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To supercharge AI clusters, check out a newly validated solution from AMD, Supermicro & Mirantis

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To supercharge AI clusters, check out a newly validated solution from AMD, Supermicro & Mirantis

Validating Supermicro hardware with Mirantis k0rdent AI represents a shift from building clusters to composing them.

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Full-stack AI infrastructure solutions are having a moment. And why not. Organizations choose these solutions to speed GPU operations, ensure efficient GPU utilization, and enforce security and compliance at scale.

One such solution is k0rdent AI, a turnkey, production-ready “super control plane” for managing complex AI environments. K0rdent automates provisioning, life-cycle management, and orchestration of infrastructure and core services.

The company behind k0rdent is Mirantis Inc. It’s privately held and based in Campbell, Calif. Founded in 2011, Mirantis today has over 800 employees.

Importantly, Mirantis is also a contributor to Kubernetes, the open-source system for automating the deployment, scaling, and management of containerized applications. Containerization is a software-deployment process that creates a single software package, known as a container, that can run on all types of devices and operating systems.

Mirantis helps organizations achieve digital self-determination by giving them complete control over their strategic infrastructure. The company’s customers include such well-known brands as Adobe, DocuSign and PayPal.

Could Supermicro benefit from the solution’s capabilities? To find out, Supermicro recently validated its modular server architecture with k0rdent.

Testing, Testing

For the validation, Supermicro used two of its own systems:

  • A Supermicro 8U GPU server (model AS -8126GS-TNMR) powered by dual AMD EPYC 9005 CPUs and up to eight AMD Instinct MI325X GPUs.
  • A Supermicro 2U Big Twin server (model AS -2124BT-HNTR) powered by dual AMD EPYC 7003 processors.

Validation began at the physical level, where the k0rdent bare-metal operator acts as a bridge between the Kubernetes API and the Supermicro servers. This delivered automated BIOS configuration, firmware updates, RAID orchestration, and deployment of a hardened host OS.

Next, the testing team deployed the AMD GPU Operator via the k0rdent catalog. GPU Operator simplifies the deployment and management of AMD Instinct GPUs with Kubernetes clusters, enabling seamless configuration and operation of GPU-accelerated workloads.

The AMD Network Operator was deployed, too. It's a control component that enables GPU-to-GPU communications in an AI cluster, managing AMD NICs in Kubernetes clusters.

Here was the test configuration:

  • Scope: Single GPU unit performance

The testers used a custom PyTorch script to measure raw compute throughput across different precisions. (PyTorch is an open-source deep learning library.)

Results Delivered

The validation successfully demonstrated the automated provisioning of production-grade Kubernetes clusters on Supermicro bare-metal hardware using k0rdent’s declarative orchestration engine and the Bare Metal Operator (BMO).

k0rdent managed the entire lifecycle of the Supermicro nodes. That went from out-of-band discovery via BMC/IPMI (Baseboard Management Controller/Intelligent Platform Management Interface) and hardware introspection…all the way to automated OS imaging and Kubernetes bootstrapping.

This eliminated manual configuration and hypervisor overhead. It also provided a high-performance, consistent, and repeatable deployment model that adheres to Cluster API (CAPI) standards.

As Supermicro explains, the validation confirms that k0rdent effectively bridges the gap between physical server management and cloud-native agility. That makes it an ideal solution for resource-intensive workloads requiring direct hardware access and deterministic performance on Supermicro infrastructure.

Conclusions

Validating Supermicro hardware with Mirantis k0rdent AI represents a shift from building clusters to composing them.

Enterprises can run their entire portfolios—from legacy apps to cutting-edge LLMs—on a single, unified, bare-metal platform with automatic deployment and comprehensive platform management from the bare metal up.

If you have customers eager to eliminate human error and inconsistencies from the AI deployment and management processes, tell them to check out this solution.

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Tech Explainer: What’s a Neocloud?

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Tech Explainer: What’s a Neocloud?

This cloud variant has arisen to meet the needs of AI developers. Find out how it differs from hyperscalers—and why your customers might want to jump on board.

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A new kind of technology demands a new kind of cloud.

Sure, it’s easy to take cloud computing for granted. After all, it’s been years since “the cloud” became part of our lives and everyday vernacular.

Over the years, clouds ranging from the simple (think Dropbox) to the fabulously complex (think multicloud ecosystems) have been powerful enough to handle whatever we’ve thrown their way.

But now our widespread adoption of AI demands a new kind of cloud.

To the rescue: Behold the neocloud!

Neoclouds offer AI workload-specific functionality as a service. And to help save enterprises and SMBs considerable expenses of time and money, neoclouds offer platforms designed to empower the rapid development and launch of the latest AI creations.

A neocloud isn’t your typical “run anything” platform. Instead, it’s optimized to run a narrow selection of highly specialized AI-centric tasks. These include AI/ML inference and training, data analytics and media rendering.

Neoclouds vs. Traditional Clouds

To better understand how neoclouds fit into the grand scheme of modern cloud architecture, it helps to compare and contrast them with their forebearer, the hyperscaler.

Hyperscalers that include Amazon Web Services (AWS), Microsoft Azure and Google Cloud also offer cloud-based services. They simply offer a much larger and less AI-specific selection.

The seemingly endless array of services these hyperscalers offer makes them ideal for developers who prize flexibility and versatility. Hyperscalers let developers combine multiple managed services to simultaneously harness the power of distributed databases, machine-learning pipelines and other components of a highly customized platform.

By contrast, neoclouds are tuned for specific workloads. They offer a narrower focus and so-called “opinionated architecture” designed to make autonomous architectural decisions. That level of specificity and autonomy changes the nature of the development process from DIY to plug & play.

 

                  

 

More-Specific Hardware, Too

To fully compare neocloud apples with hyperscaler oranges, you also need to look under the hood. The tech behind the latest cloud type makes a huge difference.

For both hyperscalers and neoclouds, we’re talking about some of the most advanced tech ever. But here again, it’s the neocloud’s laser-like focus on AI that makes it an invaluable development tool.

It’s for that reason that popping the top off an AI server like the Supermicro’s 8U server (model AS -8126GS-TNMR) will treat you to a view of truly cutting-edge CPUs, GPUs and networking gear. That gear includes a couple of server-focused AMD EPYC 9005 series processors with as many as 384 cores and up to 6TB of DDR5 memory.

For brute-force AI processing, the Supermicro A+ server also offers room for eight onboard AMD Instinct MI350X GPUs banded together via AMD Infinity Fabric Link.

Supermicro’s behemoth is also equipped with AMD ROCm. Pronounced “rock-em,” it’s a software stack designed to translate the code written by programmers into sets of instructions that AMD GPUs can understand and execute perfectly.

The Neocloud Sales Pitch, Condensed

The what and how of neoclouds are important. But if your customers are considering investing in neocloud, they’ll surely want to know about the why, as well.

So why would you want to engage a neocloud for AI development? There are four main reasons:

1. Neoclouds cut admin work, letting you concentrate instead on production.

A new eBook from Supermicro and AMD, The Smartest Path to Scalable AI, cites neoclouds for their “frictionless dev-to-prod motion.”

That’s tech business-speak for a system that handles the nitty-gritty details, getting out of your way so you can get to work. That includes one-command access to optimized hardware and preconfigured environments.

Bottom line: Less admin, more development, and faster time-to-market.

2. A neocloud delivers instant gratification, not endless development integration.

“Day 0 readiness” is the catchphrase that sums up this one. And not just for any single aspect of the neocloud platform, but for the whole stack. That includes hardware, software, and the managed offerings wrapped around them, collectively referred to as services.

Bottom line: Large models and agents start running efficiently from the get-go.

3. A neocloud is always up-to-date with the latest, greatest silicon.

The last thing you want to contend with is outdated infrastructure. That may fly when it comes to making last-decade file storage app. But creating tomorrow’s brilliant new AI requires cutting-edge tech. The problem is, that tech gets expensive. The solution? Rent, don’t buy.

Bottom line: Access to all the cool toys, with no down payment.

4. It’s already got wheels; you don’t have to reinvent them.

Neoclouds come well stocked with what are known as specialized microservices. These are pre-built, workload-specific building blocks that developers can stand on to bypass the mundanities of production and get to the good stuff.

Examples of wheels you won’t have to reinvent include distributed training orchestration, streaming ingestion services, and GPU render farms.

Bottom line: Neoclouds do the boring due diligence, and let developers get all the glory.

The Future’s Future

Neoclouds are already the future. They’re coming online now, and revealing themselves to be the greatest thing for developers since sliced bread.

But tech moves fast these days. There’s always someone thinking about the next step.

When it comes to the next step for neoclouds, that’s likely to involve deeper specialization, more compelling economics, and consolidation.

That makes sense in terms of the big picture. As both enterprises and SMBs adopt neoclouds, they’ll create more demand. That demand, in turn, should help fund expansion.

Eventually, we may see a new level of specificity. For example, one neocloud could offer low-latency SaaS production inferencing, while another may focus on analytics that cater to medical research.

What happens after that is hard to predict. But one easy-to-believe theory foretells a time in which neoclouds plug into hyperscalers. With that kind of power, imagine what tomorrow’s developers will be able to do!

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Need to cool AI hardware with safety? Check out the new solution from AMD, Supermicro & Metrum AI

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Need to cool AI hardware with safety? Check out the new solution from AMD, Supermicro & Metrum AI

The solution employs AI agents to monitor liquid-cooling systems, identifying and remediating problems quickly.

 

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Liquid cooling is great for controlling the temperature of hard-working AI servers, but the technology also has its risks. Even minor disruptions or fluctuations in a cooling system can quickly lead to massive hardware failures.

A solution to this challenge has been developed by AMD and Supermicro working with Metrum AI Inc., an Austin, Texas-based provider of industry-specific AI agents and AI evaluation products.

Their solution integrates Supermicro infrastructure, AMD computational power and ROCm software, and Metrum AI’s orchestration to deliver fast decisions that ensure safety at scale.

This solution enables multiple AI agents to collaboratively monitor signals, diagnose issues, predict failures, and coordinate corrective actions. The agents are embedded directly into a server’s cooling control plate.

Essentially, this creates a data center that is adaptive, resilient, and self-optimizing. The solution should also support the massive compute intensity of next-generation AI workloads while proactively managing their thermal and physical risks.

And unlike traditional monitoring tools, this solution can actually predict and then prevent catastrophic hydraulic failures—before they occur. And do so faster than would be possible with traditional human intervention.

Power Features

To design these multi-agent systems, the team used AMD ROCm. This open-source software delivers important benefits that include flexibility, optimized libraries and seamless integration with AMD Instinct GPUs.

Another feature that made the solution possible is the massive memory reservoir of the AMD Instinct GPUs. For example, the AMD Instinct MI355X GPU has a dedicated memory of 288 GB. This lets large-scale reasoning models operate fully in-memory.

The structural foundation of this platform is the Supermicro 8U server (model AS -8126GS-TNMR) powered by dual AMD EPYC 9005 Series CPUs and supporting up to eight AMD Instinct MI325X or MI350X GPUs.

Unlike standard servers, these systems are engineered with direct-to-chip cooling headers that expose flow, temperature and pressure data directly through Redfish interfaces. (Redfish is a standard designed to deliver simple and secure management for converged systems, hybrid IT and software-defined data centers.) This empowers the agents to monitor and adjust cooling performance in real time.

The combination of specific technologies creates what’s known as a Unified Computational Fabric. There, the AMD EPYC processors feed continuous Redfish data directly into the Supermicro Instinct GPUs via PCIe 5, eliminating I/O bottlenecks.

This synergy powers the platform to sustain real-time adaptive control loops across dozens of racks, and quickly. It’s a capability that conventional air-cooled and CPU-based infrastructures can’t deliver.

Smart Racks

The autonomous cooling system was built on a distributed multi-agent architecture designed specifically for liquid-cooled AI environments. Unlike conventional systems, where monitoring is either centralized or based on human intervention, the solution places intelligence directly at the rack level.

In this setup, lightweight agents continuously monitor telemetry, interpret changes in flow and pressure, and coordinate rapid remediation actions across the data center. This creates a resilient, high-resolution control fabric that can respond to thermal events in milliseconds.

At the base of the stack, AMD ROCm supplies the core libraries, tools, compilers and runtimes for GPU-accelerated compute on AMD Instinct GPUs. And Kubernetes orchestration and the AMD GPU Operator enable containerized deployment, GPU scheduling, and lifecycle management at a multirack scale. (Kubernetes is an open-source system for automating the deployment, scaling and management of containerized applications.)

Above this layer, the AMD Enterprise AI Suite delivers higher-level services. The suite is a full-stack of enterprise-ready AI. The services it delivers include solution blueprints, AI workbench, and a resource manager for unified model deployment, optimization and infrastructure governance.

Metrum AI extends these platform components into a specialized multiagent architecture. It supports real-time telemetry ingestion, large-model reasoning and autonomous cooling control.

 

 

 

Test Results: Fast Yet Stable

All that sounds good in theory, but does it really work?

To find out, the solution was tested by Metrum AI along two dimensions: telemetry ingestion thruput and large-model inference stability.

When monitoring a full deployment of 200 racks (1,000 servers), the system successfully processed more than 13,000 Redfish telemetry endpoints per minute. Simultaneously, it maintained over 8,000 tokens/second of multiagent large-model reasoning.

This demonstrated that as the infrastructure added complexity, the centralized coordination architecture did not become a bottleneck. Also, the test shows that every agent received real-time, high-resolution sensor context, regardless of facility size.

Across all benchmarks, the integrated solution demonstrated stable, real-time, end-to-end autonomous operation under data-center scale load.

So do you have customers who are eager to try liquid cooling, but concerned about the risks? If so, tell them about this new AI-powered solution from AMD, Supermicro and Metrum AI.

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Meet Supermicro’s new AMD-powered edge systems

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Meet Supermicro’s new AMD-powered edge systems

The five new systems range from compact systems to rackmount servers. They’re designed for use outside of the traditional data center.

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Supermicro has five new edge systems, all powered by AMD EPYC processors.

The new hardware are components of Supermicro’s end-to-end portfolio of edge systems. They represent three system categories: compact edge systems, compact edge servers and rackmount edge servers. All are designed for use at the edge—that is, outside the traditional data center.

Supermicro describes the new servers as “purpose-built solutions for a scalable, performance-efficient edge infrastructure.” To that end, the company is offering different configurations for different workloads.

For AI workloads, Supermicro is offering GPU-optimized platforms. For sensor and network integration, there are flexible I/O offerings. For harsh environmental and industrial settings, fanless designs. And for mobile deployments and small spaces, there are compact form-factor designs.

The new edge servers are designed for users in industries that include healthcare, retail, telecom and manufacturing. All share the need for reliable compact systems.

Here’s a look at the five new servers, arranged by system category.

Category 1: Compact edge systems

Supermicro’s compact edge systems are designed to run specialized workloads and connect on-prem equipment to enterprise networks. They can include both fanless gateways built for harsh industrial settings and high-performance edge servers running advanced AI models.

Organizations that deploy edge systems can reduce latency, lower bandwidth costs, improve security, and ensure continuous operations for mission-critical applications.

Supermicro’s new entry in this category is a mini 1U embedded system, model number AS -E300-14GR. It’s powered by the user’s choice of a single AMD EPYC 4004 or 4005 processor with up to 16 cores and 32 threads.

This air-cooled system measures 15 x 10.9 x 5.6 inches, and it weighs just 7.5 pounds.

Intended applications include healthcare, surveillance, AI inference, digital signage and point-of-sale. For AI inference, the system also supports up to 4 GPU accelerator cards.

Category 2: Compact edge servers

These edge servers combine specialized features with expansion options that include support for GPU accelerators. They’re designed for AI inference in industries such as retail, manufacturing and smart spaces.

For this category, Supermicro offers 2 new AMD-powered servers.

The first is a 2U compact AI system, model number AS -2116S-TNRT. It’s powered by a single fifth-generation AMD EPYC 9005 series processor with up to 192 cores, 384 threads and 384MB of cache. This server supports up to 1 double-width or 5 single-width GPUs. And it’s air-cooled by 4 fans.

The other new system is a compact 1U short-depth server, model number AS -1115S-FWTRT. This server measures 17.2 x 16.9 x 1.7 inches, and it weighs 15 pounds.

This short-depth server is powered by a single AMD EPYC 8004 processor. And it’s air-cooled by up to 6 fans.

The system has been designed for applications that include virtualization, firewall, cloud services, CDN (content delivery network), 5G networks, and VRAN/ORAN (virtual and open radio access network) for telecom.

Category 3: Rackmount edge servers

These are mainly short-depth servers designed for embedded, edge and telecom workloads that include RAN and edge AI. They offer a modern combo: high-density compute plus GPU compatibility.

For this category, Supermicro offers two AMD-powered systems. The first is a 2U Hyper-E server, model AS -2115HE-FTNR. This server is powered by a single AMD EPYC 9004/9005 series processor with up to 160 cores and 320 threads.

The box can accommodate up to 3 double-width GPUs. And it’s packed with up to 6TB of DDR5 memory. For extra flexibility, the server is available in both front and rear I/O models.

For this category, Supermicro also recently introduced a ultra-short-depth, low-power edge and embedded platform, model number AS -116R-FNR. It measures 17.2 x 9.8 x 1.7 inches.

This system is powered by a single AMD EPYC 4005 processor. And it can handle up to 192GB of DDR5 memory.

Have clients looking for compact yet powerful systems and servers they can run outside the traditional data center? Tell them about the new AMD-powered Supermicro edge.

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Looking for AI's ROI? Try purpose-fitting

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Looking for AI's ROI? Try purpose-fitting

Delivering an AI return on investment can be challenging. A new IDC white paper offers a solution: leverage infrastructure to the use case.

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Companies can build a strong return on investment (ROI) for their AI projects—but only if they understand how to leverage different infrastructure solutions for different AI use cases. In other words, they need to know how to do purpose-fitting.

That’s the case argued in a new IDC white paper sponsored by AMD and Supermicro.

The paper’s two co-authors are Peter Rutten, research VP in IDC’s worldwide infrastructure research group and global research lead of the firm’s performance-intensive computing practice; and Madhumitha Sathish, research manager for high-performance computing at IDC and lead of the firm’s AI infrastructure research.

Rutten and Sathish find not all is well in the world of AI. In a survey conducted by IDC this past September, fewer than half of companies worldwide said their AI-related projects have delivered any measurable business outcomes. And only about one in 10 companies (11.4%) said they’re obtaining measurable business results from more than 75% of their AI projects.

What’s blocking AI progress? According to the IDC survey, these are the top reasons:

  • Competition for resources: cited by 34% of survey respondents
  • Resistance to process change: cited by 30%
  • Difficulty quantifying AI’s ROI: 28%
  • Regulatory uncertainty: also 28% (multiple responses were allowed)

“Cost continues to be a major hurdle,” the authors write.

And the biggest cost? Over 60% of companies surveyed by IDC said it’s around developing and deploying AI is specialized infrastructure.

Four Questions

It doesn’t have to be this way, the IDC authors argue. Instead, AI-using organizations can build a strong ROI for their projects with purpose-fitting.

To do this, managers should ask (and answer) 4 important questions:

  • Who decides what is your relevant AI use case? A separate IDC survey finds that fewer than one in three organizations involve IT during an AI initiative’s conceptual stage.
  • What kind of AI model do you need? There are many, including machine learning, GenAI, agentic AI, deep neural network, etc. Not all require major capital expenditures.
  • How will you obtain this AI model? Each approach involves trade-offs. For example, most businesses fine-tune or customize an existing commercial model. But this approach involves both licensing costs and training costs.
  • Have you considered the biggest factors that impact AI infrastructure needs? These factors include AI model types, number of parameters, volume of training data, query response times, and query size.

By taking these factors into account, the authors say, enterprises can develop AI options that match their AI use case, creating a purpose-built infrastructure solution.

Spectrum Choices

To contain AI infrastructure costs, the IDC authors recommend that managers develop what they call a “spectrum of options” based on 7 factors: Complexity, parameter count, data volume, model accuracy, time to value, query response latency, and query size.

When these factors are low or small, an AI project is in the blue zone, which implies lower costs. As these factors become higher or larger, the project moves into the green and red zones, which imply higher costs, as shown in the IDC chart below.

Hardware system requirements can vary by spectrum, too.

Blue zone projects, those with the lowest infrastructure costs, can be run on CPU-based, air-cooled systems, or even a PC or workstation.

Green zone projects, those with intermediate infrastructure costs, can run on systems powered by CPUs with built-in accelerators and lighter co-processors.

And red zone projects, those with the highest infrastructure zones, require rack-scale systems with high-end CPUs, GPUs and liquid-cooling.

But wait, there’s more. The IDC authors point to several additional considerations:

  • Is there more than one AI use case in development? Typically, there are. If that’s the case, then that will need to be built into the needs projection.
  • How rapidly will the AI use case evolve over time? For example, if the number of users is projected to grow substantially, then the accounting must consider new infrastructure that will be required.
  • How often will the AI model require generational updates? Many models are constantly being improved, expanded and retrained, and these updates will deliver infrastructure impacts.

Better Together

The IDC authors say AI-using companies would do well to consider AMD-powered Supermicro systems. The two suppliers work with a vast ecosystem of partners to offer alternatives and options.

AMD and Supermicro demystify complexity, helping companies plan their AI projects faster and better. And they offer reliable, high-performance platforms that support AI workloads across a wide range of deployment scales.

“AMD and Supermicro,” the IDC authors write, “have developed some of the most versatile, powerful and well-tailored solutions available today.”

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Tech Explainer: What’s an AI Factory?

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Tech Explainer: What’s an AI Factory?

Discover how AI factories work—and how your clients might benefit from building an AI factory of their own.

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How can you tell that the AI Era is here? One way is by noticing that large enterprises are increasingly focused on mass producing AI models.

It’s no longer enough to have a decent set of working AI models to power Spotify’s suggestion engine or Accenture’s Big Data analytics.

To keep up with—and surpass—the Joneses, Spotify and Accenture will need dedicated systems that work every day to create, evaluate and iterate their AI models.

These systems are called AI factories. Somewhat like a factory that creates physical widgets, an AI factory churns out new and updated AI models. This continual AI production process helps enterprises react quickly to market demands and competition.

Make no mistake: The development of AI factories represents a turning point in the evolution of AI-powered business.

No. 2 with a Bullet

This theory is supported by some of IT’s top thinkers. They include Tom Davenport, a professor, speaker and author; and Randy Bean, a corporate advisor.

Davenport and Bean co-wrote an article that appeared earlier this month In the Sloan Management Review: Five trends in AI and data science for 2026. In their article, the authors place AI factories in the Number 2 spot. AI factories, they say, will be adopted by users and “all-in” AI adopters that include consumer products makers, banks and software companies.

As Davenport and Bean explain, an AI factory combines technology platforms, methods, data and previously developed algorithms to make building AI systems easy and fast. The authors’ all-important message: Watch this space.

How AI Factories Work

To fully understand the concept of an AI factory, it can help to think of the traditional smoke-belching, brick-and-mortar factories it’s named for.

Of course, there are some differences. A physical factory takes in raw materials, uses machines to process them, and produces physical products.

By contrast, an AI factory takes in data (such as text, audio, images and logs), runs that data through massive compute engines, and outputs AI models for recommendations, predictions, automation and generative content.

Another difference: Unlike the static products that emerge from traditional factories, the products of AI factories are virtual. They learn and grow as new data, infrastructure and techniques become available. In this way, AI factories help their organizations keep up with rapid changes and market shifts.

For instance, a new AI model produced by an enterprise’s AI factory can be continuously retrained as new data becomes available. While each new iteration deployed in the field busily suggests which Netflix movie to watch next, a newer version is constantly being developed in the background. When the new suggestion engine is ready, Netflix can seamlessly slide it into place.

Why Your Clients Probably Need an AI Factory

It’s good to understand the abstract benefits of an AI factory. But your clients will also want to know how building one can translate into business results.

Here’s the bottom line. An AI factory can:

  • Dramatically reduce the cost of business intelligence. Once an AI factory is built and a given AI model is trained, that model can run continuously, serving millions of decisions, predictions, etc., for a fraction of its initial cost. In other words, the cost per additional decision rapidly collapses toward zero.
  • Help organizations maintain a decisive competitive advantage. This happens on two levels. First, maintaining a constant production stream of AI models and iterations helps your clients meet market demands as quickly as possible. And second, having that ability to react faster to customer needs and economic conditions can help create and sustain an advantage over competitors.
  • Turn data into capital. Many organizations are ill-equipped to analyze and monetize all the data they collect. All that piled-up data can seem like an albatross around their neck. But by building an AI factory, the organization can harness that otherwise squandered data and put it to work.

Further, companies that don’t build an AI factory could find themselves at a competitive disadvantage. Davenport and Bean, in their Sloan Management Review article, say companies that lack an AI factory will find building AI at scale both expensive and time-consuming.

Stumbling Blocks? A Couple

Building an AI factory isn’t always easy. Enterprises can run into serious roadblocks.

For one, siloed, inconsistent or low-trust data can make for a messy AI production process. As programmers say, “garbage in, garbage out.” In other words, if the data is messy, the analysis will be, too.

Another thing that can wreak havoc on the virtual factory floor are talent bottlenecks. There are only so many data scientists to go around, and they’re in high demand. Finding the right employees is a key component here—even in an age of super-smart robots.

Another trap your clients need to watch out for are bureaucratic hold-ups. Legal, compliance and trust issues can cause AI projects to grind to a halt.

The AI Factory Future

Like everything else in the fast-moving AI world, AI factories are changing. In the near future, AI factories will likely focus on the immediacy of real-time, always-on learning.

As AI factories shift to nearly continuous adaptation, enterprises will use their AI model updates to keep pace with rapidly changing market conditions and customer demands.

Another likely future is inferencing at the edge. For “edge,” think vehicles, devices and brick-and-mortar factories. Organizations that move inferencing closer to where data is created can lower system latency (that is, increase speed) and reduce cloud costs.

Another factor that could make a big impact on AI factories is new software and hardware integrations. A recent Supermicro webinar on AI factories and related technology showed how enterprises can benefit from integrating software platforms such as Supermicro’s SuperCloud Composer (SCC) and Power Asset Orchestrator (PAO).

Supermicro says this potent combination allows operators to gain total visibility into AI Factories. It can also optimize everything from GPU telemetry to real-time grid pricing.

Overall, it’s safe to assume that when these and other updates are deployed, AI factories will quickly become part of the common AI infrastructure. In so doing, they’ll touch nearly every aspect of our daily lives.

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2025: Look Back at the Year’s Top Advances

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2025: Look Back at the Year’s Top Advances

Catch up on 2025’s highlights: ROCm 7.0, liquid-cooled AI servers, server processors for SMBs, and a MicroBlade server that’s highly efficient.

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2025 was a year to remember. But in case you’ve forgotten, here are some of the year’s top advances.

ROCm for the AI Era

This past fall, AMD introduced version 7.0 of its ROCm software stack. This latest edition features capabilities designed especially for AI.

ROCm, part of AMD’s portfolio since 2016, translates code written by human programmers into instruction sets that AMD GPUs and CPUs can understand and execute.

Now AMD has purpose-built ROCm 7.0 for GenAI, large-scale AI training, and AI inferencing. Essentially, ROCm now offers the tools and runtime to make the most complex GPU workloads run efficiently.

The full ROCm 7.0 stack contains multiple components. These include drivers, a Heterogeneous Interface for Portability (HIP), math and AI libraries, compilers and system-management tools.

Liquid-Cooled AI Servers

Supermicro introduced two rackmount AI servers in June, both of them powered by AMD Instinct MI350 Series GPUs and dual AMD EPYC 9005 CPUs.

One of the two new servers, Supermicro model number AS -4126GS-NMR-LCC, is a 4U liquid-cooled system. This server can handle up to eight GPUs, the user’s choice of AMD’s Instinct MI325X or MI355X.

The other server, Supermicro model number AS -8126GS-TNMR, is a larger 8U server that’s also air-cooled. It also offers a choice of AMD GPUs, either the AMD Instinct MI325X or AMD Instinct MI350X.

Both servers feature PCIe 5.0 connectivity; memory capacities of up to 2.3TB; support for AMD’s ROCm open-source software; and support for AMD Infinity Fabric Link connections for GPUs.

In June, Supermicro CEO Charles Liang said the new servers “strengthen and expand our industry-leading AI solutions—and give customers greater choice and better performance as they design and build the next generation of data centers.”

EPYCs for SMBs

In May, AMD introduced a CPU series designed specifically for small and medium businesses.

The processors, known as the AMD EPYC 4005 Series, bring a full suite of enterprise-level features and performance. But they’re designed for on-prem SMBs and cloud service providers who need cost-effective solutions in a 3U form factor.

“We’re delivering the right balance of performance, simplicity, and affordability,” says Derek Dicker, AMD’s corporate VP of enterprise and HPC. 

That balance includes the same AMD ‘Zen 5’ core architecture behind the AMD EPYC 9005 Series processors used in data centers run by large enterprises.

The AMD EPYC 4005 Series CPUs for SMBs come in a single-socket package. Depending on model, they offer anywhere from 6 to 16 cores and boosted performance of up to 5.7 GHz.

One model of the AMD EPYC 4005 line also includes integrated AMD 3D V-Cache technology for a larger 128MB L3 cache and lower latency.

MicroBlades for CSPs

The AMD EPYC 4005 Series processors made a star appearance in November, when Supermicro introduced a 6U, 20-node MicroBlade server (model number MBA-315R-1G) powered by the new CPUs.

These servers are intended for small and midsize cloud service providers.

Each blade is powered by a single AMD EPYC 4005 CPU. When 20 blades are combined in the system’s 6U form factor, the system offers 3.3x higher density than a traditional 1U server. It also reduces cabling by up to 95%, saves up to 70% space, and lowers energy costs by up to 30%.

This MicroBlade system with an AMD EPYC 4005 processor is also available as a motherboard (model number BH4SRG) for use in Supermicro A+ servers.

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Happy holidays from all of us at Performance Intensive Computing, and best wishes for the new year! We look forward to serving you in 2026.

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Research Roundup: Server Sales Rise, AI Helps Customer Service, Social Media is for Adults, LLMs Know What You Need

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Research Roundup: Server Sales Rise, AI Helps Customer Service, Social Media is for Adults, LLMs Know What You Need

Catch up on the latest research from leading technology analysts and market watchers.

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Servers set a new sales record. AI is augmenting customer service workers, rather than replacing them. Most adults use social media. And finely-tuned LLMs can identify customer needs better than a human professional.

That’s some of the latest from leading tech analysts and market researchers. And here’s your research roundup.

Servers Set Record Sales

Server sales set a new record in this year’s third quarter. Global sales of these systems rose 61% year-on-year, reaching an all-time quarterly high of $112.4 billion, according to market watcher IDC.

Of that total, $76.3 billion came from x86 servers, representing nearly 70% of the total. Compared with the year-earlier quarter, that marked an increase of about 33%, IDC says.

A much bigger jump came from sales of non-x86 servers. Those sales rose 197% year-on-year, reaching a worldwide total in Q3 of $36.2 billion.

Another fast-growing sector is servers with embedded GPUs. In Q3, sales of these AI-ready servers rose nearly 50% year-on-year, IDC says. Systems with GPUs now represent more than half of all server market revenue.

Growth of server sales in the quarter varied by region. The biggest sales rise was in the United States, where Q3 server sales rose nearly 80% year-on-year, IDC says. Other fast growers included Canada (with server sales up 70%), China (38%) and Japan (28%).

AI Helps Customer Service

Artificial intelligence is mainly augmenting, rather than replacing, customer-service workers, finds a new survey.

The survey, conducted by technology analyst firm Gartner, finds that only one in five customer-service leaders (20%) have reduced staffing due to AI. Even better, about half the respondents (55%) said their staffing levels have remained stable, even as AI has enabled them to handle higher customer-service volume.

AI can even lead to the creation of new jobs. Four in 10 respondents to the Gartner survey (42%) said their organizations are hiring specialized roles to support AI deployment and management. These new roles include AI strategists, conversational AI designers and automation analysts.

The survey, conducted by Gartner in October, collected responses from 321 customer service and support leaders.

Social Media for Adults? Yes!

If anyone tells you social media is strictly for kids, set them straight. A poll conducted by Pew Research finds the vast majority of U.S. adults use social media.

Specifically, YouTube is used by over eight in 10 U.S. adults (84%), the survey finds. And Facebook is used by seven in 10 U.S. adults (71%).

Another social media platform popular with grownups is Instagram. The Pew survey finds it’s used by fully half of U.S. adults.

Plenty of other social media sites are used by U.S. adults, too, if in smaller numbers. They include TikTok (used by 37%), WhatsApp (32%), Reddit (26%), Snapchat (25%) and X (21%).

The survey was conducted by Pew earlier this year, and it drew responses from 5,022 U.S. adults.

The LLM Knows What You Want

Large language models can identify customer needs better than an expert, finds a recent research paper from MIT.

To conduct their experiment, the paper’s three co-authors—John Hauser of MIT Sloan, Artem Timoshenko of Northwestern’s Kellogg School, and MIT pre-doc Chengfeng Mao—fine-tuned an LLM using studies supplied by a market research firm.

They then compared the output of their finely-tuned LLM with that of human analysts and untrained LLMs. The test asked consumers about their preferences for wood stains. In all, consumers were asked about eight primary customer needs and 30 secondary needs.

The results: The fine-tuned LLM identified 100% of the customers’ primary and secondary needs. By comparison, the human analysts missed a few, identifying 87% of the primary needs and 80% of the secondary needs.

That said, actually understanding the needs of wood-stain customers remains a job for humans, says Hauser, a professor of marketing at MIT Sloan.

“If you have to pull customer needs out of a story, the supervised fine-tuned LLM can do it,” he says. “But if you ask an LLM what customers care about when staining a deck, its answers are superficial.”

Want to learn more? Read the full paper.

 

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Tech Explainer: What are CPU Cores, Threads, Cache & Nodes?

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Tech Explainer: What are CPU Cores, Threads, Cache & Nodes?

Today’s CPUs are complex. Find out what the key components actually do—and why, in an age of AI, they still matter.

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In the age of artificial intelligence, CPUs still matter. A central processor’s parts—cores, threads, cache and nodes—are as important as any AI accelerator.

But what exactly do those CPU parts do? And why, in an age of AI, do they still matter?

These questions are easy to overlook given AI’s focus on the GPU. To be sure, graphical processors are important for today’s AI workloads. But the humble CPU also remains a vital component.

If the GPU is AI’s turbocharger, then the CPU is the engine that makes the whole car go. As Dan McNamara, AMD’s GM of compute and enterprise AI business, said at the recent AMD Financial Analysts Day, “AI requires leadership CPUs.”

So here’s a look at the most important components of today’s data-center x86 CPUs. And an explanation of why they matter.

Cores: Heavy Lifting

The central processing unit is the brain of any PC or server. It reads instructions, does the complex math, and coordinates the system’s every task.

Zooming into the architecture of a CPU, it’s the individual cores that put the “PU” in CPU. Each fully independent processing unit can run its own task, virtual machine (VM) or container.

Modern enterprise-class CPUs such as AMD’s EPYC 9005 Series offer anywhere from 8 to 192 cores each. They operate at up to 5GHz.

These cores are built using AMD’s ‘Zen’ architecture. It’s a fundamental core design that offers enhancements vital to data centers, including improved instructions-per-clock (IPC), branch prediction, caches and efficiency.

Performance like that is a must-have when it comes to a data center’s most demanding tasks. That’s especially true for compute-intensive database operations and API-heavy microservices such as authentication, payment gateways and search.

Having more cores in each CPU also enables IT managers to run more workloads per server. That, in turn, helps organizations lower their hardware and operating costs, simplify IT operations, and more easily scale operations.

Threads: Helping Cores Do More

A modern CPU core needs to multitask, and that’s where having multiple threads is essential. A single CPU core with two threads can juggle two tasks by switching between them very quickly. In a CPU with a high core count, a productivity-multiplier like that becomes exponentially more effective.

This capability delivers two important benefits. One, it helps ensure that each CPU core stays productive, even if one task stalls. And two, it boosts the CPU’s overall output.

For example, the AMD EPYC 9965 processor boasts 192 cores with a total of 384 threads. That kind of multitasking horsepower helps smooth request handling for web services and microservices. It also improves VM responsiveness and helps AI workloads run more efficiently under heavy loads.

Cache: Speedy but Short-Term Memory

The unsung heroes of CPU design? That would be cache.

The main job of a CPU cache is to help the cores juggle data with low latency. Remember, less latency is always better.

As a result, CPU cache enables databases to run faster, improve VM density and reduce latency.

Your average CPU cache is arranged in three layers:

  • L1 cache is very small and very fast. Each core has its own L1 cache, which holds around 32 KB of instructions and data. The L1 cache sends that data to a register— a tiny, ultra-fast storage location the core uses to acquire the data used for calculations.
  • L2 cache is also exclusive to each core. At around 1MB, this cache is bigger than L1, but it’s also a little slower. L2 cache holds any data that doesn’t fit in the L1 cache. Working together, the L1 and L2 caches can quickly pass data back and forth until ultimately, the L1 cache passes the data to the core.
  • L3 cache is shared by all cores in a CPU, and it acts as a buffer for passing data between the CPU and main memory. Sizes vary widely. In an 8-core AMD EPYC processor, the L3 cache is just 64MB. But in AMD’s 192-core CPU, the L3 Cache gets as big as 348MB.

Some AMD CPUs, including the AMD EPYC 9845, also include a 3D V-Cache. This AMD innovation stacks an additional cache on top of the L3 cache (hence the name 3D). Stacking the two caches vertically adds storage without increasing the overall size of the CPU.

The added 3D V-Cache also improves performance for workloads that benefit from a larger cache. Examples include scientific simulations and big data.

Nodes: Power & Efficiency

When it comes to CPU nodes, smaller is better. A smaller node size can deliver benefits that include lower power consumption, increased efficiency, and more compute performance per watt.

Nodes are expressed in nanometers (nm)—that’s one billionth of a meter—which describe the tiny size of transistors on a chip.

The latest AMD EPYC 9005-series architectures, ‘Zen 5’ and ‘Zen 5c,’ are built on 4nm and 3nm nodes, respectively.

Each of these individual performance gains may seem tiny when considered on a per-chip basis. But in the aggregate, they can make a huge difference. That’s especially true for resource-intensive workloads such as AI training and inferencing.

Coming Soon: Smaller, Faster CPUs

AMD’s near-term roadmap tells us we can expect its AMD EPYC CPUs to continue getting smaller, faster and more efficient.

Those manufacturing and performance gains will likely come from more cores per CPU socket, bigger and more efficient caches. Earlier this year, AMD said the next generation of its EPYC processors, codenamed Venice, will be brought up on TSMC’s advanced 2nm process technology.

Enterprises will be able to parlay those improvements into better performance under multi-tenant loads and reduced latency overall. The latter is particularly vital for modern operations.

The bottom lie: Denser CPU cores mean big business, both for processor makers such as AMD and the server vendors such as Supermicro that rely on these CPUs.

Denser CPUs are also vital for enterprises now transforming their data centers for AI. Because adding space is so slow and costly, these organizations are instead looking to pack more compute power per rack. Smaller, more powerful CPUs are an important part of their solution.

Minimum CPU size with maximum power? It’s coming soon to a data center near you.

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Check out Supermicro’s new AMD GPU-powered server—it’s air-cooled

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Check out Supermicro’s new AMD GPU-powered server—it’s air-cooled

Supermicro’s new 10U server is powered by AMD’s EPYC CPUs and Instinct MI355X GPUs. And it’s kept cool by nearly 20 fans.

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What do you do if you need GPU power for AI and other compute-intensive workloads, but lack the infrastructure for liquid cooling?

Supermicro has the answer. The company just introduced a 10U server powered by AMD Instinct MI355X GPUs that’s air-cooled.

The new server, showcased at the recent SC25 conference in St. Louis, is Supermicro model AS -A126GS-TNMR.

Each server is powered by the customer’s choice of dual AMD EPYC 9004 or 9005 Series CPUs with up to 384 cores and 768 threads. The system also features a total of eight AMD Instinct MI355X onboard OAM GPU accelerator modules, which are air-cooled. (OAM is short for OCP Accelerator Module, an industry-standard form factor for AI hardware.) In addition, these accelerated GPU servers offer up to 6TB of DDR5 system memory.

While the systems are air-cooled with up to 19 heavy-duty fans, there’s no penalty in terms of cooling capacity. In fact, AMD has boosted the GPU’s thermal design point (TDP)—the maximum amount of heat a server’s cooling system can handle—from 1000W to 1400W.

Also, compared with the company’s air-cooled 8U server based on AMD Instinct MI350X GPUs, the 10U server offers up to double-digit more performance, according to Supermicro . For end users, that means faster data processing.

More Per Rack

The bigger picture: Supermicro’s new 10U option lets customers unlock higher performance per rack. And with their choice of 10U air cooling or 4U liquid cooling, both powered by the latest AMD EPYC processors.

Supermicro’s GPU solutions are designed to offer maximum performance for AI and inference at scale. And they’re intended for use by both cloud service providers and enterprises.

Are your customers looking for a GPU-powered server that’s air cooled? Tell them about these new Supermicro 10U servers. And let them know that these systems are ready to ship now.

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