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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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Retail in the Spotlight: Making Shelf Space for AI

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Retail in the Spotlight: Making Shelf Space for AI

Learn how retailers including Amazon, Sephora and Walmart are applying artificial intelligence to deliver real business benefits—and help their shoppers find just the right product.

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Retailers are relying more and more on artificial intelligence. And the reason is simple: AI technology can help retailers engage customers, lower operational costs and increase revenue.

Indeed, over 70% of retailers anticipate a significant ROI from AI in the next year, according to accounting firm KPMG.

Their customers approve of AI, too. In a poll conducted earlier this year by vision AI provider Everseen, two out of three consumers said AI makes shopping more convenient.

That’s a true win-win scenario.

Customer-facing

On the retail customer side, AI provides helpful features such as support chatbots and personal shopping assistants. AI can also offer visual search, letting customers upload photos of products they like and find similar items in real time.

AI is also capable of creating personalized recommendations that go far beyond the typical “people who bought X also bought Y” message.

For example, the AI behind Amazon’s industry-leading recommendation engine takes into account a customer’s shopping habits all the way back to the time they first created an account. Then the engine combines that data with whatever demographic information it can dig up or infer. The result: Customers receive genuinely useful suggestions.

Amazon also has a retail-focused chatbot called Rufus that can answer online shoppers’ questions about a products they haven’t bought yet, but are only considering. To do this work, the GenAI-powered shopping assistant has been trained on a potent mix of data that includes the entire Amazon catalog, customer reviews, community interviews and information from the public web.

This lets consumers ask Rufus just about anything. For example, “Are these shoes good for narrow feet?” will get an answer. And so will “Can this sharpener create the 16-degree angle recommended by the maker of my fancy Japanese chef’s knife?”

If you’re looking for a bit more wow factor, consider the Sephora Virtual Artist. This AI-powered virtual try-on feature uses your smartphone’s augmented reality (AR) to show how you’d look with a particular shade of lipstick, eye shadow or other makeup.

Don’t care for one shade? Sephora’s AI will suggest a better one based on your skin tone. Then it will find your color in stock at a store near you—along with a complimentary foundation, blush and eye liner.

Behind the Scenes

Deploying AI helps retailers save time and money. That’s especially true for those with big warehouses and complex supply chains.

Both Walmart and Amazon employ small armies of AI-enabled robots to zip around their warehouses. These tireless heavy-lifters find what they’re looking for by scanning bar- and QR-codes. Once they locate a product, their robotic arms grab it off even the highest shelf. Then the robots efficiently transport the products to their shipping departments.

These AI-powered robots can also report to other parts of the system, many of which use AI as well. One example is an inventory-control AI module that forecasts demand and makes sure the warehouse stays well-stocked. Another is a bot designed to manage complex supply chains by calculating trends, market prices, availability and shipping times.

Increasingly, retailers rely on AI for marketing too. They use retail bots to keep an eye on customer sentiment and emerging trends by scraping online reviews and social media posts. This information can also help retailers deal with customer-service issues before they get out of hand. And AI systems provide vital market data that retailers can use as they plan and launch new product lines.

Retail Power

Retail AI software is hugely powerful, but the hardware matters too. Deprived of enough power to collect, analyze and act on terabytes of daily data, AI is just reams of pointless code.

So retailers rely on purpose-built retail AI hardware solutions. That includes the Supermicro AS -2115HE-FTNR server.

This retail AI-server is powered by 5th gen AMD EPYC processors and has room for up to 6TB of ECC DDR5 memory and four GPUs. Retailers can also configure the system with up to 6 hot-swappable drives and their choice of air or liquid cooling.

The improved density in Supermicro’s multi-node racks helps retail organizations achieve a lower total cost of ownership by reducing server counts and energy demands.

Retail’s Future

AI is becoming more sophisticated every day. Soon, powerful new features will catalyze a paradigm shift in retail operations.

As agentic AI changes from a fascinating new design to a daily mainstay, hyper-personalized, frictionless and predictive digital online shopping will eventually become the norm. Retail stores will standardize AI-enabled smart shelves that control inventory, display dynamic pricing and direct shoppers to related items.

Behind the scenes, AI will help retail organizations further cut waste and lower their carbon footprints by better managing inventory and supply chains.

How long will we have to wait for our new AI-powered shopping experience? At the rate things are moving these days, not long at all.

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Looking for business benefits from GenAI? Supermicro, AMD & PioVation have your solution

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Looking for business benefits from GenAI? Supermicro, AMD & PioVation have your solution

Struggling to deliver business benefits from Generative AI? Supermicro, AMD and PioVation have a new solution that not only works out-of-the-box, but is also highly scalable.

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Experimenting with Generative AI can be fun, but CEOs and corporate boards aren’t interested in fun. They want to see real business results—things like an enhanced customer experience, more innovative products, streamlined operations and lower TCO. And they want to see them now.

Getting GenAI to deliver these kinds of business results isn’t easy. A recent report from MIT finds that despite nearly $40 billion worth of enterprise investment in GenAI, 95% of the organizations are getting “zero return.”

That estimate is based on solid numbers. The MIT researchers reviewed over 300 AI projects, interviewed with more than 50 organizations, and surveyed some 150 senior leaders.

The latest forecasts aren’t much cheerier. Research firm Gartner this summer predicted that by the end of this year, nearly a third of all GenAI projects (30%) will be abandoned after the proof-of-concept stage. Gartner says the projects will be cut due to poor data quality, inadequate risk controls, escalating costs and unclear business value.

“After last year’s hype, executives are impatient to see returns on GenAI investments,” says Gartner analyst Rita Sallam. “Yet organizations are struggling to prove and realize value.”

That’s About to Change

Supermicro, AMD and startup PioVation have partnered to jointly develop a GenAI solution that offers a pre-validated, turnkey infrastructure for deploying large language models (LLMs). The benefits include lower deployment overhead, enhanced observability, and ensured control of sovereign data.

Partner PioVation is a developer of AI platforms for enterprises, government agencies, and small and midsize businesses. Its products can be run either on-premises or in PioVation’s cloud in Munich, Germany. The company, founded in 2024 by former AMD executive Mazda Sabony, has formed partnerships with several companies, including AMD and Supermicro.

The GenAI solution being offered by the three companies has been designed to scale all the way from compact on-prem clusters up to large-scale multi-tenant cloud environments. And its architecture integrates Supermicro rack-level systems, AMD Instinct GPUs, and PioVation’s agentic AI platform, PioSphere. The result, the companies say, is out-of-the box agentic AI at any scale.

Full Stack

The Supermicro-AMD-PioVation offering is a full-stack solution. An autonomous microservice chains LLM prompts, invokes domain-specific tools, and integrates seamlessly with your existing systems via REST (an architectural style for distributed hypermedia systems), gRPC (a remote procedure call framework), or event streams running on the pre-validated Supermicro server powered by AMD Instinct GPUs.

Another feature is the solution’s Model Context Protocol (MCP). It lets agents interact with external tools in a way that’s both modular and composable. The MCP also governs how tools are registered, discovered, invoked and composed dynamically at runtime. This includes input/output serialization, maintaining execution context, and enforcing consistency across tool chains. MCP also enables context-aware tool usage, making every agent interoperable, auditable and enterprise-ready from the start.

The solution is available in three topologies, each designed for different operational scales and use cases:

  • MiniStack: For SMBs, pilots, research and the edge.
  • EdgeCluster: For regulated sites, branches and other locations where high availability is required.
  • Cloud Deployment: For cloud service providers (CSPs), enterprises and AI providers.

All three versions include a unified agent dashboard, role-based access control, and policy enforcement.

Business Benefits

The three partners haven’t forgotten about the need for GenAI to deliver real business results that can keep CEOs and corporate boards happy. To that end, the solution offers benefits that include:

  • Turnkey deployment: PioSphere’s Cloud OS has been prevalidated on the Supermicro platform powered by AMD GPUs.
  • Unified operations stack: A tightly integrated environment eliminates fragmented AI tooling.
  • No-code agent development: A PioVation feature known as AgentStudio lets nontechnical users design, deploy and iterate AI agents using a no-code interface.
  • Sovereign data control: Built-in controls support national and regional compliance frameworks, including Europe’s GDPR and the United States’ HIPAA.
  • Multi-tenant scalability: An organization can create separate, secure environments for different business units or clients, yet they’ll all share a common infrastructure footprint.
  • Integrated LLM operations and agent life-cycle management: Users can integrate any LLM published on the Hugging Face or Kaggle communities with one-click connectors. Other built-in features include RAG (retrieval augmented generation) pipelines and full agent life-cycle tools.
  • Intelligent autoscaling: During workload spikes, the solution’s dynamic autoscaling ensures resource utilization, cost efficiency and seamless performance.

Put it all together, and you have a solution that goes far beyond mere experimentation. The three partners—Supermicro, AMD and PioVation—are serious about helping your GenAI projects deliver serious benefits for the business.

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Tech Explainer: What’s a short-depth server?

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Tech Explainer: What’s a short-depth server?

Do your customer have locations that need server compute power, but lack data centers? Short-depth servers to the rescue!

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There are times when a standard-sized server just won’t do. Maybe your customer’s branch office or retail store has space constraints. Maybe they have concerns over portability. Or maybe their sustainability goals demand a solution that requires low power and efficient cooling.

For these and other related situations, short-depth servers can fit the bill. These relatively diminutive boxes are designed for use in less-than-ideal physical spaces that nevertheless demand high-performance IT infrastructure.

What kinds of organizations could benefit from short-depth server? Consider your local retail store. It’s likely been laid out using a calculus that prioritizes profit per square inch. This means the store’s best spots are dedicated to attracting buyers and generating revenue.

While that’s smart in terms of retail finance, it may not leave much room for vital infrastructure. That includes the servers that power the store’s point of sale (POS), security, advertising and data-collection systems.

This is a case where short-depth servers can help. These systems provide high levels of compute, storage and networking—without needing tall data center racks, elaborate cooling systems or other supporting infrastructure.

Other good candidates for using short-depth servers include remote branch offices, telco edge installations and industrial environments. In other words, any location that needs enterprise-level servers, but is short on space.

Small but Mighty

What’s more, today’s short-depth servers can handle some serious workloads.

Consider, for instance, the Supermicro WIO A+ Server (AS -1115SV-WTNRT), powered by AMD EPYC 8004 series processors. This short-depth server is engineered to tackle a variety of workloads, including virtualization, firewall applications, database, storage, edge and cloud computing.

The WIO A+ ships as a 1U form factor with a depth of just 23.5 inches. Compared with one of Supermicro’s big 8U multi-GPU servers, which has a depth of more than 33 inches, the short-depth server is short indeed.

Yet despite its diminutive size, this Supermicro server is packed with a ton of power—and room to grow. A single AMD EPYC processor sits at the center of the action, aided by either one double-width or two single-width GPUs.

This server also has room for up to 768GB of ECC DDR5 memory. And it can accommodate up to 10 hot-swap drives for NVMe, SAS or SATA storage.

As if that weren’t enough, Supermicro also includes room in this server cabinet for two PCIe 5.0 x16 full-height, full-length (FHFL) expansion cards. There’s also space for a single PCIe 5.0 x16 low-profile (LP) card.

More Power for Smaller Space

Fitting enough tech into a short-depth server can be a challenge. To do this, Supermicro’s designers had a few tricks up their sleeves.

For one, they used a custom motherboard instead of the more common ATX or EEB types. This creates more space in the smaller chassis. It also lets the designers employ a high-density component layout. The processors, GPUs, drives and other elements are placed closer to each other than they could be in a standard server.

Supermicro’s designers also deployed low-profile heat sinks. These use pipes that direct the heat toward fans. To save space, the fans are smaller than usual, but make up the difference by running faster. Sure, faster fans can create more noise. But it’s a worthy trade-off to avoid system failure due to overheating.

Are there downsides to the smaller form factor? There can be. For one, constrained airflow could force a system to throttle both processor and GPU performance in an effort to prevent heat-related issues. This could be an issue when running highly resource-intensive VM workloads.

For another, the smaller power supply units (PSUs) used in many short-depth servers may necessitate a less-powerful configuration than a user might prefer. For example, Supermicro’s short-depth server includes two 860-watt power supplies. That’s far less available power than the company’s multi-GPU powerhouse, which comes with six 5,250-watt PSUs. Of course, from another perspective, the need for less power can be seen as a benefit, especially at remote edge locations.

Short-depth servers represent a useful trade-off. While they give up some power and expandability, their reduced sizes can help IT pros make the most of tight spaces.

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How Supermicro/AMD servers boost AI boost performance with MangoBoost

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How Supermicro/AMD servers boost AI boost performance with MangoBoost

Supermicro and MangoBoost are together delivering an optimized end-to-end GenAI stack. It’s based on Supermicro servers powered by AMD Instinct GPUs and running MangoBoost’s LLMBoost software.

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While many organizations are implementing AI for business, many are also discovering that deploying and operating large language models (LLMs) at scale isn’t easy.

They’re finding that the hardware demands are intense. And so are the performance and cost trade-offs. Also, with AI workloads increasingly demanding multi-node GPU clusters, orchestration and tuning can be complex.

To address these challenges, Supermicro and MangoBoost Inc. are working together to deliver an optimized end-to-end GenAI stack. They’ve combined Supermicro’s robust AMD Instinct GPU server portfolio with MangoBoost’s LLMBoost software.

Meet MangoBoost

If you’re unfamiliar with MangoBoost, the company offers programmable solutions that improve data-center application performance while lowering CPU overhead. MangoBoost was founded three years ago; today it operates in the United States, Canada and South Korea.

MangoBoost’s core product is called the Data Processing Unit. It ensures full compatibility with general-purpose GPUs, accelerators and storage devices, enabling cost-efficient and standardized AI infrastructures.

MangoBoost also offers a ready-to-deploy, full-stack AI inference server. Known as Mango LLMBoost, it’s available from the Big Three cloud providers—AWS, Microsoft Azure and Google Cloud.

LLMBoost helps organizations accelerate both the training and deploying LLM at scale. Why is this so challenging? Because once a model is ready for inference, developers face what’s known as a “productization tax.”

Integrating the machine-learning processing pipeline into the rest of the application often requires additional time and engineering effort. And this can lead to delays.

Mango LLMBoost addresses these challenges by creating an easy-to-use container. This lets LLM experts optimize their models, then select suitable GPUs on demand.

MangoBoost’s inference engine uses three forms of GPU parallelism, allowing GPUs to balance their compute, memory and network-resource usage. In addition, the software’s intelligent job scheduling optimizes cluster-wide GPU resources, ensuring that the load is balanced equally across GPU nodes.

LLMBoost also ensures the effective use of low-latency GPU caches and high-bandwidth memory through quantization. This reduces the data footprint, but without lowering accuracy.

Complementing Hardware

MangoBoost’s LLMBoost software complements the powerful hardware with a full-stack, production-ready AI MLOps platform. It includes:

  • Plug-and-play deployment: Pre-built Docker images and an intuitive command-line interface (CLI) both help developers to launch LLM workloads quickly.
  • OpenAI-compatible API: Lets developers integrate LLM endpoints with minimal code changes.
  • Kubernetes-native orchestration: Provides automated deployment and management of autoscaling, load balancing and job scheduling for seamless operation across both single- and multi-node clusters.
  • Full-stack performance auto-tuning: Unlike conventional auto-tuners that handle model hyper-parameters only, LLMBoost optimizes every layer from the inference and training back-ends to network configurations and GPU runtime parameters. This ensures maximum hardware utilization, yet without requiring any manual tuning.

Proof of Performance

Supermicro and MangoBoost collaborating to deliver an optimized end-to-end Generative AI stack sounds good. But how does the combined solution actually perform?

To find out, Supermicro, AMD and MangoBoost recently tested their combined solution using real-world GenAI workloads. Here are the results:

  • LLMBoost reduced training time by 40% for two-node training, down to 13.3 minutes on a server built around a dual-node AMD Instinct MI325X. The training was done running Llama 2 70B, an LLM with 70 billion parameters, with LoRA (low-rank adaptation).
  • LLMBoost achieved a 1.96X higher throughput for multiple-node inference on Supermicro AMD servers. That was up to over 61,000 tokens/sec. on a dual-node AMD Instinct MI325X configuration.
  • In-house LLM inference with Llama 4 Maverick and Scout models achieved near-linear scaling on AMD Instinct MI325X nodes. (Maverick is designed for fast responses at low cost; Scout, for long-document analysis.) This shows that Supermicro systems are ready for real-time GenAI deployment.
  • Load balancing: The researchers used LLaVA, an image-capturing model, on three setups. The heterogeneous dual-node configuration—eight AMD Instinct MI300X GPUs and eight AMD Instinct MI325X GPUs—achieved 96% of the sum of individual single-node runs. This demonstrates minimal overhead and high efficiency.

Are your customers looking for a turnkey GenAI cluster solution that’s high-performance, flexible and easy to operate? Then tell them that Supermicro, AMD and MangoBoost have their solution—and the proof that it works.

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Need AI for financial services? Supermicro and AMD have your solution

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Need AI for financial services? Supermicro and AMD have your solution

Financial services companies are making big investments in AI. To speed their time to leadership, Supermicro and AMD are partnering to deliver advanced computing systems.

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Financial services companies earn their keep by investing in stocks, bonds and other financial instruments. Now these companies are also making big investments in artificial intelligence technology.

To help these financial services industry (FSI) players adopt AI, Supermicro and AMD are working together. The two are partnering to offer advanced computing solutions designed to empower and speed the finance industry’s move to technology and business leadership.

FSI companies can use these systems to:

  • Detect risks faster, uncovering patterns and anomalies by ingesting ever-larger data sets
  • Supercharge trading with AI in both the front- and back-office
  • Modernize core processes to lower costs while boosting resilience
  • Engage and delight customers by meeting—even exceeding—their expectations

Big Spenders

Already, FSI spending on AI technology is substantial. Last year, when management consulting firm Bain & Co. surveyed nearly 110 U.S. FSI firms, it found that those respondents with annual revenue of at least $5 billion were spending an average of $221 million on AI.

The companies were getting a good return on AI, too. Bain found that 75% of financial services companies said their generative AI initiatives were either achieving or exceeding their expected value. In addition, the GenAI users reported an average productivity gain across all uses of an impressive 20%.

Based on those findings, Bain estimates that by embracing AI, FSI firms can reduce their customer-service costs by 20% to 30% while increasing their revenue by about 5%. 

Electric Companies

One big issue facing all users of AI is meeting the technology’s energy needs. Power consumption is a big-ticket item, accounting for about 40% of all data center costs, according to professional services firm Deloitte.

Greater AI adoption could push that even higher. Deloitte believes global data center electric consumption could double by as soon as 2030, driven by big increases in GenAI training and inference.

As Deloitte points out, some of that will be the result of new hardware requirements. While general-purpose data center CPUs typically run at 150 to 200 watts per chip, the GPUs used for AI run at up to 1,200 watts per chip.

This can also increase the power demand per rack. As of early 2024, data centers typically supported rack power requirements of at least 20 kilowatts, Deloitte says. But with growth of GenAI, that’s expected to reach 50 kilowatts per rack by 2027.

That growth is almost sure to come. Market watcher Grand View Research expects the global market for GPUs in data centers of all industries to rise over the next eight years at a compound annual growth rate (CAGR) of nearly 36%. That translates into data-center GPU sales leaping from $14.48 billion worldwide last year to $190.1 billion in 2033, Grand View predicts.

Partner Power

FSI companies don’t have to meet these challenges alone. Supermicro and AMD have partnered to deliver advanced computing systems that deliver high levels of compute performance and flexibility, yet with a comparatively low total cost of ownership (TCO).

They’re boosting performance with high-performing, dense 4U servers using the latest AMD EPYC CPUs and AMD Instinct GPUs. Some of these servers offer up to 60 storage drive bays, 9TB of DDR5 RAM and 192 CPU cores.

For AI workloads, AMD offers the AMD EPYC 9575F AI host node. It has 64 cores and a maximum boost frequency of up to 5 GHz.

Flexibility is another benefit. Supermicro offers modular Datacenter Building Block Solutions. These include system-level units that have been pre-validated to ease the task of data-center design, among other offerings.

AMD and Supermicro are also offering efficiencies that lower the cost of transforming with AI. Supermicro’s liquid cooling slashes the total cost of ownership (TCO). AMD processors are designed for power efficiency. And SMC’s multi-mode design gives you more processing capability per rack.

Are you working with FSI customers looking to lead the way with AI investments? The latest Supermicro servers powered by AMD CPUs and GPUs have your back.

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Validate, test and benchmark the latest AMD-powered servers with Supermicro JumpStart

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Validate, test and benchmark the latest AMD-powered servers with Supermicro JumpStart

Get a free test drive on cutting-edge Supermicro servers powered by the latest AMD CPUs and GPUs.

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How would you like free access to Supermicro’s first-to-market, high-end H14 servers powered by the latest AMD EPYC CPUs and Instinct GPUs?

Now it’s yours via your browser—and the Supermicro JumpStart program.

JumpStart offers you remote access to Supermicro servers. There, you can validate, test and benchmark your workloads. And assuming you qualify, using JumpStart is absolutely free.

While JumpStart has been around for some time, Supermicro has recently refreshed the program by including some of its latest H14 servers:

  • 8U server with eight AMD Instinct MI325X GPUs, dual AMD EPYC 9005 Series CPUs, 2TB of HBM3 memory (Supermicro model AS -8126GS-TNMR)
  • 2U server with dual AMD EPYC 9005 Series processors and up to 1.5TB of DDR5 memory (AS -2126HS-TN).
  • 1U cloud server with a single AMD EPYC 9005 Series processor (AS -1116CS-TN)

Supermicro has also updated JumpStart systems with its 1U E3.S all-Flash storage systems powered by a single AMD EPYC processor, so you can also test-drive the latest PCIe drives. Also, several of Supermicro’s H13 AMD-powered are available for remote access on JumpStart, as well.

How It Works

Getting started with JumpStart is easy:

Step 1: On the main JumpStart page, browse the available systems, then click the “get access” or “request access” button for the system you want to try. Then select your preferred system and time slot.

Step 2: Sign in. You can either login with your Supermicro single sign-on (SSO) account or create a new free account. Supermicro will then qualify your account and reach out with further instructions.

Step 3: When your chosen time arrives, secure access to your system. Most JumpStart sessions last for one week. If you need more time, that can often be negotiated with your Supermicro sales reps.

It's that simple.

Once you’re connected to a server via JumpStart, you can have up to three sessions open: one VNC (virtual network computing), one SSH (secure shell), and one IPMI (intelligent platform management interface).

JumpStart also protects your privacy. After your JumpStart trial is completed, the server and storage devices are manually erased. In addition, the BIOS and firmware are reflashed, and the operating system is re-installed with new credentials.

More protection is offered, too. A jump server is used as a proxy. This means that the server you’re testing can use the internet to get files, but it is not directly addressable via the internet.

That said, it’s recommended that you do not use the test servers for processing sensitive or confidential data. Instead, Supermicro advises the use of anonymized data only—mainly because the servers may follow security policies that differ from your own.

So what are you waiting for? Try out JumpStart and get free remote access to Supermicro’s cutting-edge servers powered by the latest AMD CPUs and GPUs.

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Tech Explainer: What is agentic AI?

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Tech Explainer: What is agentic AI?

Find out how new artificial intelligence systems can make decisions and take actions autonomously—that is, without human intervention.

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We’re on the precipice of a major AI evolution. Welcome to the era of agentic AI.

The official definition of agentic AI is artificial intelligence capable of making autonomous decisions. That is, without human oversight or intervention.

You can imagine agentic AI as a robot on a mission. This robot has been designed to think like a human. Give it a goal, and the robot can then evaluate the ongoing situation, reacting intelligently in pursuit of that defined goal.

For example, imagine you’re planning a visit to wineries in California’s Napa Valley. A standard AI chatbot like ChatGPT could help you find the closest airport with car-rental agencies, identify which airlines fly there, and locate nearby hotels. But it would still be up to you to compare prices and actually make the reservations.

But what if instead, your robot could autonomously plan—and book!—the entire trip based on your preferences? For example, you might engage an agentic AI like AutoGPT by telling it something like this:

“I want to go to Napa Valley and visit wineries. I don’t want to spend more than $3,000. I prefer Chardonnay and Syrah wines. I once had a bad experience with American Airlines. It would be fun to drive a convertible. A 3-star hotel is fine as long as it’s got good reviews.”

The promise of agentic AI is that it would use that information to plan and book your trip. The agentic AI would find you the best flight, car and hotel by interacting with each company’s APIs or even their own agentic AI—here referred to as “other agents.” This is also known as machine-to-machine (M2M) communications.

Your robot agent could also make your reservations at vineyards with critically acclaimed Chardonnay and Syrah wines. And it might even plan your route using details as granular as the range of the discounted rag-top Ford Mustang it found near the airport.

Agentic AI for Organizations

This personal Napa Valley scenario is one of those nice-to-have kinds of things. But for organizations, agentic AI has far more potential. This technology could eventually transform every major industry and vertical market.

For example, a retailer might use agentic AI to autonomously adjust a product’s price based on the current inventory level, availability and competitive brands.

A manufacturer could use an AI agent to manage procurement and create dynamic forecasting, saving the company time and money.

And in the public sector, agentic AI could help a government agency better respond to public-health emergencies like the next global pandemic. The AI could model viral transmission patterns, then send additional resources to the areas that need them the most.

In each case, we’re talking about the potential for a tireless virtual robot workforce. Once you give an agentic AI a mission, it can proceed without any further human intervention, saving you countless hours and dollars.

Training: Standard AI vs. Agentic

For all types of AI, one big issue is training. That’s because an AI system on its own doesn’t really know anything. To be useful, it first has to be trained.

And with training, there’s a huge difference between the way you train a standard AI and the way you train an AI that’s agentic. It’s as dramatic as the difference between programming a calculator and onboarding a new (human) intern.

With a standard AI chatbot, the system is trained to answer questions based on a relatively narrow set of parameters. To accomplish this, engineers provide massive amounts of data via large language models (LLMs). They then train the bot through supervised learning. Eventually, inferencing enables the AI to make predictions based on user input and available data.

By contrast, training an agentic AI focuses on memory, autonomy, planning and using available tools. Here, LLMs are paired with prompt engineering, long-term memory systems and feedback loops. These elements work together to create a type of intelligent thought process—the kind you hope your new intern is capable of!

Then, at the inferencing stage, the AI does far more than just answer questions. Instead, agentic AI inferencing enables the system to interpret goals, create plans, ask for help and, ultimately, execute tasks autonomously.

Nuts and Bolts

The IT infrastructure that powers agentic AI is no different from the horsepower behind your average chatbot. There’s just a lot more of it.

That’s because agentic AI, in comparison with standard AI, makes more inference calls, reads and writes more files, and queries more APIs. It also engages a persistent memory. That way, the AI can continuously access collected information as it works towards its goals.

However, having a slew of GPUs and endless solid-state storage won’t be enough to sustain what will likely be the meteoric growth of this cutting-edge technology. As agentic AI becomes more vital, IT managers will need a way to feed the fast-growing beast.

Supermicro’s current H14 systems—they include the GPU A+ Server—are powered by AMD EPYC 9005-series processors and fitted with up to 8 AMD Instinct MI325X Accelerators. Supermicro has designed these high-performance solutions to tackle the most challenging AI workloads.

Looking ahead, at AMD’s recent “Advancing AI” event, CEO Lisa Su introduced Helios, AMD’s vision for agentic AI infrastructure. Su said Helios will deliver the compute density, memory bandwidth, performance and scale-out bandwidth needed for the most demanding AI workloads. What’s more, Helios will come packaged as a ready-to-deploy AI rack solution that accelerates users’ time to market.

Helios, planned for release in 2026, will use several forthcoming products: AMD Instinct MI400 GPUs, AMD 6th Gen EPYC CPUs, and AMD Pensando “Vulcano” network interface cards (NICs). All will be integrated in an OCP-compliant rack that supports both UALink and Ultra Ethernet. And eventually, Helios will appear in turnkey systems such as the Supermicro H14 series.

What’s Next?

What else does agentic AI have in store for us? While no one has a crystal ball, it’s reasonable to assume we’ll see increasingly sophisticated agents infiltrating nearly every aspect of our lives.

For instance, agentic AI could eventually develop the ability to work autonomously on long-term, multifaceted projects—everything from advertising campaigns to biomedical research.

Agentic AI is also likely to learn how to debug its own logic and develop new tools. These capabilities are referred to by the pros as self-reflection and self-improvement, respectively.

One day in the not-too-distant future, we could even see massive teams of specialized AI agents working together under a single robotic project manager.

Think this is starting to sound like “The Matrix”? You ain’t seen nothin’ yet.

 

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Deploy GenAI with confidence: Validated Server Designs from Supermicro and AMD

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Deploy GenAI with confidence: Validated Server Designs from Supermicro and AMD

Learn about the new Validated Design for AI clusters from Supermicro and AMD. It can save you time, reduce complexity and improve your ROI.

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The task of designing, building and connecting a server system that can run today’s artificial intelligence workloads is daunting.

Mainly, because there are a lot of moving parts. Assembling and connecting them all correctly is not only complicated, but also time-consuming.

Supermicro and AMD are here to help. They’ve recently co-published a Verified Design document that explains how to build an AI cluster. The PDF also tells you how you can acquire an AMD-powered Supermicro cluster for AI pre-built, with all elements connected, configured and burned in before shipping.

Full-Stack for GenAI

Supermicro and AMD are offering a fully validated, full-stack solution for today’s Generative AI workloads. The system’s scale can be easily adjusted from as few as 16 nodes to as many as 1,024—and points in between.

This Supermicro solution is based on three AMD elements: the AMD Instinct MI325X GPU, AMD Pensando Pollara 400 AI network interface card (NIC), and AMD EPYC CPU.

These three AMD parts are all integrated with Supermicro’s optimized servers. That includes network cabling and switching.

The new Validated Design document is designed to help potential buyers understand the joint AMD-Supermicro solution’s key elements. To shorten your implementation time, the document also provides an organized plan from start to finish.

Under the Cover

This comprehensive report—22 pages plus a lengthy appendix—goes into a lot of technical detail. That includes the traffic characteristics of AI training, impact of large “elephant” flows on the network fabric, and dynamic load balancing. Here’s a summary:

  • Foundations of AI Fabrics: Remote Direct Memory Access (RDMA), PCIe switching, Ethernet, IP and Border Gateway Protocol (BGP).
  • Validated Design Equipment and Configuration: Server options that optimize RDMA traffic with minimal distance, latency and silicon between the RDMA-capable NIC (RNIC) and accelerator.
  • Scaling Out the Accelerators with an Optimized Ethernet Fabric: Components and configurations including the AMD Pensando Pollara 400 Ethernet NIC and Supermicro’s own SSE-T8196 Ethernet switch.
  • Design of the Scale Unit—Scaling Out the Cluster: Designs are included for both air-cooled and liquid-cooled setups.
  • Resource Management and Adding Locality into Work Placement: Covering the Simple Linux Utility for Resource Management (SLURM) and topology optimization including the concept of rails.
  • Supermicro Validated AMD Instinct MI325 Design: Shows how you can scale the validated design all the way to 8,000 AMD MI325X GPUs in a cluster.
  • Storage Network Validated Design: Multiple alternatives are offered.
  • Importance of Automation: Human errors are, well, human. Automation can help with tasks including the production of detailed architectural drawings, output of cabling maps, and management of device firmware.
  • How to Minimize Deployment Time: Supermicro’s Rack Scale Solution Stack offers a fully integrated, end-to-end solution. And by offering a system that’s pre-validated, this also eases the complexity of multi-vendor integration.

Total Rack Solution

Looking to minimize implementation times? Supermicro offers a total rack scale solution that’s fully integrated and end-to-end.

This frees the user from having to integrate and validate a multi-vendor solution. Basically, Supermicro does it for you.

By leveraging industry-leading energy efficiency, liquid and air-cooled designs, and global logistics capabilities, Supermicro delivers a cost-effective and future-proof solution designed to meet the most demanding IT requirements.

The benefits to the customer include reduced operational overhead, a single point of accountability, streamlined procurement and deployment, and maximum return on investment.

For onsite deployment, Supermicro provides a turnkey, fully optimized rack solution that is ready to run. This helps organizations maximize efficiency, lower costs and ensure long-term reliability. It includes a dedicated on-site project manager.

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Tech Explainer: What’s special about an AI server?

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Tech Explainer: What’s special about an AI server?

What’s in an AI server that a general-purpose system lacks?

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The Era of Artificial Intelligence requires its own class of servers, and rightly so. The AI tech that increasingly powers our businesses, finance, entertainment and scientific research is some of the most resource-intensive in history. Without AI servers, all this would grind to a halt.

But why? What’s so special about AI servers? And how are they able to power successive evolutions of large language models, generative AI, machine learning, and all the other AI-based workloads we’ve come to rely on day in and day out?

Put another way: What do AI servers have that standard servers don’t?

The answer can be summed up in a single word: More.

When it comes to AI servers, it’s all about managing a symphony. The musical instruments include multiple processors, GPUs, memory modules, networking hardware and expansion options.

Sure, your average general-purpose server has many similar components. But both the quantity and performance of each component is considerably lower than those of an AI server. That helps keep the price affordable, heat low, and workload options open. But it certainly doesn’t have the integrated GPU needed to run AI workloads.

Best of the Beasts

Supermicro specializes in the deployment of jaw-dropping power. The company’s newest 8U GPU Server (AS -8126GS-TNMR) is engineered to chew through the world’s toughest AI workloads. It’s powered by dual AMD EPYC processors and eight AMD Instinct MI350X or Instinct MI325X accelerators. This server can tackle AI workloads while staying cool and scaling up to meet increasing demand.

Keeping AI servers from overheating can be a tough job. Even a lowly, multipurpose business server kicks off a lot of heat. Temperatures build up around vital components like the CPU, GPU and storage devices. If that heat hangs around too long, it can lead to performance issues and, eventually, system failure.

Preventing heat-related issues in a single general-purpose server can be accomplished with a few heatsinks and small-diameter fans. But when it comes to high-performance, multi-GPU servers like Supermicro’s new 4U GPU A+ Server (AS -4126GS-NMR-LCC), liquid cooling becomes a must-have.

It’s also vital that AI servers be designed with expansion in mind. When an AI-powered app becomes successful, IT managers must be able to scale up quickly and without interruption.

Supermicro’s H14 8U 8-GPU System sets the standard for scalability. The H14 offers up to 20 storage drives and up to 12 PCI Express 5.0 (PCIe) x16 expansion slots.

Users can fill these high-bandwidth slots with a dizzying array of optional hardware, including:

  • Network Interface Cards (NICs) like the new AI-focused AMD AI NIC for high-speed networking.
  • NVMe storage to provide fast disk access.
  • Field Programmable Gate Array (FPGA) modules, which can be set up for custom computation and reconfigured after deployment.
  • Monitoring and control management cards. These enable IT staff to power servers on and off remotely, and also access BIOS settings.
  • Additional GPUs to aid in AI training and inferencing.
  • AI Accelerators. The AMD Instinct series is designed to tackle computing for AI, both training and inference.

A Different Class of Silicon

Hardware like the Supermicro GPU Server epitomizes what it means to be an AI server. That’s due in part to the components it’s designed to house. We’re talking about some of the most advanced processing tech available today.

As mentioned above, that tech comes courtesy of AMD, whose 5th Gen AMD EPYC 9005 series processors and recently announced AMD Instinct MI350 Series GPUs are powerful enough to tackle any AI workload.

AMD’s Instinct MI350 accelerators deliver a 4x generation-on-generation AI compute increase and a 35x generational leap in inferencing.

Say the word, and Supermicro will pack your AI Server with dual AMD EPYC processors containing up to 192 cores. They’ll install the latest AMD Instinct M1350X platform with 8 GPUs, fill all 24 DIMM slots with 6TB of DDR5 memory, and add an astonishing 16 NVMe U.2 drives. 

Advances Just Around the Corner

It seems like each new day brings stories about bold advances in AI. Apparently, our new robot friends may have the answer to some very human questions like, how can we cure our most insidious diseases? And how do we deal with the looming threat of climate crisis?

The AI models that could answer those questions—not to mention the ones that will help us find even better movies on Netflix—will require more power as they grow.

To meet those demands, AI server engineers are already experimenting with the next generation of advanced cooling for dense GPU clusters, enhanced hardware-based security, and new, more scalable modular infrastructure.

In fact, AI server designers have begun using their own AI models to create bigger and better AI servers. How very meta.

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