them,” Jenna observed. “It’s not uncommon for me to talk to customers who are aggressively deleting data and introducing constraints on their environment to manage costs.”

Ocient’s technology allows companies to control these expenses by streamlining data processing, preparation, and exploration stages directly on the platform.

“As customers need to do more, particularly in the age of AI, they’re going to need to be more efficient at that core foundational layer,” Jenna noted.

Ocient’s approach reduces the need to transfer data between systems, which minimizes security risks and operational overhead.

The company also supports AI initiatives by incorporating in-database machine learning capabilities, allowing clients to train and deploy models directly within Ocient’s platform.

“We launched Ocient ML last year, bringing ML directly to the data in Ocient,” Jenna said. This functionality enables clients to explore, prepare, and process data with fewer resources and less time, making the pipeline for predictive AI and general AI more streamlined and cost-effective.

Customer satisfaction is a high priority for Ocient, especially as clients tackle high-compute tasks requiring seamless integration with existing systems. Jenna described the role of Ocient’s Customer Solutions and Workload Services Team, which helps clients manage data pipelines and identify ways to optimize processing and pre-processing tasks. This hands-on support ensures that Ocient’s clients can realize the platform's benefits from day one, which has led to a high retention rate among their users.

“By the time they go with Ocient, they've already seen everything working, and they're realizing value from day one,” Jenna highlighted. “That drives incredible stickiness with our customers.”

Ocient’s SSD-exclusive architecture means that clients can avoid energy-intensive hard disk drives while still achieving the performance levels they require.

“From our first day, we’ve always optimized for performance at scale and for efficiency,” Jenna said. By showing clients the comparative footprint of legacy systems versus Ocient’s solution, Ocient often reveals a 50 to 90 percent reduction in operational costs and energy consumption.

Jenna also expressed hope for more transparency around the energy consumption of software applications, particularly as AI applications increase in popularity.

So what’s the TechArena take? Hyperscale data environments are projected to account for over 50% of global data center capacity by 2026, so, in short, Ocient is in the catbird seat. Their technology is well-positioned to serve organizations requiring high-efficiency, large-scale data solutions.

With continued innovations, strategic partnerships, and a firm commitment to efficiency and sustainability, Ocient is leading a transformative shift in how organizations manage and leverage their data. And their  commitment to sustainable practices help set it apart in a rapidly changing industry.

Interested in learning more? Listen to the full podcast and visit Ocient.com or connect with them on LinkedIn.

As hyperscalers, supercomputing operators, and advanced enterprises globally rush to modernize and expand operations, CoolIT has emerged as a key partner for data center cooling – providing efficient, reliable solutions that have been proven in the market for 20 years.

Liquid cooling – once the target of skepticism across a data center industry that balked at its complexity and didn’t need it yet – has exploded on the data center scene as one of the key enablers of heavy AI workloads. During the OCP Summit 2024, new liquid cooling entrants could be seen in every direction. And what’s more, established players in other parts of data center infrastructure have begun planting liquid cooling flags.

It’s more than apparent that the opportunity is gargantuan. But there are few liquid cooling players that come to this data center modernization party with 20 years of expertise. CoolIT is one of them.

I thoroughly enjoyed the opportunity to sit down during OCP Summit with Charles Robison, Director of Marketing for CoolIT Systems, to learn more about this critical player in the AI - data center landscape.

From Chips to Data Centers

CoolIT’s liquid cooling tech has its roots in making cooling chips for high-performance gaming platforms. This foundational knowledge of chip-level cooling enabled the company to quickly pivot when data center demand began to skyrocket.

Today, CoolIT’s offerings include cold plates and advanced cooling loops designed to support Original Equipment Manufacturers (OEMs) and Original Design Manufacturers (ODMs).

But as Charles highlighted, its core offering is cold plate technology, which targets hotspots on chips, delivering focused cooling where it’s needed most, ensuring systems operate optimally even under high workloads.

Why the Focus on Direct Liquid Cooling

In the liquid cooling industry, various methods are available, including immersion cooling and rear-door heat exchangers. CoolIT focuses on single-phase direct liquid cooling (DLC) because it is proven, reliable, and scalable. With liquid cooling technologies tested across generations of servers from brands like HPE and Dell, CoolIT’s cold plates stand out for their reliability. Single-phase DLC is particularly effective for handling high thermal design power (TDP) in modern chips, including hot spots that require targeted cooling. By focusing on DLC, CoolIT addresses both the demand for scalable solutions and the ability to cool today’s high-powered chips.

While CoolIT recognizes the value of other cooling approaches, such as immersion cooling, DLC remains the most feasible option for wide-scale deployment. As Robinson explained, “Our technology has been through multiple generations… it’s a proven technology and a scalable technology.” This commitment to proven solutions ensures data center operators have reliable and consistent performance, a necessity in an industry where operational continuity is crucial.

CoolIT’s reputation as an end-to-end provider has been bolstered by their ability to cover every aspect of data center cooling. They supply a full suite of products, from cold plate loops that deliver direct cooling to chips, to Coolant Distribution Units (CDUs) that manage the overall cooling flow. This comprehensive approach ensures that customers receive a streamlined solution, custom-tailored to their needs – making CoolIT’s systems a reliable choice in a high-stakes environment where consistency is paramount.

Charles aptly pointed out that when Jensen Huang of NVIDIA announced liquid cooling as the future of data centers, it marked a turning point for the liquid cooling segment. Once confined to high-performance computing (HPC) and academia, it is now erupting onto the data center infrastructure scene as a solution that significantly enhances energy efficiency.

“Liquid cooling has crossed the chasm; it’s now a mainstream approach,” Charles said.

With their single-phase direct liquid cooling, data centers can handle AI-driven workloads that push conventional cooling to its limits.

Quality. Service. Support.

“Quality, as they say, is job one,” Charles said, explaining CoolIT’s top priorities.

Reliability is at the center of CoolIT’s approach. The cooling systems they produce are meticulously designed, using technologies like friction stir welding to create a single, molecular-level fusion of cold plates. This construction minimizes the potential for leaks, ensuring longevity and reliability. CoolIT employs rigorous quality control, from inspecting incoming parts to conducting end-of-line testing. Every system is tested before it leaves the factory, guaranteeing that clients, including major server manufacturers like Dell and HPE, receive a flawless product.

Beyond the product itself, CoolIT offers a comprehensive service network covering more than 70 countries, ensuring seamless deployment and ongoing support. CoolIT assists customers at every stage, from design consultation to installation and commissioning, making it easy for operators to integrate liquid cooling into their existing data centers.

“So you want to figure out how to design?” Charles said. “We'll help you with that. Would you like to install it? Yep. We've got an installation team that will come in. And we'll put together a secondary fluid network if you need…We'll help you figure that out. We'll commission it. So we'll put the fluid in and we'll actually get it running.”

Scaling for Growth

Looking into 2025 and beyond, the demand for liquid cooling will only increase as data centers handle denser and more complex workloads. CoolIT has invested significantly in expanding their production capabilities, scaling up by 25 times to meet the growing demand. This capacity enables CoolIT to handle both brownfield deployments – where liquid-to-air CDUs can be introduced to existing data centers – and greenfield projects that require a more comprehensive cooling solution from the ground up.

The scale of CoolIT’s investment in production reflects the growth potential they see in the market. As Robinson noted, “We certainly see just a massive deployment of liquid cooling… we have multi-gigawatt manufacturing capacity within our shop.” This preparation positions CoolIT as a capable partner for any data center operator looking to future-proof their infrastructure against the demands of tomorrow’s computing workloads.

Educating the Industry and Pioneering Change

To ease the industry’s shift toward liquid cooling, CoolIT emphasizes education and industry collaboration. As a founding member of the Liquid Cooling Coalition, CoolIT is committed to informing operators, policymakers, and the broader industry about the benefits and applications of liquid cooling. Through these initiatives, CoolIT hopes to normalize the adoption of liquid cooling, fostering an industry-wide shift toward more efficient, sustainable practices.

So what’s the TechArena take? I’m so grateful to Solidigm and our Data Insights series for opening the door to this delightful discussion. As for CoolIT, in short, they are rocking liquid cooling. Their solutions have proven themselves in one of the most demanding environments — data centers powering AI. And while this space is nascent and has more promise than volume deployments, as we head into the second half of the decade, we at TechArena are expecting a hockey stick ramp for this segment of the industry. We also are keen to see how CoolIT leverages this massive opportunity towards financial returns. Listen to the full podcast here.

I was lucky to catch up with Eddie Ramirez, VP of Marketing for Arm’s infrastructure business, at the recent OCP Summit. Eddie was last on the show at last OCP Summit talking about Arm’s focus on development of a data center ecosystem, and I was keen to learn about the progress the company had made in this arena. Arm’s advancements in data center technology are making a mark on innovative data center infrastructure with a focus on efficiency, chiplet innovation, scalable solution design.

During the recent OCP Summit 2024, Data Insights podcast co-host Jeneice Wnorowski of Solidigm and I had the pleasure of welcoming Eddie back to the TechArena to better understand the company’s impact across the industry. Arm’s big announcement this year at OCP Summit centered around the power of chiplets to accelerate silicon design. Chiplet technology enables multiple processing units to be combined in a single package, streamlining custom chip design. Arm’s Total Design program enables partners to adopt their cores efficiently, with configurations that cater to diverse needs, from general-purpose tasks to specialized AI processing. This modular integration approach enables flexibility, supporting efficient scaling for data centers that need adaptable configurations for different workloads.

Eight different partners within Arm’s Total Design program announced chiplet projects that they've kicked off, ranging from 16-core to 64-core setups that can be used in a variety of products. One partnership in particular brings together Samsung Foundry, a Korean ASIC design partner, ADTechnology, and Rebellions AI - a startup delivering TPU accelerators. Through this collaboration, Arm has demonstrated how its program helps deliver an integrated design that enables 3X greater performance efficiency than conventional GPU-based solutions – underscoring the power of best in breed chiplet solutions’ role in data center applications. When seeing where chiplet design is going with Arm, it comes as no surprise that this was a focus of OCP Summit, land of the hyperscalers. Arm cores have gained traction among the major players – AWS, Microsoft and Google – which all now integrate the technology in their home-grown designs – utilizing them for internal workloads as well as customer instances.

It's been in Arm’s DNA to provide compute efficient architectures. Their design delivers up to 60% higher power efficiency than x86 servers, allowing cloud providers to reduce power consumption and total cost of ownership (TCO) while achieving sustainability goals. This energy-saving approach is the key to Arm’s success with the hyperscalers, Eddie said, providing them a huge benefit and positioning Arm as an optimal choice for large-scale workloads.

With the rise of AI, the need for GPUs is amplified, especially to train large-scale models. However, CPUs remain essential, particularly for the inference stage, where AI models process data and provide real-time predictions. Unlike training, which demands high power, inference tasks can be handled efficiently by CPUs. Arm-based processors offer a cost-effective solution, balancing performance with reduced energy consumption.

Arm’s reach extends beyond computing into networking and storage within the data center. Arm cores are now embedded in top-of-rack switches, data processing units (DPUs), and baseboard management controllers (BMCs), enhancing efficiency in high-speed data transmission and storage. By deploying ARM cores across types of infrastructure, data centers achieve better resource management and power optimization, aligning with performance demands from AI workloads. This integrated approach allows data centers to streamline operations and enhance energy efficiency at every level.

Arm’s Neoverse platform - the company's infrastructure-focused product line – includes high-performance cores and interconnect IPs for data centers and edge environments. Neoverse’s adaptable architecture enables Arm partners to integrate the latest technology and expand it with additional I/O or storage features.

V3 of the Neoverse platform enhances Arm-based systems’ performance and flexibility, making them suitable for AI and data processing applications. This scalable approach enables data centers to meet growing performance needs without compromising power efficiency.

So what’s the TechArena take? I love chiplets and love what Arm is doing with an ecosystem. This design innovation makes sense for a wide array of use cases, and Arm’s foundation will help the industry move further, faster. Arm’s commitment to energy efficiency, modularity, and open collaboration also aligns well to Open Compute Project tenets, transforming data center infrastructure and offering true differentiation in a crowded field. Through programs like Total Design and platforms like Neoverse, Arm is responsibly building efficient and scalable solutions that meet the demands of AI, cloud, and edge applications.

There is a lot of disruption in the compute landscape with AI acceleration taking center stage. I see two paths of opportunity for Arm…one as a “head node” alternative to x86 with noted energy efficiency advantages, the other as a chiplet core with integration of TPU or other acceleration chiplets as alternative to GPU. Both are exciting to see gain traction in the market, and we’ll keep watching this space for more.

Listen to the full podcast here.

Join Allyson Klein and Jeniece Wnorowski in this episode of Data Insights as they discuss key takeaways from the 2024 OCP Summit with Scott Shadley, focusing on AI advancements and storage innovations.

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In this episode of Data Insights by Solidigm, Ravi Kuppuswamy of AMD unpacks the company’s innovations in data center computing and how they adapt to AI demands while supporting traditional workloads.

Join host Allyson Klein and co-host Jeniece Wnorowski in this episode of Data Insights as they chat with Gigabyte's Chen Lee about AI innovations and the future of server technology at OCP Summit.

Live from OCP Summit 2024, this Data Insights podcast explores how Ocient’s innovative platform is optimizing compute-intensive data workloads, delivering efficiency, cost savings, and sustainability.

Join Allyson Klein and Jeniece Wnorowski as they chat with Eddie Ramirez from Arm about how chiplet innovations and compute efficiency are driving AI and transforming data center architecture.

Learn how CoolIT Systems is driving efficiency and performance in AI and data centers with cutting-edge liquid cooling solutions in our latest Data Insights podcast.

Jeniece Wronowski and I recently got the chance to sit down with Gregory Lebourg of OVHcloud, a major European cloud provider that’s been making significant strides in the global cloud market. With a focus on sustainability, data sovereignty, and competitive pricing, OVHcloud is challenging carving out a space that’s distinctly European. Our conversation delved into OVHcloud’s unique approach, their mission, and the trends they see shaping the future of cloud computing.

One of the first things that Gregory emphasized was OVH’s identity as a European service provider. While the cloud market is dominated by American and Chinese giants, OVHcloud stands out as a provider deeply rooted on the continent, adhering to European standards and business practices. This isn't just about where they’re based, but about how they operate. Data sovereignty is at the core of their operations, and OVHcloud ensures that customer data remains protected under European regulations, providing a significant advantage for businesses looking to avoid the complexities of non-EU data jurisdiction.

Gregory leads OVH’s sustainability practices, and in terms of environmental impact, OVHcloud is setting a very high bar. They’ve adopted a circular economy approach, focusing on minimizing waste and optimizing resource efficiency across everything they do. In our chat, Gregory shared that OVH data centers are equipped with custom water-cooling systems that reduce energy consumption by up to 50% compared to traditional air-cooling methods. This innovative approach has earned them an impressive PUE (Power Usage Effectiveness) rating of around 1.1 across most of their facilities, which is significantly better than industry averages. But that’s not all. They also use refurbished servers, which helps them keep costs low while reducing their carbon footprint. OVHcloud’s data centers operate on a massive scale, with more than 400,000 servers across 33 data centers globally. Despite this scale, they’ve managed to maintain competitive pricing without compromising on performance, a feat they attribute to their sustainability practices and vertically integrated supply chain.

We also had a chance to discuss pricing with Gregory, and it became clear that OVHcloud’s commitment to affordability is about more than just competing with other cloud giants — it’s part of their mission to democratize cloud access. By controlling their supply chain and building their servers in-house, they’re able to offer services at 20%-50% lower costs than the major competitors. This cost advantage has been crucial in helping small and medium-sized businesses access high-performance cloud services that might otherwise be out of reach.

One area where OVHcloud is particularly focused is in supporting multi-cloud strategies. Businesses are increasingly looking for flexibility in their cloud environments, and OVHcloud has responded by offering a range of services that can integrate seamlessly with other providers. This approach provides customers with more choices and enables them to build cloud architectures that suit their unique needs.

In today’s digital landscape, data security and privacy are critical concerns. OVHcloud takes a strong stance on data sovereignty, a major selling point for European customers wary of foreign jurisdiction over their data. They’ve also aligned their services with GDPR (General Data Protection Regulation) requirements, which gives customers the peace of mind that their data is protected according to some of the strictest standards in the world.

During our chat, Gregory underscored their commitment to transparency and compliance. They’re actively involved in initiatives like GAIA-X, which aims to create a federated and secure data infrastructure for Europe. This aligns with OVHcloud’s long-term vision of building a robust digital ecosystem in Europe that champions trust and user control over data.

When it comes to future technologies, Gregory shared that OVH is keeping its eyes on the horizon. They’re particularly interested in quantum computing and AI, areas they believe will transform the cloud landscape in the coming decade. Their partnership with the French government on the Plan Quantum initiative exemplifies their proactive approach to these technologies. As Gregory sees it, quantum computing holds the potential to revolutionize data processing and encryption, making it a game-changer for sectors like finance, healthcare, and defense. Meanwhile, OVH is investing in AI-driven tools that will enhance cloud services, offering more intelligent insights and automation options for customers.

So what’s the TechArena take? After our conversation, I walked away with a sense that OVHcloud is setting a very high standard for innovative cloud services, designed for their market and aimed at delivery with sustainability in mind. Services are high-performance, affordable, and sustainable, reflecting European customer priorities. Their commitment to data sovereignty is particularly timely, as businesses are becoming increasingly pressured to manage these aspects to keep aligned with government regulations. OVHcloud’s approach is refreshing in a market dominated by a few powerful players. For businesses in Europe and beyond, OVHcloud is proving to be a compelling alternative to the usual suspects, and I’m excited to see how they continue to evolve in the years to come.

Listen to the full conversation with OVHcloud here.

During this episode of Data Insights sponsored by Solidigm, Grégory Lebourg – Global Environmental Director at OVHcloud – discusses how companies can meet their environmental goals effectively.

I love hearing from providers on how they’re grappling with delivery of cloud services to support customer adoption of AI. Jeniece Wronowski and I got that chance in a recent episode of our Data Insights podcast when we hosted Ian McClarty, President of PhoenixNAP, for a deep dive into the evolving role of AI in data centers and how bare metal cloud is meeting the demand for infrastructure that’s up to the AI performance challenge.

Our conversation started with Ian sharing his view on the enormous impact AI is having on data centers and the unique demand they bring to both operators and their customers. They require immense compute power as well as low latency communication, putting significant strain on traditional cloud infrastructure. Ian pointed out how the explosion of data—from IoT devices, streaming, and cloud applications—continues to fuel the AI boom, and that AI can’t be treated as another workload in the data center. It demands a completely fresh approach to data center infrastructure, something Ian and his team at PhoenixNAP are laser-focused on providing. 

Ian then turned to bare metal cloud offerings, something PhoenixNAP is famous for delivering, and how they are particularly suited to meet AI’s growing infrastructure needs. Unlike typical cloud solutions that share resources, bare metal cloud provides dedicated servers that give companies access to raw, non virtualized hardware. This is key, Ian explained, for resource- hungry AI workloads. Companies working on AI algorithms need the ability to quickly scale, spin up resources on demand, and process huge amounts of data—capabilities that bare metal cloud supports seamlessly.  

Ian highlighted three key advantages to this approach: performance, scalability, and control. In traditional virtualized environments, AI workloads can face latency issues or performance bottlenecks due to resource sharing. In addition, bare metal cloud allows for rapid scaling, whether a company needs to deploy a few servers for small-scale training or dozens for large AI models. The infrastructure can be customized and scaled up or down based on demand providing flexibility that is crucial as AI workloads can vary significantly in terms of compute power needed. Control is equally important, and Ian stressed how organizations want more control over their infrastructure when it comes to AI. With bare metal cloud, companies have the freedom to configure the hardware environment to suit their specific needs, which is especially important for workloads involving sensitive or proprietary data. This level of customization and control just isn’t possible in shared cloud environments. 

As we love to do on the Data Insights series, we turned the conversation to sustainability. With recent reports placing energy consumption of data centers forecasted to represent up to 20% of the world’s energy supply due to the rise of AI, operators are grappling with driving efficiency across every vector of computing. Ian acknowledged the industry’s responsibility to address the environmental impact and noted that PhoenixNAP is taking proactive steps to design data centers with energy efficiency in mind, from improving cooling technologies to optimizing server utilization. PhoenixNAP is also exploring renewable energy utilization to power their facilities. While the journey to a fully sustainable data center is ongoing, the strides they’re making are encouraging. Ian believes that future innovations in both hardware and software will make sustainability not just an add-on but a core feature of high-performance computing environments. 

The conversation made it clear that PhoenixNAP is primed for infrastructure transformation to support AI’s growth. The company’s focus on performance, flexibility, and sustainability positions it uniquely to meet the challenges and opportunities that AI presents. I left the conversation energized about the possibilities bare metal cloud offers for AI innovation and the impact it will have across industries. 

Tune in to the full episode for more insights from Ian and how PhoenixNAP is reshaping the future of data centers. 

During our latest Data Insights podcast, sponsored by Solidigm, Ian McClarty of PhoenixNAP shares how AI is shaping data centers, discusses the rise of Bare Metal Cloud solutions, and more.

Kelley Osburn gets storage. As an industry veteran and leader at Graid Technology, Kelley recently shared his insights on how the storage arena is rapidly transforming to fuel AI workloads and how his company’s SupremeRAID™ solution – a revolutionary approach to tackling modern data storage challenges – is hitting a sweet spot in the market.

So why is traditional RAID no longer sufficient? Kelley explained how these configurations struggle with high-performance computing demands, especially in data-intensive environments. He emphasized the need for innovation in data storage as the exponential growth in data continues to challenge existing systems, explaining that RAID's original purpose was to provide redundancy and protection against disk failures. While this redundancy is still valued, it lacks the performance desired by many customers.

As data stores grow and speed-of-delivery of data becomes more urgent, innovation to the approach helps extend RAID solution viability while meeting customer demand. Graid's SupremeRAID™ solution, for example, optimizes storage performance by offloading RAID tasks to a dedicated hardware device, enhancing speed and efficiency without compromising data integrity. This makes it an ideal solution for customers managing massive amounts of data for applications like AI, machine learning, and big data analytics.

Kelley detailed the core value of Graid’s solution, describing how SupremeRAID™ addresses critical bottlenecks in traditional storage systems by offering unprecedented performance gains while reducing the computational load on CPU and system resources. The innovation lies in its architecture, which integrates both hardware and software in a way that eliminates RAID-specific processing burdens from the host server, thus allowing server resources to focus on other tasks. The result is a solution that dramatically improves throughput and reduces latency, creating a more balanced and efficient data environment.

In addition to AI and ML, SupremeRAID™ also proves to be a valuable tool in applications in media and entertainment, where high-resolution content creation, editing, and rendering demand significant data processing power. Its ability to handle these workloads without compromising performance makes it a game-changer for companies managing large data sets.

Industry Implications and Future Outlook 

So what are the broader implications of Graid's innovations for the storage industry? Kelley explained that as companies continue to generate vast quantities of data, the demand for more efficient and performant storage solutions will only grow. Graid’s SupremeRAID™ is positioned to address these challenges head-on, providing enterprises with the tools they need to manage, protect, and access their data faster and more reliably than ever before. This will rely on underlying storage media delivering the performance and density required for these tasks, and Kelley pointed to Graid’s strategic collaboration with Solidigm as an example of how high performance QLC memory delivers unique value to customers.

Looking to the future, Graid plans to continue evolving its technology to meet the ever-increasing demands of the data economy. As data volumes grow, so too will the need for innovative storage solutions that can handle not just the size, but the speed and complexity of modern workloads. SupremeRAID™ represents a critical step in that direction, offering a glimpse into the future of RAID technology and its role in addressing the data challenges of tomorrow.

Want to learn more? Check the episode here.

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Join Allyson Klein and Jeniece Wnorowski as they chat with Rita Kozlov from Cloudflare about their innovative cloud solutions, AI integration, and commitment to privacy and sustainability.

Allyson Klein and Jeniece Wnorowski chat with Kelley Osburn of Graid about SupremeRAID™ and its role in tackling high-performance storage challenges in data-driven environments.

With GPU-driven AI training ruling the moment, we have finally come to the asymptotic moment for liquid cooling to overtake air cooled data center infrastructure for many environments. Consider, for a moment, that NVIDIA Blackwell-based racks are drawing from 60kW to 120kW per rack, a dramatic shift from the historic 5-10kW per rack delivered to fuel general purpose applications. When you extrapolate that power across football fields of racks for a hyperscale training cluster, you realize that there’s a LOT of heat to extract. The debate has quickly shifted from air vs liquid to what type of liquid to utilize, opening the door for market disruption and new player entry.

This is why I was so excited to talk to Dr. Kelley Mullick, vice president of technology advancement at Iceotope. Kelley joined Iceotope, a Sheffield, England-based immersion cooling startup, last year, bringing with her a technology leadership pedigree and the notable achievement of having delivered the first industry liquid cooling warranty while at Intel in 2022. Her PhD in chemical engineering and lengthy engagement in industry standards work places her squarely in the middle of liquid cooling advancement.

So why liquid cooling? Kelley confirmed that AI is the primary driver for urgency in transition to liquid cooling due to its serial computing nature, but also stated that broader commitments to sustainability have driven hyperscalers to consider liquid alternatives. She outlined the three alternatives in play in the liquid market: cold plate, tank immersion and precision liquid cooling. While all are more effective and efficient than air, each of the alternatives offer different advantages for consideration. Cold plate has the advantage that it has been widely deployed in HPC environments and utilizes air to cool parts of the chassis where liquid plates are not uniquely targeted, supporting retrofit opportunities for existing infrastructure. Tank immersion delivers a solution where heat can be captured for secondary usage but is also delivered at a weight that requires reinforcement of flooring in existing data center tile flooring, likely limiting to greenfield buildouts. Finally, precision liquid is somewhat of a hybrid, offering advantages of immersion cooling with alternative chemistries to water and similarities to cold plate, offering deployment in existing vertical racks.

If this complexity wasn’t enough, there’s also the topic of chemistry, and it’s here that Kelley really lit up. To start, the options for liquid cooling are water (used in cold plate designs) and dielectric fluid (used in cold plate, immersion, and precision designs). Dielectric fluid is composed of hydrocarbon or fluoridated hydrocarbon fluid with most vendors targeting hydrocarbon options because of its non-toxic composition and ability to be recycled. For two phase cooling solutions, however, only fluoridated hydrocarbon solutions can be used, introducing toxic chemicals into the data center and representing increased challenges from a circularity perspective.

Iceotope is delivering a pretty special chemistry within this landscape. Kelley explained that solutions are delivering precision cooling at up to 1500 watts with thermal resistance 0.037 Kelvin/watt, at par with fluoridated solutions with a sustainable and environmentally friendly chemistry. This technology is delivered in adaptable form factors including racks, power shelves and more, enabling customers to deploy across data center and edge environments. Kelley also noted that different types of infrastructure from GPUs and CPUs to storage JBODs can be submerged in dielectric fluid. Iceotope has done extensive testing of material compatibility to ensure customer deployments will keep cool without reliability erosion.

What’s the TechArena take? We were delighted that we were able to feature this story on our Data Insights series sponsored by Solidigm as cooling is critical to delivery of the data pipeline. Iceotope is delivering disruptive technology in this space, and I expect to hear much more about their solutions as we head into the OCP Summit this fall. If liquid cooling is not on your radar today…put it on your radar. With hyperscalers moving rapidly to liquid alternatives, we expect solutions to scale to meet edge requirements and broader scale AI configurations in data centers. To learn more, check out the interview and visit Iceotope’s site.

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