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Research Roundup: Server Sales Rise, AI Helps Customer Service, Social Media is for Adults, LLMs Know What You Need

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Research Roundup: Server Sales Rise, AI Helps Customer Service, Social Media is for Adults, LLMs Know What You Need

Catch up on the latest research from leading technology analysts and market watchers.

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Servers set a new sales record. AI is augmenting customer service workers, rather than replacing them. Most adults use social media. And finely-tuned LLMs can identify customer needs better than a human professional.

That’s some of the latest from leading tech analysts and market researchers. And here’s your research roundup.

Servers Set Record Sales

Server sales set a new record in this year’s third quarter. Global sales of these systems rose 61% year-on-year, reaching an all-time quarterly high of $112.4 billion, according to market watcher IDC.

Of that total, $76.3 billion came from x86 servers, representing nearly 70% of the total. Compared with the year-earlier quarter, that marked an increase of about 33%, IDC says.

A much bigger jump came from sales of non-x86 servers. Those sales rose 197% year-on-year, reaching a worldwide total in Q3 of $36.2 billion.

Another fast-growing sector is servers with embedded GPUs. In Q3, sales of these AI-ready servers rose nearly 50% year-on-year, IDC says. Systems with GPUs now represent more than half of all server market revenue.

Growth of server sales in the quarter varied by region. The biggest sales rise was in the United States, where Q3 server sales rose nearly 80% year-on-year, IDC says. Other fast growers included Canada (with server sales up 70%), China (38%) and Japan (28%).

AI Helps Customer Service

Artificial intelligence is mainly augmenting, rather than replacing, customer-service workers, finds a new survey.

The survey, conducted by technology analyst firm Gartner, finds that only one in five customer-service leaders (20%) have reduced staffing due to AI. Even better, about half the respondents (55%) said their staffing levels have remained stable, even as AI has enabled them to handle higher customer-service volume.

AI can even lead to the creation of new jobs. Four in 10 respondents to the Gartner survey (42%) said their organizations are hiring specialized roles to support AI deployment and management. These new roles include AI strategists, conversational AI designers and automation analysts.

The survey, conducted by Gartner in October, collected responses from 321 customer service and support leaders.

Social Media for Adults? Yes!

If anyone tells you social media is strictly for kids, set them straight. A poll conducted by Pew Research finds the vast majority of U.S. adults use social media.

Specifically, YouTube is used by over eight in 10 U.S. adults (84%), the survey finds. And Facebook is used by seven in 10 U.S. adults (71%).

Another social media platform popular with grownups is Instagram. The Pew survey finds it’s used by fully half of U.S. adults.

Plenty of other social media sites are used by U.S. adults, too, if in smaller numbers. They include TikTok (used by 37%), WhatsApp (32%), Reddit (26%), Snapchat (25%) and X (21%).

The survey was conducted by Pew earlier this year, and it drew responses from 5,022 U.S. adults.

The LLM Knows What You Want

Large language models can identify customer needs better than an expert, finds a recent research paper from MIT.

To conduct their experiment, the paper’s three co-authors—John Hauser of MIT Sloan, Artem Timoshenko of Northwestern’s Kellogg School, and MIT pre-doc Chengfeng Mao—fine-tuned an LLM using studies supplied by a market research firm.

They then compared the output of their finely-tuned LLM with that of human analysts and untrained LLMs. The test asked consumers about their preferences for wood stains. In all, consumers were asked about eight primary customer needs and 30 secondary needs.

The results: The fine-tuned LLM identified 100% of the customers’ primary and secondary needs. By comparison, the human analysts missed a few, identifying 87% of the primary needs and 80% of the secondary needs.

That said, actually understanding the needs of wood-stain customers remains a job for humans, says Hauser, a professor of marketing at MIT Sloan.

“If you have to pull customer needs out of a story, the supervised fine-tuned LLM can do it,” he says. “But if you ask an LLM what customers care about when staining a deck, its answers are superficial.”

Want to learn more? Read the full paper.

 

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Tech Explainer: What are CPU Cores, Threads, Cache & Nodes?

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Tech Explainer: What are CPU Cores, Threads, Cache & Nodes?

Today’s CPUs are complex. Find out what the key components actually do—and why, in an age of AI, they still matter.

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In the age of artificial intelligence, CPUs still matter. A central processor’s parts—cores, threads, cache and nodes—are as important as any AI accelerator.

But what exactly do those CPU parts do? And why, in an age of AI, do they still matter?

These questions are easy to overlook given AI’s focus on the GPU. To be sure, graphical processors are important for today’s AI workloads. But the humble CPU also remains a vital component.

If the GPU is AI’s turbocharger, then the CPU is the engine that makes the whole car go. As Dan McNamara, AMD’s GM of compute and enterprise AI business, said at the recent AMD Financial Analysts Day, “AI requires leadership CPUs.”

So here’s a look at the most important components of today’s data-center x86 CPUs. And an explanation of why they matter.

Cores: Heavy Lifting

The central processing unit is the brain of any PC or server. It reads instructions, does the complex math, and coordinates the system’s every task.

Zooming into the architecture of a CPU, it’s the individual cores that put the “PU” in CPU. Each fully independent processing unit can run its own task, virtual machine (VM) or container.

Modern enterprise-class CPUs such as AMD’s EPYC 9005 Series offer anywhere from 8 to 192 cores each. They operate at up to 5GHz.

These cores are built using AMD’s ‘Zen’ architecture. It’s a fundamental core design that offers enhancements vital to data centers, including improved instructions-per-clock (IPC), branch prediction, caches and efficiency.

Performance like that is a must-have when it comes to a data center’s most demanding tasks. That’s especially true for compute-intensive database operations and API-heavy microservices such as authentication, payment gateways and search.

Having more cores in each CPU also enables IT managers to run more workloads per server. That, in turn, helps organizations lower their hardware and operating costs, simplify IT operations, and more easily scale operations.

Threads: Helping Cores Do More

A modern CPU core needs to multitask, and that’s where having multiple threads is essential. A single CPU core with two threads can juggle two tasks by switching between them very quickly. In a CPU with a high core count, a productivity-multiplier like that becomes exponentially more effective.

This capability delivers two important benefits. One, it helps ensure that each CPU core stays productive, even if one task stalls. And two, it boosts the CPU’s overall output.

For example, the AMD EPYC 9965 processor boasts 192 cores with a total of 384 threads. That kind of multitasking horsepower helps smooth request handling for web services and microservices. It also improves VM responsiveness and helps AI workloads run more efficiently under heavy loads.

Cache: Speedy but Short-Term Memory

The unsung heroes of CPU design? That would be cache.

The main job of a CPU cache is to help the cores juggle data with low latency. Remember, less latency is always better.

As a result, CPU cache enables databases to run faster, improve VM density and reduce latency.

Your average CPU cache is arranged in three layers:

  • L1 cache is very small and very fast. Each core has its own L1 cache, which holds around 32 KB of instructions and data. The L1 cache sends that data to a register— a tiny, ultra-fast storage location the core uses to acquire the data used for calculations.
  • L2 cache is also exclusive to each core. At around 1MB, this cache is bigger than L1, but it’s also a little slower. L2 cache holds any data that doesn’t fit in the L1 cache. Working together, the L1 and L2 caches can quickly pass data back and forth until ultimately, the L1 cache passes the data to the core.
  • L3 cache is shared by all cores in a CPU, and it acts as a buffer for passing data between the CPU and main memory. Sizes vary widely. In an 8-core AMD EPYC processor, the L3 cache is just 64MB. But in AMD’s 192-core CPU, the L3 Cache gets as big as 348MB.

Some AMD CPUs, including the AMD EPYC 9845, also include a 3D V-Cache. This AMD innovation stacks an additional cache on top of the L3 cache (hence the name 3D). Stacking the two caches vertically adds storage without increasing the overall size of the CPU.

The added 3D V-Cache also improves performance for workloads that benefit from a larger cache. Examples include scientific simulations and big data.

Nodes: Power & Efficiency

When it comes to CPU nodes, smaller is better. A smaller node size can deliver benefits that include lower power consumption, increased efficiency, and more compute performance per watt.

Nodes are expressed in nanometers (nm)—that’s one billionth of a meter—which describe the tiny size of transistors on a chip.

The latest AMD EPYC 9005-series architectures, ‘Zen 5’ and ‘Zen 5c,’ are built on 4nm and 3nm nodes, respectively.

Each of these individual performance gains may seem tiny when considered on a per-chip basis. But in the aggregate, they can make a huge difference. That’s especially true for resource-intensive workloads such as AI training and inferencing.

Coming Soon: Smaller, Faster CPUs

AMD’s near-term roadmap tells us we can expect its AMD EPYC CPUs to continue getting smaller, faster and more efficient.

Those manufacturing and performance gains will likely come from more cores per CPU socket, bigger and more efficient caches. Earlier this year, AMD said the next generation of its EPYC processors, codenamed Venice, will be brought up on TSMC’s advanced 2nm process technology.

Enterprises will be able to parlay those improvements into better performance under multi-tenant loads and reduced latency overall. The latter is particularly vital for modern operations.

The bottom lie: Denser CPU cores mean big business, both for processor makers such as AMD and the server vendors such as Supermicro that rely on these CPUs.

Denser CPUs are also vital for enterprises now transforming their data centers for AI. Because adding space is so slow and costly, these organizations are instead looking to pack more compute power per rack. Smaller, more powerful CPUs are an important part of their solution.

Minimum CPU size with maximum power? It’s coming soon to a data center near you.

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Check out Supermicro’s new AMD GPU-powered server—it’s air-cooled

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Check out Supermicro’s new AMD GPU-powered server—it’s air-cooled

Supermicro’s new 10U server is powered by AMD’s EPYC CPUs and Instinct MI355X GPUs. And it’s kept cool by nearly 20 fans.

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What do you do if you need GPU power for AI and other compute-intensive workloads, but lack the infrastructure for liquid cooling?

Supermicro has the answer. The company just introduced a 10U server powered by AMD Instinct MI355X GPUs that’s air-cooled.

The new server, showcased at the recent SC25 conference in St. Louis, is Supermicro model AS -A126GS-TNMR.

Each server is powered by the customer’s choice of dual AMD EPYC 9004 or 9005 Series CPUs with up to 384 cores and 768 threads. The system also features a total of eight AMD Instinct MI355X onboard OAM GPU accelerator modules, which are air-cooled. (OAM is short for OCP Accelerator Module, an industry-standard form factor for AI hardware.) In addition, these accelerated GPU servers offer up to 6TB of DDR5 system memory.

While the systems are air-cooled with up to 19 heavy-duty fans, there’s no penalty in terms of cooling capacity. In fact, AMD has boosted the GPU’s thermal design point (TDP)—the maximum amount of heat a server’s cooling system can handle—from 1000W to 1400W.

Also, compared with the company’s air-cooled 8U server based on AMD Instinct MI350X GPUs, the 10U server offers up to double-digit more performance, according to Supermicro . For end users, that means faster data processing.

More Per Rack

The bigger picture: Supermicro’s new 10U option lets customers unlock higher performance per rack. And with their choice of 10U air cooling or 4U liquid cooling, both powered by the latest AMD EPYC processors.

Supermicro’s GPU solutions are designed to offer maximum performance for AI and inference at scale. And they’re intended for use by both cloud service providers and enterprises.

Are your customers looking for a GPU-powered server that’s air cooled? Tell them about these new Supermicro 10U servers. And let them know that these systems are ready to ship now.

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Tech Explainer: What’s new in AMD ROCm 7?

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Tech Explainer: What’s new in AMD ROCm 7?

Learn how the AMD ROCm software stack has been updated for the era of AI.

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While GPUs have become the digital engines of our increasingly AI-powered lives, controlling them accurately and efficiently can be tricky.

That’s why, in 2016, AMD created ROCm. Pronounced rock-em, it’s a software stack designed to translate the code written by programmers into sets of instructions that AMD GPUs can understand and execute perfectly.

If the GPUs in today’s cutting-edge AI servers are the orchestra, then ROCm is the sheet music being played.

AMD introduced the latest version, ROCm 7.0, earlier this fall. Version 7.0 is designed for the new world of AI.

How ROCm works

ROCm is a platform created by AMD to run programs on its AI-focused GPUs, the Instinct MI350 Series accelerators. AMD calls the latest version, ROCm 7.0, an AI-ready powerhouse designed for performance, efficiency and productivity.

Providing that kind of facility is a matter of far more than just simple software. ROCm is actually an expansive collection of tools, drivers and libraries.

What’s in the collection? The full ROCm stack contains:

  • Drivers that enable a computer’s operating system to communicate with any installed AMD GPUs.
  • The Heterogeneous Interface for Portability (HIP), a coding system for users to create and run custom GPU programs.
  • Math and AI libraries including specialized tools like deep learning operations, fast math routines, matrix multiplication, and tensor ops. These AI building blocks are pre-built to help developers accelerate production.
  • Compilers that turn code into GPU instructions.
  • System-management tools that developers can use to debug applications and optimize GPU performance.

Help Me, GPU

The latest version of ROCm is purpose-built for generative AI and large-scale AI inferencing and training. While developers rely on GPUs for parallel processing, performing many tasks at once, GPUs are general-purpose processors. To achieve the best performance for AI workloads, developers need a software bridge that turns their high-level code into GPU-optimized instructions. That bridge is ROCm.

ROCm lets developers run AI frameworks that include PyTorch effectively on AMD GPUs. ROCm converts application code into instructions designed for the hardware. In this way, ROCm helps organizations improve performance, scale workloads across multiple GPUs, and meet increasing demand without sacrificing reliability.
 
For demanding AI workloads such as those using Mixture of Experts (MoE) models, ROCm is essential for execution. MoE models activate only a small group of expert networks for each input, resulting in sparse workloads that are efficient, but hard to schedule. ROCm ensures that GPUs can perform these sparse operations at scale, maintaining high throughput and accuracy across clusters.
 
In other words, ROCm provides the tools and runtime to make even the most complex GPU workloads run efficiently. It connects AI developers with the hardware that supports their applications.
 
That’s important. While increased demand is what every enterprise wants, it still brings challenges that leave little room for mistakes.
 
Open Source Power

But wait, there's more. AMD ROCm has another clever trick up its sleeve: open-source integration.

By using popular open-source frameworks, ROCm lets enterprises and developers run large-scale inference workloads more efficiently. This open source approach also empowers the same organizations and developers to break free of proprietary software and vendor-locked ecosystems.

Free from those dependencies, users can scale AI clusters by deploying commodity components instead of being locked into a single vendor’s hardware. Ultimately, that can lead to lower hardware and licensing costs.

This approach also empowers users to customize their AI operations. In this way, AI systems can be developed to better suit the unique requirements of an organization’s applications, environments and end users.

Another Layer

While ROCm serves the larger market, the recent release of AMD’s new Enterprise AI Suite shows the company’s commitment to developing tools specifically for enterprise-class organizations.

AMD says the new suite can to take enterprises from bare metal server to enterprise-ready AI software in mere minutes.

To accomplish this, the suite provides four additional components: solution blueprints, inference microservices, AI Workbench, and a dedicated resource manager.

These tools are designed to help enterprises better scale their AI workloads, predict costs and capacity, and accelerate time-to-production.

Always Be Developing

Along with these product releases, AMD is being perfectly clear about its focus on AI development. At the company’s recent Financial Analyst Day, AMD CEO Lisa Su explained that over the last five years, the cost of AMD’s AI-related investments and acquisitions has topped $100 billion. That includes building up a staff of some 25,000 engineers.

Looking ahead, Su told financial analysts that AMD’s data-center AI business is on track to draw revenue in the “tens of billions of dollars” by 2027. She also said that over the next three to five years, AMD expects its data-center AI revenue to enjoy a compound annual growth rate (CAGR) of over 80%.

AMD’s roadmap points to updates that will focus on further boosts to performance, productivity and scalability. The company may accomplish these gains by offering more streamlined build and packaging systems, more optimized training and inferencing, and broader hardware support. It’s also reasonable to expect improved virtualization and multi-tenant support.

That said, if you want your speculation about future AI-centric ROCm improvements to be as accurate as possible, your best bet may be to ask an AI chatbot…powered by Supermicro and AMD, of course.

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Research Roundup: IT budgets, server sales, IoT analytics, AI at work

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Research Roundup: IT budgets, server sales, IoT analytics, AI at work

Catch up on the latest intelligence from leading IT market watchers and pollsters.

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Global IT spending is on track to top $6 trillion next year. Server sales in this year’s second quarter nearly doubled. IoT is gaining real-time analytics. And more people say they use AI at work.

That’s the latest from leading IT market watchers and pollsters. And here’s your Research Roundup.

IT Spending Forecast

IT spending worldwide will rise by nearly 10% next year, topping $6 trillion for the first time, predicts Gartner.

The industry watcher says a big driver of spending is the rise of Generative AI. It’s bringing new features and functionality, and these cost more money, Gartner says. It predicts global spending on software will total $1.43 trillion in 2026 — that's 15% higher than it will be this year.

Other fast-growing sectors, Gartner predicts, will be data center systems (with 2026 spending growth projected at 19%), IT services (8.7%) and devices (6.8%).

But IT buyers aren’t waiting for 2026. “A significant budget flush is anticipated before the end of the year,” says Gartner analyst John-David Lovelock.

Q2 Server Sales Nearly Doubled

In the second quarter, global spending on servers nearly doubled, rising by 97.3% year-on-year, according to IDC. The market watcher attributes this rise to what it calls a “mass deployment” of GPUs.

Server sales by units were also strong. In Q2, they rose by nearly 16% from the year-ago quarter, IDC says.

Buyers of servers generally fall into one of two camps: either large cloud service providers (CSPs) or end-user organizations. Looking ahead, IDC expects CSPs to continue expanding their infrastructure through at least 2029. End users, by contrast, will work to balance spending between their on-premises deployments and cloud services they buy from others.

Looking ahead to the next five years, IDC forecasts global server sales rising by a compound annual growth rate (CAGR) of nearly 29%. Shorter term, IDC predicts global server sales will rise from $455.4 billion this year to $565.9 billion next year, a one-year increase of just over 24%.

IoT Going Real-Time

For Internet of Things (IoT) deployments, the dominant technology priority is real-time analytics.

So finds a survey conducted by market watcher Omdia that reached over 600 enterprises in 10 countries. Fully 82% of organizations surveyed said they either use real-time data processing capabilities now or plan to soon.

“Strong adoption of 5G and edge computing are laying the groundwork for real-time analytics,” says John Canali, an Omdia market analyst. “We’re seeing IoT evolve from simple data collection to process automation.”

All this IoT creates a lot of data. To process it all, over 75% of enterprises are supplementing their IoT systems with additional services such as AI and machine learning, Omdia finds. Their larger goal: Transforming business operations from reactive to predictive.

Will there be a payoff, and if so, how quick? Nearly all respondents to the Omdia survey (95%) said they expect to see measurable benefits from IoT within two years.

More People Using AI at Work

Artificial intelligence is slowly but surely working its way into real life, finds a survey by the Pew Research Center. The survey finds that roughly one in five U.S. workers (21%) now use AI in their jobs. That’s up from 16% a year ago.

Pew conducted the survey in September, connecting with respondents both online and by phone. Responses were collected from 8,750 randomly selected U.S. adults.

As the survey shows, some things about AI haven’t changed. A year ago, Pew found that a tiny 2% of U.S. adults were doing all or most of their work with AI. This year it was also 2%.

Similarly, last year about two-thirds of Pew’s respondents (65%) said they don’t use AI much or at all while on the job. That response rate was also unchanged this time.

One factor has changed: Fewer people admit to a lack of AI knowledge. A year ago, 17% of U.S. adults surveyed by Pew said they had not heard or read much about AI. This year, that group shrank to 12%.

Another change: More people think at least some parts of their job could be done by AI. A year ago, 31% of respondents agreed with that statement; this year, that rose to 36% of all.

 

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Tech Explainer: What’s liquid cooling? And why might your data center need it now?

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Tech Explainer: What’s liquid cooling? And why might your data center need it now?

Liquid cooling offers big efficiency gains over traditional air. And while there are upfront costs, for data centers with high-performance AI and HPC servers, the savings can be substantial. Learn how it works.

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Increasingly resource-intensive AI workloads are creating more demand for advanced data center cooling systems. Today, the most efficient and cost-effective method is liquid cooling.

A liquid-cooled PC or server relies on a liquid rather than air to remove heat from vital components that include CPUs, GPUs and AI accelerators. The heat produced by these components is transferred to a liquid. Then the liquid carries away the heat to where it can be safely dissipated.

Most computers don’t require liquid cooling. That’s because general-use consumer and business machines don’t generate enough heat to justify liquid cooling’s higher upfront costs and additional maintenance.

However, high-performance systems designed for tasks such as gaming, scientific research and AI can often operate better, longer and more efficiently when equipped with liquid cooling.

How Liquid Cooling Works

For the actual coolant, most liquid systems use either water or dielectric fluids. Before water is added to a liquid cooler, it’s demineralized to prevent corrosion and build-up. And to prevent freezing and bacterial growth, the water may also be mixed with a combination of glycol, corrosion inhibitors and biocides.

Thus treated, the coolant is pushed through the system by an electric pump. A single liquid-cooled PC or server will need to include its own pump. But for enterprise data center racks containing multiple servers, the liquid is pumped by what’s known as an in-rack cooling distribution unit (CDU). Then the liquid is distributed to each server via a coolant distribution manifold (CDM).

As the liquid flows through the system, it’s channeled into cold plates that are mounted atop the system’s CPUs, GPUs, DIMM modules, PCIe switches and other heat-producing components. Each cold plate has microchannels through which the liquid flows, absorbing and carrying away each component’s thermal energy.

The next step is to safely dissipate the collected heat. To accomplish this, the liquid is pumped back through the CDU, which sends the now-hot liquid to a mechanism that removes the heat. This is typically done using chillers, cooling towers or heat exchangers.

Finally, the cooled liquid is sent back to the systems’ heat-producing components to begin the process again.

Liquid Pros & Cons

The most compelling aspect of liquid cooling is its efficiency. Water moves heat up to 25 times better than air while using less energy to do it. In comparison with traditional air, liquid cooling can reduce cooling energy costs by up to 40%.

But there’s more to the efficiency of liquid cooling than just cutting costs. Liquid cooling also enables IT managers to move servers closer together, packing in more power and storage per square foot. Given the high cost of data center real estate, and the fullness of many data centers, that’s an important benefit.

In addition, liquid cooling can better handle the latest high-powered processing components. For instance, Supermicro says its DLC-2 next-generation Direct Liquid-Cooling solutions, introduced in May, can accommodate warmer liquid inflow temperatures while also enhancing AI per watt.

But liquid cooling systems have their downsides, too. For one, higher upfront costs can present a barrier for entry. Sure, data center operators will realize a lower total cost of ownership (TCO) over the long run. But when deploying a liquid-cooled data center, they must still contend with initial capital expense (CapEx) outlays—and justifying those costs to the CFO.

For another, IT managers might think twice about the additional complexity and risks of a liquid cooling solution. More components and variables mean more things that can go wrong. Data center insurance premiums may rise too, since a liquid cooling system can always spring a leak.

Driving Demand: AI

All that said, the market for liquid cooling systems is primed for serious growth.

As AI workloads become increasingly resource-intensive, IT managers are deploying more powerful servers to keep up with demand. These high-performance machines produce more heat than previous generations. And that creates increased demand for efficient, cost-effective cooling solutions.

How much demand? This year, the data center liquid cooling market is projected to drive global sales of $2.84 billion, according to Markets and Markets.

Looking ahead, the industry watcher expects the global liquid cooling market to reach $21.14 billion by 2032. If that happens, the rise will represent a compound annual growth rate (CAGR) over the projected period of 33%.

Coming Soon: Immersive Cooling

In the near future, AI workloads will likely become even more demanding. This means data centers will need to deploy—and cool—ultra-dense AI server clusters that produce tremendous amounts of heat.

To deal with this extra heat, IT managers may need the next step in data center cooling: immersion.

With immersion cooling, an entire rack of servers is submerged horizontally in a tank filled with what’s known as dielectric fluid. This is a non-conductive liquid that ensures the server’s hardware can operate while submerged, and without short-circuiting.

Immersion cooling is being developed along two paths. The most common variety is called single-phase, and it operates similarly to an aquarium’s water filter. As pumps circulate the dielectric fluid around the servers, the fluid is heated by the server’s components. Then it’s cooled by an external heat exchanger.

The other type of immersion cooling is known as two-phase. Here, the system uses water treated to have a relatively low boiling point—around 50 C / 122 F. As this water is heated by the immersed server, it boils, creating a vapor that rises to condensers installed at the top of the tank. The vapor is there condensed to a cooler liquid, then allowed to drip back down into the tank.

This natural convection means there’s no need for electric pumps. It’s a glimpse of a smarter, more efficient liquid future, coming soon to a data center near you.

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Retail AI at the edge: Now here from Supermicro, AMD & Wobot.ai

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Retail AI at the edge: Now here from Supermicro, AMD & Wobot.ai

Retailers can now use AI to analyze in-store videos, thanks to a new system from Supermicro, AMD and Wobot.ai.

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Artificial intelligence is being adapted for specific industry verticals. That now includes retail.

Supermicro, AMD and Wobot Intelligence Inc., a video intelligence supplier, are partnering to provide retailers with a short-depth server they can use to drive AI-powered analysis of their in-store videos. With these analyses, retailers can improve store operations, elevate the customer experience and boost sales.

The new server system was recently showcased by the three partners at NRF Europe 2025, an international conference for retailers. This year’s NRF Europe was held in Paris, France, in mid-September.

The new retail system is based on a Supermicro 1U server, model AS -1115S-FWTRT. It’s a short-depth front I/O system powered by a single AMD EPYC 8004 processor.

The server’s other features include dual 10G ports, dual 2.5-inch drive bays, up to 768GB of DDR5 memory, and an 800W redundant platinum power supply. This server is air-cooled by as many as six heavy-duty fans, and it supports a pair of single-width GPUs.

Good to Go

The retail system’s video-analysis software, provided by Wobot.ai, features a single dashboard, performance benchmarking, and easy installation and configuration. It’s designed to work with a user’s existing CCTV setup.

The company’s WoConnect app helps users connect digital video recorders (DVRs) and network video recorders (NVRs) in their private network to their Wobot.ai account. The app routes the user’s camera feeds to the AI.

Target use cases for retailers include store operations, loss prevention and compliance, customer behavior and footfall analysis.

More specifically, retailers can use the system to conduct video analyses that include:

  • Zone-based analytics: Which areas of the store draw the most attention? Which products draw interaction? How do customers move through the store?
  • Heat maps and event tracking: Visualize “crowd magnets” to improve future sales.
  • Customer-path analysis: Observe which sections of the store customers explore the most, and also see where they linger.

Using the system, retailers can enjoy a long list of benefits that include accelerated checkout processes, fewer customer walkaways, fine-tuned staffing levels, and improved product placement.

For example, a chain of juice bars with nearly 145 locations in California turned to Wobot.ai for help speeding customer service and improving employee productivity. Based on its video analyses, the retailer worked with Wobot.ai to design a pilot program for 10 stores. In just three months, the pilot delivered additional revenue in the test stores equivalent to 2% to 2.5% a year.

Wobot.ai also offers its video intelligence systems to other verticals, including hospitality, food service and security.

Edgy

One important feature of the new server is that it allows retailers to run real-time AI-powered video analysis at the edge. The Supermicro server is housed in a short-depth form factor, meaning it can be run in retail sites that lack a full-fledged data center.

Similarly, the system’s AMD EPYC 8004 processor has been optimized for power efficiency—important for installations at the edge. Featuring up to 64 ‘Zen4c’ dense cores, this AMD processor is specifically designed for intelligent edge and communications workloads.

By processing the AI analysis on-premises, the new system also offers low latency and high levels of privacy. Wobot.ai says its software is scalable across literally thousands of locations.

And the software is designed to be integrated easily with retailers’ existing camera infrastructure. In this way, it offers fast time-to-value and a quick return on investment.

Do you have retail customers looking for an edge—with AI at the edge? Tell them about this new retail solution today.

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4 IT events this fall you won’t want to miss

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4 IT events this fall you won’t want to miss

Important IT industry events are coming in October and November--with lots of participation from AMD and Supermicro. 

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Summer’s over…somehow it’s already October…and that means it’s time to attend important IT industry conferences, summits and other get-togethers.

Here’s your Performance Intensive Computing preview of four top events coming this month and next.

OCP Global Summit

  • Where & when: San Jose, California; Oct. 13-16, 2025
  • Who it’s for: This event, sponsored by the Open Compute Project (OCP), is for anyone interested in redesigning open source hardware to support the changing demands on compute infrastructure. This year’s theme: “Leading the future of AI.”
  • Who will be there: Speakers this year include Vik Malyala, senior VP of technology and AI at Supermicro; Mark Papermaster, CTO of AMD; Johnson Eung, staff growth product manager in AI at Supermicro; Shane Corban, senior director of technical product management at AMD; and Morris Ruan, director of product management at Supermicro.
     
  • Fun facts: AMD is a Diamond sponsor, and Supermicro is an Emerald sponsor.

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AMD AI Developer Day

  • Where & when: San Francisco, Oct. 20, 2025
  • Who it’s for: Developers of artificial intelligence applications and systems. Workshop topics will include developing multi-model, multi-agent systems; generating videos using open source tools; and developing optimized kernals.
  • Who will be there: Speakers will include executives from the University of California, Berkeley; Red Hat AI; Google DeepMind; and OpenAI. Also speaking will be execs from Ollama, an open source platform for AI models; Unsloth AI, an open source AI startup; vLLM, a library for large language model (LLM) inference and serving; and SGLang, an LLM framework.
  • Fun facts:
    • Supermicro is a conference sponsor.
    • During the conference, winners of the AMD Developer Challenge will be announced. The grand prize winner will take home $100,000.
    • AMD, PyTorch and Unsloth AI are co-sponsoring a virtual hackathon, the Synthetic Data AI Agents Challenge, on Oct. 18-20. The first-prize winners will receive $3,000 plus 1,200 hours of GPU credits.

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AI Infra Summit

  • Where & when: San Francisco; Nov. 7, 2025
  • Who it’s for: Anyone interested in the convergence of AI innovation and scalable infrastructure. This event is being hosted by Ignite, a go-to-market provider for the technology industry.
  • Who will be there: The speaker lineup is still TBA, but is promised to include enterprise technology leaders, AI and machine learning engineers, cloud and data center architects, venture capital investors, and infrastructure vendors.
  • Fun facts:
    • This is a hybrid event. You can attend either live or online.
    • AMD and Supermicro are Stadium-level sponsors.

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SC25

  • Where & when: St. Louis, Missouri; Nov. 16-21, 2025
  • Who it’s for: The global supercomputing community, including those working in high performance computing (HPC), networking, storage and analysis. This year’s theme: “HPC ignites.”
  • Who will be there: Speakers will feature nearly a dozen AMD executives, including Rob Curtis, a Fellow in Data Center Platform Engineering; Shelby Lockhart, a software system engineer; and Nuwan Jayasena, a Fellow in AMD Research. They and other speakers will appear in panels, presentations of papers, workshops, tutorials and more.
     
  • Fun facts: SC25 will feature a series of noncommercial “Birds of a Feather” sessions that allow attendees to openly discuss topics of mutual interest.

 

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Research Roundup: Cloud infrastructure spending, AI PoCs, preemptive security, AI worries

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Research Roundup: Cloud infrastructure spending, AI PoCs, preemptive security, AI worries

Get the latest insights from leading IT researchers, industry analysts and market watchers.

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Global spending on cloud infrastructure services rose in the latest quarter by over 20%. Only about one in four AI tests in the Asia/Pacific are moving on to full production. Cybersecurity is about to become preemptive. And the rise of AI has many U.S. adults concerned.

That’s the latest from leading IT researchers, market watchers and pollsters. And here’s your research roundup.

Cloud Infrastructure Spending: Up, Up and Away

Global spending on cloud infrastructure services rose 22% year-on-year in this year’s second quarter (April, May and June), reaching a total of $95.3 billion, according to market watcher Canalys. This marks the sector’s fourth consecutive quarter of year-on-year growth topping 20%.

All that demand was driven by three main forces, Canalys says: AI consumption, revived legacy migrations, and cloud-native scale-ups.

Also during Q2, the Big Three cloud providers—Amazon Web Services, Google Cloud and Microsoft Azure—held their collective 65% share of the market. What’s more, customer spending with the Big Three increased in the quarter by 27% year-on-year, Canalys says.

Customer demand for AI is shifting, too. “An increasing number of enterprises are seeking the capability to switch between different AI models based on specific business requirements,” says Canalys senior analyst Yi Zhang. Their goal: an optimal balance of performance, cost and application fit.

AI PoC to Production? Not Many Yet

Organizations in the Asia/Pacific region are experimenting with AI, but fewer than one in four of their AI applications (23%) have moved from proof-of-concept (PoC) to production, finds industry analyst IDC.

One result, says IDC researcher Abhishek Kumar: “Many Asian businesses are reassessing how to launch and scale AI.”

Part of this reassessment involves a shift to new AI approaches based on end-to-end platforms. However, moving to these approaches won’t be easy, Kumar says. Organizations need to understand not only each vendor’s approach, but also how the proposed systems align with their own organization’s requirements.

IDC recommends that organizations start thinking of their AI suppliers as partners, not just providers. Though we’ve heard that before, this time it’s different: AI is likely to dramatically reshape entire workflows.

Cybersecurity’s Future: Preemptive

Detection and response are currently the main cybersecurity techniques, but that’s about to change, predicts Gartner. The research firm believes that by 2030, over half of all cybersecurity spending worldwide will instead go to technologies that are preemptive.

“Preemptive cybersecurity will soon be “the new gold standard,” asserts Gartner VP Carl Manion.

Why the shift? Because detection/response-based cybersecurity will no longer be enough to keep assets safe from AI-enabled attackers, Manion says.

As part of this shift, organizations will move away from one-size-fits-all security solutions, instead adopting approaches that are more targeted. These could include security systems for specific verticals, such as healthcare and finance; specific application types, such as industrial control systems; and specific threat actor methods, such as supply-chain attacks.

Preemptive cybersec could also include what are known as autonomous cyber immune systems (ACIS). Like a biological immune system, an ACIS will be able to both detect attacks and fight them off.

Resistance to this shift will be futile, Manion says. Organizations that stick with older detection and response security systems, will be exposing their products, services and customers to what he calls “a new, rapidly escalating level of danger.”

AI has U.S. Adults Fretting

The rise of artificial intelligence has U.S. adults concerned, finds a new poll by Pew Research. A majority of respondents say they believe the rising use of AI will worsen people’s ability to think creatively, form meaningful relationships, make difficult decisions and solve problems.

The poll, conducted by Pew in June, reached over 5,000 adults who live in the United States. Pew released the poll results earlier this month.

Overall, more than half the survey respondents (57%) rated the societal risks of AI as high. Only one in four (25%) said the benefits of AI are high.

Other findings include:

  • Creative thinking: In the poll, more than half the respondents (53%) said increased use of AI will worsen people’s ability to think creatively. Only 16% thought increased use of AI would improve this ability. Another 16% said it would be neither better nor worse, and a final 16% wasn’t sure.
  • Relationships: Exactly half the respondents (50%) believe increased use of AI will worsen people’s ability to form meaningful relationships. Only 5% believe wider AI use would improve this ability. A quarter (25%) thought there would be no change, while one in five (20%) weren’t sure.
  • Decisions: More than a third of respondents (40%) believe increased use of AI will worsen our ability to make difficult decisions. Fewer than one in five (19%) expect AI to improve this ability. About the same number (20%) foresee no change, and the same percentage said they weren’t sure.
  • Problem-solving: This was a closer contest. Over a third of respondents (38%) said wider use of AI will worsen our ability to solve problems, while more than a quarter (29%) said it would improve this ability. Fifteen percent expect no change, and 17% weren’t sure.
  • Deepfakes: Over three-quarters of respondents (76%) said it’s important to be able to detect whether a picture, video or text was created by AI. But over half of all (53%) also said they’re not confident they can make these detections.

These concerns aside, the AI market still has plenty of room for growth. A recent forecast from Grand View Research has global AI sales rising from about $280 billion last year to nearly $3.5 trillion in 2033. That would represent an impressive 8-year compound annual growth rate (CAGR) of just over 30%.

 

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Vultr, Supermicro, AMD team to offer hi-performance cloud compute & AI infrastructure

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Vultr, Supermicro, AMD team to offer hi-performance cloud compute & AI infrastructure

Vultr, a global provider of cloud services, now offers Supermicro servers powered by AMD Instinct GPUs.

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Supermicro servers powered by the latest AMD Instinct GPUs and supported by the AMD ROCm open software ecosystem are at the heart of a global cloud infrastructure program offered by Vultr.

Vultr calls itself a modern hyperscaler, meaning it provides cloud solutions for organizations facing complex AI and HPC workloads, high operational costs, vendor lock-in, and the need for rapid insights.

Launched in 2014, Vultr today offers services from 32 data centers worldwide, which it says can reach 90% of the world’s population in under 40 milliseconds. Vultr’s services include cloud instances, dedicated servers, cloud GPUs, and managed services for database, cloud storage and networking.

Vultr’s customers enjoy benefits that include costs 30% to 50% lower than those of the hyperscalers and 20% to 30% lower than those of other independent cloud providers. These customers—there are over 220,000 of them worldwide—also enjoy Vultr’s full native AI stack of compute, storage and networking.

Vultr is the flagship product of The Constant Co., based in West Palm Beach, Fla. The company was founded by David Aninowsky, an entrepreneur who also started GameServers.com and served as its CEO for 18 years.

Now Vultr counts among its partners AMD, which joined the Vultr Cloud Alliance, a partner program, just a year ago. In addition, AMD’s venture group co-led a funding round this past December that brought Vultr $333 million.

Expanded Data Center

Vultr is now expanding its relationship with Supermicro, in part because that company is first to market with the latest AMD Instinct GPUs. Vultr is now offering Supermcro systems powered by AMD Instinct MI355X, MI325X and MI300X GPUs. And as part of the partnership, Supermicro engineers work on-site with Vultr technicians.

Vultr is also relying on Supermicro for scaling. That’s a challenge for large AI implementations, as these configurations require deep expertise for both integration and operations.

Among Vultr’s offerings from Supermicro is a 4U liquid-cooled server (model AS -4126GS-NMR-LCC) with dual AMD EPYC 9005/9004 processors and up to eight AMD GPUs—the user’s choice of either MI325X or MI355X.

Another benefit of the new arrangement is access to AMD’s ROCm open source software environment, which will be made available within Vultr’s composable cloud infrastructure. This AMD-Vultr combo gives users access to thousands of open source, pre-trained AI models & frameworks.

Rockin’ with ROCm

AMD’s latest update to the software is ROCm 7, introduced in July and now live and ready to use. Version 7 offers advancements that include big performance gains, advanced features for scaling AI, and enterprise-ready AI tools.

One big benefit of AMD ROCm is that its open software ecosystem eliminates vendor lock-in. And when integrated with Vultr, ROCm supports AI frameworks that include PyTorch and TensorFlow, enabling flexible, rapid innovation. Further, ROCm future-proofs AI solutions by ensuring compatibility across hardware, promoting adaptability and scalability.

AMD’s roadmap is another attraction for Vultr. AMD products on tap for 2026 include the Instinct 400 family (codename Helios), new EPYC CPUs (Venice) and an 800-Gbit NIC (Vulcano).

Conversely, Vultr is a big business for AMD. Late last year, a tech blog reported that Vultr’s first shipment of AMD Instinct MI300X GPUs numbered “in the thousands.”

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