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AMD presents its vision for the AI future: open, collaborative, for everyone

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AMD presents its vision for the AI future: open, collaborative, for everyone

Check out the highlights of AMD’s Advancing AI event—including new GPUs, software and developer resources.

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AMD advanced its AI vision at the “Advancing AI” event on June 12. The event, held live in the Silicon Valley city of San Jose, Calif., as well as online, featured presentations by top AMD executives and partners.

As many of the speakers made clear, AMD’s vision for AI is that it be open, developer-friendly, collaborative and useful to all.

AMD certainly believes the market opportunity is huge. During the day’s keynote, CEO Lisa Su said AMD now believes the total addressable market (TAM) for data-center AI will exceed $500 billion by as soon as 2028.

And that’s not all. Su also said she expects AI to move beyond the data center, finding new uses in edge computers, PCs, smartphone and other devices.

To deliver on this vision, Su explained, AMD is taking a three-pronged approach to AI:

  • Offer a broad portfolio of compute solutions.
  • Invest in an open development ecosystem.
  • Deliver full-stack solutions via investments and acquisitions.

The event, lasting over two hours, was also filled with announcements. Here are the highlights.

New: AMD Instinct MI350 Series

At the Advancing AI event, CEO Su formally announced the company’s AMD Instinct MI350 Series GPUs.

There are two models, the MI350X and MI355X. Though both are based on the same silicon, the MI355X supports higher thermals.

These GPUs, Su explained, are based on AMD’s 4th gen Instinct architecture, and each GPU comprises 10 chiplets containing a total of 185 billion transistors. The new Instinct solutions can be used for both AI training and AI inference, and they can also be configured in either liquid- or air-cooled systems.

Su said the MI355X delivers a massive 35x general increase in AI performance over the previous-generation Instinct MI300. For AI training, the Instinct MI355X offers up to 3x more throughput than the Instinct MI300. And in comparison with a leading competitive GPU, the new AMD GPU can create up to 40% more tokens per dollar.

AMD’s event also featured several representatives of companies already using AMD Instinct MI300 GPUs. They included Microsoft, Meta and Oracle.

Introducing ROCm 7 and AMD Developer Cloud

Vamsi Boppana, AMD’s senior VP of AI, announced ROCm 7, the latest version of AMD’s open-source AI software stack. ROCm 7 features improved support for industry-standard frameworks; expanded hardware compatibility; and new development tools, drivers, APIs and libraries to accelerate AI development and deployment.

Earlier in the day, CEO Su said AMD’s software efforts “are all about the developer experience.” To that end, Boppana introduced the AMD Developer Cloud, a new service designed for rapid, high-performance AI development.

He also said AMD is giving developers a 25-hour credit on the Developer Cloud with “no strings.” The new AMD Developer Cloud is generally available now.

Road Map: Instinct MI400, Helios rack, Venice CPU, Vulcano NIC

During the last segment of the AMD event, Su gave attendees a sneak peek at several forthcoming products:

  • Instinct MI400 Series: This GPU is being designed for both large-scale AI inference and training. It will be the heart of the Helios rack solution (see below) and provide what Su described as “the engine for the next generation of AI.” Expect performance of up to 40 petaflops, 432GB of HBM4 memory, and bandwidth of 19.6TB/sec.
  • Helios: The code name for a unified AI rack solution coming in 2026. As Su explained it, Helios will be a rack configuration that functions like a single AI engine, incorporating AMD’s EPYC CPU, Instinct GPU, Pensando Pollara network interface card (NIC) and ROCm software. Specs include up to 72 GPUs in a rack and 31TB of HBM3 memory.
  • Venice: This is the code name for the next generation of AMD EPYC server CPUs, Su said. They’ll be based on a 2nm form, feature up to 256 cores, and offer a 1.7x performance boost over the current generation.
  • Vulcano: A future NIC, it will be built using a 3nm form and feature speeds of up to 800Gb/sec.

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Research Roundup: Tariffs, the data center next door, agentic supply chains, cyber AI, and ransomware

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Research Roundup: Tariffs, the data center next door, agentic supply chains, cyber AI, and ransomware

Catch up on the latest IT market research and analysis.

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U.S. tariffs could slow IT spending worldwide. Many Americans are okay with a data center next door. Supply chains could start making decisions on their own. AI is both a dangerous cyber threat and a great cyber defense. And ransomware continues to morph.

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

Tariff Tremors

Uncertainty related to President Trump’s tariffs has led IT market watcher IDC to lower its estimate for global IT spending this year.

At the start of this year, IDC had expected global IT spending to rise by 10% this year. Then, in March, the company lowered that, saying spending would grow by just 5%. Now IDC, citing rising uncertainty over tariffs, is hedging its bet, pegging growth at anywhere from 5% to 9%.

Regardless of the exact impact on overall IT spending, IDC feels confident that tariffs will indeed have an impact on the IT industry.

In an April blog post, four IDC analysts wrote, “New tariffs will have an inflationary impact on technology prices in the U.S., as well as causing significant disruption to supply chains.”  

The impact of tariffs should be felt most immediately in compute, storage and network hardware as well as datacenter construction, the IDC analysts wrote, adding: “Even sectors such as software and services will be affected if tariffs are longer lived.”

Data Center NIMBY? No

Seven in 10 Americans say they’re comfortable with a data center being built within a few miles of their home—that is, if it’s done sustainably and with community input.

That’s from a survey of 600 U.S. adults conducted for Modine, a provider of thermal-management products.

The survey’s key findings include:

  • Nearly half of U.S. adults (47%) say they’d be fine with a data center being built within five miles of their home.
  • Americans’ top concerns about having a data center nearby are: increased energy demand (cited by 63% of respondents); noise pollution (60%); and lower property values (52%).
  • About six in 10 respondents (62%) say they’d like local data-center owners to contribute to community initiatives such as schools and infrastructure.
  • Slightly over half the respondents (55%) favor tax breaks to encourage responsible data-center development.

Agentic AI & Supply Chains

You may have already heard the term agentic AI. It refers to the idea that artificial intelligence systems can operate autonomously, without human intervention.

IT research firm Gartner predicts that fully half of all supply-chain management solutions will include agentic AI capabilities, and by as soon as 2030. This means future supply-chain systems will use intelligent agents to make and act on decisions, all without a human’s oversight.

Further, these agentic AI systems will provide what Gartner calls a virtual workforce. AI agents will assist, offload and augment human work along with more traditional software applications.

Gartner also says agentic AI systems could help supply-chain managers improve efficiency and contribute more to their organizations’ profit growth. Mainly, by enhancing resource efficiency, automating complex tasks, and introducing new business models.

“AI agents will autonomously complete tasks without relying on explicit inputs or predefined outcomes,” says Kaitlynn Sommers, a senior analyst at Gartner. “Agents will continuously learn from real-time data and adapt to evolving conditions and complex demands.”

AI: Both Cyber Friend and Cyber Foe

AI is both the greatest threat to cybersecurity and cybersecurity’s greatest defense, say management consultants McKinsey & Co.

AI is reshaping the cybersecurity landscape, write four McKinsey analysts in a new blog post. This technology brings new opportunities, as well as new threats.

For one, conducting a cyberattack is relatively fast and easy with AI. Criminals can use AI to create convincing phishing emails, fake websites and deepfake videos. They can also use machine learning to observe an attack, then modify their tactics based on the results, making future attacks more effective.

But AI is also what McKinsey calls a “game changer” for cybersecurity defense. Organizations can use AI to detect, react to, and recover from attacks with greater speed. And AI-driven anomaly detection can help organizations detect cyberattacks before they escalate.

Integration of AI into cybersecurity solutions is vital, McKinsey says. Especially because more than nine in 10 AI capabilities will come from third-party vendors. With integration, AI can be added to mainstream cyber tools such as zero trust, SASE and security-posture management.

The State of Ransomware: Slightly Worse

Ransomware is getting worse. In 2024, the percentage of users worldwide who were affected by ransomware increased by nearly half a percentage point, says security firm Kaspersky in 2025 “State of Ransomware” report. That may sound like a small increase, but ransomware criminals focus on quality of their victims rather than the quantity.

The frequency of attacks varies greatly by geographical region, Kaspersky finds. The highest rate is found in the Middle East, where nearly one in 100 users (0.72%) were attacked in 2024. Next worse was APAC, with an attack rate of 0.6%. The global average was 0.44%.

“Ransomware is one of the most pressing cybersecurity threats facing organizations today,” says Dmitry Galov, head of a Kaspersky research center. “Building cyber awareness at every level is just as important as investing in the right technology.”

New ransomware trends identified by Kaspersky:

  • AI use: Ransomware groups are using AI tools to enhance development and evade detection. One example is FunkSec, a group that uses AI to take a contrarian approach to ransomware; instead of attacking a few high-value targets, FunkSec makes many attacks for low ransoms.
  • Ransomware-as-a-Service: Criminals who lack technical development skills can now just buy a ransomware package on the dark web. There are even ransomware platforms that offer malware, tech support and even revenue-sharing affiliate programs.
  • Unconventional vulnerabilities: Attackers are increasingly targeting overlooked entry points. These include IoT devices, smart appliances and misconfigured hardware. In this way, the bad guys can capitalize on expanding attack surfaces created by interconnected systems.
  • LLM proliferation: Criminals can take advantage of large language models sold on the dark web, which lower the technical barriers to creating malicious code, phishing campaigns and social-engineering attacks. One example is LowCode, which provides an AI-assisted drag-and-drop interface for software development.

 

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Tech Explainer: What’s a NIC? And how can it empower AI?

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Tech Explainer: What’s a NIC? And how can it empower AI?

With the acceleration of AI, the network interface card is playing a new, leading role.

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The humble network interface card (NIC) is getting a status boost from AI.

At a fundamental level, the NIC enables one computing device to communicate with others across a network. That network could be a rendering farm run by a small multimedia production house, an enterprise-level data center, or a global network like the internet.

From smartphones to supercomputers, most modern devices use a NIC for this purpose. On laptops, phones and other mobile devices, the NIC typically connects via a wireless antenna. For servers in enterprise data centers, it’s more common to connect the hardware infrastructure with Ethernet cables.

Each NIC—or NIC port, in the case of an enterprise NIC—has its own media access control (MAC) address. This unique identifier enables the NIC to send and receive relevant packets. Each packet, in turn, is a small chunk of a much larger data set, enabling it to move at high speeds.

Networking for the Enterprise

At the enterprise level, everything needs to be highly capable and powerful, and the NIC is no exception. Organizations operating full-scale data centers rely on NICs to do far more than just send emails and sniff packets (the term used to describe how a NIC “watches” a data stream, collecting only the data addressed to its MAC address).

Today’s NICs are also designed to handle complex networking tasks onboard, relieving the host CPU so it can work more efficiently. This process, known as smart offloading, relies on several functions:

  • TCP segmentation offloading: This breaks big data into small packets.
  • Checksum offloading: Here, the NIC independently checks for errors in the data.
  • Receive side scaling: This helps balance network traffic across multiple processor cores, preventing them from getting bogged down.
  • Remote Direct Memory Access (RDMA): This process bypasses the CPU and sends data directly to GPU memory.

Important as these capabilities are, they become even more vital when dealing with AI and machine learning (ML) workloads. By taking pressure off the CPU, modern NICs enable the rest of the system to focus on running these advanced applications and processing their scads of data.

This symbiotic relationship also helps lower a server’s operating temperature and reduce its power usage. The NIC does this by increasing efficiency throughout the system, especially when it comes to the CPU.

Enter the AI NIC

Countless organizations both big and small are clamoring to stake their claims in the AI era. Some are creating entirely new AI and ML applications; others are using the latest AI tools to develop new products that better serve their customers.

Either way, these organizations must deal with the challenges now facing traditional Ethernet networks in AI clusters. Remember, Ethernet was invented over 50 years ago.

AMD has a solution: a revolutionary NIC it has created for AI workloads, the AMD AI NIC card. Recently released, this NIC card is designed to provide the intense communication capabilities demanded by AI and ML models. That includes tightly coupled parallel processing, rapid data transfers and low-latency communications.

AMD says its AI NIC offers a significant advancement in addressing the issues IT managers face as they attempt to reconcile the broad compatibility of an aging network technology with modern AI workloads. It’s a specialized network accelerator explicitly designed to optimize data transfer within back-end AI networks for GPU-to-GPU communication.

To address the challenges of AI workloads, what’s needed is a network that can support distributed computing over multiple GPU nodes with low jitter and RDMA. The AMD AI NIC is designed to manage the unique communication patterns of AI workloads and offer high throughput across all available links. It also offers congestion avoidance, reduced tail latency, scalable performance, and fast job-completion times.

Validated NIC

Following rigorous validation by the engineers at Supermicro, the AMD AI NIC is now supported on the Supermicro 8U GPU Server (AS -8126GS-TNMR). This behemoth is designed specifically for AI, deep learning, high-performance computing (HPC), industrial automation, retail and climate modeling.

In this configuration, AMD’s smart AI-focused NIC can offload networking tasks. This lets the Supermicro SuperServer’s dual AMD EPYC 9000-series processors run at even higher efficiency.

In the Supermicro server, the new AMD AI NIC occupies one of the myriad PCI Express x16 slots. Other optional high-performance PCIe cards include a CPU-to-GPU interconnect and up to eight AMD Instinct GPU accelerators.

In the NIC of time

A chain is only as strong as its weakest link. The chain that connects our ever-expanding global network of AI operations is strengthened by the advent of NICs focused on AI.

As NICs grow more powerful, these advanced network interface cards will help fuel the expansion of the AI/ML applications that power our homes, offices, and everything in between. They’ll also help us bypass communication bottlenecks and speed time to market.

For SMBs and enterprises alike, that’s good news indeed.

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Oil & gas spotlight: Fueling up with AI

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Oil & gas spotlight: Fueling up with AI

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

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

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

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

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

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

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

Powerful Solutions

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

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

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

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

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

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To make room for AI, modernize your data center

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To make room for AI, modernize your data center

A new report finds the latest AMD-powered Supermicro servers can modernize the data center, lowering TCO and making room for AI systems.

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Did you know that dramatic improvements in processor power can enable your corporate customers to lower their total cost of ownership (TCO) by consolidate servers and modernizing their data centers?

Server consolidation is a hot topic in the context of AI. Many data centers are full and running with all the power that’s available. So how can they make room for new AI systems? Also, how can they get the kind of power that today’s AI systems require?

One answer: with consolidation. 

Four in One

All this is especially relevant in light of a new report from Principled Technologies.

The report, prepared for AMD, finds that an organization that upgrades to new Supermicro servers powered by the current 5th generation AMD EPYC processors can consolidate servers on a 4:1 ratio.

In other words, the level of performance that previously required four older servers can now be delivered with just one.

Further, Principled found that organizations that make this upgrade can also free up data-center space; lower operating costs by up to $2.8 million over five years; shrink power-consumption levels; and reduce the maintenance load on sys admins.

Testing Procedures

Here’s how Principled figured all this out. To start, they obtained two systems:

Next, Principled’s researchers compared the transactional database performance of the two servers. They did this with HammerDB TPROC-C, an open-source benchmarking tool for online transaction processing (OLTP) workloads.

To ensure the systems were sufficiently loaded, Principled also measured both servers’ CPU and power utilization rates, pushing both servers to 80% CPU core utilization.

Then Principled calculated a consolidation ratio. That is, how many of the older servers would be needed to do the same level of work done by just 1 new server?

Finally, Principled calculated the expected 5-year costs for software licensing, power, space and maintenance. These calculations were made for both the older and new Supermicro servers, so they could be compared.

The Results

So what did Principled find? Here are the key results:

  • Performance upgrades: The new servers, based on AMD 5th Gen EPYC processors, is much more powerful. To match the database performance of just 1 new server, the testers required 4 of the older servers.
  • Lower operating costs: Consolidating those four older servers onto just one new server could lower an organization’s TCO by over 60%, saving up to an estimated $2.8 million over five years. The estimated 5-year TCO for the legacy server was $4.68 million, compared with $1.78 million for the new system.
  • Lower software license costs: Much of the savings would come from consolidating software licenses. They’re typically charged on a per-core basis, and the new test server needed only about a third as many cores as did the four older systems: 96 cores on the new system, compared with a total of 256 cores on the four older servers.
  • Reduced power consumption: To run the same benchmark, the new system needed only about 40% of the power required by the four older servers.
  • Lower space and cooling requirements: Space savings were calculated by comparing data-center footprint costs, taking into account the 4:1 consolidation and rack space needed. Cooling costs were factored in, too. The savings here were pretty dramatic, even if the figures were relatively low. The new system’s space costs were just $476, or 75% lower than the legacy system’s cost of $1,904.
  • Reduced maintenance costs: This was estimated with the assumption that one full-time sys admin with an annual salary of roughly $100K is responsible for 100 servers. The savings here brought a cost of over $26K for the older setup down to about $6,500 for the new, for a reduction of 75%.

Implicit in the results, though not actually calculated, is the way these reductions could also free up funding, floor space and other resources that organizations can then use for new AI systems.

So if your customers are grappling with finding new resources for AI, tell them about these test results. Upgrading to servers based on the latest processors could be the answer.

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Research roundup: Edge computing, supply chain, AI sentiment, back to the office

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Research roundup: Edge computing, supply chain, AI sentiment, back to the office

Catch up on the latest IT market research, forecasts, surveys and more.

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Spending on edge computing is rising. Supply chains are getting automated. AI sentiment depends on who you ask. And back-to-the-office strategies will require new, integrated technology.

That’s some of the latest from leading IT market researchers, analysts and pollsters. And here’s your Performance Intensive Computing research roundup.

Edge Computing: Hot

How hot? Well, global spending on edge solutions, set to approach $261 billion this year, will grow at a compound annual growth rate (CAGR) of nearly 14%, reaching $380 billion by 2028, predicts market watcher IDC.

"Most industries benefit from the ability to process data closer to the source, leading to faster decision-making, improved security and cost savings,” says IDC researcher Alexandra Rotaru. Those industries, she adds, include retail, industrial manufacturing, utilities, high tech, healthcare and life sciences.

Retail and services will account for nearly 28% of global spending on edge solutions this year, making it the leading vertical sector for edge spending, IDC expects. Use cases for this sector include video analytics, real-time carrier performance, and optimized operations.

Edge computing’s fastest-growing sector over the next five years will be financial services, IDC says. That sector’s use cases—including augmented fraud analysis—should spur its edge-solutions spending to exceed a CAGR of 15%.

Not Your Father’s Supply Chain

If you think supply chain managers still rely on clipboards and spreadsheets, think again. There’s a brave new world of supply chain technology that includes agentic AI, ambient invisible intelligence and an augmented connected workforce, according to a new report from IT advisors Gartner. If you don’t know what those are, read on.

Supply chain managers have been under pressure since at least the pandemic. Now they’re looking to boost productivity, gain value from digital investments, and adopt new and innovative operating models. To do all this, Gartner says, they’ll adopt advanced supply-chain tech, including:

  • Agentic AI: The term “agentic” basically means AI systems that can make decisions and solve problems autonomously—that is, without human intervention. Supply chains can use it to adjust stock levels based on realtime demand forecasts.
  • Ambient Invisible Intelligence: This technology provides real-time visibility into end-to-end supply chains with small, inexpensive smart tags and sensors. It’s especially useful for monitoring food and other perishables.
  • Augmented connected workforce: By digitizing standard operating procedures, this technology aims to fill skills gaps in the supply chain workforce.
  • Decision Intelligence: Decisions are being automated by this combo of decision modeling, AI and analytics. The technology can also be used to improve the quality of automated decisions over time.

AI Sentiment? Depends on Who You Ask

Is artificial intelligence a positive force leading to innovation, higher productivity and better decisions? Or a negative leading to unemployment and inaccurate results? As a slew of recent surveys show, it depends on who you ask:

  • Nearly three-quarters (72%) of small-business owners have a positive view of AI, finds a survey of 500 business owners by Paychex. Of those small-biz owners now using AI, 66% say it’s improved productivity. Others say they’ve enjoyed AI-driven cost savings (cited by 44%), revenue growth (40%) and improved recruiting (35%).
  • The same percentage (72%) of C-suite executives say their companies have faced challenges when adopting Generative AI, according to a survey conducted by Writer, a GenAI provider. These challenges include internal power struggles, poor return on investment (ROI), underperforming tools, and clashing perspectives among executives and employers.
  • AI can help humans overcome our biases, say nearly half the 2,000 Americans and Canadians recently polled by AI litigation platform Alexi. In addition, nearly three-quarters of them (72%) also support increasing AI literacy in school curriculums by 2026. 
  • Just over half of U.S. workers (52%) say they’re worried about the future impact of AI, and nearly a third (32%) fear AI will lead to fewer job opportunities for them in the future, finds a Pew Research Center survey of nearly 5,300 employed U.S. adults. Only 6% of those Pew surveyed believe workplace AI will lead to more job opportunities for them in the long term.

Back to the Office? Consolidate Tech

Still working from home? Maybe not for long. About one in three businesses (34%) plan to increase office attendance, according to a survey of 200 business executives worldwide conducted by Eptura, a provider of workplace systems.

However, the transition back to the office isn’t always going smoothly, and Eptura says one reason is disconnected technology. The company’s survey finds that half the respondents (50%) manage workplace operations with an average of 17 standalone technologies.

To make sense of this proliferation of systems, formats and dashboards, companies are turning to human intervention. About a third of organizations Eptura surveyed (37%) say they use 11 or more full-time employees to collate, analyze and report on workplace data.

The solution, Eptura says, will come with a unified operational approach with integrated technology across the enterprise. To get there, companies may need to consolidate operational data; implement AI to enhance the workforce experience (planned by 77% of respondents); and hire a new digital workplace leader, a move already in the works at three-quarters of those surveyed.

 

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Research Roundup: IT & cloud infrastructure spending rise, tech jobs stay strong, 2 security threats worsen

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Research Roundup: IT & cloud infrastructure spending rise, tech jobs stay strong, 2 security threats worsen

Catch up on the latest IT industry trends and statistics from leading market watchers and analysts.

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Three of every four CFOs plan to increase their organizations’ IT spending this year. Spending on cloud infrastructure services rose 20% last year. Unemployment among IT workers is lower than the national average. And two types of cyber attacks are bigger threats than ever.

That’s some of the latest from leading IT industry watchers and researchers. And here’s your Performance Intensive Computing roundup.

CFOs: More IT Spending

If it’s true that a rising tide lifts all boats, you might prepare to set sail now. A new survey finds that a majority of corporate CFOs plan to boost their technology budgets this year.

The survey, conducted this past fall by research group Gartner, reached just over 300 CFOs and other senior finance leaders. Gartner published its findings this month, and they include:

  • Over three-quarters of CFOs surveyed (77%) plan to boost spending in the technology category this year.
  • Nearly half the CFOs (47%) plan to increase technology spending by 10% or more this year compared with last year.
  • Nearly a third (30%) plan to increase technology spending by 4% to 9% year-on-year.
  • And fewer than one in 10 CFOs (9%) plan to decrease technology spending this year.

Cloud Infrastructure: Spending Rises

One of those lifted ships: cloud infrastructure services.

In the fourth quarter of 2024, global spending on these services rose 20% year-on-year, according to new metrics from market watcher Canalys.

Global spending for the full year also rose 20%, Canalys said. Spending on cloud infrastructure services hit $321.3 billion last year, up from $267.7 billion in 2023.

The key driver of the growth? That would be AI. The technology “significantly accelerated” cloud adoption, Canalys says.

Looking ahead, Canalys expects global spending on cloud infrastructure services this year to rise by another 19%.

Tech Employment: Mostly Strong

Also on an upswing: technology employment.

New figures from the U.S. Bureau of Labor show that across all sectors of the U.S. economy, tech occupations grew by about 228,000 jobs.

Within the tech industry alone, the picture was more mixed. More than 13,700 jobs were filled in IT services and software development, but in telecom, 7,900 workers lost their jobs.

Tech is still a good industry to work in. The industry’s unemployment rate in January was 2.9%, compared with a national rate of 4%.

“Tech hiring activity was solid across the key categories,” says Tim Herbert, chief research officer at CompTIA, an industry trade group. “Employers continue to balance the need for foundational tech talent and skills with the push into next-gen fields.”

Security: Phishing, DDoS Both Worsen

Two kinds of cyber threats are getting worse:

  • The number of phishing attempts blocked worldwide last year by Kaspersky rose 26% over the previous year.
  • Distributed Denial of Services (DDoS) attacks increased by 82% last year, according to a new report from Zayo Group.

Kaspersky, a cybersecurity and digital privacy company, says it blocked more than 893 million phishing attempts last year, up from 710 million in 2023.

In many instances, the attackers mimicked the websites and social media feeds of well-known brands, including Airbnb, Booking and TikTok. Others falsely presented product giveaways from celebrities. In one, actress Jennifer Aniston was falsely shown promoting a giveaway of 10,000 laptop computers — a giveaway that did not exist.

Separately, Zayo Group, a provider of communications infrastructure, has published its biannual DDoS insights report, and the findings aren’t pretty. The attack volume rose from 90,000 incidents in 2023 to 165,000 incidents last year.

In a DDoS attack, the bad guys make a machine or network resource unavailable by disrupting the services of a host connected to a network. Often they do this by flooding the target system with requests, overloading the system and preventing requests that are legit from being fulfilled

In one worrisome change, the bad guys are increasing the scale of their DDoS attacks by using large botnets, compromised IoT devices and AI.

“The sophistication of DDoS attacks continues to grow,” says Max Clauson, a senior VP at Zayo. “Cybercriminals are finding ways to exploit cloud services, higher-bandwidth availability, and new vulnerabilities in software and network protocols.”

Also, Zayo finds the targets of DDoS attacks are shifting:

  • Telecom is still the most targeted sector, representing 42% of all observed incidents. But that’s down from 48% in 2023.
  • Attacks on the finance industry grew. In 2023 finance represented just 3.5% of all observed instances. In 2024 that doubled to 7%.
  • In healthcare, the total number of DDoS attacks more than tripled from 2023 to 2024, rising by a whopping 223%.

 

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

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

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

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

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

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

Under the Hood

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

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

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

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

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

Converged Computing

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

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

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

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

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

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

Supermicro Servers

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

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

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

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

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

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

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Research Roundup: AI edition

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Research Roundup: AI edition

Catch up on the latest AI trends spotted by leading IT market watchers.

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Spending on artificial intelligence infrastructure is exploding. So is spending on AI for supply chains. But disappointing results on early GenAI tests is causing some CIOs to worry about the ROI.

That’s some of the latest intelligence from leading IT market watchers and researchers. And here’s your research roundup.

AI Infrastructure: $100B and Beyond

Behind every AI implementation is the need for high-end infrastructure. And spending on this type of equipment is expected to grow rapidly.

Market watcher IDC predicts that global spending on AI infrastructure will exceed $100 billion by 2028. Last year this spending totaled roughly $70 billion.

The AI infrastructure market has enjoyed double-digit growth for the last four and a half years, driven primarily by investments in servers, IDC says. In the first half of 2024, servers accounted for nearly 90% of all AI infrastructure spending.

Covered in IDC’s definition of AI infrastructure are servers and storage used for AI platforms, AI and AI-enabled applications, and AI applications development & deployment software.

AI Servers: $200B

That could be only the tip of the iceberg. Gartner researchers now predict worldwide spending on AI-optimized servers will top $200 billion this year. They also say that’s more than double what’s expected to be spent on more traditional servers.

About 70% of that $200 billion will be spent not by end users, but instead by big IT services companies and hyperscalers, Gartner expects. By 2028, the hyperscalers—large cloud providers including AWS, Google Cloud and Microsoft Azure—will operate AI-optimized servers collectively worth about $1 trillion.

Worth noting: This AI spending is part of an even bigger trend. Gartner predicts overall IT spending will rise this year by nearly 10%, reaching a global total of $5.6 trillion.

AI for Supply Chain: Huge

The use of AI in supply chain management is growing at a super-fast compound annual growth rate (CAGR) of 30%. This spending will jump from $3.5 billion in 2023 to $22.7 billion by 2030, according to a new forecast from ResearchAndMarkets.

Supply chain health became a major concern during the pandemic. Now companies realize they need supply chains that are resilient, adaptable and efficient. And AI can help.

The fastest-growing supply chain sector for AI is expected to be forecasting. There, AI can be used to predict future demand for various products. These forecasts can then be used by manufacturers and their partners to optimize inventories and production plans.

GenAI: Where’s the Value?

This year, Generative AI will fail to create its expected value, predicts ABI Research.

Many GenAI proof-of-concept trials have been disappointing, with failure rates as high as 80% to 90%, ABI says. This is seriously cooling some red-hot expectations.

As a result, some enterprise CIOs will turn away from GenAI. Instead, ABI expects, they’ll adopt more traditional AI approaches that solve business problems and deliver a clearer ROI.

ABI’s jaundiced view of GenAI gets some support from Gartner. In its 2025 IT market forecast, Gartner says GenAI is sliding toward the “trough of disillusionment.”

That phrase comes from Gartner’s Hype Cycle. It states that most innovations progress through a pattern of over-enthusiasm and disillusionment, followed by eventual productivity.

While businesses may still be searching for GenAI’s ROI, a growing number of teens are certainly finding it. About one in four U.S. teens (26%) used ChatGPT for schoolwork last year, according to a new Pew Research Center survey. That’s double the percentage of teens who did so in 2023.

AI’s New Mandate: Trust

A much more positive view comes from Accenture’s 25th annual technology vision report. The consulting firm's report says AI is accelerating across enterprises faster than any other prior technology.

What’s more, nearly 70% of executives polled by Accenture said they believe AI brings new urgency to re-invention and how tech systems and processes are designed, built and run.

An even bigger group, 80% of those polled, told Accenture that natural language processing (NLP) will increase collaboration between humans and robots.

One possible barrier to AI progress is the matter of trust. More than 75% of the executives polled by Accenture believe AI’s true benefits must be built on a foundation of trust.

Accenture CEO Julie Sweet agrees. “Unlocking the benefits of AI,” she says, “will only be possible if leaders seize the opportunity to inject and develop trust in its performance and outcomes.” 

 

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Tech Explainer: CPUs and GPUs for AI training and inferencing

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Tech Explainer: CPUs and GPUs for AI training and inferencing

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

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

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

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

Two Steps to AI

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

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

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

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

AI Training

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

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

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

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

AI Inferencing

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

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

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

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

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

Enterprise Grade

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

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

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

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

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

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