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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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2024: A look back at the year’s best

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2024: A look back at the year’s best

Let's look back at 2024, a year when AI was everywhere, AMD introduced its 5th Gen EPYC processors, and Supermicro led with liquid cooling.

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You couldn't call 2024 boring.

If anything, the year was almost too exciting, too packed with important events, and moving much too fast.

Looking back, a handful of 2024’s technology events stand out. Here are a few of our favorite things.

AI Everywhere

In March AMD’s chief technology officer, Mark Papermaster, made some startling predictions that turned out to be absolutely true.

Speaking at an investors’ event sponsored by Arete Research, Papermaster said, “We’re thrilled to bring AI across our entire product portfolio.” AMD has indeed done that, offering AI capabilities from PCs to servers to high-performance GPU accelerators.

Papermaster also said the buildout of AI is an event as big as the launch of the internet. That certainly sounds right.

He also said AMD believes the total addressable market for AI through 2027 to be $400 billion. If anything, that was too conservative. More recently, consultants Bain & Co. predicted that figure will reach $780 billion to $990 billion.

Back in March, Papermaster said AMD had increased its projection for full-year AI sales from $2 billion to $3.5 billion. That’s probably too low, too.

AMD recently reported revenue of $3.5 billion for its data-center group for just the third quarter alone. The company attributed at least some of the group’s 122% year-on-year increase to the strong ramp of AMD Instinct GPU shipments.

5th Gen AMD EPYC Processors

October saw AMD introduce the fifth generation of its powerful line of EPYC server processors.

The 5th Gen AMD EPYC processors use the company’s new ‘Zen 5’ core architecture. It includes over 25 SKUs offering anywhere from 8 to 192 cores. And the line includes a model—the AMD EPYC 9575F—designed specifically to work with GPU-powered AI solutions.

The market has taken notice. During the October event, AMD CEO Lisa Su told the audience that nearly one in three servers worldwide (34%) are now powered by AMD EPYC processors. And Supermicro launched its new H14 line of servers that will use the new EPYC processors.

Supermicro Liquid Cooling

As servers gain power to add AI and other compute-intensive capabilities, they also run hotter. For data-center operators, that presents multiple challenges. One big one is cost: air conditioning is expensive. What’s more, AC may be unable to cool the new generation of servers.

Supermicro has a solution: liquid cooling. For some time, the company has offered liquid cooling as a data-center option.

In November the company took a new step in this direction. It announced a server that comes with liquid cooling only.

The server in question is the Supermicro 2U 4-node FlexTwin, model number AS -2126FT-HE-LCC. It’s a high-performance, hot-swappable, high-density compute system designed for HPC workloads.

Each 2U system comprises 4 nodes, and each node is powered by dual AMD EPYC 9005 processors. (The previous-gen AMD EPYC 9004s are supported, too.)

To keep cool, the FlexTwin server uses a direct-to-chip (D2C) cold plate liquid cooling setup. Each system also runs 16 counter-rotating fans. Supermicro says this cooling arrangement can remove up to 90% of server-generated heat.

AMD Instinct MI325X Accelerator

A big piece of AMD’s product portfolio for AI is its Instinct line of accelerators. This year the company promised to maintain a yearly cadence of new Instinct models.

Sure enough, in October the company introduced the AMD Instinct MI325X Accelerator. It’s designed for Generative AI performance and working with large language models (LLMs). The system offers 256GB of HBM3E memory and up to 6TB/sec. of memory bandwidth.

Looking ahead, AMD expects to formally introduce the line’s next member, the AMD Instinct MI350, in the second half of next year. AMD has said the new accelerator will be powered by a new AMD CDNA 4 architecture, and will improve AI inferencing performance by up to 35x compared with the older Instinct MI300.

Supermicro Edge Server

A lot of computing now happens at the edge, far beyond either the office or corporate data center.

Even more edge computing is on tap. Market watcher IDC predicts double-digit growth in edge-computing spending through 2028, when it believes worldwide sales will hit $378 billion.

Supermicro is on it. At the 2024 MWC, held in February in Barcelona, the company introduced an edge server designed for the kind of edge data centers run by telcos.

Known officially as the Supermicro A+ Server AS -1115SV-WTNRT, it’s a 1U short-depth server powered by a single AMD EPYC 8004 processor with up to 64 cores. That’s edgy.

Happy Holidays from all of us at Performance Intensive Computing. We look forward to serving you in 2025.

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Tech Explainer: Why does PCIe 5.0 matter? And what’s coming next?

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Tech Explainer: Why does PCIe 5.0 matter? And what’s coming next?

PCIe 5.0 connects high-speed components to servers and PCs. Versions 6 & 7, coming soon, will deliver even higher speeds for tomorrow’s AI workloads.

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You’ve no doubt heard of PCIe 5.0. But what is it exactly? And why does it matter?

As the name and number imply, PCIe 5.0 is the fifth generation of the Peripheral Component Interconnect Express interface standard. PCIe essentially sets the rules for connecting high-speed components such as GPUs, networking cards and storage devices to servers, desktop PCs and other devices.

To be sure, these components could be connected via a number of other interface standards, such as USB-C and SATA.

But PCIe 5.0 alone offers extremely high bandwidth and low latency. That makes it a better choice for mission-critical enterprise IT operations and resource-intensive AI workloads.

Left in the Dust

The 5th generation of PCIe was released in May 2019, bringing significant improvements over PCIe 4.0. These include:

  • Increased Bandwidth. PCIe 5.0 has a maximum throughput of 32 giga-transfers per second (GT/s)—effectively double the bandwidth of its predecessor. In terms of data transfer, 32 GT/s translates to around 4 GB of data throughput per lane in each direction. That allows for a total of 64 GB/s in a 16-lane PCIe-based GPU. That’s perfect for modern GPU-dependent workflows such as AI-inferencing.
  • Lower Latency. Keeping latency as low as possible is crucial for applications like gaming, high-performance computing (HPC) and AI workloads. High latency can inhibit data retrieval and processing, which in turn hurts both application performance and the user experience. The latency of PCIe 5.0 varies depending on multiple factors, including network connectivity, attached devices and workloads. But it’s safe to assume an average latency of around 100 nanoseconds (ns) — roughly 50% less than PCIe 4.0. And again, with latency, lower is better.
  • Enhanced Data-Center Features. Modern data-center operations are among the most demanding. That’s especially true for IT operations focused on GenAI, machine learning and telecom. So it’s no surprise that PCIe 5.0 includes several features focused on enhanced operations for data centers. Among the most notable is increased bandwidth and faster data access for NVMe storage devices. PCIe 5.0 also includes features that enhance power management and efficiency.

Leveraging PCIe 5

AMD is a front-runner in the race to help enterprises cope with modern AI workloads. And the company has been quick to take advantage of PCIe 5.0’s performance improvements. Take, for example, the AMD Instinct MI325X Accelerator.

This system is a leading-edge accelerator module for generative AI, inference, training and HPC. Each discrete AMD Instinct MI325X offers a 16-lane PCIe Gen 5 host interface and seven AMD Infinity Fabric links for full connectivity between eight GPUs in a ring.

By leveraging a PCIe 5.0 connection, AMD’s accelerator can offer I/O-to-host-CPU and scale-out network bandwidths of 128 GB/sec.

AMD is also using PCIe on its server processors. The new 5th generation AMD EPYC server processors take advantage of PCIe 5.0’s impressive facility. Specifically, the AMD EPYC 9005 Series processors support 128 PCIe 5 I/O lanes in a single-socket server. For dual-socket servers, support increases to 160 lanes.

Supermicro is another powerful force in enterprise IT operations. The company’s behemoth H14 8-GPU system (model number AS-8126GS-TNMR2) leverages AMD EPYC processors and AMD Instinct accelerators to help enterprises deploy the largest AI and large language models (LLMs).

The H14’s standard configuration includes eight PCIe 5.0 x16 low-profile slots and two full-height slots. Users can also opt for a PCIe expansion kit, which adds two additional PCIe 5.0 slots. That brings the grand total to an impressive 12 PCIe 5.0 16-lane expansion slots.

PCIe 6.0 and Beyond

PCIe 5.0 is now entering its sixth year of service. That’s not a long time in the grand scheme of things. But the current version might feel ancient to IT staff who need to eke out every shred of bandwidth to support modern AI workloads.

Fortunately, a new PCIe generation is in the works. The PCIe 6.0 specification, currently undergoing testing and development, will offer still more performance gains over its predecessor.

PCI-SIG, an organization committed to developing and enhancing the PCI standard, says the 6.0 platform’s upgrades will include:

  • A data rate of up to 64 GT/sec., double the current rate and providing a maximum bidirectional bandwidth of up to 256 GB/sec for x16 lanes
  • Pulse Amplitude Modulation with 4 levels (PAM4)
  • Lightweight Forward Error Correct (FEC) and Cyclic Redundancy Check (CRC) to mitigate the bit error rate increase associated with PAM4 signaling
  • Backwards compatibility with all previous generations of PCIe technology

There’s even a next generation after that, PCIe 7.0. This version could be released as soon as 2027, according to the PCI-SIG. That kind of speed makes sense considering the feverish rate at which new technology is being developed to enable and expand AI operations.

It’s not yet clear how accurate those release dates are. But one thing’s for sure: You won’t have to wait long to find out.

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Tech Explainer: What is the AMD “Zen” core architecture?

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Tech Explainer: What is the AMD “Zen” core architecture?

Originally launched in 2017, this CPU architecture now delivers high performance and efficiency with ever-thinner processes.

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The recent release of AMD’s 5th generation processors—formerly codenamed Turin—also heralded the introduction of the company’s “Zen 5” core architecture.

“Zen” is AMD’s name for a design ethos that prioritizes performance, scalability and efficiency. As any CTO will tell you, these 3 aspects are crucial for success in today’s AI era.

AMD originally introduced its “Zen” architecture in 2017 as part of a broader campaign to steal market share and establish dominance in the all-important enterprise IT space.

Subsequent generations of the “Zen” design have markedly increased performance and efficiency while delivering ever-thinner manufacturing processes.

Now and Zen

Since the “Zen” core’s original appearance in AMD Ryzen 1000-series processors, the architecture’s design philosophy has maintained its focus on a handful of vital aspects. They include:

  • A modular design. Known as Infinity Fabric, it facilitates efficient connectivity among multiple CPU cores and other components. This modular architecture enhances scalability and performance, both of which are vital for modern enterprise IT infrastructure.
  • High core counts and multithreading. Both are common to EPYC and Ryzen CPUs built using the AMD “Zen” core architecture. Simultaneous multithreading enables each core to process 2 threads. In the case of EPYC processors, this makes AMD’s CPUs ideal for multithreaded workloads that include Generative AI, machine learning, HPC and Big Data.
  • Advanced manufacturing processes. These allow faster, more efficient communication among individual CPU components, including multithreaded cores and multilevel caches. Back in 2017, the original “Zen” architecture was manufactured using a 14-nanometer (nm) process. Today’s new “Zen 5” and “Zen 5c” architectures (more on these below) reduce the lithography to just 4nm and 3nm, respectively.
  • Enhanced efficiency. This enables IT staff to better manage complex enterprise IT infrastructure. Reducing heat and power consumption is crucial, too, both in data centers and at the edge. The AMD “Zen” architecture makes this possible by offering enterprise-grade EPYC processors that offer up to 192 cores, yet require a maximum thermal design power (TDP) of only 500W.

The Two-Fold Path

The latest, fifth generation “Zen” architecture is divided into two segments: “Zen 5” and “Zen 5c.”

“Zen 5” employs a 4-nanometer (nm) manufacturing process to deliver up to 128 cores operating at up to 4.1GHz. It’s optimized for high per-core performance.

“Zen 5c,” by contrast, offers a 3nm lithography that’s reserved for AMD EPYC 96xx, 97xx, 98xx, and 99xx series processors. It’s optimized for high density and power efficiency.

The most powerful of these CPUs—the AMD EPYC 9965—includes an astonishing 192 cores, a maximum boost clock speed of 3.7GHz, and an L3 cache of 384MB.

Both “Zen 5” and “Zen 5c” are key components of the 5th gen AMD EPYC processors introduced earlier this month. Both have also been designed to achieve double-digit increases in instructions per clock cycle (IPC) and equip the core with the kinds of data handling and processing power required by new AI workloads.

Supermicro’s Satori

AMD isn’t the only brand offering bold, new tech to harried enterprise IT managers.

Supermicro recently introduced its new H14 servers, GPU-accelerated systems and storage servers powered by AMD EPYC 9005 Series processors and AMD Instinct MI325X Accelerators. A number of these servers also support the new AMD “Turin” CPUs.

The new product line features updated versions of Supermicro’s vaunted Hyper system, Twin multinode servers, and AI-inferencing GPU systems. All are now available with the user’s choice of either air or liquid cooling.

Supermicro says its collection of purpose-built powerhouses represents one of the industry’s most extensive server families. That should be welcome news for organizations intent on building a fleet of machines to meet the highly resource-intensive demands of modern AI workloads.

By designing its next-generation infrastructure around AMD 5th Generation components, Supermicro says it can dramatically increase efficiency by reducing customers’ total data-center footprints by at least two-thirds.

Enlightened IT for the AI Era

While AMD and Supermicro’s advances represent today’s cutting-edge technology, tomorrow is another story entirely.

Keeping up with customer demand and the dizzying pace of AI-based innovation means these tech giants will soon return with more announcements, tools and design methodologies. AMD has already promised a new accelerator, the AMD Instinct MI350, will be formally announced in the second half of 2025.

As far as enterprise CTOs are concerned, the sooner, the better. To survive and thrive amid heavy competition, they’ll need an evolving array of next-generation technology. That will help them reduce their bottom lines even as they increase their product offerings—a kind of technological nirvana.

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Do your customers need more room for AI? AMD has an answer

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Do your customers need more room for AI? AMD has an answer

If your customers are looking to add AI to already-crowded, power-strapped data centers, AMD is here to help. 

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How can your customers make room for AI in data centers that are already full?

It’s a question that’s far from academic. Nine in 10 tech vendors surveyed recently by the Uptime Institute expect AI to be widely used in data centers in the next 5 years.

Yet data center space is both hard to find and costly to rent. Vacancy rates have hit new lows, according to real-estate services firm CBRE Group.

Worse, this combination of supply shortages and high demand is driving up data center pricing and rents. Across North America, CRBE says, pricing is up by 20% year-on-year.

Getting enough electric power is an issue, too. Some utilities have told prospective data-center customers they won’t get the power they requested until the next decade, reports The Wall Street Journal. In other cases, strapped utilities are simply giving customers less power than they asked for.

So how to help your customers get their data centers ready for AI? AMD has some answers. And a free software tool to help.

The AMD Solution

AMD’s solution is simple, with just 2 points:

  • Make the most of existing data-center real estate and power by consolidating existing workloads.
  • Replace the low-density compute of older, inefficient and out-of-warranty systems with compute that’s newer, denser and more efficient.

AMD is making the case that your customers can do both by moving from older Intel-based systems to newer ones that are AMD-based.

For example, the company says, replacing servers based on Intel Xeon 6143 Sky Lake processors with those based on AMD EPYC 9334 CPUs can result in the need for 73% fewer servers, 70% fewer racks and 69% less power.

That could include Supermicro servers powered by AMD EPYC processors. Supermicro H13 servers using AMD EPYC 9004 Series processors offer capabilities for high-performance data centers.

AMD hasn’t yet done comparisons with either its new 5th gen EPYC processors (introduced last week) or Intel’s 86xx CPUs. But the company says the results should be similar.

Consolidating processor-based servers can also make room in your customers’ racks for AMD Instinct MI300 Series accelerators designed specifically for AI and HPC workloads.

For example, if your customer has older servers based on Intel Xeon Cascade Lake processors, migrating them to servers based on AMD EPYC 9754 processors instead can gain them as much as a 5-to-1 consolidation.

The result? Enough power and room to accommodate a new AI platform.

Questions Answered

Simple doesn’t always mean easy. And you and your customers may have concerns.

For example, isn’t switching from one vendor to another difficult?

No, says AMD. The company cross-licenses the X86 instruction set, so on its processors, most workloads and applications will just work.

What about all those cores on AMD processors? Won’t they raise a customer’s failure domain too high?

No, says AMD. Its CPUs are scalable enough to handle any failure domain from 8 to 256 cores per server.

Wouldn’t moving require a cold migration? And if so, wouldn’t that disrupt the customer’s business?

Again, AMD says no. While moving virtual machines (VMs) to a new architecture does require a cold migration, the job can be done without any application downtime.

That’s especially true if you use AMD’s free open-source tool known as VAMT, short for VMware Architecture Migration Tool. VAMT automates cold migration. In one AMD test, it migrated hundreds of VMs in just an hour.

So if your customers among those struggling to find room for AI systems in their already-crowded and power-strapped data centers, tell them consider a move to AMD.

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