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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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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 Generative AI is rocking the tech business—in a good way

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How Generative AI is rocking the tech business—in a good way

With ChatGPT the newest star of tech, generative AI has emerged as a major market opportunity for traditional hardware and software suppliers. Here’s some of what you can expect from AMD and Supermicro.

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The seemingly overnight adoption of generative AI systems such as ChatGPT is transforming the tech industry.

A year ago, AI tech suppliers focused mainly on providing systems for training. For good reason: AI training is technically demanding.

But now the focus has shifted onto large language model (LLM) inferencing and generative AI.

Take ChatGPT, the AI chatbot built on a large language model. In just the first week after its launch, ChatGPT gained over a million users. Since then, it has attracted more than 100 million users who now generate some 10 million queries a day. OpenAI, ChatGPT’s developer, says the system has thus far processed approximately 300 billion words from over a million conversations.

It's not all fun and games, either. In a new Gartner poll of 2,500 executive leaders, nearly half the respondents said all the publicity around ChatGPT has prompted their organizations to increase their AI spending.

In the same survey, nearly 1 in 5 respondents already have generative AI in either pilot or production mode. And 7 in 10 are experimenting with or otherwise exploring the technology.

Top priority

This virtual explosion has gotten the attention of mainstream tech providers such as AMD. During the company’s recent first-quarter earnings call, CEO Lisa Su said, “We’re very excited about our opportunity in AI. This is our No. 1 strategic priority.”

And AMD is doing a lot more than just talking about AI. For one, the company has consolidated all its disparate AI activities into a single group that will be led by Victor Peng. He was previously general manager of AMD’s adaptive and embedded products group, which recently reported record first-quarter revenue of $1.6 billion, a year-on-year increase of 163%.

This new AI group will focus mainly on strengthening AMD’s AI software ecosystem. That will include optimized libraries, models and frameworks spanning all of the company’s compute engines.

Hardware for AI

AMD is also offering a wide range of AI hardware products for everything from mobile devices to powerful servers.

For data center customers, AMD’s most exciting hardware product is its Instinct MI300 Accelerator. Designed for both supercomputing HPC and AI workloads, the device is unusual in that it contains both a CPU and GPU. The MI300 is now being sampled with selected large customers, and general shipments are set to begin in this year’s second half.

Other AMD hardware components for AI include its “Genoa” EPYC processors for servers, Alveo accelerators for inference-optimized solutions, and embedded Versal AI Core series.

Several of AMD’s key partners are offering important AI products, too. That includes Supermicro. It now offers Universal GPU systems powered by AMD Instinct MI250 accelerator and optional EPYC CPUs.

These systems include the Supermicro AS 4124GQ-TNMI server. It’s powered by dual AMD EPYC 7003 Series processors and up to four AMD Instinct MI250 accelerators.

Help for AI developers

AMD has also made important moves on the developer front. Also during its Q1 earnings call, AMD announced expanded capabilities for developers to build robust AI solutions leveraging its products.

The moves include new updates to PyTorch 2.0. This open-source framework now offers native support for ROCm software and the latest TensorFlow-ZenDNN plug-in, which enables neural-network inferencing on AMD EPYC CPUs.

ROCm is an open software platform allowing researchers to tap the power of AMD Instinct accelerators to drive scientific discoveries. The latest version, ROCm 5.0, supports major machine learning (ML) frameworks, including TensorFlow and PyTorch. This helps users accelerate AI workloads.

TensorFlow is an end-to-end platform designed to make it easy to build and deploy ML models. And ZenDNN is a deep neural network library that includes basic APIs optimized for AMD CPU architectures.

Just the start

Busy as AMD and Supermicro have been with AI products, you should expect even more. As Gartner VP Francis Karamouzis says, “The generative AI frenzy shows no sign of abating.”

That sentiment gained support from AMD’s Su during the company’s Q1 earnings call.

“It’s a multiyear journey,” Su said in response to an analyst’s question about AI. “This is the beginning for what we think is a significant market opportunity for the next 3 to 5 years.”

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Tech Explainer: How does Gaming as a Service work?

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Tech Explainer: How does Gaming as a Service work?

Gaming as a Service is a streaming platform that pushes content from the cloud to personal devices on demand. Though it’s been around for years, in some ways it’s just getting started.

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The technology known as Gaming as a Service has been around for 20 years. But in many ways it’s just getting started.

The technology is already enjoyed by literally millions of gamers worldwide. But new advances in AI and edge computing are making a big difference. So are faster, more consistent internet connections.

And coming soon should be a mix of virtual and augmented reality (VR & AR) headsets. They could bring gaming to a whole new level.

But how does GaaS work? Let’s take a look.

Cloud + edge = GaaS

GaaS is to video games what Netflix is to movies. Like Netflix, GaaS is a streaming platform that pushes content from the cloud to PCs, smartphones and other personal devices (including gaming consoles with the appropriate updates) on demand.

GaaS originates in the cloud. There, data centers packed with powerful servers maintain the gaming environment, process user commands, determine interaction between players and the virtual world, and deliver real-time results to players.

If the cloud is GaaS’s brains, then edge computing networks are its arms. They reach out to a worldwide base of users, connecting their devices to the gaming cloud.

Edge devices also keep things speedy by amplifying or, if necessary, taking over various processing duties. This helps reduce latency, the time lag between when a command is issued and when it’s executed.

Latency is especially detrimental to gamers. They rely on split-second actions that can make the difference between winning and losing. For them, lower latency is always better.

Device choice

GaaS is innovative at the user end, too. GaaS can interface with a wide array of client devices. That offers gamers far more flexibility than they get with traditional gaming models.

With GaaS, users are no longer tied to a specific gaming PC or console such as the Microsoft Xbox or Sony PlayStation. Instead, gamers can use any supported device with a decent GPU and a stable internet connection speed of at least 10 to 15 Mbps.

To be sure, some GaaS games—one example is the super-popular Fortnite—require a mobile or desktop app. But these apps are usually free.

Other cloud-based games are designed to work with any standard web browser. This lets a gamer pick up wherever they left off, using nearly any internet-connected device anywhere in the world.

Big business

If all this sounds attractive, it is. One of the first GaaS titles, World of Warcraft, is still active nearly 20 years after its initial launch. In 2015—the last time its publisher, Blizzard Entertainment, reported usage numbers—World of Warcraft had 5.5 million players.

Even more popular is Fortnite, introduced in 2017. Today it has more than 350 million registered users. In part, that’s because of the game’s flexible business model: Fortnite players can sign up and enjoy basic gameplay for free.

Instead of charging these users a fee, Fortnite’s developer, Epic Games, makes money from literally millions of micro-transactions. These include in-game purchases of weapons and accessories, access to tournaments and other gated experiences, and the purchase of a new “season,” released four times a year.

Super-popular games like Fortnite and World of Warcraft have help create a lucrative and compelling business model. This, in turn, has given rise to a new breed of GaaS tech providers.

One such operation is Blacknut, a France-based cloud gaming platform. Together with Australian outfit Radian Arc, Blacknut provides a GaaS digital infrastructure powered by AMD-based GPU servers designed and distributed by Supermicro.

What could go wrong?

Does GaaS have a downside? Sure. No platform is without its flaws.

For one, cloud gamers are at the mercy of the cloud. If a cloud provider experiences a slowdown or outage, a game can disappear until the issue is resolved.

For another, unlike a collection of game titles on physical media, GaaS gamers never really own the games they play. For example, if Epic decided to shut down Fortnite tomorrow, 350+ million gamers would have no choice but to look for alternate entertainment.

Internet access can be an issue, too. Those of us in first-world cities tend to take our high-speed connections for granted. The rest of the world may not be so lucky.

Future of GaaS

Looking ahead, the future of GaaS appears bright.

Advances in AI-powered cloud and edge computing will encourage game developers to create more nuanced and immersive content than ever before.

Faster and more consistent internet connections will help. They’ll give more power to both the bandwidth-hungry devices we use today and the shiny, new objects of desire we’ll clamor for tomorrow.

Tomorrow’s devices will surely include a mixture of VR and AR headsets. These could attach to other smart devices that enhance gameplay, like the interactive bodysuits foretold by movies such as Ready Player One.

GaaS will get smaller, too, as new mobile devices come to the market. Cloud-gaming titles, already a mainstay of mobile gamers, should be further empowered by next-generation mobile processors and faster, more reliable wireless data connections like 5G.

We’re witnessing the evolution of gaming as multiple clients interact with low latencies and high-quality graphics. Welcome to the future.

 

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Gaming as a Service gets a platform boost

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Gaming as a Service gets a platform boost

Gaming as a Service gets a boost from Blacknut’s new platform for content providers that’s powered by Supermicro and Radian Arc.

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Getting into Gaming as a Service? Cloud gaming provider Blacknut has released a new platform for content providers that’s powered by Supermicro and Radian Arc.

This comprehensive edge and cloud architecture provides content providers worldwide with bundled and fully managed game licensing, in-depth content metadata and a global hybrid-cloud solution.

If you’re not into gaming yet, you might want to be. Interactive entertainment and game streaming are on the rise.

Last year, an estimated 30 million paying users spent a combined $2.4 billion on cloud gaming services, according to research firm Newzoo. Looking ahead, Newzoo expects this revenue to more than triple by 2025, topping $8 billion. That would make the GaaS market an attractive investment for content providers.

What’s more, studies show that Gen Z consumers (aged 11 to 26 years old) spend over 12 hours a week playing video games. That’s more time than they spend watching TV, by about 30 minutes a week.

Paradigm shift

This data could signal a paradigm shift that challenges the dominance of traditional digital entertainment. That could include subscription video on demand (SVOD) such as Netflix as well as content platforms including ISPs, device manufacturers and media companies.

To help content providers capture younger, more tech-savvy consumers, Blacknut, Supermicro and Radian Arc are lending their focus to deploying a fully integrated GaaS platform. Blacknut, based in France, offers cloud-based gaming. Australia-based Radian Arc provides digital infrastructure and cloud game technology.

The system offers IT hardware solutions at the edge and the core, system management software and extensive IP. Blacknut’s considerable collection includes a catalog of over 600 AAA to indie games.

Blacknut is also providing white-glove services that include:

  • Onboard games wish lists and help establishing exclusive publisher agreements
  • Support for Bring Your Own Game (BYOG) and freemium game models
  • Assistance with the development of IP-licensed games designed in partnership with specialized studios
  • Marketing support to help providers develop go-to-market plans and manage subscriber engagement

The tech behind GaaS

Providers of cloud-based content know all too well the challenge of providing customers with high-availability, low-latency service. The right technology is a carefully choreographed ballet of hybrid cloud infrastructure, modern edge architecture and the IT expertise required to make it all run smoothly.

At the edge, Blacknut’s GaaS offering operates on Radian Arc’s GPU Edge Infrastructure-as-a-Service platform powered by Supermicro GPU Edge Infrastructure solutions.

These hardware solutions include flexible GPU servers featuring 6 to 8 directly attached GPUs and AMD EPYC processors. Also on board are cloud-optimized, scalable management servers and feature-rich ToR networking switches.

Combined with Blacknut’s public and private cloud infrastructure, an impressive array of hardware and software solutions come together. These can create new ways for content providers to quickly roll out their own cloud-gaming products and capture additional market share.

Going global

The Blacknut GaaS platform is already live in 45 countries and is expanding via distribution partnerships with over-the-top providers and carriers.

The solution can also be pre-embedded in set-top boxes and TV ecosystems. Indeed, it has already found its way onto such marquis devices as Samsung Gaming Hub, LG Gaming Shelf and Amazon FireTV.

To learn more about the Blacknut GaaS platform powered by Radian Arc and Supermicro, check out this new solution brief:

 

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How to help your customers invest in AI infrastructure

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How to help your customers invest in AI infrastructure

The right AI infrastructure can help your customers turn data into actionable information. But building and scaling that infrastructure can be challenging. Find out why—and how you can make it easier. 

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Get smarter about helping your customers create an infrastructure for AI systems that leverage their data into actionable information.

A new Supermicro white paper, Investing in AI Infrastructure, shows you how.

As the paper points out, creating an AI infrastructure is far from easy.

For one, there’s the risk of underinvesting. Market watcher IDC estimates that AI will soon represent 10% to 15% of the typical organization’s total IT infrastructure. Organizations that fall short here could also fall short on delivering critical information to the business.

Sure, your customers could use cloud-based AI to test and ramp up. But cloud costs can rise fast. As The Wall Street Journal recently reported, some CIOs have even established internal teams to oversee and control their cloud spending. That makes on-prem AI data center a viable option.

“Every time you run a job on the cloud, you’re paying for it,” says Ashish Nadkarni, general manager of infrastructure systems, platforms and technologies at IDC. “Whereas on-premises, once you buy the infrastructure components, you can run applications multiple times.”

Some of those cloud costs come from data-transfer fees. First, data needs to be entered into a cloud-based AI system; this is known as ingress. And once the AI’s work is done, you’ll want to transfer the new data somewhere else for storage or additional processing, a process of egress.

Cloud providers typically charge 5 to 20 cents per gigabyte of egress. For casual users, that may be no big deal. But for an enterprise using massive amounts of AI data, it can add up quickly.

4 questions to get started

But before your customer can build an on-prem infrastructure, they’ll need to first determine their AI needs. You can help by gathering all stakeholders and asking 4 big questions:

  • What are the business challenges we’re trying to solve?
  • Which AI capabilities and capacities can deliver the solutions we’ll need?
  • What type of AI training will we need to deliver the right insights from your data?
  • What software will we need?

Keep your customer’s context in mind, too. That might include their industry. After all, a retailer has different needs than a manufacturer. But it could include their current technology. A company with extensive edge computing has different data needs than does one without edge devices.

“It’s a matter of finding the right configuration that delivers optimal performance for the workloads,” says Michael McNerney, VP of marketing and network security at Supermicro.

Help often needed

One example of an application-optimized system for AI training is the Supermicro AS-8125GS-TNHR, which is powered by dual AMD EPYC 9004 Series processors. Another option are the Supermicro Universal GPU systems, which support AMD’s Instinct MI250 accelerators.

The system’s modularized architecture helps standardize AI infrastructure design for scalability and power efficiency despite complex workloads and workflow requirements enterprises have, such as AI, data analytics, visualization, simulation and digital twins.

Accelerators work with traditional CPUs to enable greater computing power, yet without slowing the system. They can also shave milliseconds off AI computations. While that may not sound like much, over time those milliseconds “add up to seconds, minutes, hours and days,” says Matt Kimball, a senior analyst at Moor Insights & Strategy.

Roll with partner power

To scale AI across an enterprise, you and your customers will likely need partners. Scaling workloads for critical tasks isn’t easy.

For one, there’s the challenge of getting the right memory, storage and networking capabilities to meet the new high-performance demands. For another, there’s the challenge of finding enough physical space, then providing the necessary electric power and cooling.

Tech suppliers including Supermicro are standing by to offer you agile, customizable and scalable AI architectures.

Learn more from the new Supermicro white paper: Investing in AI Infrastructure.

 

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What is the AMD Instinct MI300A APU?

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What is the AMD Instinct MI300A APU?

Accelerate HPC and AI workloads with the combined power of CPU and GPU compute. 

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The AMD Instinct MI300A APU, set to ship in this year’s second half, combines the compute power of a CPU with the capabilities of a GPU. Your data-center customers should be interested if they run high-performance computing (HPC) or AI workloads.

More specifically, the AMD Instinct MI300A is an integrated data-center accelerator that combines AMD Zen 4 cores, AMD CDNA3 GPUs and high-bandwidth memory (HBM) chiplets. In all, it has more than 146 billion transistors.

This AMD component uses 3D die stacking to enable extremely high bandwidth among its parts. In fact, nine 5nm chiplets that are 3D-stacked on top of four 6nm chiplets with significant HBM surrounding it.

And it’s coming soon. The AMD Instinct MI300A is currently in AMD’s labs. It will soon be sampled with customers. And AMD says it’s scheduled for shipments in the second half of this year. 

‘Most complex chip’

The AMD Instinct MI300A was publicly displayed for the first time earlier this year, when AMD CEO Lisa Su held up a sample of the component during her CES 2023 keynote. “This is actually the most complex chip we’ve ever built,” Su told the audience.

A few tech blogs have gotten their hands on early samples. One of them, Tom’s Hardware, was impressed by the “incredible data throughput” among the Instinct MI300A’s CPU, GPU and memory dies.

The Tom’s Hardware reviewer added that will let the CPU and GPU work on the same data in memory simultaneously, saving power, boosting performance and simplifying programming.

Another blogger, Karl Freund, a former AMD engineer who now works as a market researcher, wrote in a recent Forbes blog post that the Instinct MI300 is a “monster device” (in a good way). He also congratulated AMD for “leading the entire industry in embracing chiplet-based architectures.”

Previous generation

The new AMD accelerator builds on a previous generation, the AMD Instinct MI200 Series. It’s now used in a variety of systems, including Supermicro’s A+ Server 4124GQ-TNMI. This completely assembled system supports the AMD Instinct MI250 OAM (OCP Acceleration Module) accelerator and AMD Infinity Fabric technology.

The AMD Instinct MI200 accelerators are designed with the company’s 2nd gen AMD CDNA Architecture, which encompasses the AMD Infinity Architecture and Infinity Fabric. Together, they offer an advanced platform for tightly connected GPU systems, empowering workloads to share data fast and efficiently.

The MI200 series offers P2P connectivity with up to 8 intelligent 3rd Gen AMD Infinity Fabric Links with up to 800 GB/sec. of peak total theoretical I/O bandwidth. That’s 2.4x the GPU P2P theoretical bandwidth of the previous generation.

Supercomputing power

The same kind of performance now available to commercial users of the AMD-Supermicro system is also being applied to scientific supercomputers.

The AMD Instinct MI25X accelerator is now used in the Frontier supercomputer built by the U.S. Dept. of Energy. That system’s peak performance is rated at 1.6 exaflops—or over a billion billion floating-point operations per second.

The AMD Instinct MI250X accelerator provides Frontier with flexible, high-performance compute engines, high-bandwidth memory, and scalable fabric and communications technologies.

Looking ahead, the AMD Instinct MI300A APU will be used in Frontier’s successor, known as El Capitan. Scheduled for installation late this year, this supercomputer is expected to deliver at least 2 exaflops of peak performance.

 

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