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At Computex, AMD & Supermicro CEOs describe AI advances you’ll be adopting soon

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At Computex, AMD & Supermicro CEOs describe AI advances you’ll be adopting soon

At Computex Taiwan, Lisa Su of AMD and Charles Liang of Supermicro delivered keynotes that focused on AI, liquid cooling and energy efficiency.

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The chief executives of both AMD and Supermicro used their Computex keynote addresses to describe their companies’ AI products and, in the case of AMD, pre-announce important forthcoming products.

Computex 2024 was held this past week in Taipei, Taiwan, with the conference theme of “connecting AI.” Exhibitors featured some 1,500 companies from around the world, and keynotes were delivered by some of the IT industry’s top executives.

That included Lisa Su, chairman and CEO of AMD, and Charles Liang, founder and CEO of Supermicro. Here's some of what they previewed at Computex 2024

Lisa Su, AMD: Top priority is AI

Su of AMD presented one of this Computex’s first keynotes. Anyone who thought she might discuss topics other than AI was quickly set straight.

“AI is our number one priority,” Su told the crowd. “We’re at the beginning of an incredibly exciting time for the industry as AI transforms virtually every business, improves our quality of life, and reshapes every part of the computing market.”

AMD intends to lead in AI solutions by focusing on three priorities, she added: delivering a broad portfolio of high-performance, energy-efficient compute engines (including CPUs, GPUs and NPUs); enabling an open and developer-friendly ecosystem; and co-innovating with partners.

The latter point was supported during Su’s keynote by brief visits from several partner leaders. They included Pavan Dhavulari, corporate VP of Windows devices at Microsoft; Christian Laforte, CTO of Stability AI; and (via a video link) Microsoft CEO Satya Nadella.

Fairly late in Su’s hour-plus keynote, she held up AMD’s forthcoming 5th gen EPYC server processor, codenamed Turin. It’s scheduled to ship by year’s end.

As Su explained, Turin will feature up to 192 cores and 384 threads, up from the current generation’s max of 128 cores and 256 threads. Turin will contain 13 chiplets built in both 3-nm and 6-nm processor technology. Yet it will be available as a drop-in replacement for existing EPYC platforms, Su said.

Turin processors will use AMD’s new ‘Zen5’ cores, which Su also announced at Computex. She described AMD’s ‘Zen5’ as “the highest performance and most energy-efficient core we’ve ever built.”

Su also discussed AMD’s MI3xx family of accelerators. The MI300, introduced this past December, has become the fastest ramping product in AMD’s history, she said. Microsoft’s Nadella, during his short presentation, bragged that his company’s cloud was the first to deliver general availability of virtual machines using the AMD MI300X accelerator.

Looking ahead, Su discussed three forthcoming Instinct accelerators on AMD’s road map: The MI325, MI350 and MI400 series.

The AMD Instinct MI325, set to launch later this year, will feature more memory (up to 288GB) and higher memory bandwidth (6TB/sec.) than the MI300. But the new component will still use the same infrastructure as the MI300, making it easy for customers to upgrade.

The next series, MI350, is set for launch next year, Su said. It will then use AMD’s new CDNA4 architecture, which Su said “will deliver the biggest generational AI leap in our history.” The MI350 will be built on 3nm process technology, but will still offer a drop-in upgrade from both the MI300 and MI325.

The last of the three, the MI400 series, is set to start shipping in 2026. That’s also when AMD will deliver a new generation of CDNA, according to Su.

Both the MI325 and MI350 series will leverage the same industry standard universal baseboard OCP server design used by MI300. Su added: “What that means is, our customers can adopt this new technology very quickly.”

Charles Liang, Supermicro: Liquid cooling is the AI future

Liang dedicated his Computex keynote to the topics of liquid cooling and “green” computing.

“Together with our partners,” he said, “we are on a mission to build the most sustainable data centers.”

Liang predicted a big change from the present, where direct liquid cooling (DLC) has a less-than-1% share of the data center market. Supermicro is targeting 15% of new data center deployments in the next year, and Liang hopes that will hit 30% in the next two years.

Driving this shift, he added, are several trends. One, of course, is the huge uptake of AI, which requires high-capacity computing.

Another is the improvement of DLC technology itself. Where DLC system installations used to take 4 to 12 months, Supermicro is now doing them in just 2 to 4 weeks, Liang said. Where liquid cooling used to be quite expensive, now—when TCO and energy savings are factored in—“DLC can be free, with a big bonus,” he said. And where DLC systems used to be unreliable, now they are high performing with excellent uptime.

Supermicro now has capacity to ship 1,000 rack scale solutions with liquid cooling per month, Liang said. In fact, the company is shipping over 50 liquid-cooled racks per day, with installations typically completed within just 2 weeks.

“DLC,” Liang said, “is the wave of the future.”

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Supermicro intros MicroCloud server powered by AMD EPYC 4004 CPUs

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Supermicro intros MicroCloud server powered by AMD EPYC 4004 CPUs

Supermicro’s latest 3U server, the Supermicro MicroCloud, supports up to 10 nodes of AMD’s entry-level server processor. With this server and the high-density enclosure, Supermicro offers an efficient, high-density and affordable solution for SMBs, corporate departments and branches, and hosted IT service providers.

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Supermicro’s latest H13 server is powered by the AMD EPYC 4004 series processors introduced last month. Designated the Supermicro MicroCloud AS -3015MR-H10TNR, this server is designed to run cloud-native workloads for small and midsized businesses (SMBs), corporate departments and branch offices, and hosted IT service providers.

Intended workloads for the new server include web hosting, cloud gaming and content-delivery networks.

10 Nodes, 3U Form

This new Supermicro MicroCloud server supports up to 10 nodes in a 3U form factor. In addition, as many as 16 enclosures can be loaded into a single track, providing a total of 160 individual nodes.

Supermicro says customers using the new MicroCloud server can increase their computing density by 3.3X compared with industry-standard 1U rackmount servers at rack scale.

The new server also supports high-performance peripherals with either two PCIe 4.0 x8 add-on cards or one x16 full-height, full-width GPU accelerator. System memory maxes out at 192GB. And the unit gets air-cooled by five heavy-duty fans.

4004 for SMBs

The AMD EPYC 4004 series processors bring an entry-level family of CPUs to AMD’s EPYC line. They’re designed for use in entry-level servers used by organizations that typically don’t require either hosting on the public cloud or more powerful server processors.

The new AMD EPYC 4004 series is initially offered as eight SKUs, all designed for use in single-processor systems. They offer from 8 to 16 ‘Zen 4’ cores with up to 32 threads; 128MB of L3 cache; 2 DDR channels with a memory capacity of up to 192GB; and 28 lanes of PCIe 5 connectivity.

More Than One

Supermicro is also using the new AMD EPYC 4004 series processors to power three other server lines.

That includes a 1U server designed for web hosting and SMB applications. A 2U server aimed specifically at companies in financial services. And towers intended for content creation, entry-level servers, workstations and even desktops.

All are designed to be high-density, efficient and affordable. Isn’t that what your SMB customers are looking for?

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Meet AMD's new Alveo V80 Compute Accelerator Card

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Meet AMD's new Alveo V80 Compute Accelerator Card

AMD’s new Alveo V80 Compute Accelerator Card has been designed to overcome performance bottlenecks in compute-intensive workloads that include HPC, data analytics and network security.

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Are you or your customers looking for an accelerator for memory-bound applications with large data sets that require FPGA hardware adaptability? If so, then check out the new AMD Alveo V80 Compute Accelerator Card.

It was introduced by AMD at ISC High Performance 2024, an event held recently in Hamburg, Germany.

The thinking behind the new component is that for large-scale data processing, raw computational power is only half the equation. You also need lots of memory bandwidth.

Indeed, AMD’s new hardware adaptable accelerator is purpose-built to overcome performance bottlenecks for compute-intensive workloads with large data sets common to HPC, data analytics and network security applications. It’s powered by AMD’s 7nm Versal HBM Series adaptive system-on-chip (SoC).

Substantial gains

AMD says that compared with the previous-generation Alveo U55C, the new Alveo V80 offers up to 2x the memory bandwidth, 2x the PCIe bandwidth, 2x the logic density, and 4x the network bandwidth (820GB/sec.).

The card also features 4x200G networking, PCIe Gen4 and Gen5 interfaces, and DDR4 DIMM slots for memory expansion.

Appropriate workloads for the new AMD Alveo V80 include HPC, data analytics, FinTech/Blockchain, network security, computational storage, and AI compute.

In addition, the AMD Alveo V80 can scale to hundreds of nodes over Ethernet, creating compute clusters for HPC applications that include genomic sequencing, molecular dynamics and sensor processing.

Developers, too

A production board in a PCIe form factor, the AMD Alveo V80 is designed to offer a faster path to production than designing your own PCIe card.

Indeed, for FPGA developers, the V80 is fully enabled for traditional development via the Alveo Versal Example Design (AVED), which is available on Github.

This example design provides an efficient starting point using a pre-built subsystem implemented on the AMD Versal adaptive SoC. More specifically, it targets the new AMD Alveo V80 accelerator.

Supermicro offering

The new AMD accelerator is already shipping in volume, and you can get it from either AMD or an authorized distributor.

In addition, you can get the Alveo V80 already integrated into a partner-provided server.

Supermicro is integrating the new AMD Alveo V80 with its AMD EPYC processor-powered A+ servers. These include the Supermicro AS-4125GS-TNRT, a compact 4U server for deployments where compute density and memory bandwidth are critical.

Early user

AMD says one early customer for the new accelerator card is the Commonwealth Scientific Industrial Research Organization (CSIRO), the national research organization of Australia.

CSIRO plans to upgrade an older setup with 420 previous-generation AMD Alveo U55C accelerator cards, replacing them with the new Alveo V80.

 Because the new part is so much more powerful than its predecessor, the organization expects to reduce the number of cards it needs by two-thirds. That, in turn, should shrink the data-center footprint required and lower system costs.

If those sound like benefits you and your customers would find attractive, check out the AMD Alveo V80 links below.

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Research Roundup: AI edition

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Research Roundup: AI edition

Catch up on the latest research and analysis around artificial intelligence.

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Generative AI is the No. 1 AI solution being deployed. Three in 4 knowledge workers are already using AI. The supply of workers with AI skills can’t meet the demand. And supply chains can be helped by AI, too.

Here’s your roundup of the latest in AI research and analysis.

GenAI is No. 1

Generative AI isn’t just a good idea, it’s now the No. 1 type of AI solution being deployed.

In a survey recently conducted by research and analysis firm Gartner, more than a quarter of respondents (29%) said they’ve deployed and are now using GenAI.

That was a higher percentage than any other type of AI in the survey, including natural language processing, machine learning and rule-based systems.

The most common way of using GenAI, the survey found, is embedding it in existing applications. For example, using Microsoft Copilot for 365. This was cited by about 1 in 3 respondents (34%).

Other approaches mentioned by respondents included prompt engineering (cited by 25%), fine-tuning (21%) and using standalone tools such as ChatGPT (19%).

Yet respondents said only about half of their AI projects (48%) make it into production. Even when that happens, it’s slow. Moving an AI project from prototype to production took respondents an average of 8 months.

Other challenges loom, too. Nearly half the respondents (49%) said it’s difficult to estimate and demonstrate an AI project’s value. They also cited a lack of talent and skills (42%), lack of confidence in AI technology (40%) and lack of data (39%).

Gartner conducted the survey in last year’s fourth quarter and released the results earlier this month. In all, valid responses were culled from 644 executives working for organizations in the United States, the UK and Germany.

AI ‘gets real’ at work

Three in 4 knowledge workers (75%) now use AI at work, according to the 2024 Work Trend Index, a joint project of Microsoft and LinkedIn.

Among these users, nearly 8 in 10 (78%) are bringing their own AI tools to work. That’s inspired a new acronym: BYOAI, short for Bring Your Own AI.

“2024 is the year AI at work gets real,” the Work Trend report says.

2024 is also a year of real challenges. Like the Gartner survey, the Work Trend report finds that demonstrating AI’s value can be tough.

In the Microsoft/LinkedIn survey, nearly 8 in 10 leaders agreed that adopting AI is critical to staying competitive. Yet nearly 6 in 10 said they worry about quantifying the technology’s productivity gains. About the same percentage also said their organization lacks an AI vision and plan.

The Work Trend report also highlights the mismatch between AI skills demand and supply. Over half the leaders surveyed (55%) say they’re concerned about having enough AI talent. And nearly two-thirds (65%) say they wouldn’t hire someone who lacked AI skills.

Yet fewer than 4 in 10 users (39%) have received AI training from their company. And only 1 in 4 companies plan to offer AI training this year.

The Work Trend report is based on a mix of sources: a survey of 31,000 people in 31 countries; labor and hiring trends on the LinkedIn site; Microsoft 365 productivity signals; and research with Fortune 500 customers.

AI skills: supply-demand mismatch

The mismatch between AI skills supply and demand was also examined recently by market watcher IDC. It expects that by 2026, 9 of every 10 organizations will be hurt by an overall IT skills shortage. This will lead to delays, quality issues and revenue loss that IDC predicts will collectively cost these organizations $5.5 trillion.

To be sure, AI skills are currently the most in-demand skill for most organizations. The good news, IDC finds, is that more than half of organizations are now using or piloting training for GenAI.

“Getting the right people with the right skills into the right roles has never been more difficult,” says IDC researcher Gina Smith. Her prescription for success: Develop a “culture of learning.”

AI helps supply chains, too

Did you know AI is being used to solve supply-chain problems?

It’s a big issue. Over 8 in 10 global businesses (84%) said they’ve experienced supply-chain disruptions in the last year, finds a survey commissioned by Blue Yonder, a vendor of supply-chain solutions.

In response, supply-chain executives are making strategic investments in AI and sustainability, Blue Yonder finds. Nearly 8 in 10 organizations (79%) said they’ve increased their investments in supply-chain operations. Their 2 top areas of investment were sustainability (cited by 48%) and AI (41%).

The survey also identified the top supply-chain areas for AI investment. They are planning (cited by 56% of those investing in AI), transportation (53%) and order management (50%).

In addition, 8 in 10 respondents to the survey said they’ve implemented GenAI in their supply chains at some level. And more than 90% said GenAI has been effective in optimizing their supply chains and related decisions.

The survey, conducted by an independent research firm with sponsorship by Blue Yonder, was fielded in March, with the results released earlier this month. The survey received responses from more than 600 C-suite and senior executives, all of them employed by businesses or government agencies in the United States, UK and Europe.

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AMD intros entry-level server CPUs for SMBs, enterprise branches, hosted service providers

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AMD intros entry-level server CPUs for SMBs, enterprise branches, hosted service providers

The new AMD EPYC 4004 processors extend the company’s ‘Zen 4’ core architecture into a line of entry-level systems for small and midsized businesses, schools, branch IT and regional providers of hosted IT services.

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AMD has just introduced the AMD EPYC 4004 processors, bringing a new entry-level line to its family of 4th gen server processors.

To deliver these new processors, AMD has combined the architecture of its Ryzen 7000 series processors with the packaging of its EPYC line of server processors. The result is a line of CPUs that lowers the entry-level pricing for EPYC-powered servers.

The AMD EPYC 4004 processors are designed for use in entry-level servers and towers, systems that typically retail for $1,500 to $3,000. That’s a price level affordable for most small and medium businesses, enterprise IT branches, public school districts, and regional providers of hosted IT services. It’s even less than the retail price for some high-end processor CPUs.

Many SMBs can’t afford either hosting on the public cloud or AMD’s more powerful server processors. As a result, they often make do with using PCs as servers. The new AMD processors aim to change that.

There are lots of reasons why a real server offers a better solution. These reasons include greater performance and scalability, higher rates of dependability and easier management.

Under the hood

The new AMD EPYC 4004 series is initially offered as eight SKUs, all designed for use in single-processor systems. They offer from 8 to 16 ‘Zen 4’ cores with up to 32 threads; 128MB of L3 cache; 2 DDR channels with a memory capacity of up to 192GB; and 28 lanes of PCIe 5 connectivity.

Two of the new SKUs—4584PX and 4484PX—offer AMD’s 128MB 3D V-Cache technology. As the name implies, V-Cache is a 3D vertical cache designed to offer faster interconnect density, greater energy efficiency and higher per-core performance for cache-hungry applications.

All the new AMD EPYC 4004 processors use AMD’s AM5 socket. That makes them incompatible with AMD’s higher-end EPYC 8004 and EPYC 9004 server processors, which use a different socket.

OEM support

AMD is working with several server OEMs to get systems built around the new EPYC 4004 processors to market quickly. Among these OEMs is Supermicro, which is supporting the new AMD CPUs in select towers and servers.

That includes Supermicro’s H13 MicroCloud system, a high-density, 3U rackmount system for the cloud. It has now been updated with additional performance offered by the AMD EPYC 4004.

Supermicro’s H13 MicroCloud retails for about $10K, making it more expensive than most entry-level servers. But unlike those less-expensive servers, the MicroCloud offers 8 single-processor nodes for applications requiring multiple discrete servers, such as e-commerce sites, code development, cloud gaming and content creation.

AMD says shipments of the new AMD EPYC 4004 Series processors, as well as of OEM systems powered by the new CPUs, are expected to begin during the first week of June. Pre-sales orders of the new processors, AMD adds, have already been strong.

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Tech Explainer: Why the Rack is Now the Unit

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Tech Explainer: Why the Rack is Now the Unit

Today’s rack scale solutions can include just about any standard data center component. They can also save your customers money, time and manpower.

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Are your data center customers still installing single servers and storage devices instead of full-rack solutions? If so, they need to step up their game. Today, IT infrastructure management is shifting toward rack scale integrations. Increasingly, the rack is the unit.

A rack scale solution can include just about any standard data center component. A typical build combines servers, storage devices, network switches and other rack products like power-management and cooling systems. Some racks are loaded with the same type of servers, making optimization and maintenance easier.

With many organizations developing and deploying resource-intensive AI-enabled applications, opting for fully integrated turnkey solutions that help them become more productive faster makes sense. Supermicro is at the vanguard of this movement.

The Supermicro team is ready and well-equipped to design, assemble, test, configure and deploy rack scale solutions. These solutions are ideal for modern datacenter workloads, including AI, deep learning, big data and vSAN.

Why rack scale?

Rack scale solutions let your customers bypass the design, construction and testing of individual servers. Instead of spending precious time and money building, integrating and troubleshooting IT infrastructure, rack scale and cluster-level solutions arrive preconfigured and ready to run.

Supermicro advertises plug-and-play designs. That means your customers need only plug in and connect to their networks, power and optional liquid cooling. After that, it’s all about getting more productivity faster.

Deploying rack scale solutions could enable your customers to reduce or redeploy IT staff, help them optimize their multicloud deployments, and lower their environmental impact and operating costs.

Supermicro + AMD processors = lower costs

Every organization wants to save time and money. Your customers may also need to adhere to stringent environmental, social and governance (ESG) policies to reduce power consumption and battle climate change.

Opting for AMD silicon helps increase efficiency and lower costs. Supermicro’s rack scale solutions feature 4th generation AMD EPYC server processors. These CPUs are designed to shrink rack space and reduce power consumption in your customers’ data center.

AMD says its EPYC-series processors can:

  • Run resource-intensive workloads with fewer servers
  • Reduce operational and energy costs
  • Free up precious data center space and power, then re-allocate this capacity for new workloads and services

Combined with a liquid-cooling system, Supermicro’s AMD-powered rack scale solutions can help reduce your customer’s IT operating expenses by more than 40%.

More than just the hardware

The right rack scale solution is about more than just hardware. Your customers also need a well-designed, fully integrated solution that has been tested and certified before it leaves the factory.

Supermicro provides value-added services beyond individual components to create a rack scale solution greater than the sum of its parts.

You and your customers can collaborate with Supermicro product managers to determine the best platform and components. That includes selecting optimum power supplies and assessing network topology architecture and switches.

From there, Supermicro will optimize server, storage and switch placement at rack scale. Experienced hardware and software engineers will design, build and test the system. They’ll also install mission-critical software benchmarked to your customer’s requirements.

Finally, Supermicro performs strenuous burn-in tests and delivers thoroughly tested L12 clusters to your customer’s chosen site. It’s a one-stop shop that empowers your customers to maximize productivity from day one.

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Supermicro, Vast collaborate to deliver turnkey AI storage at rack scale

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Supermicro, Vast collaborate to deliver turnkey AI storage at rack scale

Supermicro and Vast Data are jointly offering an AMD-based turnkey solution that promises to simplify and accelerate AI and data pipelines.

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Supermicro and Vast Data are collaborating to deliver a turnkey, full-stack solution for creating and expanding AI deployments.

This joint solution is aimed at hyperscalers, cloud service providers (CSPs) and large, data-centric enterprises in fintech, adtech, media and entertainment, chip design and high-performance computing (HPC).

Applications that can benefit from the new joint offering include enterprise NAS and object storage; high-performance data ingestion; supercomputer data access; scalable data analysis; and scalable data processing.

Vast, founded in 2016, offers a software data platform that enterprises and CSPs use for data-intensive computing. The platform is based on a distributed systems architecture, called DASE, that allows a system to run read and write operations at any scale. Vast’s customers include Pixar, Verizon and Zoom.

By collaborating with Supermicro, Vast hopes to extend its market. Currently, Vast sells to infrastructure providers at a variety of scales. Some of its largest customers have built 400 petabyte storage systems, and a few are even discussing systems that would store up to 2 exabytes, according to John Mao, Vast’s VP of technology alliances.

Supermicro and Vast have engaged with many of the same CSPs separately, supporting various parts of the solution. By formalizing this collaboration, they hope to extend their reach to new customers while increasing their sell-through to current customers.

Vast is also looking to the Supermicro alliance to expand its global reach. While most of Vast’s customers today are U.S.-based, Supermicro operates in over 100 countries worldwide. Supermicro also has the infrastructure to integrate, test and ship 5,000 fully populated racks per month from its manufacturing plants in California, Netherlands, Malaysia and Taiwan.

There’s also a big difference in size. Where privately held Vast has about 800 employees, publicly traded Supermicro has more than 5,100.

Rack solution

Now Vast and Supermicro have developed a new converged system using Supermicro’s Hyper A+ servers with AMD EPYC 9004 processors. The solution combines 2 separate Vast servers. 

This converged system is well suited to large service providers, where the typical Supermicro-powered Vast rack configuration will start at about 2PB, Mao adds.

Rack-scale configurations can cut costs by eliminating the need for single-box redundancy. This converged design makes the system more scalable and more cost-efficient.

Under the hood

One highlight of the joint project: It puts Vast’s DASE architecture on Supermicro’s  industry-standard servers. Each server will have both the compute and storage functions of a Vast cluster.

At the same time, the architecture is disaggregated via a high-speed Ethernet NVMe fabric. This allows each node to access all drives in the cluster.

The Vast platform architecture uses a series of what the company calls an EBox. Each EBox, in turn, contains 2 kinds of storage servers in a container environment: CNode (short for Compute Node) and DNode (short for Data Node). In a typical EBox, one CNode interfaces with client applications and writes directly to two DNode containers.

In this configuration, Supermicro’s storage servers can act as a hardware building block to scale Vast to hundreds of petabytes. It supports Vast’s requirement for multiple tiers of solid-state storage media, an approach that’s unique in the industry.

CPU to GPU

At the NAB Show, held recently in Las Vegas, Supermicro’s demos included storage servers, each powered by a single-socket AMD EPYC 9004 Series processor.

With up to 128 PCIe Gen 5 lanes, the AMD processor empowers the server to connect more SSDs via NVMe with a single CPU. The Supermicro storage server also lets users move data directly from storage to GPU memory supporting Nvidia’s GPU Direct storage protocol, essentially bypassing a GPU cluster’s CPU using RDMA.

If you or your customers are interested in the new Vast solution, get in touch with your local Supermicro sales rep or channel partner. Under the terms of the new partnership, Supermicro is acting as a Vast integrator and OEM. It’s also Vast’s only rack-scale partner.

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Which media server should you use when you absolutely can’t lose data?

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Which media server should you use when you absolutely can’t lose data?

A new Linus Tech Tip video shows a real-world implementation of Supermicro storage servers powered by AMD EPYC processors to provide super-high reliability.

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Are your customers looking for a top-performing media server? And are you looking for a surprisingly entertaining video review of the best one? Then look no further. You’ll find both in the latest Linus Tech Tip video.

This episode, sponsored by Supermicro, is entitled “This Server CANNOT Lose Data.” That gives you an idea of its primary focus: high reliability.

And that reliability is delivered courtesy of a sophisticated server/storage cluster featuring Supermicro GrandTwin A+ multinode servers.

Myriad redundancies

What makes the GrandTwin so reliable? Redundancy. As video host Linus Sebastian exclaims, “Inside this 2U are 4 independent computers!”

Each computer, or node, is powered by a 2.45GHz AMD EPYC processor with up to 128 cores and a 256MB L3 cache. Each node also has 4 front hot-swap 2.5-inch drive bays that can hold petabytes of either NVMe or SATA storage.

The GrandTwin’s nodes can handle up to 3TB of DDR5 ECC server memory. They also have dual M.2 slots for boot drives and 6 PCIe Gen 5 x16 slots for networking, graphics and other expansion cards.

GrandTwin’s high-availability design extends all the way down to its dual power supplies. To ensure the system always has a reliable flow of power to all its vital components, Supermicro added two redundant 2200-watt titanium-level PSUs.

Handling the heat generated by this monster machine is paramount. The GrandTwin takes care of all that hot air via 4 high-speed fans—one fan in each PSU, plus 2 dedicated heavy-duty 8-cm. fans spinning at more than 17,000 RPM.

Prime processing

At the core of each of the GrandTwin’s 4 nodes is an AMD 9004-series processor. Linus’ prized media server, known as “Whonnock 10,” sports an AMD EPYC 9534 CPU in each node.

The EPYC 9534’s cores—there are 64 of them—operate at 2.45GHz and can boost up to 3.7GHz. And because each EPYC processor boasts 12 memory channels, the GrandTwin can address up to 12TB of memory systemwide.

Don’t call it overkill

As Linus says with unbridled enthusiasm, when it comes to redundancy, the name of the game is avoiding “split brain.”

The dreaded split brain can occur when redundant servers have their own object storage. The failure of even a single system can lead to a situation in which each server believes it has the correct data.

If there are only 2 servers, proving which system is correct is impossible. On the other hand, operating 3 or more servers allows the system to resolve the argument and determine the correct data.

Linus and company installed 2 GrandTwin A+ servers. That gives them the 8 redundant systems recommended by their preferred NVMe file system, WEKA.

A multitude of use cases

Your customers may have to contend with thousands of hours of high-resolution videos, like Linus and his cohorts. Or they may develop AI-enabled applications, provide cloud gaming, or host mission-critical web applications.

Whatever the use case, they can benefit from high-reliability servers designed with built-in redundancies. When failure is not an option, your customers need a server that, as the video puts it, “CANNOT lose data.”

That means helping your customers deploy Supermicro GrandTwin A+ servers powered by AMD EPYC processors. It’s the ultimate high-reliability system.

After all, as Linus says, “You only server once.”

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How CSPs can accelerate the data center

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How CSPs can accelerate the data center

A new webinar, now available on demand, offers cloud service providers an overview of new IDC research, outlines roadblocks, and offers guidelines for future success.

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Are you a cloud services provider—or a CSP wannabe—wondering how to expand your data center in ways that will both keep your customers happy and help you turn a profit?

If so, a recent webinar sponsored by Supermicro and AMD can help. Entitled Accelerate Your Cloud: Best Practices for CSPs, it was moderated by Wendell Wenjen, director of storage market development at Supermicro. Best of all, you can now view this webinar on demand.

Here’s a taste of what you’ll see:

IDC research on CSP buying plans

The webinar’s first speaker is Ashish Nadkarni, group VP and GM of worldwide infrastructure research at IDC. He summarizes new IDC research on technology adoption trends and strategies among service providers.

Sales growth, IDC says, is coming mainly in 4 areas: Infrastructure as a Service (IaaS), hardware (both servers and storage), software and IT services. The good news, Nadkarni adds, is that all 4 can be offered by service providers.

Data centers remain important, Nadkarni says. Not everyone wants to use the public cloud, and not every workload belongs there.

IDC expects that 5 key technologies will be immune to budget cuts:

  • AI and automation
  • Security, risk and compliance
  • Optimization of IT infrastructure and IT operations
  • Back-office applications (HR, SCM and ERP)
  • Customer experience initiatives (for example, chatbots)

Generative AI dominates the conversation, Nadkarni said, and for good reason: IDC expects that this year, GenAI will double the productive use of unstructured data, helping workers discover new insights and knowledge.

Supply-chain issues remain a daunting challenge, IDC finds. Delays can hurt a CSP’s ability to deliver projects, increase the cost of delivering services, and even impair service quality. Owning the supply chain will remain vital.

Other tactics for change, Nadkarni said, include offering a transformation road map; working with a full-stack portfolio provider; and developing a long-term vision for why customers will want to do business with you.

10 steps to data-center scaling

Next up in the webinar is Sim Upadhyayula, VP of solutions enablement at Supermicro. He offered a list of 10 essential steps for scaling a CSP data center.

Topping his list: standardize and scale. There’s no way you can know exactly which workloads will dominate in the future. So be modular. That way, you can scale in smaller increments, keeping customers happy while controlling your costs.

Next on the list: optimize for applications. Unlike big enterprises, most CSPs cannot afford to build application silos. Instead, leading providers will develop an architecture that can cater to all. That means using standard hardware that can later be optimized for specific workloads.

Common challenges

Suresh Andani, AMD’s senior director of product management for server cloud, is up next. He discusses 3 key CSP challenges:

  • Market disruption: Caused by a changing ISV landscape, and by increasing power and cooling costs.
  • Aging infrastructure: Service providers with older systems find them costly to maintain, unable to keep pace with customers’ business demands, and vulnerable to increasingly dangerous security threats.
  • Expanding demands: Customers keep raising the bar on core workloads AI, cloud-native applications, digital transformation, the hybrid workforce and security enhancements.

During the webinar’s concluding roundtable discussion, Andani also emphasized the importance of marrying the right infrastructure with your workloads. That way, he said, CSPs can operate efficiently, making the most of their power and compute cycles.

“Work with your vendors to provide the best compute solutions,” Andani of AMD advised. “Later you can offer a targeted infrastructure for high performance compute, another for enterprise workloads, another for gaming, and another for rendering.”

Lean on your providers, he added, to provide the right solution, whether your target is performance or cost.

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Tech Explainer: What’s the difference between AI training and AI inference?

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Tech Explainer: What’s the difference between AI training and AI inference?

AI training and inference may be two sides of the same coin, but their compute needs can be quite different. 

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Artificial Intelligence (AI) training and inference are two sides of the same coin. Training is the process of teaching an AI model how to perform a given task. Inference is the AI model in action, drawing its own conclusions without human intervention.

Take a theoretical machine learning (ML) model designed to detect counterfeit one-dollar bills. During the training process, AI engineers would feed the model large data sets containing thousands, or even millions, of pictures. And tell the training application which are real and which are counterfeit.

Then inference could kick in. The AI model could be uploaded to retail locations, then run to detect bogus bills.

A deeper look at training

That’s the high level. Let’s dig in a bit deeper.

Continuing with our bogus-bill detecting workload, during training, the pictures fed to the AI model would include annotations telling the AI how to think about each piece of data.

For instance, the AI might see a picture of a dollar bill with an embedded annotation that essentially tells the model “this is legal tender.” The annotation could also identify characteristics of a genuine dollar, such as the minute details of the printed iconography and the correct number of characters in the bill’s serial number.

Engineers might also feed the AI model pictures of counterfeit bills. That way, the model could learn the tell-tale signs of a fake. These might include examples of incomplete printing, color discrepancies and missing watermarks.

On to inference

One the training is complete, inference can take over.

Still with our example of counterfeit detection, the AI model could now be uploaded to the cloud, then connected with thousands of point-of-sale (POS) devices in retail locations worldwide.

Retail workers would scan any bill they suspect might be fake. The machine learning model, in turn, would then assess the bill’s legitimacy.

This process of AI inference is autonomous. In other words, once the AI enters inference, it’s no longer getting help from engineers and app developers.

Using our example, during inference the AI system has reached the point where it can reliably discern both legal and counterfeit bills. And it can do so with a high enough success percentage to satisfy its human controllers.

Different needs

AI training and inference also have different technology requirements. Basically, training is far more resource-intensive. The focus is on achieving low-latency operation and brute force.

Training a large language model (LLM) chatbot like the popular ChatGPT often forces its underlying technology to contend with more than a trillion parameters. An AI parameter is a variable learned by the LLM during training. These parameters include configuration settings and components that define the LLM’s behavior.)

To meet these requirements, IT operations must deploy a system that can bring to bear raw computational power in a vast cluster.

By contrast, inference applications have different compute requirements. “Essentially, it’s, ‘I’ve trained my model, now I want to organize it,’” explained AMD executive VP and CTO Mark Papermaster in a recent virtual presentation.

AMD’s dual-processor solution

Inferencing workloads are both more concise and less demanding than those for training. Therefore, it makes sense to run them on more affordable GPU-CPU combination technology like the AMD Instinct MI300A.

The AMD Instinct MI300A is an accelerated processing unit (APU) that combines the facility of a standard AI accelerator with the efficiency of AMD EPYC processors. Both the CPU and GPU elements can share memory, dramatically enhancing efficiency, flexibility and programmability.

A single AMD MI300A APU packs 228 GPU compute units, 24 of AMD’s ‘Zen 4’ CPU cores, and 128GB of unified HBM3 memory. Compared with the previous-generation AMD MI250X accelerators, this translates to approximately 2.6x the workload performance per watt using FP32.

That’s a significant increase in performance. It’s likely to be repeated as AI infrastructure evolves along with the proliferation of AI applications that now power our world.

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