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Tech Explainer: What’s special about an AI server?

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Tech Explainer: What’s special about an AI server?

What’s in an AI server that a general-purpose system lacks?

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The Era of Artificial Intelligence requires its own class of servers, and rightly so. The AI tech that increasingly powers our businesses, finance, entertainment and scientific research is some of the most resource-intensive in history. Without AI servers, all this would grind to a halt.

But why? What’s so special about AI servers? And how are they able to power successive evolutions of large language models, generative AI, machine learning, and all the other AI-based workloads we’ve come to rely on day in and day out?

Put another way: What do AI servers have that standard servers don’t?

The answer can be summed up in a single word: More.

When it comes to AI servers, it’s all about managing a symphony. The musical instruments include multiple processors, GPUs, memory modules, networking hardware and expansion options.

Sure, your average general-purpose server has many similar components. But both the quantity and performance of each component is considerably lower than those of an AI server. That helps keep the price affordable, heat low, and workload options open. But it certainly doesn’t have the integrated GPU needed to run AI workloads.

Best of the Beasts

Supermicro specializes in the deployment of jaw-dropping power. The company’s newest 8U GPU Server (AS -8126GS-TNMR) is engineered to chew through the world’s toughest AI workloads. It’s powered by dual AMD EPYC processors and eight AMD Instinct MI350X or Instinct MI325X accelerators. This server can tackle AI workloads while staying cool and scaling up to meet increasing demand.

Keeping AI servers from overheating can be a tough job. Even a lowly, multipurpose business server kicks off a lot of heat. Temperatures build up around vital components like the CPU, GPU and storage devices. If that heat hangs around too long, it can lead to performance issues and, eventually, system failure.

Preventing heat-related issues in a single general-purpose server can be accomplished with a few heatsinks and small-diameter fans. But when it comes to high-performance, multi-GPU servers like Supermicro’s new 4U GPU A+ Server (AS -4126GS-NMR-LCC), liquid cooling becomes a must-have.

It’s also vital that AI servers be designed with expansion in mind. When an AI-powered app becomes successful, IT managers must be able to scale up quickly and without interruption.

Supermicro’s H14 8U 8-GPU System sets the standard for scalability. The H14 offers up to 20 storage drives and up to 12 PCI Express 5.0 (PCIe) x16 expansion slots.

Users can fill these high-bandwidth slots with a dizzying array of optional hardware, including:

  • Network Interface Cards (NICs) like the new AI-focused AMD AI NIC for high-speed networking.
  • NVMe storage to provide fast disk access.
  • Field Programmable Gate Array (FPGA) modules, which can be set up for custom computation and reconfigured after deployment.
  • Monitoring and control management cards. These enable IT staff to power servers on and off remotely, and also access BIOS settings.
  • Additional GPUs to aid in AI training and inferencing.
  • AI Accelerators. The AMD Instinct series is designed to tackle computing for AI, both training and inference.

A Different Class of Silicon

Hardware like the Supermicro GPU Server epitomizes what it means to be an AI server. That’s due in part to the components it’s designed to house. We’re talking about some of the most advanced processing tech available today.

As mentioned above, that tech comes courtesy of AMD, whose 5th Gen AMD EPYC 9005 series processors and recently announced AMD Instinct MI350 Series GPUs are powerful enough to tackle any AI workload.

AMD’s Instinct MI350 accelerators deliver a 4x generation-on-generation AI compute increase and a 35x generational leap in inferencing.

Say the word, and Supermicro will pack your AI Server with dual AMD EPYC processors containing up to 192 cores. They’ll install the latest AMD Instinct M1350X platform with 8 GPUs, fill all 24 DIMM slots with 6TB of DDR5 memory, and add an astonishing 16 NVMe U.2 drives. 

Advances Just Around the Corner

It seems like each new day brings stories about bold advances in AI. Apparently, our new robot friends may have the answer to some very human questions like, how can we cure our most insidious diseases? And how do we deal with the looming threat of climate crisis?

The AI models that could answer those questions—not to mention the ones that will help us find even better movies on Netflix—will require more power as they grow.

To meet those demands, AI server engineers are already experimenting with the next generation of advanced cooling for dense GPU clusters, enhanced hardware-based security, and new, more scalable modular infrastructure.

In fact, AI server designers have begun using their own AI models to create bigger and better AI servers. How very meta.

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Meet Supermicro’s newest AI servers, powered by AMD Instinct MI350 Series GPUs

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Meet Supermicro’s newest AI servers, powered by AMD Instinct MI350 Series GPUs

Supermicro’s new AI servers are powered by a combination of AMD EPYC CPUs and AMD Instinct GPUs.

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Supermicro didn’t waste any time supporting AMD’s new Instinct MI350 Series GPUs. The same day AMD formally introduced the new GPUs, Supermicro announced two rack-mount servers that support them.

The new servers, members of Supermicro’s H14 generation of GPU optimized solutions, feature dual AMD EPYC 9005 CPUs along with the AMD Instinct MI350 series GPUs. They’re aimed at organizations looking to achieve a formerly tough combination: maximum performance at scale in their AI-driven data centers, but also a lower total cost of ownership (TCO).

To make the new servers easy to upgrade and scale, Supermicro has designed the new servers around its proven building-block architecture.

Here’s a quick look at the two new Supermicro servers:

4U liquid-cooled system with AMD Instinct MI355X GPU

This system, model number AS -4126GS-NMR-LCC, comes with a choice of dual AMD EPYC 9005 or 9004 Series CPUs, both with liquid cooling.

On the GPU front, users also have a choice of the AMD Instinct MI325X or brand-new AMD Instinct MI355X. Either way, this server can handle up to 8 GPUs.

Liquid cooling is provided by a single direct-to-chip cold plate. Further cooling comes from 5 heavy-duty fans and an air shroud.

8U air-cooled system with AMD Instinct MI350X GPU

This system, model number AS -8126GS-TNMR, comes with a choice of dual AMD EPYC 9005 or 9004 Series CPUs, both with air cooling.

This system also supports both the AMD Instinct MI325X and AMD Instinct MI350X GPUs. Also like the 4U server, this system supports up to 8 GPUs.

Air cooling is provided by 10 heavy-duty fans and an air shroud.

The two systems also share some features in common. These include PCIe 5.0 connectivity, large memory capacities (up to 2.3TB), and support for both AMD’s ROCm open-source software and AMD Infinity Fabric Link connections for GPUs.

“Supermicro continues to lead the industry with the most experience in delivering high-performance systems designed for AI and HPC applications,” says Charles Liang, president and CEO of Supermicro. “The addition of the new AMD Instinct MI350 series GPUs to our GPU server lineup strengthens and expands our industry-leading AI solutions and gives customers greater choice and better performance as they design and build the next generation of data centers.”

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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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Oil & gas spotlight: Fueling up with AI

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Oil & gas spotlight: Fueling up with AI

AI is helping industry players that include BP, Chevron and Shell automate a wide range of important use cases. To serve them, AMD and Supermicro offer powerful accelerators and servers.

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What’s artificial intelligence good for? For managers in the oil and gas industry, quite a lot.

Industry players that include Shell, BP, ExxonMobil and Chevron are already using machine learning and AI. Use cases include predictive maintenance, seismic data analysis, reservoir management and safety monitoring, says a recent report by Chirag Bharadwaj of consultants Appinventiv.

AI’s potential benefits for oil and gas companies are substantial. Anurag Jain of AI consultants Oyelabs cites estimates of AI lowering oil production costs by up to $5 a barrel with a 25% productivity gain, and increasing oil reserves by as much as 20% with enhanced resource recovery.

Along the same lines is a recent report from market watcher Global Growth Insights. It says adoption of AI in North American oil shale drilling has increased production efficiency by an impressive 20%.

All this has led Jain of Oyelabs to expect a big increase in the oil and gas industry’s AI spend. He predicts the industry’s worldwide spending on AI will rise from $3 billion last year to nearly $5.3 billion in 2028.

Assuming Jain is right, that would put the oil and gas industry’s AI spend at about 15% of its total IT spend. Last year, the industry spent nearly $20 billion on all IT goods and services worldwide, says Global Growth Insights.

Powerful Solutions

All this AI activity in the oil and gas industry hasn’t passed the notice of AMD and Supermicro. They’re on the case.

AMD is offering the industry its AMD Instinct MI300A, an accelerator that combines CPU cores and GPUs to fuel the convergence of high-performance computing (HPC) with AI. And Supermicro is offering rackmount servers driven by this AMD accelerator.

Here are some of the benefits the two companies are offering oil and gas companies:

  • An APU multi-chip architecture that enables dense compute, high-bandwidth memory integration, and chips for both CPU and GPU all in one.
  • Up to 2.6x the HPC performance/watt vs. the older AMD Instinct MI250X.
  • Up to 5.1x the AI-training workload performance with INT8 vs. the AMD Instinct MI250X. (INT8 is a fixed-point representation using 8 bits.)
  • Up to 128GB of unified HBM3 memory dedicated to GPUs. (HBM3 is a high-bandwidth memory chip technology that offers increased bandwidth, memory capacity and power efficiency, all in a smaller form factor.)
  • Double-precision power up to 122.6 TFLOPS with FP64 matrix HPC performance. (FP64 is a double-precision floating point format using 64 bits in memory.)
  • Complete, pre-validated solutions that are ready for rack-scale deployment on day one. These offer the choice of either 2U (liquid cooled) or 4U (air cooled) form factors.
     

If you have customers in oil and gas looking to get into AI, tell them about these Supermicro and AMD solutions.

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Healthcare in the spotlight: Big challenges, big tech

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Healthcare in the spotlight: Big challenges, big tech

To meet some of their industry’s toughest challenges, healthcare providers are turning to advanced technology.

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Healthcare providers face some tough challenges. Advanced technology can help.

As a recent report from consultants McKinsey & Co. points out, healthcare providers are dealing with some big challenges. These include rising costs, workforce shortages, an aging population, and increased competition from nontraditional parties.

Another challenge: Consumers expect their healthcare providers to offer new capabilities, such as digital scheduling and telemedicine, as well as better experiences.

One way healthcare providers hope to meet these two challenge streams is with advanced technology. Three-quarters of U.S. healthcare providers increased their IT spending in the last year, according to a survey conducted by consultants Bain & Co. The same survey found that 15% of healthcare providers already have an AI strategy in place, up from just 5% who had a strategy in 2023.

Generative AI is showing potential, too. Another survey, this one done by McKinsey, finds that over 70% of healthcare organizations are now either pursuing GenAI proofs-of-concept or are already implementing GenAI solutions.

Dynamic Duo

There’s a catch to all this: As healthcare providers adopt AI, they’re finding that the required datasets and advanced analytics don’t run well on their legacy IT systems.

To help, Supermicro and AMD are working together. They’re offering healthcare providers heavy-duty compute delivered at rack scale.

Supermicro servers powered by AMD Instinct MI300X GPUs are designed to accelerate AI and HPC workloads in healthcare. They offer the levels of performance, density and efficiency healthcare providers need to improve patient outcomes.

The AMD Instinct MI300X is designed to deliver high performance for GenAI workloads and HPC applications. It’s designed with no fewer than 304 high-throughput compute units. You also get AI-specific functions and 192GB of HBM3 memory, all of it based on AMD’s CDNA 3 architecture.

Healthcare providers can use Supermicro servers powered by AMD GPUs for next-generation research and treatments. These could include advanced drug discovery, enhanced diagnostics and imaging, risk assessments and personal care, and increased patient support with self-service tools and real-time edge analytics.

Supermicro points out that its servers powered by AMD Instinct GPUs deliver massive compute with rack-scale flexibility, as well as high levels of power efficiency.

Performance:

  • The powerful combination of CPUs, GPUs and HBM3 memory accelerates HPC and AI workloads.
  • HBM3 memory offers capacities of up to 192GB dedicated to the GPUs.
  • Complete solutions ship pre-validated, ready for instant deployment.
  • Double-precision power can serve up to 163.4 TFLOPS.

Flexibility:

  • Proven AI building-block architecture streamlines deployment at scale for the largest AI models.
  • An open AI ecosystem with AMD ROCm open software.
  • A unified computing platform with AMD Instinct MI300X plus AMD Infinity fabric and infrastructure.
  • Thanks to a modular design and build, users move faster to the correct configuration.

Efficiency:

  • Dual-zone cooling innovation, used by some of the most efficient supercomputers on the Green500 supercomputer list.
  • Improved density with 3rd Gen AMD CDNA, delivering 19,456 stream cores.
  • Chip-level power intelligence enables the AMD Instinct MI300X to deliver big power performance.
  • Purpose-built silicon design of the 3rd Gen AMD CDNA combines 5nm and 6nm fabrication processes.

Are your healthcare clients looking to unleash the potential of their data? Then tell them about Supermicro systems powered by the AMD MI300X GPUs.

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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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AMD Instinct MI300A blends GPU, CPU for super-speedy AI/HPC

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AMD Instinct MI300A blends GPU, CPU for super-speedy AI/HPC

CPU or GPU for AI and HPC? You can get the best of both with the AMD Instinct MI300A.

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The AMD Instinct MI300A is the world’s first data center accelerated processing unit for high-performance computing and AI. It does this by integrating both CPU and GPU cores on a single package.

That makes the AMD Instinct MI300A highly efficient at running both HPC and AI workloads. It also makes the MI300A powerful enough to accelerate training the latest AI models.

Introduced about a year ago, the AMD Instinct MI300A accelerator is shipping soon. So are two Supermicro servers—one a liquid-cooled 2U system, the other an air-cooled 4U—each powered by four MI300A units.

Under the Hood

The technology of the AMD Instinct MI300A is impressive. Each MI300A integrates 24 AMD ‘Zen 4’ x86 CPU cores with 228 AMD CDNA 3 high-throughput GPU compute units.

You also get 128GB of unified HBM3 memory. This presents a single shared address space to CPU and GPU, all of which are interconnected into the coherent 4th Gen AMD Infinity architecture.

Also, the AMD Instinct MI300A is designed to be used in a multi-unit configuration. This means you can connect up to four of them in a single server.

To make this work, each APU has 1 TB/sec. of bidirectional connectivity through eight 128 GB/sec. AMD Infinity Fabric interfaces. Four of the interfaces are dedicated Infinity Fabric links. The other four can be flexibly assigned to deliver either Infinity Fabric or PCIe Gen 5 connectivity.

In a typical four-APU configuration, six interfaces are dedicated to inter-GPU Infinity Fabric connectivity. That supplies a total of 384 GB/sec. of peer-to-peer connectivity per APU. One interface is assigned to support x16 PCIe Gen 5 connectivity to external I/O devices. In addition, each MI300A includes two x4 interfaces to storage, such as M.2 boot drives, plus two USB Gen 2 or 3 interfaces.

Converged Computing

There’s more. The AMD Instinct MI300A was designed to handle today’s convergence of HPC and AI applications at scale.

To meet the increasing demands of AI applications, the APU is optimized for widely used data types. These include FP64, FP32, FP16, BF16, TF32, FP8 and INT8.

The MI300A also supports native hardware sparsity for efficiently gathering data from sparse matrices. This saves power and compute cycles, and it also lowers memory use.

Another element of the design aims at high efficiency by eliminating time-consuming data copy operations. The MI300A can easily offload tasks easily between the CPU and GPU. And it’s all supported by AMD’s ROCm 6 open software platform, built for HPC, AI and machine learning workloads.

Finally, virtualized environments are supported on the MI300A through SR-IOV to share resources with up to three partitions per APU. SR-IOV—short for single-root, input/output virtualization—is an extension of the PCIe spec. It allows a device to separate access to its resources among various PCIe functions. The goal: improved manageability and performance.

Fun fact: The AMD Instinct MI300A is a key design component of the El Capitan supercomputer recently dedicated by Lawrence Livermore Labs. This system can process over two quintillion (1018) calculations per second.

Supermicro Servers

As mentioned above, Supermicro now offers two server systems based on the AMD Instinct MI300A APU. They’re 2U and 4U systems.

These servers both take advantage of AMD’s integration features by combining four MI300A units in a single system. That gives you a total of 912 GPUs, 96 CPUs, and 512GB of HBM3 memory.

Supermicro says these systems can push HPC processing to Exascale levels, meaning they’re very, very fast. “Flop” is short for floating point operations per second, and “exa” indicates a 1 with 18 zeros after it. That’s fast.

Supermicro’s 2U server (model number AS -2145GH-TNMR-LCC) is liquid-cooled and aimed at HPC workloads. Supermicro says direct-to-chip liquid-cooling technology enables a nice TCO with over 51% data center energy cost savings. The company also cites a 70% reduction in fan power usage, compared with air-cooled solutions.

If you’re looking for big HPC horsepower, Supermicro’s got your back with this 2U system. The company’s rack-scale integration is optimized with dual AIOM (advanced I/O modules) and 400G networking. This means you can create a high-density supercomputing cluster with as many as 21 of Supermicro’s 2U systems in a 48U rack. With each system combining four MI300A units, that would give you a total of 84 APUs.

The other Supermicro server (model number AS -4145GH-TNMR) is an air-cooled 4U system, also equipped with four AMD Instinct MI300A accelerators, and it’s intended for converged HPC-AI workloads. The system’s mechanical airflow design keeps thermal throttling at bay; if that’s not enough, the system also has 10 heavy-duty 80mm fans.

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Tech Explainer: CPUs and GPUs for AI training and inferencing

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Tech Explainer: CPUs and GPUs for AI training and inferencing

Which is best for AI – a CPU or a GPU? Like much in life, it depends.

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While central processing units and graphics processing units serve different roles in AI training and inferencing, both roles are vital to AI workloads.

CPUs and GPUs were both invented long before the AI era. But each has found new purpose as the robots conduct more of our day-to-day business.

Each has its tradeoffs. Most CPUs are less expensive than GPUs, and they typically require less electric power. But that doesn’t mean CPUs are always the best choice for AI workloads. Like lots of things in life, it depends.

Two Steps to AI

A typical AI application involves a two-step process. First training. Then inferencing.

Before an AI model can be deployed, it must be trained. That could include suggesting which movie to watch next on Netflix or detecting fake currency in a retail environment.

Once the AI model has been deployed, it can begin the inferencing process. In this stage, the AI application interfaces with users, devices and other models. Then it autonomously makes predictions and decisions based on new input.

For example, Netflix’s recommendation engine is powered by an AI model. The AI was first trained to consider your watching history and stated preferences, as well as to review newly available content. Then the AI employs inferencing—what we might call reasoning—to suggest a new movie or TV show you’re likely to enjoy.

AI Training

GPU architectures like those found in the AMD Instinct MI325X accelerator offers highly parallel processing. In other words, a GPU can perform many calculations simultaneously.

The AMD Instinct MI325X has more than 300 GPU compute units. They make the accelerator faster and more adept at both processing large datasets and handling the repetitious numerical operations common to the training process.

These capabilities also mean GPUs can accelerate the training process. That’s especially true for large models, such as those that underpin the networks used for deep learning.

CPUs, by contrast, excel at general-purpose tasks. Compared with a GPU, a CPU will be better at completing sequential tasks that require logic or decision-making. For this reason, a CPU’s role in AI training is mostly limited to data preprocessing and coordinating GPU tasks.

AI Inferencing

However, when it comes to AI inferencing, CPUs play a much more significant role. Often, inferencing can be a relatively lightweight workload, because it’s not highly parallel. A good example is the AI capability present in modern edge devices such as the latest iOS and Android smartphones.

As mentioned above, the average CPU also consumes less power than a GPU. That makes a CPU a better choice in situations where heat and battery life are important.

However, not all inferencing applications are lightweight, and such workloads may not be appropriate for CPUs. One example is autonomous vehicles. They will require massive parallel processing in real-time to ensure safety and optimum efficiency.

In these cases, GPUs will play a bigger role in the AI inferencing process, despite their higher cost and power requirements.

Powerful GPUs are already used for AI inferencing at the core. Examples include large-scale cloud services such as AWS, Google Cloud and Microsoft Azure.

Enterprise Grade

Enterprises often conduct AI training and inferencing on a scale so massive, it eclipses those found in edge environments. In these cases, IT engineers must rely on hugely powerful systems.

One example is the Supermicro AS -8125GS-TNMR2 server. This 8U behemoth—weighing in at 225 pounds—can operate up to eight AMD Instinct MI300X accelerators. And it’s equipped with dual AMD EPYC processors, the customer’s choice of either the 9004 or 9005 series.

To handle some of the world’s most demanding AI workloads, Supermicro’s server is packed with an astonishing amount of tech. In addition to its eight GPUs, the server also has room for a pair of AMD EPYC 9005-series processors, 6TB of ECC DDR5 memory, and 18 hot-swap 2.5-inch NVMe and SATA drives.

That makes the Supermicro system one of the most capable and powerful servers now available. And as AI evolves, tech leaders including AMD and Supermicro will undoubtedly produce more powerful CPUs, GPUs and servers to meet the growing demand.

What will the next generation of AI training and inferencing technology look like? To find out, you won’t have to wait long.

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AMD’s new ROCm 6.3 makes GPU programming even better

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AMD’s new ROCm 6.3 makes GPU programming even better

AMD recently introduced version 6.3 of ROCm, its open software stack for GPU programming. New features included expanded OS support and other optimizations.

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There’s a new version of AMD ROCm, the open software stack designed to enable GPU programming from low-level kernel all the way up to end-user applications.  

The latest version, ROCm 6.3, adds features that include expanded operating system support, an open-source toolkit and more.

Rock On

AMD ROCm provides the tools for HIP (the heterogeneous-computing interface for portability), OpenCL and OpenMP. These include compilers, APIs, libraries for high-level functions, debuggers, profilers and runtimes.

ROCm is optimized for Generative AI and HPC applications, and it’s easy to migrate existing code into. Developers can use ROCm to fine-tune workloads, while partners and OEMs can integrate seamlessly with AMD to create innovative solutions.

The latest release builds on ROCm 6, which AMD introduced last year. Version 6 added expanded support for AMD Instinct MI300A and MI300X accelerators, key AI support features, optimized performance, and an expanded support ecosystem.

The senior VP of AMD’s AI group, Vamsi Boppana, wrote in a recent blog post: “Our vision is for AMD ROCm to be the industry’s premier open AI stack, enabling choice and rapid innovation.”

New Features

Here’s some of what’s new in AMD ROCm 6.3:

  • rocJPEG: A high-performance JPEG decode SDK for AMD GPUs.
  • ROCm compute profiler and system profiler: Previously known as Omniperf and Omnitrace, these have been renamed to reflect their new direction as part of the ROCm software stack.
  • Shark AI toolkit: This open-source toolkit is for high-performance serving of GenAI and  LLMs. Initial release includes support for the AMD Instinct MI300.
  • PyTorch 2.4 support: PyTorch is a machine learning library used for applications such as computer vision and natural language processing. Originally developed by Meta AI, it’s now part of the Linux Foundation umbrella.
  • Expanded OS support: This includes added support for Ubuntu 24.04.2 and 22.04.5; RHEL 9.5; and Oracle Linux 8.10. In addition, ROCm 6.3.1 includes support for both Debian 12 and the AMD Instinct MI325X accelerator.
  • Documentation updates: ROCm 6.3 offers clearer, more comprehensive guidance for a wider variety of use cases and user needs.

Super for Supermicro

Developers can use ROCm 6.3 to create tune workloads and create solutions for Supermicro GPU systems based on AMD Instinct MI300 accelerators.

Supermicro offers three such systems:

Are your customers building AI and HPC systems? Then tell them about the new features offered by AMD ROCm 6.3.

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