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How AMD and Supermicro are working together to help you deliver AI

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How AMD and Supermicro are working together to help you deliver AI

AMD and Supermicro are jointly offering high-performance AI alternatives with superior price and performance.

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When it comes to building AI systems for your customers, a certain GPU provider with a trillion-dollar valuation isn’t the only game in town. You should also consider the dynamic duo of AMD and Supermicro, which are jointly offering high-performance AI alternatives with superior price and performance.

Supermicro’s Universal GPU systems are designed specifically for large-scale AI and high-performance computing (HPC) applications. Some of these modular designs come equipped with AMD’s Instinct MI250 Accelerator and have the option of being powered by dual AMD EPYC processors.

AMD, with a newly formed AI group led by Victor Peng, is working hard to enable AI across many environments. The company has developed an open software stack for AI, and it has also expanded its partnerships with AI software and framework suppliers that now include the PyTorch Foundation and Hugging Face.

AI accelerators

In addition, AMD’s Instinct MI300A data-center accelerator is due to ship in this year’s fourth quarter. It’s the successor to AMD’s MI200 series, based on the company’s CDNA 2 architecture and first multi-die CPU, which powers some of today’s fastest supercomputers.

The forthcoming Instinct MI300A is based on AMD’s CDNA 3 architecture for AI and HPC workloads, which uses 5nm and 6nm process tech and advanced chiplet packaging. Under the MI300A’s hood, you’ll find 24 processor cores with Zen 4 tech, as well as 128GB of HBM3 memory that’s shared by the CPU and GPU. And it supports AMD ROCm 5, a production-ready, open source HPC and AI software stack.

Earlier this month, AMD introduced another member of the series, the AMD Instinct MI300X. It replaces three Zen 4 CPU chiplets with two CDNA 3 chiplets to create a GPU-only system. Announced at AMD’s recent Data Center and AI Technology Premier event, the MI300X is optimized for large language models (LLMs) and other forms of AI.

To accommodate the demanding memory needs of generative AI workloads, the new AMD Instinct MI300X also adds 64GB of HBM3 memory, for a new total of 192GB. This means the system can run large models directly in memory, reducing the number of GPUs needed, speeding performance, and reducing the user’s total cost of ownership (TCO).

AMD also recently introduced the AMD Instinct Platform, which puts eight MI300X systems and 1.5TB of memory in a standard Open Compute Project (OCP) infrastructure. It’s designed to drop into an end user’s current IT infrastructure with only minimal changes.

All this is coming soon. The AMD MI300A started sampling with select customers earlier this quarter. The MI300X and Instinct Platform are both set to begin sampling in the third quarter. Production of the hardware products is expected to ramp in the fourth quarter.

KT’s cloud

All that may sound good in theory, but how does the AMD + Supermicro combination work in the real world of AI?

Just ask KT Cloud, a South Korea-based provider of cloud services that include infrastructure, platform and software as a service (IaaS, PaaS, SaaS). With the rise of customer interest in AI, KT Cloud set out to develop new XaaS customer offerings around AI, while also developing its own in-house AI models.

However, as KT embarked on this AI journey, the company quickly encountered three major challenges:

  • The high cost of AI GPU accelerators: KT Cloud would need hundreds of thousands of new GPU servers.
  • Inefficient use of GPU resources in the cloud: Few cloud providers offer GPU virtualization due to overhead. As a result, most cloud-based GPUs are visible to only 1 virtual machine, meaning they cannot be shared by multiple users.
  • Difficulty using large GPU clusters: KT is training Korean-language models using literally billions of parameters, requiring more than 1,000 GPUs. But this is complex: Users would need to manually apply parallelization strategies and optimizations techniques.

The solution: KT worked with Moreh Inc., a South Korean developer of AI software, and AMD to design a novel platform architecture powered by AMD’s Instinct MI250 Accelerators and Moreh’s software.

The entire AI software stack was developed by Moreh from PyTorch and TensorFlow APIs to GPU-accelerated primitive operations. This overcomes the limitations of cloud services and large AI model training.

Users do not need to insert or modify even a single line of existing source code for the MoAI platform. They also do not need to change the method of running a PyTorch/TensorFlow program.

Did it work?

In a word, yes. To test the setup, KT developed a Korean language model with 11 billion parameters. Training was then done on two machines: one using Nvidia GPUs, the other being the AMD/Moreh cluster equipped with AMD Instinct MI250 accelerators, Supermicro Universal GPU systems, and the Moreh AI platform software.

Compared with the Nvidia system, the Moreh solution with AMD Instinct accelerators showed 116% throughput (as measured by tokens trained per second), and 2.05x higher cost-effectiveness (measured as throughput per dollar).

Other gains are expected, too. “With cost-effective AMD Instinct accelerators and a pay-as-you-go pricing model, KT Cloud expects to be able to reduce the effective price of its GPU cloud service by 70%,” says JooSung Kim, VP of KT Cloud.

Based on this test, KT built a larger AMD/Moreh cluster of 300 nodes—with a total of 1,200 AMD MI250 GPUs—to train the next version of the Korean language model with 200 billion parameters.

It delivers a theoretical peak performance of 434.5 petaflops for fp16/bf16 (a native 16-bit format for mixed-precision training) matrix operations. That should make it one of the top-tier GPU supercomputers in the world.

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Tech Explainer: Green Computing, Part 2 — Holistic strategies

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Tech Explainer: Green Computing, Part 2 — Holistic strategies

Holistic green computing strategies can help both corporate and individual users make changes for the better.

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Green computing allows us to align the technology that powers our lives with the sustainability goals necessary to battle the climate crisis.

In Part 1 of our Tech Explainer on green computing, we looked at data-center architecture best practices and component-level green engineering. Now we’ll investigate holistic green computing strategies that can help both corporate and individual users change for the better.

Green manufacturing and supply chain

The manufacturing process can account for up to 70% of the natural resources used in the lifecycle of a PC, server or other digital device. And an estimated 76% of all global trade passes through a supply chain. So it’s more important than ever to reform processes that could harm the environment.

AMD’s efforts to advance environmental sustainability in partnership with its suppliers is a step in the right direction. The AMD Supply Chain is currently on track to ensure two important goals: that 80% of its suppliers source renewable energy, and that 100% make public their emissions-reduction goals, both by 2025.

To reduce the environmental impact of IT manufacturing, tech providers are replacing the toxic chemicals used in computer manufacturing with alternatives that are more environmentally friendly.

Materials such as the brominated flame retardants found in plastic casings are giving way to eco-friendly, non-toxic silicone compounds. Traditional non-recyclable plastic parts are being replaced by parts made from both bamboo and recyclable plastics, such as polycarbonate resins. And green manufacturers are working to eliminate other toxic chemicals, including lead in solder and cadmium and selenium in circuit boards.

Innovation in green manufacturing can identify and improve hundreds, if not thousands, of industry-standard practices. No matter how small an improvement is when employed to create millions of devices, it can make a big difference.

Green enterprise

Today’s enterprise data-center managers are working to maximize server performance while also minimizing their environmental impact. Leading-edge green methodologies include two important moves: reducing power usage at the server level and extending hardware lifecycles to create less waste.

Supermicro, an authority on energy-efficient data center design, is empowering this movement by creating new servers engineered for green computing.

One such server is Supermicro’s 4-node BigTwin. The BigTwin features disaggregated server architecture that reduces e-waste by enabling subsystem upgrades.

As technology improves, IT managers can replace components like the CPU, GPU and memory. This extends the life of the chassis, power supplies and cooling systems that might otherwise end up in a landfill.

Twin and Blade server architectures are more efficient because they share power supplies and fans. This can significantly lower their power usage, making them a better choice for green data centers.

The upgraded components that go into these servers now include high-efficiency processors like the AMD EPYC 9654. The infographic below, courtesy of AMD, shows how 4th Gen AMD EPYC processors can power 2,000 virtual machines using up to 35% fewer servers than the competition:

EPYC green infographic

As shown, the potential result is up to 29% less energy consumed annually. That kind of efficiency can save an estimated 35 tons of carbon dioxide—the equivalent of 38 acres of U.S. forest carbon sequestration every year.

Green data centers also employ advanced cooling systems. For instance, Supermicro’s servers include optional liquid cooling. Using fluid to carry heat away from critical components allows IT managers to lower fan speeds inside each server and reduce HVAC usage in data centers.

Deploying efficient cooling systems like these lowers a data center’s Power Usage Effectiveness (PUE), thus reducing carbon emissions from power generation.

Changing for the better, together

No single person, corporation or government can stave off the worst effects of climate crisis. If we are to win this battle, we must work together.

Engineers, industrial designers and data scientists have their work cut out for them. By fueling the evolution of green computing, they—and their corporate managers—can provide us with the tools we need to go green and safeguard our environment for generations to come.

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Tech Explainer: Green Computing, Part 1 - What does the data center demand?

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Tech Explainer: Green Computing, Part 1 - What does the data center demand?

The ultimate goal of Green Computing is net-zero emissions. To get there, organizations can and must innovate, conducting an ongoing campaign to increase efficiency and reduce waste.

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The Green Computing movement has begun in earnest and not a moment too soon. As humanity faces the existential threat of climate crisis, technology needs to be part of the solution. Green computing is a big step in the right direction.

The ultimate goal of Green Computing is net-zero emissions. It’s a symbiotic relationship between technology and nature in which both SMBs and enterprises can offset carbon emissions, drastically reduce pollution, and reuse/recycle the materials that make up their products and services.

To get there, the tech industry will need to first take a long, hard look at the energy it uses and the waste it produces. Using that information, individual organizations can and must innovate, conducting an ongoing campaign to increase efficiency and reduce waste.

It’s a lofty goal, sure. But after all the self-inflicted damage we’ve done since the dawn of the Industrial Revolution, we simply have no choice.

The data-center conundrum

All digital technology requires electricity to operate. But data centers use more than their share.

Here’s a startling fact: Each year, the world’s data centers gobble up at least 200 terawatts of energy. That’s roughly 2% of all the electricity used on this planet annually.

What’s more, that figure is likely to increase as new, power-hungry systems are brought online and new data centers are opened. And the number of global data centers could grow from 700 in 2021 to as many as 1,200 by 2026, predicts Supermicro.

At that rate, data-center energy consumption could account for up to 8% of global energy usage by 2030. That’s why tech leaders including AMD and Supermicro are rewriting the book on green computing best practices.

A Supermicro white paper, Green Computing: Top 10 Best Practices For A Green Data Center, suggests specific actions you and your customers can take now to reduce the environmental impact of your data centers:

  • Right-size systems to match workload requirements
  • Share common scalable infrastructure
  • Operate at higher ambient temperature
  • Capture heat at the source via aisle containment and liquid cooling
  • Optimize key components (i.e., CPU, GPU, SSD, etc.) for workload performance per watt
  • Optimize hardware refresh cycle to maintain efficiency
  • Optimize power delivery
  • Utilize virtualization and power management
  • Source renewable energy and green manufacturing
  • Consider climate impact when making site selection

Green components

Rethinking data-center architectures is an excellent way to leverage green computing from a macro perspective. But to truly make a difference, the industry needs to consider green computing at the component level.

This is one area where AMD is leading the charge. Its mission: increase the energy efficiency of its CPUs and hardware accelerators. The rest of the industry should follow suit.

In 2021 AMD announced its goal to deliver a 30x increase in energy efficiency for both AMD EPYC CPUs and AMD Instinct accelerators for AI and HPC applications running on accelerated compute nodes—and to do so by 2025.

Taming AI energy usage

The golden age of AI has begun. New machine learning algorithms will give life to a population of hyper-intelligent robots that will forever alter the nature of humanity. If AI’s most beneficent promises come to fruition, it could help us live, eat, travel, learn and heal far better than ever before.

But the news isn’t all good. AI has a dark side, too. Part of that dark side is its potential impact on our climate crisis.

Researchers at the University of Massachusetts, Amherst, illustrated this point by performing a life-cycle assessment for training several large AI models. Their findings, published by Supermicro, concluded that training a single AI model can emit more than 626,000 pounds of carbon dioxide. That’s approximately 5 times the lifetime emissions of your average American car.

A comparison like that helps put AMD’s environmental sustainability goals in perspective. Affecting a 30x energy efficiency increase in the components that power AI could bring some much-needed light to AI’s dark side.

In fact, if the whole technology sector produces practical innovations similar to those from AMD and Supermicro, we might have a fighting chance in the battle against climate crisis.

Continued…

Part 2 of this 3-part series will take a closer look at the technology behind green computing—and the world-saving innovations we could see soon.

 

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Genoa-X: a deeper dive into AMD’s new EPYC processors optimized for technical computing

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Genoa-X: a deeper dive into AMD’s new EPYC processors optimized for technical computing

AMD has introduced its EPYC 9X84X series processors, formerly codenamed Genoa-X. The new CPUs are designed specifically for technical workloads, and they support up to 1.1GB of L3 Cache.

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AMD is responding to greater specialization in the data center by creating workload-optimized versions of its 4th gen EPYC server processors.

That now includes the AMD EPYC 9x84X series processors, formerly codenamed Genoa-X.

These new CPUs are optimized for technical computing workloads. Those include engineering simulation, product design, structural design, aerodynamics modeling and electronic design automation (EDA).

Big cache

A key feature of the new AMD EPYC 9x84X processors is the new 2nd generation of AMD’s 3D V-Cache technology. It supports more than 1GB of L3 Cache on a 96-core CPU. The larger cache can feed the CPU faster with data needed for large and complex simulations.

Speaking at AMD’s Data Center and AI Technology Premier earlier this month, Dan McNamara, GM of AMD’s server business, said this will deliver a “new dimension” of workload optimization. This will help users get to market faster with higher-quality products while also reducing their OpEx budgets, he added.

The new AMD EPYC 9x84X processors also use the new AMD Zen 4c cores, the company’s new EPYC processors optimized for cloud-native workloads. The 94X8X CPUs are also socket-compatible with earlier Genoa processors. And they offer security protection with AMD Infinity Guard, the company’s suite of hardware-level security features.

It’s worth noting that AMD last year introduced a similar optimization for its Milan series processors. Those processors were code-named Milan-X.

Total ecosystem

To create a complete technical-computing environment, AMD has been working closely with developers of highly technical software. These partners include Altair, Ansys, Cadence, Dassault Systemes, Siemens and Synopsys.

Hardware partners are jumping in, too. Supermicro recently announced that its entire line of Supermicro H13 AMD-based systems now support 4th gen AMD EPYC processors with AMD 3D V-cache technology.

As this table shows, courtesy of AMD, the AMD EPYC 9x84X series now comes in 3 SKUs:

In addition, all 3 SKUs support both DDR5 memory and PCIe 5.0 connectivity.

The new AMD EPYC 9x84X processors are available now. OEM systems based on these processors are expected to start shipping in the third quarter.

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Bergamo: a deeper dive into AMD’s new EPYC processor for cloud-native workloads

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Bergamo: a deeper dive into AMD’s new EPYC processor for cloud-native workloads

Bergamo is AMD’s first-ever server processor designed specifically for cloud-native workloads. Learn how it works.  

 

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Bergamo is the former codename for AMD’s new 4th gen EPYC 97X4 processors optimized for cloud-native workloads, which the company introduced earlier this month.

AMD is responding to the increasingly specialized nature of data center workloads by optimizing its server processors for specific workloads. This month AMD introduced two examples: Bergamo (97X4) for cloud and Genoa-X (9XX4X) for technical computing.

The AMD EPYC 97X4 processors are AMD’s first-ever designed specifically for cloud-native workloads. And they’re shipping now in volume to AMD’s hyperscale customers that include Facebook parent company Meta and partners including Supermicro.

Speaking of Supermicro, that company this week announced that the new AMD EPYC 97X4 processors can now be included in its entire line of Supermicro H13 AMD-based systems.

Zen mastery

The main difference between the AMD EPYC 97X4 and AMD’s general-purpose Genoa series processors comes down to the core chiplet. The 97X4 CPUs use a new design called “Zen 4c.” It’s an update on the AMD Zen 4 core used in the company’s Genoa processors.

Where AMD’s original Zen 4 was designed for the highest performance per core, the new Zen 4c has been designed for a sweet spot of both density and power efficiency.

As AMD CEO Lisa Su explained during the company’s recent Data Center and AI Technology Premier event, AMD achieved this by starting with the same RTL design as Zen 4. AMD engineers then optimized this physical layout for power and area. They also redesigned the L3 cache hierarchy for greater throughput.

The result: a design that takes up about 35% less area yet offers substantially better performance per watt.

Because the start from the Zen 4’s design, the new 97X4 processors are both software- and platform-compatible with Genoa. The idea is that end users can mix and match 97X4- and Genoa-based servers, depending on their specific workloads and computing needs.

Basic math

Another difference is that where Genoa processors offer up to 96 cores per socket, the new 97X4 processors offer up to 128.

Here’s how it’s done: Each AMD 97X4 system-on-chip (SoC) contains 8 core complex dies (CCDs). In turn, each CCD contains 16 Zen 4c cores. So 8 CCDs x 16 cores = a total of 128 cores.

As the table below shows, courtesy of AMD, there are three SKUs for the new EPYC 97X5 series processors:

For security, all 3 SKUs support AMD Infinity Guard, a suite of hardware-level security features, and AMD Infinity Architecture, which lets system builders and cloud architects get maximum power while still ensuring security.

Are your customers looking for servers to handle their cloud-native applications? Tell them to look into the new AMD EPYC 97X4 processors.

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AMD intros CPUs, cache, AI accelerators for cloud, enterprise data centers

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AMD intros CPUs, cache, AI accelerators for cloud, enterprise data centers

AMD strengthens its commitment to the cloud and enterprise data centers with new "Bergamo" CPUs, "Genoa-X" cache, Instinct accelerators.

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This week AMD strengthened its already strong commitment to the cloud and enterprise markets. The company announced several new products and partnerships at its Data Center and AI Technology Premier event, which was held in San Francisco and simultaneously broadcast online.

“We’re focused on pushing the envelope in high-performance and adaptive computing,” AMD CEO Lisa Su told the audience, “creating solutions to the world’s most important challenges.”

Here’s what’s new:

Bergamo: That’s the former codename for the new 4th gen AMD EPYC 97X4 processors. AMD’s first processor designed specifically for cloud-native workloads, it packs up to 128 cores per socket using AMD’s new Zen 4c design to deliver lots of power/watt. Each socket contains 8 chiplets, each with up to 16 Zen 4c cores; that’s twice as many cores as AMD’s earlier Genoa processors (yet the two lines are compatible). The entire lineup is available now.

Genoa-X: Another codename, this one is for AMD’s new generation of AMD 3D V-Cache technology. This new product, designed specifically for technical computing such as engineering simulation, now supports over 1GB of L3 cache on a 96-core CPU. It’s paired with the new 4th gen AMD EPYC processor, including the high-performing Zen4 core, to deliver high performance/core.

“A larger cache feeds the CPU faster with complex data sets, and enables a new dimension of processor and workload optimization,” said Dan McNamara, an AMD senior VP and GM of its server business.

In all, there are 4 new Genoa-X SKUs, ranging from 16 to 96 cores, and all socket-compatible with AMD’s Genoa processors.

Genoa: Technically, not new, as this family of data-center CPUs was introduced last November. But what is new is AMD’s new focus for the processors on AI, data-center consolidation and energy efficiency.

AMD Instinct: Though AMD had already introduced its Instinct MI300 Series accelerator family, the company is now revealing more details.

This includes the introduction of the AMD Instinct MI300X, an advanced accelerator for generative AI based on AMD’s CDNA 3 accelerator architecture. It will support up to 192GB of HBM3 memory to provide the compute and memory efficiency needed for large language model (LLM) training and inference for generative AI workloads.

AMD also introduced the AMD Instinct Platform, which brings together eight MI300X accelerators into an industry-standard design for the ultimate solution for AI inference and training. The MI300X is sampling to key customers starting in Q3.

Finally, AMD also announced that the AMD Instinct MI300A, an APU accelerator for HPC and AI workloads, is now sampling to customers.

Partner news: Mark your calendar for June 20. That’s when Supermicro plans to explore key features and use cases for its Supermicro 13 systems based on AMD EPYC 9004 series processors. These Supermicro systems will feature AMD’s new Zen 4c architecture and 3D V-Cache tech.

This week Supermicro announced that its entire line of H13 AMD-based systems are now available with support for the 4th gen AMD EPYC processors with Zen 4c architecture and V-Cache technology.

That includes Supermicro’s new 1U and 2U Hyper-U servers designed for cloud-native workloads. Both are equipped with a single AMD EPYC processor with up to 128 cores.

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Why your AI systems can benefit from having both a GPU and CPU

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Why your AI systems can benefit from having both a GPU and CPU

Like a hockey team with players in different positions, an AI system with both a GPU and CPU is a necessary and winning combo. This mix of processors can bring you and your customers both the lower cost and greater energy efficiency of a CPU and the parallel processing power of a GPU. With this team approach, your customers should be able to handle any AI training and inference workloads that come their way.

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Sports teams win with a range of skills and strengths. A hockey side can’t win if everyone’s playing goalie. The team also needs a center and wings to advance the puck and score goals, as well as defensive players to block the opposing team’s shots.

The same is true for artificial intelligence systems. Like a hockey team with players in different positions, an AI system with both a GPU and CPU is a necessary and winning combo.

This mix of processors can bring you and your customers both the lower cost and greater energy efficiency of a CPU and the parallel processing power of a GPU. With this team approach, your customers should be able to handle any AI training and inference workloads that come their way.

In the beginning

One issue: Neither CPUs nor GPUs were originally designed for AI. In fact, both designs predate AI by many years. Their origins still define how they’re best used, even for AI.

GPUs were initially designed for computer graphics, virtual reality and video. Getting pixels to the screen is a task where high levels of parallelization speed things up. And GPUs are good at parallel processing. This has allowed them to be adapted for HPC and AI workloads, which analyze and learn from large volumes of data. What’s more, GPUs are often used to run HPC and AI workloads simultaneously.

GPUs are also relatively expensive. For example, Nvidia’s new H100 has an estimated retail price of around $25,000 per GPU. Your customers may incur additional costs from cooling—GPUs generate a lot of heat. GPUs also use a lot of power, which can further raise your customer’s operating costs.

CPUs, by contrast, were originally designed to handle general-purpose computing. A modern CPU can run just about any type of calculation, thanks to its encompassing instruction set.

A CPU processes data sequentially, rather than in parallel, and that’s good for linear and complex calculations. Compared with GPUs, a comparable CPU generally is less expensive, needs less power and runs cooler.

In today’s cost-conscious environment, every data center manager is trying to get the most performance per dollar. Even a high-performing CPU has a cost advantage over comparable GPUs that can be extremely important for your customers.

Team players

Just as a hockey team doesn’t rely on its goalie to score points, smart AI practitioners know they can’t rely on their GPUs to do all types of processing. For some jobs, CPUs are still better.

Due to a CPU’s larger memory capacity, they’re ideal for machine learning training and inference, as long as the scale is relatively small. CPUs are also good for training small neural networks, data preparation and feature extraction.

CPUs offer other advantages, too. They’re generally less expensive than GPUs. In today’s cost-conscious environment, where every data center manager is trying to get the most performance per dollar, that’s extremely important. CPUs also run cooler than GPUs, requiring less (and less expensive) cooling.

GPUs excel in two main areas of AI: machine learning and deep learning (ML/DL). Both involve the analysis of gigabytes—or even terabytes—of data for image and video processing. For these jobs, the parallel processing capability of a GPU is a perfect match.

AI developers can also leverage a GPU’s parallel compute engines. They can do this by instructing the processor to partition complex problems into smaller, more manageable sub-problems. Then they can use libraries that are specially tuned to take advantage of high levels of parallelism.

Theory into practice

That’s the theory. Now let’s look at how some leading AI tech providers are putting the team approach of CPUs and GPUs into practice.

Supermicro offers its Universal GPU Systems, which combine Nvidia GPUs with CPUs from AMD, including the AMD EPYC 9004 Series.

An example is Supermicro’s H13 GPU server, with one model being the AS 8215GS-TNHR. It packs an Nvidia HGX H100 multi-GPU board, dual-socket AMD EPYC 9004 series CPU, and up to 6TB of DDR5 DRAM memory.

For truly large-scale AI projects, Supermicro offers SuperBlade systems designed for distributed, midrange AI and ML training. Large AI and ML workloads can require coordination among multiple independent servers, and the Supermicro SuperBlades are designed to do just that. Supermicro also offers rack-scale, plug-and-play AI solutions powered by the company’s GPUs and turbocharged with liquid cooling.

The Supermicro SuperBlade is available with a single AMD EYPC 7003/7002 series processors with up to 64 cores. You also get AMD 3D V-Cache, up to 2TB of system memory per node, and a 200Gbps InfiniBand HDR switch. Within a single 8U enclosure, you can install up to 20 blades.

Looking ahead, AMD plans to soon ship its Instinct MI300A, an integrated data-center accelerator that combines three key components: AMD Zen 4 CPUs, AMD CDNA3 GPUs, and high-bandwidth memory (HBM) chiplets. This new system is designed specifically for HPC and AI workloads.

Also, the AMD Instinct MI300A’s high data throughput lets the CPU and GPU work on the same data in memory simultaneously. AMD says this CPU-GPU partnership will help users save power, boost performance and simplify programming.

Truly, a team effort.

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How ILM creates visual effects faster & cheaper with AMD-powered Supermicro hardware

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How ILM creates visual effects faster & cheaper with AMD-powered Supermicro hardware

ILM, the visual-effects company founded by George Lucas, is using AMD-powered Supermicro servers and workstations to create the next generation of special effects for movies and TV.

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AMD and Supermicro are helping Industrial Light & Magic (ILM) create the future of visual movie and TV production.

ILM is the visual-effects company founded by George Lucas in 1975. Today it’s still on the lookout for better, faster tech. And to get it, ILM leans on Supermicro for its rackmount servers and workstations, and AMD for its processors.

The servers help ILM reduce render times. And the workstations enable better collaboration and storage solutions that move data faster and more efficiently.

All that high-tech gear comes together to help ILM create some of the world’s most popular TV series and movies. That includes “Obi-Wan Kenobi,” “Transformers” and “The Book of Boba Fett.”

It’s a huge task. But hey, someone’s got to create all those new universes, right?

Power hungry—and proud of it

No one gobbles up compute power quite like ILM. Sure, it may have all started with George Lucas dropping an automotive spring on a concrete floor to create the sound of the first lightsaber. But these days, it’s all about the 1s and 0s—a lot of them.

An enormous amount of compute power goes into rendering computer-generated imagery (CGI) like special effects and alien characters. So much power, in fact, that it can take weeks or even months to render an entire movie’s worth of eye candy.

Rendering takes not only time, but also money and energy. Those are the three resources that production companies like ILM must ration. They’re under pressure to manage cash flow and keep to tight production schedules.

By deploying Supermicro’s high-performance and multinode servers powered by AMD’s EPYC processors , ILM gains high core counts and maximum throughput—two crucial components of faster rendering.

Modern filmmakers are also obliged to manage data. Storing and moving terabytes of rendering and composition information is a constant challenge, especially when you’re trying to do it quickly and securely.

The solution to this problem comes in the form of high-performance storage and networking devices. They can shift vast swaths of information from here to there without bottlenecks, overheating or (worst-case scenario) total failure.

EPYC stories

This is the part of the story where CPUs take back some of the spotlight. GPUs have been stealing the show ever since data scientists discovered that graphic processors are the keys to unlocking the power of AI. But producing the next chapter of the “Star Wars” franchise means playing by different rules.

AMD EPYC processors play a starring role in ILM’s render farms. Render farms are big collections of networked server-class computers that work as a team to crunch a metric ton of data.

A typical ILM render farm might contain dozens of high-performance computers like the Supermicro BigTwin. This dual-node processing behemoth can house two 3rd gen AMD EPYC processors, 4TB of DDR5 memory per node and a dozen 2.5-inch hot-swappable solid-state drives (SSDs). In case the specs don’t speak for themselves, that’s an insane amount of power and storage.

For ILM, lighting and rendering happen inside an application by Isotropix called Clarisse. Our hero, Clarisse, relies on CPU rather than GPU power. Unlike most 3D apps, which are single-threaded, Clarisse also features unusually efficient multi-threading.

This lets the application take advantage of the parallel-processing power in AMD’s EPYC CPUs to complete more tasks simultaneously. The results: shorter production times and lower costs.

Coming soon: StageCraft

ILM is taking its tech show on the road with an end-to-end virtual production solution called StageCraft. It exists as both a series of Los Angeles and Vancouver-based sites—ILM calls them “volumes”—as well as mobile pop-up volumes waiting to happen anywhere in the United States and Europe.

The introduction of StageCraft is interesting for a couple of reasons. For one, this new production environment makes ILM’s AMD-powered magic wand accessible to a wider range of directors, producers and studios.

For another, StageCraft could catalyze the proliferation of cutting-edge creative tech. This, in turn, could lead to the same kind of competition, efficiency increases and miniaturization that made 4K filmmaking a feature of everyone’s mobile phones.

StageCraft could also usher in a new visual language. The more people with access to high-tech visualization technology, the more likely it is that some unknown aspiring auteur will pop up, seemingly out of nowhere, to change the nature of entertainment forever.

Kinda’ like how George Lucas did it back in the day.

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Absolute Hosting finds the sweet spot with AMD-powered Supermicro servers

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Absolute Hosting finds the sweet spot with AMD-powered Supermicro servers

Absolute Hosting, a South African provider of hosting services to small and midsize businesses, sought to upgrade its hardware, improve its performance, and lower its costs. The company achieved all three goals with AMD-powered Supermicro servers.

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Some brands are so strong, customers ask for them by name. They ask for a Coke when thirsty, click on Amazon.com when shopping online, visit a Tesla showroom when thinking of buying an electric car.

For Absolute Hosting Ltd., a South Africa-based provider of hosting and other digital services for small and midsize businesses (SMBs), it’s not one brand, but two: Supermicro and AMD. More specifically, the combination of Supermicro servers powered by AMD EPYC processors.

“Clients who have switched over to us have been amazed by the performance of our AMD EPYC-powered servers,” says Jade Benson, the founder of Absolute Hosting and now its managing director.

Benson and his colleagues find the Supermicro-AMD brand so powerful, they offer it by name. Check out Absolute Hosting's website, and you’ll see the AMD and Supermicro brands called out by name.

SMB specialists

It wasn’t always the case. Back in 2011, when Benson founded Absolute Hosting, the company served local South African tech resellers. Five years later, in 2016, the company shifted its focus to offering hosting and virtual server services to local SMBs.

One of its hosting services is virtual private servers. VPS hosting provides dedicated resources to each customer’s website, allowing for more control, customization and scalability than they’d get with shared hosting. That makes VPS hosting ideal for businesses that require lots of resources, enjoy high traffic, or need a great deal of control over their hosting environment.

Today Absolute Hosting owns about 100 physical servers and manages roughly 300 VPS servers for clients. The company also supplies its 5,000 clients with other hosting services, including Linux web, WordPress and email.

‘We kept seeing AMD’

Absolute Hosting’s shift to AMD-powered Supermicro servers was driven by its own efforts to refresh and upgrade its hardware, improve its performance and lower its own costs. Initially, the company rented dedicated servers from a provider that relied exclusively on Supermicro hardware.

“So when we decided to purchase our own hardware, we made it a requirement to use Supermicro,” Benson says. “And we kept seeing AMD as the recommended option.”

The new servers were a quick success. Absolute Hosting tested them with key benchmarks, including Cinebench, a cross-platform test suite, and Passmark, which compares the performance of CPUs. And it found them leading for every test application.

Absolute Hosting advertised the new offering on social media and quickly had enough business for 100 VPS servers. The company ran a public beta for customers and allowed the local IT community to conduct their own stress tests.

“The feedback we received was phenomenal,” Benson says. “Everyone was blown away.”

Packing a punch

Absolute Hosting’s solution is based on Supermicro’s AS-2115GT-HNTF GrandTwin server. It packs four hot-pluggable nodes into a 2U rackmount form factor.

Each node includes an AMD EPYC CPU; 12 memory slots for up to 3TB of DDR5 memory; flexible bays for storage or I/O; and up to four hot-swap 2.5-inch NVMe/SATA storage drives.

Absolute Hosting currently uses the AMD EPYC 7003 Series processors. But the Supermicro server now supports the 4th gen AMD EPYC 9004 Series processors, and Benson plans to move to them soon.

Benson considers the AMD-powered Supermicro servers a serious competitive advantage. “There are only a few people we don’t tell about AMD,” he says. “That’s our competitors.”

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Research roundup: AI edition

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Research roundup: AI edition

AI is busting out all over. AI is getting prioritized over all other digital investments. The AI market is forecast to grow by over 20% a year through 2030. AI worries Americans about the potential impact on hiring. And AI needs to be safeguarded against the risk of misuse.

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AI is busting out all over. AI is getting prioritized over all other digital investments. The AI market is forecast to grow by over 20% a year through 2030. AI worries Americans about the potential impact on hiring. And AI needs to be safeguarded against the risk of misuse.

That’s some of the latest AI research from leading market watchers. And here’s your research roundup.

The AI priority

Nearly three-quarters (73%) of companies are prioritizing AI over all other digital investments, finds a new report from consultants Accenture. For these AI projects, the No. 1 focus area is improving operational resilience; it was cited by 90% of respondents.

Respondents to the Accenture survey also say the business benefits of AI are real. While only 9% of companies have achieved maturity across all 6 areas of AI operations, they averaged 1.4x higher operating margins than others. (Those 6 areas, by the way, are AI, data, processes, talent, collaboration and stakeholder experiences.)

Compared with less-mature AI operations, these companies also drove 42% faster innovation, 34% better sustainability and 30% higher satisfaction scores.

Accenture’s report is based on its recent survey of 1,700 executives in 12 countries and 15 industries. About 7 in 10 respondents held C-suite-level job titles.

The AI market

It’s no surprise that the AI market is big and growing rapidly. But just how big and how rapidly might surprise you.

How big? The global market for all AI products and services, worth some $428 billion last year, is on track to top $515 billion this year, predicts market watcher Fortune Business Insights.

How fast? Looking ahead to 2030, Fortune Insights expects the global AI market that year to hit $2.03 trillion. If so, that would mark a compound annual growth rate (CAGR) of nearly 22%.

What’s driving this big, rapid growth? Several factors, says Fortune, including the surge in the number of applications, increased partnering and collaboration, a rise in small-scale providers, and demand for hyper-personalized services.

The AI impact

What, me worry? About six in 10 Americans (62%) believe AI will have a major impact on workers in general. But only 28% believe AI will have a major effect on them personally.

So finds a recent poll by Pew Research of more than 11,000 U.S. adults.

Digging a bit deeper, Pew found that nearly a third of respondents (32%) believe AI will hurt workers more than help; the same percentage believe AI will equally help and hurt; about 1 in 10 respondents (13%) believe AI will help more than hurt; and roughly 1 in 5 of those answering (22%) aren’t sure.

Respondents also widely oppose the use of AI to augment regular management duties. Nearly three-quarters of Pew’s respondents (71%) oppose the use of AI for making a final hiring decision. Six in 10 (61%) oppose the use of AI for tracking workers’ movements while they work. And nearly as many (56%) oppose the use of AI for monitoring workers at their desks.

Facial-recognition technology fared poorly in the survey, too. Fully 7 in 10 respondents were opposed to using the technology to analyze employees’ facial expressions. And over half (52%) were opposed to using facial recognition to track how often workers take breaks. However, a small majority (45%) favored the use of facial recognition to track worker attendance; about a third (35%) were opposed and one in five (20%) were unsure.

The AI risk

Probably the hottest form of AI right now is generative AI, as exemplified by the ChatGPT chatbot. But given the technology’s risks around security, privacy, bias and misinformation, some experts have called for a pause or even a halt on its use.

Because that’s unlikely to happen, one industry watcher is calling for new safeguards. “Organizations need to act now to formulate an enterprisewide strategy for AI trust, risk and security management,” says Avivah Litan, a VP and analyst at Gartner.

What should you do? Two main things, Litan says.

First, monitor out-of-the-box usage of ChatGPT. Use your existing security controls and dashboards to catch policy violations. Also, use your firewalls to block unauthorized use, your event-management systems to monitor logs for violations, and your secure web gateways to monitor disallowed API calls.

Second, for prompt engineering usage—which uses tools to create, tune and evaluate prompt inputs and outputs—take steps to protect the sensitive data used to engineer prompts. A good start, Litan says, would be to store all engineered prompts as immutable assets.

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