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Need AI for financial services? Supermicro and AMD have your solution

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Need AI for financial services? Supermicro and AMD have your solution

Financial services companies are making big investments in AI. To speed their time to leadership, Supermicro and AMD are partnering to deliver advanced computing systems.

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Financial services companies earn their keep by investing in stocks, bonds and other financial instruments. Now these companies are also making big investments in artificial intelligence technology.

To help these financial services industry (FSI) players adopt AI, Supermicro and AMD are working together. The two are partnering to offer advanced computing solutions designed to empower and speed the finance industry’s move to technology and business leadership.

FSI companies can use these systems to:

  • Detect risks faster, uncovering patterns and anomalies by ingesting ever-larger data sets
  • Supercharge trading with AI in both the front- and back-office
  • Modernize core processes to lower costs while boosting resilience
  • Engage and delight customers by meeting—even exceeding—their expectations

Big Spenders

Already, FSI spending on AI technology is substantial. Last year, when management consulting firm Bain & Co. surveyed nearly 110 U.S. FSI firms, it found that those respondents with annual revenue of at least $5 billion were spending an average of $221 million on AI.

The companies were getting a good return on AI, too. Bain found that 75% of financial services companies said their generative AI initiatives were either achieving or exceeding their expected value. In addition, the GenAI users reported an average productivity gain across all uses of an impressive 20%.

Based on those findings, Bain estimates that by embracing AI, FSI firms can reduce their customer-service costs by 20% to 30% while increasing their revenue by about 5%. 

Electric Companies

One big issue facing all users of AI is meeting the technology’s energy needs. Power consumption is a big-ticket item, accounting for about 40% of all data center costs, according to professional services firm Deloitte.

Greater AI adoption could push that even higher. Deloitte believes global data center electric consumption could double by as soon as 2030, driven by big increases in GenAI training and inference.

As Deloitte points out, some of that will be the result of new hardware requirements. While general-purpose data center CPUs typically run at 150 to 200 watts per chip, the GPUs used for AI run at up to 1,200 watts per chip.

This can also increase the power demand per rack. As of early 2024, data centers typically supported rack power requirements of at least 20 kilowatts, Deloitte says. But with growth of GenAI, that’s expected to reach 50 kilowatts per rack by 2027.

That growth is almost sure to come. Market watcher Grand View Research expects the global market for GPUs in data centers of all industries to rise over the next eight years at a compound annual growth rate (CAGR) of nearly 36%. That translates into data-center GPU sales leaping from $14.48 billion worldwide last year to $190.1 billion in 2033, Grand View predicts.

Partner Power

FSI companies don’t have to meet these challenges alone. Supermicro and AMD have partnered to deliver advanced computing systems that deliver high levels of compute performance and flexibility, yet with a comparatively low total cost of ownership (TCO).

They’re boosting performance with high-performing, dense 4U servers using the latest AMD EPYC CPUs and AMD Instinct GPUs. Some of these servers offer up to 60 storage drive bays, 9TB of DDR5 RAM and 192 CPU cores.

For AI workloads, AMD offers the AMD EPYC 9575F AI host node. It has 64 cores and a maximum boost frequency of up to 5 GHz.

Flexibility is another benefit. Supermicro offers modular Datacenter Building Block Solutions. These include system-level units that have been pre-validated to ease the task of data-center design, among other offerings.

AMD and Supermicro are also offering efficiencies that lower the cost of transforming with AI. Supermicro’s liquid cooling slashes the total cost of ownership (TCO). AMD processors are designed for power efficiency. And SMC’s multi-mode design gives you more processing capability per rack.

Are you working with FSI customers looking to lead the way with AI investments? The latest Supermicro servers powered by AMD CPUs and GPUs have your back.

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Validate, test and benchmark the latest AMD-powered servers with Supermicro JumpStart

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Validate, test and benchmark the latest AMD-powered servers with Supermicro JumpStart

Get a free test drive on cutting-edge Supermicro servers powered by the latest AMD CPUs and GPUs.

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How would you like free access to Supermicro’s first-to-market, high-end H14 servers powered by the latest AMD EPYC CPUs and Instinct GPUs?

Now it’s yours via your browser—and the Supermicro JumpStart program.

JumpStart offers you remote access to Supermicro servers. There, you can validate, test and benchmark your workloads. And assuming you qualify, using JumpStart is absolutely free.

While JumpStart has been around for some time, Supermicro has recently refreshed the program by including some of its latest H14 servers:

  • 8U server with eight AMD Instinct MI325X GPUs, dual AMD EPYC 9005 Series CPUs, 2TB of HBM3 memory (Supermicro model AS -8126GS-TNMR)
  • 2U server with dual AMD EPYC 9005 Series processors and up to 1.5TB of DDR5 memory (AS -2126HS-TN).
  • 1U cloud server with a single AMD EPYC 9005 Series processor (AS -1116CS-TN)

Supermicro has also updated JumpStart systems with its 1U E3.S all-Flash storage systems powered by a single AMD EPYC processor, so you can also test-drive the latest PCIe drives. Also, several of Supermicro’s H13 AMD-powered are available for remote access on JumpStart, as well.

How It Works

Getting started with JumpStart is easy:

Step 1: On the main JumpStart page, browse the available systems, then click the “get access” or “request access” button for the system you want to try. Then select your preferred system and time slot.

Step 2: Sign in. You can either login with your Supermicro single sign-on (SSO) account or create a new free account. Supermicro will then qualify your account and reach out with further instructions.

Step 3: When your chosen time arrives, secure access to your system. Most JumpStart sessions last for one week. If you need more time, that can often be negotiated with your Supermicro sales reps.

It's that simple.

Once you’re connected to a server via JumpStart, you can have up to three sessions open: one VNC (virtual network computing), one SSH (secure shell), and one IPMI (intelligent platform management interface).

JumpStart also protects your privacy. After your JumpStart trial is completed, the server and storage devices are manually erased. In addition, the BIOS and firmware are reflashed, and the operating system is re-installed with new credentials.

More protection is offered, too. A jump server is used as a proxy. This means that the server you’re testing can use the internet to get files, but it is not directly addressable via the internet.

That said, it’s recommended that you do not use the test servers for processing sensitive or confidential data. Instead, Supermicro advises the use of anonymized data only—mainly because the servers may follow security policies that differ from your own.

So what are you waiting for? Try out JumpStart and get free remote access to Supermicro’s cutting-edge servers powered by the latest AMD CPUs and GPUs.

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Tech Explainer: What is agentic AI?

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Tech Explainer: What is agentic AI?

Find out how new artificial intelligence systems can make decisions and take actions autonomously—that is, without human intervention.

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We’re on the precipice of a major AI evolution. Welcome to the era of agentic AI.

The official definition of agentic AI is artificial intelligence capable of making autonomous decisions. That is, without human oversight or intervention.

You can imagine agentic AI as a robot on a mission. This robot has been designed to think like a human. Give it a goal, and the robot can then evaluate the ongoing situation, reacting intelligently in pursuit of that defined goal.

For example, imagine you’re planning a visit to wineries in California’s Napa Valley. A standard AI chatbot like ChatGPT could help you find the closest airport with car-rental agencies, identify which airlines fly there, and locate nearby hotels. But it would still be up to you to compare prices and actually make the reservations.

But what if instead, your robot could autonomously plan—and book!—the entire trip based on your preferences? For example, you might engage an agentic AI like AutoGPT by telling it something like this:

“I want to go to Napa Valley and visit wineries. I don’t want to spend more than $3,000. I prefer Chardonnay and Syrah wines. I once had a bad experience with American Airlines. It would be fun to drive a convertible. A 3-star hotel is fine as long as it’s got good reviews.”

The promise of agentic AI is that it would use that information to plan and book your trip. The agentic AI would find you the best flight, car and hotel by interacting with each company’s APIs or even their own agentic AI—here referred to as “other agents.” This is also known as machine-to-machine (M2M) communications.

Your robot agent could also make your reservations at vineyards with critically acclaimed Chardonnay and Syrah wines. And it might even plan your route using details as granular as the range of the discounted rag-top Ford Mustang it found near the airport.

Agentic AI for Organizations

This personal Napa Valley scenario is one of those nice-to-have kinds of things. But for organizations, agentic AI has far more potential. This technology could eventually transform every major industry and vertical market.

For example, a retailer might use agentic AI to autonomously adjust a product’s price based on the current inventory level, availability and competitive brands.

A manufacturer could use an AI agent to manage procurement and create dynamic forecasting, saving the company time and money.

And in the public sector, agentic AI could help a government agency better respond to public-health emergencies like the next global pandemic. The AI could model viral transmission patterns, then send additional resources to the areas that need them the most.

In each case, we’re talking about the potential for a tireless virtual robot workforce. Once you give an agentic AI a mission, it can proceed without any further human intervention, saving you countless hours and dollars.

Training: Standard AI vs. Agentic

For all types of AI, one big issue is training. That’s because an AI system on its own doesn’t really know anything. To be useful, it first has to be trained.

And with training, there’s a huge difference between the way you train a standard AI and the way you train an AI that’s agentic. It’s as dramatic as the difference between programming a calculator and onboarding a new (human) intern.

With a standard AI chatbot, the system is trained to answer questions based on a relatively narrow set of parameters. To accomplish this, engineers provide massive amounts of data via large language models (LLMs). They then train the bot through supervised learning. Eventually, inferencing enables the AI to make predictions based on user input and available data.

By contrast, training an agentic AI focuses on memory, autonomy, planning and using available tools. Here, LLMs are paired with prompt engineering, long-term memory systems and feedback loops. These elements work together to create a type of intelligent thought process—the kind you hope your new intern is capable of!

Then, at the inferencing stage, the AI does far more than just answer questions. Instead, agentic AI inferencing enables the system to interpret goals, create plans, ask for help and, ultimately, execute tasks autonomously.

Nuts and Bolts

The IT infrastructure that powers agentic AI is no different from the horsepower behind your average chatbot. There’s just a lot more of it.

That’s because agentic AI, in comparison with standard AI, makes more inference calls, reads and writes more files, and queries more APIs. It also engages a persistent memory. That way, the AI can continuously access collected information as it works towards its goals.

However, having a slew of GPUs and endless solid-state storage won’t be enough to sustain what will likely be the meteoric growth of this cutting-edge technology. As agentic AI becomes more vital, IT managers will need a way to feed the fast-growing beast.

Supermicro’s current H14 systems—they include the GPU A+ Server—are powered by AMD EPYC 9005-series processors and fitted with up to 8 AMD Instinct MI325X Accelerators. Supermicro has designed these high-performance solutions to tackle the most challenging AI workloads.

Looking ahead, at AMD’s recent “Advancing AI” event, CEO Lisa Su introduced Helios, AMD’s vision for agentic AI infrastructure. Su said Helios will deliver the compute density, memory bandwidth, performance and scale-out bandwidth needed for the most demanding AI workloads. What’s more, Helios will come packaged as a ready-to-deploy AI rack solution that accelerates users’ time to market.

Helios, planned for release in 2026, will use several forthcoming products: AMD Instinct MI400 GPUs, AMD 6th Gen EPYC CPUs, and AMD Pensando “Vulcano” network interface cards (NICs). All will be integrated in an OCP-compliant rack that supports both UALink and Ultra Ethernet. And eventually, Helios will appear in turnkey systems such as the Supermicro H14 series.

What’s Next?

What else does agentic AI have in store for us? While no one has a crystal ball, it’s reasonable to assume we’ll see increasingly sophisticated agents infiltrating nearly every aspect of our lives.

For instance, agentic AI could eventually develop the ability to work autonomously on long-term, multifaceted projects—everything from advertising campaigns to biomedical research.

Agentic AI is also likely to learn how to debug its own logic and develop new tools. These capabilities are referred to by the pros as self-reflection and self-improvement, respectively.

One day in the not-too-distant future, we could even see massive teams of specialized AI agents working together under a single robotic project manager.

Think this is starting to sound like “The Matrix”? You ain’t seen nothin’ yet.

 

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Deploy GenAI with confidence: Validated Server Designs from Supermicro and AMD

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Deploy GenAI with confidence: Validated Server Designs from Supermicro and AMD

Learn about the new Validated Design for AI clusters from Supermicro and AMD. It can save you time, reduce complexity and improve your ROI.

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The task of designing, building and connecting a server system that can run today’s artificial intelligence workloads is daunting.

Mainly, because there are a lot of moving parts. Assembling and connecting them all correctly is not only complicated, but also time-consuming.

Supermicro and AMD are here to help. They’ve recently co-published a Verified Design document that explains how to build an AI cluster. The PDF also tells you how you can acquire an AMD-powered Supermicro cluster for AI pre-built, with all elements connected, configured and burned in before shipping.

Full-Stack for GenAI

Supermicro and AMD are offering a fully validated, full-stack solution for today’s Generative AI workloads. The system’s scale can be easily adjusted from as few as 16 nodes to as many as 1,024—and points in between.

This Supermicro solution is based on three AMD elements: the AMD Instinct MI325X GPU, AMD Pensando Pollara 400 AI network interface card (NIC), and AMD EPYC CPU.

These three AMD parts are all integrated with Supermicro’s optimized servers. That includes network cabling and switching.

The new Validated Design document is designed to help potential buyers understand the joint AMD-Supermicro solution’s key elements. To shorten your implementation time, the document also provides an organized plan from start to finish.

Under the Cover

This comprehensive report—22 pages plus a lengthy appendix—goes into a lot of technical detail. That includes the traffic characteristics of AI training, impact of large “elephant” flows on the network fabric, and dynamic load balancing. Here’s a summary:

  • Foundations of AI Fabrics: Remote Direct Memory Access (RDMA), PCIe switching, Ethernet, IP and Border Gateway Protocol (BGP).
  • Validated Design Equipment and Configuration: Server options that optimize RDMA traffic with minimal distance, latency and silicon between the RDMA-capable NIC (RNIC) and accelerator.
  • Scaling Out the Accelerators with an Optimized Ethernet Fabric: Components and configurations including the AMD Pensando Pollara 400 Ethernet NIC and Supermicro’s own SSE-T8196 Ethernet switch.
  • Design of the Scale Unit—Scaling Out the Cluster: Designs are included for both air-cooled and liquid-cooled setups.
  • Resource Management and Adding Locality into Work Placement: Covering the Simple Linux Utility for Resource Management (SLURM) and topology optimization including the concept of rails.
  • Supermicro Validated AMD Instinct MI325 Design: Shows how you can scale the validated design all the way to 8,000 AMD MI325X GPUs in a cluster.
  • Storage Network Validated Design: Multiple alternatives are offered.
  • Importance of Automation: Human errors are, well, human. Automation can help with tasks including the production of detailed architectural drawings, output of cabling maps, and management of device firmware.
  • How to Minimize Deployment Time: Supermicro’s Rack Scale Solution Stack offers a fully integrated, end-to-end solution. And by offering a system that’s pre-validated, this also eases the complexity of multi-vendor integration.

Total Rack Solution

Looking to minimize implementation times? Supermicro offers a total rack scale solution that’s fully integrated and end-to-end.

This frees the user from having to integrate and validate a multi-vendor solution. Basically, Supermicro does it for you.

By leveraging industry-leading energy efficiency, liquid and air-cooled designs, and global logistics capabilities, Supermicro delivers a cost-effective and future-proof solution designed to meet the most demanding IT requirements.

The benefits to the customer include reduced operational overhead, a single point of accountability, streamlined procurement and deployment, and maximum return on investment.

For onsite deployment, Supermicro provides a turnkey, fully optimized rack solution that is ready to run. This helps organizations maximize efficiency, lower costs and ensure long-term reliability. It includes a dedicated on-site project manager.

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AMD presents its vision for the AI future: open, collaborative, for everyone

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AMD presents its vision for the AI future: open, collaborative, for everyone

Check out the highlights of AMD’s Advancing AI event—including new GPUs, software and developer resources.

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AMD advanced its AI vision at the “Advancing AI” event on June 12. The event, held live in the Silicon Valley city of San Jose, Calif., as well as online, featured presentations by top AMD executives and partners.

As many of the speakers made clear, AMD’s vision for AI is that it be open, developer-friendly, collaborative and useful to all.

AMD certainly believes the market opportunity is huge. During the day’s keynote, CEO Lisa Su said AMD now believes the total addressable market (TAM) for data-center AI will exceed $500 billion by as soon as 2028.

And that’s not all. Su also said she expects AI to move beyond the data center, finding new uses in edge computers, PCs, smartphone and other devices.

To deliver on this vision, Su explained, AMD is taking a three-pronged approach to AI:

  • Offer a broad portfolio of compute solutions.
  • Invest in an open development ecosystem.
  • Deliver full-stack solutions via investments and acquisitions.

The event, lasting over two hours, was also filled with announcements. Here are the highlights.

New: AMD Instinct MI350 Series

At the Advancing AI event, CEO Su formally announced the company’s AMD Instinct MI350 Series GPUs.

There are two models, the MI350X and MI355X. Though both are based on the same silicon, the MI355X supports higher thermals.

These GPUs, Su explained, are based on AMD’s 4th gen Instinct architecture, and each GPU comprises 10 chiplets containing a total of 185 billion transistors. The new Instinct solutions can be used for both AI training and AI inference, and they can also be configured in either liquid- or air-cooled systems.

Su said the MI355X delivers a massive 35x general increase in AI performance over the previous-generation Instinct MI300. For AI training, the Instinct MI355X offers up to 3x more throughput than the Instinct MI300. And in comparison with a leading competitive GPU, the new AMD GPU can create up to 40% more tokens per dollar.

AMD’s event also featured several representatives of companies already using AMD Instinct MI300 GPUs. They included Microsoft, Meta and Oracle.

Introducing ROCm 7 and AMD Developer Cloud

Vamsi Boppana, AMD’s senior VP of AI, announced ROCm 7, the latest version of AMD’s open-source AI software stack. ROCm 7 features improved support for industry-standard frameworks; expanded hardware compatibility; and new development tools, drivers, APIs and libraries to accelerate AI development and deployment.

Earlier in the day, CEO Su said AMD’s software efforts “are all about the developer experience.” To that end, Boppana introduced the AMD Developer Cloud, a new service designed for rapid, high-performance AI development.

He also said AMD is giving developers a 25-hour credit on the Developer Cloud with “no strings.” The new AMD Developer Cloud is generally available now.

Road Map: Instinct MI400, Helios rack, Venice CPU, Vulcano NIC

During the last segment of the AMD event, Su gave attendees a sneak peek at several forthcoming products:

  • Instinct MI400 Series: This GPU is being designed for both large-scale AI inference and training. It will be the heart of the Helios rack solution (see below) and provide what Su described as “the engine for the next generation of AI.” Expect performance of up to 40 petaflops, 432GB of HBM4 memory, and bandwidth of 19.6TB/sec.
  • Helios: The code name for a unified AI rack solution coming in 2026. As Su explained it, Helios will be a rack configuration that functions like a single AI engine, incorporating AMD’s EPYC CPU, Instinct GPU, Pensando Pollara network interface card (NIC) and ROCm software. Specs include up to 72 GPUs in a rack and 31TB of HBM3 memory.
  • Venice: This is the code name for the next generation of AMD EPYC server CPUs, Su said. They’ll be based on a 2nm form, feature up to 256 cores, and offer a 1.7x performance boost over the current generation.
  • Vulcano: A future NIC, it will be built using a 3nm form and feature speeds of up to 800Gb/sec.

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Tech Explainer: What’s a NIC? And how can it empower AI?

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Tech Explainer: What’s a NIC? And how can it empower AI?

With the acceleration of AI, the network interface card is playing a new, leading role.

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The humble network interface card (NIC) is getting a status boost from AI.

At a fundamental level, the NIC enables one computing device to communicate with others across a network. That network could be a rendering farm run by a small multimedia production house, an enterprise-level data center, or a global network like the internet.

From smartphones to supercomputers, most modern devices use a NIC for this purpose. On laptops, phones and other mobile devices, the NIC typically connects via a wireless antenna. For servers in enterprise data centers, it’s more common to connect the hardware infrastructure with Ethernet cables.

Each NIC—or NIC port, in the case of an enterprise NIC—has its own media access control (MAC) address. This unique identifier enables the NIC to send and receive relevant packets. Each packet, in turn, is a small chunk of a much larger data set, enabling it to move at high speeds.

Networking for the Enterprise

At the enterprise level, everything needs to be highly capable and powerful, and the NIC is no exception. Organizations operating full-scale data centers rely on NICs to do far more than just send emails and sniff packets (the term used to describe how a NIC “watches” a data stream, collecting only the data addressed to its MAC address).

Today’s NICs are also designed to handle complex networking tasks onboard, relieving the host CPU so it can work more efficiently. This process, known as smart offloading, relies on several functions:

  • TCP segmentation offloading: This breaks big data into small packets.
  • Checksum offloading: Here, the NIC independently checks for errors in the data.
  • Receive side scaling: This helps balance network traffic across multiple processor cores, preventing them from getting bogged down.
  • Remote Direct Memory Access (RDMA): This process bypasses the CPU and sends data directly to GPU memory.

Important as these capabilities are, they become even more vital when dealing with AI and machine learning (ML) workloads. By taking pressure off the CPU, modern NICs enable the rest of the system to focus on running these advanced applications and processing their scads of data.

This symbiotic relationship also helps lower a server’s operating temperature and reduce its power usage. The NIC does this by increasing efficiency throughout the system, especially when it comes to the CPU.

Enter the AI NIC

Countless organizations both big and small are clamoring to stake their claims in the AI era. Some are creating entirely new AI and ML applications; others are using the latest AI tools to develop new products that better serve their customers.

Either way, these organizations must deal with the challenges now facing traditional Ethernet networks in AI clusters. Remember, Ethernet was invented over 50 years ago.

AMD has a solution: a revolutionary NIC it has created for AI workloads, the AMD AI NIC card. Recently released, this NIC card is designed to provide the intense communication capabilities demanded by AI and ML models. That includes tightly coupled parallel processing, rapid data transfers and low-latency communications.

AMD says its AI NIC offers a significant advancement in addressing the issues IT managers face as they attempt to reconcile the broad compatibility of an aging network technology with modern AI workloads. It’s a specialized network accelerator explicitly designed to optimize data transfer within back-end AI networks for GPU-to-GPU communication.

To address the challenges of AI workloads, what’s needed is a network that can support distributed computing over multiple GPU nodes with low jitter and RDMA. The AMD AI NIC is designed to manage the unique communication patterns of AI workloads and offer high throughput across all available links. It also offers congestion avoidance, reduced tail latency, scalable performance, and fast job-completion times.

Validated NIC

Following rigorous validation by the engineers at Supermicro, the AMD AI NIC is now supported on the Supermicro 8U GPU Server (AS -8126GS-TNMR). This behemoth is designed specifically for AI, deep learning, high-performance computing (HPC), industrial automation, retail and climate modeling.

In this configuration, AMD’s smart AI-focused NIC can offload networking tasks. This lets the Supermicro SuperServer’s dual AMD EPYC 9000-series processors run at even higher efficiency.

In the Supermicro server, the new AMD AI NIC occupies one of the myriad PCI Express x16 slots. Other optional high-performance PCIe cards include a CPU-to-GPU interconnect and up to eight AMD Instinct GPU accelerators.

In the NIC of time

A chain is only as strong as its weakest link. The chain that connects our ever-expanding global network of AI operations is strengthened by the advent of NICs focused on AI.

As NICs grow more powerful, these advanced network interface cards will help fuel the expansion of the AI/ML applications that power our homes, offices, and everything in between. They’ll also help us bypass communication bottlenecks and speed time to market.

For SMBs and enterprises alike, that’s good news indeed.

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Meet AMD’s new EPYC CPUs for SMBs—and Supermicro servers that support them

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Meet AMD’s new EPYC CPUs for SMBs—and Supermicro servers that support them

AMD introduced the AMD EPYC 4005 series processors for SMBs and cloud service providers. And Supermicro announced that the new AMD processors are now shipping in several of its servers.

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AMD this week introduced the AMD EPYC 4005 series processors. These are purpose-built CPUs designed to bring enterprise-level features and performance to small and medium businesses.

And Supermicro, wasting no time, also announced that several of its servers are now shipping with the new AMD EPYC 4005 CPUs.

EPYC 4005

The new AMD EPYC 4005 series processors are intended for on-prem users and cloud service providers who need powerful but cost-effective solutions in a 3U height form factor.

Target customers include SMBs, departmental and branch-office server users, and hosted IT service providers. Typical workloads for servers powered by the new CPUs will include general-purpose computing, dedicated hosting, code development, retail edge deployments, and content creation, AMD says.

“We’re delivering the right balance of performance, simplicity, and affordability,” says Derek Dicker, AMD’s corporate VP of enterprise and HPC. “That gives our customers and system partners the ability to deploy enterprise-class solutions that solve everyday business challenges.”

The new processors feature AMD’s ‘Zen 5’ core architecture and come in a single-socket package. Depending on model, they offer anywhere from 6 to 16 cores; up to 192GB of dual-channel DDR5 memory; 28 lanes of PCIe Gen 5 connectivity; and boosted performance of up to 5.7 GHz. One model of the AMD EPYC 4005 line also includes integrated AMD 3D V-Cache tech for a larger 128MB L3 cache and lower latency.

On a standard 42U rack, servers powered by AMD EPYC 4005 can provide up to 2,080 cores (that’s 13 3U servers x 10 nodes/server x 16 cores/node). That level of capacity can reduce a user’s size requirements while also lowering their TCO.

The new AMD CPUs follow the AMD EPYC 4004 series, introduced this time last year. The EPYC 4004 processors, still available from AMD, use the same AM5 socket as the 4005s.

Supermicro Servers

Also this week, Supermicro announced that several of its servers are now shipping with the new AMD EPYC 4005 series processors. Supermicro also introduced a new MicroCloud 3U server that’s available in 10-node and 5-node versions, both powered by the AMD EPYC 4005 CPUs.

"Supermicro continues to deliver first-to-market innovative rack-scale solutions for a wide range of use cases,” says Mory Lin, Supermicro’s VP of IoT, embedded and edge computing.

Like the AMD EPYC 4005 CPUs, the Supermicro servers are intended for SMBs, departmental and branch offices, and hosted IT service providers.

The new Supermicro MicroCloud 10-node server features single-socket AMD processors (your choice of either 4004 or the new 4005) as well as support for one single-width GPU accelerator card.

Supermicro’s new 5-node MicroCloud server also offers a choice of AMD EPYC 4004 or 4005 series processor. In contrast to the 10-node server, the 5-node version supports one double-width GPU accelerator card.

Supermicro has also added support for the new AMD EPYC 4005 series processors to several of its existing server lines. These servers include 1U, 2U and tower servers.

Have SMB, branch or hosting customers looking for affordable compute power? Tell them to:

 

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To make room for AI, modernize your data center

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To make room for AI, modernize your data center

A new report finds the latest AMD-powered Supermicro servers can modernize the data center, lowering TCO and making room for AI systems.

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Did you know that dramatic improvements in processor power can enable your corporate customers to lower their total cost of ownership (TCO) by consolidate servers and modernizing their data centers?

Server consolidation is a hot topic in the context of AI. Many data centers are full and running with all the power that’s available. So how can they make room for new AI systems? Also, how can they get the kind of power that today’s AI systems require?

One answer: with consolidation. 

Four in One

All this is especially relevant in light of a new report from Principled Technologies.

The report, prepared for AMD, finds that an organization that upgrades to new Supermicro servers powered by the current 5th generation AMD EPYC processors can consolidate servers on a 4:1 ratio.

In other words, the level of performance that previously required four older servers can now be delivered with just one.

Further, Principled found that organizations that make this upgrade can also free up data-center space; lower operating costs by up to $2.8 million over five years; shrink power-consumption levels; and reduce the maintenance load on sys admins.

Testing Procedures

Here’s how Principled figured all this out. To start, they obtained two systems:

Next, Principled’s researchers compared the transactional database performance of the two servers. They did this with HammerDB TPROC-C, an open-source benchmarking tool for online transaction processing (OLTP) workloads.

To ensure the systems were sufficiently loaded, Principled also measured both servers’ CPU and power utilization rates, pushing both servers to 80% CPU core utilization.

Then Principled calculated a consolidation ratio. That is, how many of the older servers would be needed to do the same level of work done by just 1 new server?

Finally, Principled calculated the expected 5-year costs for software licensing, power, space and maintenance. These calculations were made for both the older and new Supermicro servers, so they could be compared.

The Results

So what did Principled find? Here are the key results:

  • Performance upgrades: The new servers, based on AMD 5th Gen EPYC processors, is much more powerful. To match the database performance of just 1 new server, the testers required 4 of the older servers.
  • Lower operating costs: Consolidating those four older servers onto just one new server could lower an organization’s TCO by over 60%, saving up to an estimated $2.8 million over five years. The estimated 5-year TCO for the legacy server was $4.68 million, compared with $1.78 million for the new system.
  • Lower software license costs: Much of the savings would come from consolidating software licenses. They’re typically charged on a per-core basis, and the new test server needed only about a third as many cores as did the four older systems: 96 cores on the new system, compared with a total of 256 cores on the four older servers.
  • Reduced power consumption: To run the same benchmark, the new system needed only about 40% of the power required by the four older servers.
  • Lower space and cooling requirements: Space savings were calculated by comparing data-center footprint costs, taking into account the 4:1 consolidation and rack space needed. Cooling costs were factored in, too. The savings here were pretty dramatic, even if the figures were relatively low. The new system’s space costs were just $476, or 75% lower than the legacy system’s cost of $1,904.
  • Reduced maintenance costs: This was estimated with the assumption that one full-time sys admin with an annual salary of roughly $100K is responsible for 100 servers. The savings here brought a cost of over $26K for the older setup down to about $6,500 for the new, for a reduction of 75%.

Implicit in the results, though not actually calculated, is the way these reductions could also free up funding, floor space and other resources that organizations can then use for new AI systems.

So if your customers are grappling with finding new resources for AI, tell them about these test results. Upgrading to servers based on the latest processors could be the answer.

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AI across AMD’s entire portfolio? Believe it!

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AI across AMD’s entire portfolio? Believe it!

A little over a year ago, AMD CTO Mark Papermaster said the company’s strategy was to offer AI everywhere. Now learn how AMD, with help from Supermicro, is bringing this strategy to life.

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A year in the fast-moving world of artificial intelligence can seem like a lifetime.

Consider:

  • A year ago, ChatGPT had fewer than 200 million weekly active users. Now this Generative AI tool has 400 million weekly users, according to developer OpenAI.
  • A year ago, no one outside of China had heard of DeepSeek. Now its GenAI chatbot is disrupting the AI industry, challenging the way some mainstream tools function.
  • About a year ago, AMD CTO Mark Papermaster said his company’s new strategy called for AI across the entire product portfolio. Now AMD, with help from Supermicro, offers AI power for the data center, cloud and desktop. AMD also offers a robust open AI stack.

‘We’re Thrilled’

AMD’s Papermaster made his comments in Feb. 2024 during a fireside chat hosted by stock research firm Arete Research.

During the interview, CTO Papermaster acknowledged that most early customers for AMD’s AI hardware were mostly big cloud hyperscalers, including AWS, Google Cloud and Microsoft Azure. But he also said new customers are coming, including both enterprises and individual endpoint users.

“We’re thrilled to bring AI across our entire portfolio,” Papermaster said.                                                                          

So how has AMD done? According to the company’s financial results for both the fourth quarter and the full year 2024, pretty good.

Aggressive Investments

During AMD’s recent report on its Q4:24 and full-year ’24 financial results, CFO Jean Hu mentioned that the company is “investing aggressively in AI.” She wasn’t kidding, as the following items show:

  • AMD is accelerating its AI software road map. The company released ROCm 6.3, which includes enhancements for faster AI inferencing on AMD Instinct GPUs. The company also shared an update on its plans for the ROCm software stack.
  • AMD announced a new GPU system in 2024, the AMD Instinct MI325X. Designed for GenAI performance, it’s built on the AMD CDNA3 architecture and offers up to 256GB of HBM3E memory and up to 6TB/sec. of bandwidth.
  • To provide a scalable AI infrastructure, AMD has expanded its partnerships. These partnerships involve companies that include Aleph, IBM, Fujitsu and Vultr. IBM, for one, plans to deploy AMD MI300X GPUs to power GenAI and HPC applications on its cloud offering.
  • AMD is offering AI power for PCs. The company added AI capabilities to its Ryzen line of processors. Dell, among other PC vendors, has agreed to use these AMD CPUs in its Dell Pro notebook and desktop systems.

Supermicro Servers

AMD partner Supermicro is on the AI case, too. The company now offers several AMD-powered servers designed specifically for HPC and AI workloads.

These include an 8U 8-GPU system with AMD Instinct MI300X GPUs. It’s designed to handle some of the largest AI and GenAI models.

There’s also a Supermicro liquid-cooled 2U 4-way server. This system is powered by the AMD Instinct MI300A, which combines CPUs and GPUs, and it’s designed to support workloads that coverge HPC and AI.

Put it all together, and you can see how AMD is implementing AI across its entire portfolio.

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Tech Explainer: What is edge computing — and why does it matter?

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Tech Explainer: What is edge computing — and why does it matter?

Edge computing, once exotic, is now a core aspect of modern IT infrastructures. 

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Edge computing is a vital aspect of our modern IT infrastructure. Its use can reduce latency, minimize bandwidth usage, and shorten response times.

This distributed computing methodology enables organizations to process data closer to its source and make decisions faster. This is referred to as operating at the edge.

For contrast, you can compare this with operating at the core, which refers to data being sent to centralized data centers and cloud environments for processing.

The edge is also a big and fast-growing business. Last year, global spending on edge computing rose by 14%, totaling $228 billion, according to market watcher IDC.

Looking ahead, IDC predicts this spend will increase to $378 billion by 2028, for a five-year compound annual growth rate (CAGR) of nearly 18%. Driving this growth will be high demand for real-time analytics, automation and enhanced customer experiences.

How does edge computing work?

Fundamentally, edge computing operates pretty much the same way that other types of computing do. The big difference is the location of the computing infrastructure relative to devices that collect the data.

For instance, a telecommunications provider like Verizon operates at the edge to better serve its customers. Rather than sending customer data to a central location, a telco can process it closer to the source.

An edge node’s proximity to end users can dramatically reduce the time it takes to transfer information to and from each user. This time is referred to as latency. And moving computing to the edge can reduce it. Edge computing can also lower data-error rates and demand for costly data-center space.

For a telco application of edge computing, the flow of data would look something like this:

1.   Users working with their smartphones, PCs and other devices create and request data. Because this happens in their homes, offices or anywhere else they happen to be, the data is said to have been created at the edge.

2.   Next, this customer data is processed by what are known as edge nodes. These are edge computing infrastructure devices placed near primary data sources.

3.   Next, the edge nodes filter the user data with algorithms and AI-enabled processing. Then the nodes send to the cloud only the most relevant data. This helps reduce bandwidth usage and costs.

Edge is Everywhere

Many verticals now rely on edge computing to increase efficiency and better serve their customers. These include energy providers, game developers and IoT appliance manufacturers.

One big vertical for the edge is retail, where major brands rely on edge computing to collect data from shoppers in real time. This helps retailers manage their stock, identify new sales opportunities, reduce shrinkage (that is, theft), and offer unique deals to their customers.

Other areas for the edge include “smart roads.” Here, roadside sensors are used to collect and process data locally to assess traffic conditions and maintenance. In addition, the reduced latency and hyper-locality provided by edge computing can speed communications, paring precious seconds when first responders are called to the scene of an accident.

Inner Workings

Like most modern computers, edge nodes rely on a laundry list of digital components. At the top of that list is a processor like the AMD EPYC Embedded 9004 and 8004 series.

AMD’s latest embedded processors are designed to balance performance and efficiency. The company’s ‘Zen 4’ and ‘Zen 4c’ 5-nanometer core architecture is optimized for always-on embedded systems. And with up to 96 cores operating as fast as 4.15 GHz, these processors can handle the AI-heavy workloads increasingly common to edge computing.

Zoom out from the smallest component to the largest, and you’re likely to find a density- and power-optimized edge platform like the Supermicro H13 WIO.

Systems like these are designed specifically for edge operations. Powered by either AC or DC current for maximum flexibility, the H13 WIO can operate at a scant 80 watts TDP. Yet to handle the most resource-intensive applications, it can scale up to 64 cores.

Getting Edgier

The near future of edge computing promises to be fascinating. As more users sign up for new services, enterprises will have to expand their edge networks to keep up with demand.

What tools will they use? To find out, see the latest edge tech from AMD and Supermicro at this year’s MWC, which kicks off in Barcelona, Spain, on March 3.

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