Run HPC workloads faster with this AMD-Supermicro combo
Compared with a CPU-only system, a Supermicro system with AMD CPUs and GPUs delivered double-digit performance gains. In benchmark tests, some HPC workloads that previously required months of simulation time were now completed in just days.
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Are you or your customers looking to boost high-performance computing (HPC) workloads with greater performance, scalability and energy efficiency?
If so, then Supermicro and AMD have what you’re looking for. Together, they’ve demonstrated dramatic improvements in HPC workloads using a Supermicro system powered by AMD Instinct MI355X GPUs.
Compared with a CPU-only system, this AMD-Supermicro setup delivered double-digit performance gains. In benchmark tests, some HPC workloads that previously required months of simulation time were now completed in just days.
That’s because HPC workloads — especially those that are compute-intensive — can benefit from GPU accelerators.
The massive parallelism of GPUs enables faster iteration and higher resolution modeling. And accelerators such as the AMD Instinct MI355X transform traditional CPU-bound HPC clusters into GPU-accelerated supercomputing platforms.
Booming Benchmarks
The improvements can be dramatic for both performance/watt and time-to-solution. But how dramatic? That’s what AMD and SMC set out to discover.
To find out, they ran benchmarks on a liquid-cooled Supermicro 4U server powered by dual AMD EPYC CPUs and eight Instinct MI355X GPUs.
The AMD Instinct MI355X is a compelling solution for HPC applications, mainly because of its massive memory capacity (up to 6TB of DDR5), high double-precision (FP64) performance, and its support of the open-source AMD ROCm software ecosystem. These features enable it to handle extensive and complex scientific modeling, simulations and data-intensive tasks efficiently and at scale.
The benchmarks were generated using Chroma (software for quantum lattice field theory), Gromacs (software for molecular dynamics) and NAMD (molecular dynamics simulations). Here were the test workloads:
Chroma QUDA BICGSTAB Clover Solver: Fast lattice quantum chromodynamics
Gromacs ADH-Dodec: High-thruput for small and medium biomolecular systems
Gromacs Cellulose-NVE: Biomolecular simulation of crystal structures
Gromacs STMV Virus: Biomolecular simulation of plant virus
NAMD large-scale MD: Molecular dynamics simulation with 1 million steps
Compared with CPU-only, results were delivered anywhere from 6x to 14x faster, depending on benchmark.
Are your customers looking for that kind of HPC advantage? Then tell them about this Supermicro and AMD solution.
You’ll find embedded systems in thousands of retail locations—but only if you know where to look. Find out how these specialized servers work, why they make sense, and how your retail customers can get started using them.
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While dedicated high-performance servers and multiuse cloud platforms command the biggest headlines in tech news, that doesn’t mean they’re the perfect fit for every use case.
Retail organizations have unique requirements. Sometimes those requirements are best served by the diminutive, unsung heroes of the server world: embedded retail servers.
Embedded systems are usually smaller and less powerful than their larger, purpose-built cousins. So where giant AI servers may offer the brute force power of a freight train, smaller embedded systems are more like a ski lift. They do only one thing, but they do it very well.
You can find embedded systems in thousands of retail sites, but you’ll have to do some hunting—their location is not always obvious. Some embedded retail servers sit under counters or in small, out-of-the-way closets. Others are attached to the backs of large color displays that offer patrons dynamic menus, ads and special deals.
High-Tech Sales
One of the most common retail embedded systems is the humble point-of-sale (POS) terminal. A quick survey of your favorite retail stores is likely to reveal a variety of versions, ranging from smart cash registers to fully autonomous self-checkout kiosks.
But POS devices are designed to do far more than just add prices and calculate tax. In a modern retail setting, these servers may also read barcodes, weigh items, process mobile payments, update inventory, schedule deliveries, and detect fraud.
These processes can become even more demanding when the embedded system must complete them without the aid of cloud services.
Why? Because without the processing power and storage of remote cloud and core servers, the embedded system has to rely on its own internal components to complete what can often be a series of very demanding tasks.
Other Use Cases
Deploying embedded retail systems becomes even more complex when a retail location doubles as a warehouse. Such is the case with supermarkets and big-box retailers like Walmart. They must be able to quickly restock their shelves whenever supplies are depleted by shoppers.
In these locations, you can often find embedded retail servers keeping track of real-time stock levels. This can be accomplished using a number of methods, including radio frequency identification (RFID) tags, shelf-based weight sensors, and AI-enabled cameras.
Another task best handled by small, embedded systems is building and energy management. Retail operations often use embedded servers connected to distributed sensors to control HVAC, lighting and security. Here, again, it’s vitally important that these systems be able to operate without an internet connection when necessary.
In this case, an embedded server’s ability to operate on its own can actually prevent physical disasters. Even deprived of remote cloud services, it may need to keep control over a store’s climate to prevent damaging stock. Likewise, store managers often rely on an embedded server’s ability to maintain 100% security system uptime to avoid theft, damage or fire.
Power to Get the Job Done
Designers of embedded retail servers have a tricky job. They need to create systems that meet a long list of disparate requirements. That’s because the most effective embedded retail servers are:
Compact enough to fit in small retail outlets
Cost-effective enough that enterprises can outfit each location with multiple servers
Powerful enough to handle multiple complex tasks and run AI applications locally
Outfitted with enough storage to collect terabytes of data
Reliable enough to run security services that store managers can rely on 24x7
Able to reliably perform with or without an internet connection
To address these concerns, systems designers like Supermicro are tasked with creating the perfect balance of power, pricing, and reliability.
Despite its small size, Supermicro’s embedded system still manages to offer some real expansion. For example, users can have Supermicro populate the server with up to 960GB of SSD storage and 192GB of DDR5 RAM. They can also opt for additional storage via the system’s dual M.2 PCIe 5.0 x4 NVMe slots.
In addition, there’s a single PCIe 5.0 x16 LP slot for an expansion card. Common options to fill that slot include PCIe-based networking cards and dedicated AI accelerators like AMD’s Instinct GPUs.
Coming Soon
What kind of features can we expect from future generations of embedded retail servers? The answer will have much to do with consumer shopping habits, economic and market shifts, and new tech that becomes available in the near future.
While we can’t make infallible predictions about those forces, we can make some assumptions.
One is that AI will become deeply integrated in embedded systems. In fact, we could soon see more systems with AI fully on-device—no cloud connection necessary.
Connectivity-wise, future embedded systems could feature not only Wi-Fi 7 integration, but also 5G cellular connections.
Embedded systems’ footprints should also shrink, even as they become more powerful. Ultra-low-power chips should enable them to operate silently with passive cooling systems and improved thermal management, which will allow designers to shrink the server’s overall size.
Bottom line: Expect embedded servers for retail to become smaller, faster and better. Isn’t that always the way when it comes to new technology?
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:
Model: AMD/Llama 3.1.8B-Instruct-FP8-KV (an open-source model from GitHub that offers a free installation service)
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.
Meet Supermicro’s latest MicroBlade, powered by AMD EPYC 4005 processors
This Supermicro/AMD platform offers a flexible, density-optimized blade architecture for a 6U system. Pack it to the max, and you’ll get 320 server nodes in a standard 48U rack.
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Supermicro has introduced a MicroBlade platform powered by the latest AMD EPYC 4005 series processors. Its intended workloads include cloud, virtualization, data services, edge computing and Software as a Service (SaaS).
These MicroBlades are offered in a new 6U system that supports up to 20 blades in a single enclosure. Each blade holds two CPU nodes, so a fully loaded system with 20 blades has 40 nodes. Fill a standard 48U rack with eight of these 6U systems, and you get a total of 160 blades with 320 server nodes.
The Supermicro system also lets customers mix newer processors with older ones. So customers can expand and upgrade only when their compute requirements change, protecting their earlier investments and providing seamless scalability.
Blade Power
Supermicro MicroBlades offer a powerful and flexible extreme-density 3U and 6U all-in-one blade architecture. Compared with standard 1U rackmount servers, they provide up to 86% more power efficiency and up to 56% improved density, Supermicro says.
The new Supermicro MicroBlade (model number MBA-315R-1DE12) is a dual-node device that measures roughly 23 x 5 x 1 inches and weighs just over 3 pounds. In addition to the two AMD processors, the MicroBlade packs in dual 25GbE LAN ports with 100G uplinks for high-speed networking. There are also two slots for up to 128GB of DDR5 memory.
The blade supports the Intelligent Platform Management Interface (IPMI) v.2.0 via Chassis Management Module. CMM offers remote control of individual server blades, power supplies, cooling fans and networking switches. This lets sys admins control maximum power consumption, manage power allocation, reboot and reset the server, and obtain BIOS configuration data—all remotely with a processor that operates independently of the managed systems.
AMD EPYC 4005 series processors are designed for entry-level systems used by small businesses and hosted IT services providers. While affordable, they offer high performance, advanced technologies and energy efficiency.
Do you have small-business or IT service provider customers looking for a flexible yet powerful server? Tell them about the new Supermicro MicroBlade platform powered by AMD EPYC 4005 series processors.
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!
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.
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.
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.”
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.
i3D Supercharges Game Hosting with AMD EPYC Processors
Discover how game host i3D boosted per-core performance, improved the user experience, and improved its TCO…all by moving to Supermicro MicroBlade servers with AMD EPYC 4000 Series processors.
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One thing hosts of fast-moving multiplayer games don’t want is jitter.
Jitter is the game industry’s term for an inconsistent user experience. It occurs when what’s known as the tick rate base workload—the frequency with which gamers are updated—differs from one game-player to another.
Keeping this tick rate consistent across gamers isn’t easy. Some games get updated hundreds of times a second.
That explains why global game hosting provider i3D.net recently refreshed its infrastructure stack. The company chose Supermicro MicroBlade servers powered by AMD EPYC 4004 Series processors.
Single-Core Rules
To understand i3D’s choice, it helps to understand how the demands of game hosting differs from those of conventional cloud hosting.
Cloud hyperscalers generally try to pack as many compute cores as possible into the smallest possible space. That’s because they want to support many virtual machines on a single node. To get this result, they buy large servers with lots of cores and plenty of memory.
By contrast, for game hosting providers, it’s single-core performance that rules. These companies want to provide the best possible performance for their users. For this purpose, core count per CPU is relatively unimportant.
One thing that really matters for game hosting is single-core performance. And to control costs, gaming providers typically scale with lots of smaller machines rather than a single big one.
All that’s important for i3D. The company, founded in the Netherlands in 2002, initially rented consumer game servers. In 2018, i3D was acquired by Paris-based Ubisoft, and today it offers not only game online services, but also cloud and compute resources, connectivity services, and colocation via its private data center.
Big Games, Big Systems
i3D planned its rollout in large part to support a new game, “Dune: Awakening.” It’s a massively multiplayer online game.
To provide the needed scale, i3D acquired Supermicro MicroCloud servers powered by AMD EPYC 4464P processors. This CPU, part of AMD’s 4th generation EPYC 4004 series, packs 12 cores, 24 threads and 64MB of cache. Yet its power consumption is just 65 watts, a level that fits most data centers.
Now that the rollout is complete, i3D has found that single-core performance with the new setup on a bare-metal is 52% higher than the previous solution.
As Paul Louvet, i3D’s senior product manager of bare metal, puts it: “AMD has the best performance out there for a very attractive cost.”
Double the Nodes
These AMD processors power i3D’s choice of Supermicro 3U MicroCloud servers with eight nodes (Supermicro model AS -3015MR-H8TNR). Each node has a single AMD CPU. This means i3D can fit 96 nodes in one rack, more than double what they could do before.
The Supermicro chassis also includes dual power supplies, bolstering reliability.
Though the upgrade involved a transition from older servers based on a competitor’s processors, i3D says the shift to AMD was seamless. I3D now has about 1,800 nodes powered by AMD EPYC processors, and it will add even more soon.
Looking ahead, i3D plans to upgrade the Supermicro servers to AMD EPYC 4545P processors. This CPU is a member of AMD’s 5th generation EPYC 4005 series, which offers ‘Zen 5’ data-center processors designed for small businesses and hosted services.
Importantly, these processors offer 16 cores to the prior generation’s 12. That will allow i3D to employ four additional cores for the same power usage.
“That’s incredible,” says Louvet of i3D. “This CPU will allow us to have much better TCO per core.”
