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Oil & gas spotlight: Fueling up with AI

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Oil & gas spotlight: Fueling up with AI

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

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

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

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

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

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

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

Powerful Solutions

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

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

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

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

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

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Healthcare in the spotlight: Big challenges, big tech

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Healthcare in the spotlight: Big challenges, big tech

To meet some of their industry’s toughest challenges, healthcare providers are turning to advanced technology.

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Healthcare providers face some tough challenges. Advanced technology can help.

As a recent report from consultants McKinsey & Co. points out, healthcare providers are dealing with some big challenges. These include rising costs, workforce shortages, an aging population, and increased competition from nontraditional parties.

Another challenge: Consumers expect their healthcare providers to offer new capabilities, such as digital scheduling and telemedicine, as well as better experiences.

One way healthcare providers hope to meet these two challenge streams is with advanced technology. Three-quarters of U.S. healthcare providers increased their IT spending in the last year, according to a survey conducted by consultants Bain & Co. The same survey found that 15% of healthcare providers already have an AI strategy in place, up from just 5% who had a strategy in 2023.

Generative AI is showing potential, too. Another survey, this one done by McKinsey, finds that over 70% of healthcare organizations are now either pursuing GenAI proofs-of-concept or are already implementing GenAI solutions.

Dynamic Duo

There’s a catch to all this: As healthcare providers adopt AI, they’re finding that the required datasets and advanced analytics don’t run well on their legacy IT systems.

To help, Supermicro and AMD are working together. They’re offering healthcare providers heavy-duty compute delivered at rack scale.

Supermicro servers powered by AMD Instinct MI300X GPUs are designed to accelerate AI and HPC workloads in healthcare. They offer the levels of performance, density and efficiency healthcare providers need to improve patient outcomes.

The AMD Instinct MI300X is designed to deliver high performance for GenAI workloads and HPC applications. It’s designed with no fewer than 304 high-throughput compute units. You also get AI-specific functions and 192GB of HBM3 memory, all of it based on AMD’s CDNA 3 architecture.

Healthcare providers can use Supermicro servers powered by AMD GPUs for next-generation research and treatments. These could include advanced drug discovery, enhanced diagnostics and imaging, risk assessments and personal care, and increased patient support with self-service tools and real-time edge analytics.

Supermicro points out that its servers powered by AMD Instinct GPUs deliver massive compute with rack-scale flexibility, as well as high levels of power efficiency.

Performance:

  • The powerful combination of CPUs, GPUs and HBM3 memory accelerates HPC and AI workloads.
  • HBM3 memory offers capacities of up to 192GB dedicated to the GPUs.
  • Complete solutions ship pre-validated, ready for instant deployment.
  • Double-precision power can serve up to 163.4 TFLOPS.

Flexibility:

  • Proven AI building-block architecture streamlines deployment at scale for the largest AI models.
  • An open AI ecosystem with AMD ROCm open software.
  • A unified computing platform with AMD Instinct MI300X plus AMD Infinity fabric and infrastructure.
  • Thanks to a modular design and build, users move faster to the correct configuration.

Efficiency:

  • Dual-zone cooling innovation, used by some of the most efficient supercomputers on the Green500 supercomputer list.
  • Improved density with 3rd Gen AMD CDNA, delivering 19,456 stream cores.
  • Chip-level power intelligence enables the AMD Instinct MI300X to deliver big power performance.
  • Purpose-built silicon design of the 3rd Gen AMD CDNA combines 5nm and 6nm fabrication processes.

Are your healthcare clients looking to unleash the potential of their data? Then tell them about Supermicro systems powered by the AMD MI300X GPUs.

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

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

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

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

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

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

Under the Hood

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

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

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

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

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

Converged Computing

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

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

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

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

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

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

Supermicro Servers

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

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

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

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

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

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

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AMD’s new ROCm 6.3 makes GPU programming even better

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AMD’s new ROCm 6.3 makes GPU programming even better

AMD recently introduced version 6.3 of ROCm, its open software stack for GPU programming. New features included expanded OS support and other optimizations.

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There’s a new version of AMD ROCm, the open software stack designed to enable GPU programming from low-level kernel all the way up to end-user applications.  

The latest version, ROCm 6.3, adds features that include expanded operating system support, an open-source toolkit and more.

Rock On

AMD ROCm provides the tools for HIP (the heterogeneous-computing interface for portability), OpenCL and OpenMP. These include compilers, APIs, libraries for high-level functions, debuggers, profilers and runtimes.

ROCm is optimized for Generative AI and HPC applications, and it’s easy to migrate existing code into. Developers can use ROCm to fine-tune workloads, while partners and OEMs can integrate seamlessly with AMD to create innovative solutions.

The latest release builds on ROCm 6, which AMD introduced last year. Version 6 added expanded support for AMD Instinct MI300A and MI300X accelerators, key AI support features, optimized performance, and an expanded support ecosystem.

The senior VP of AMD’s AI group, Vamsi Boppana, wrote in a recent blog post: “Our vision is for AMD ROCm to be the industry’s premier open AI stack, enabling choice and rapid innovation.”

New Features

Here’s some of what’s new in AMD ROCm 6.3:

  • rocJPEG: A high-performance JPEG decode SDK for AMD GPUs.
  • ROCm compute profiler and system profiler: Previously known as Omniperf and Omnitrace, these have been renamed to reflect their new direction as part of the ROCm software stack.
  • Shark AI toolkit: This open-source toolkit is for high-performance serving of GenAI and  LLMs. Initial release includes support for the AMD Instinct MI300.
  • PyTorch 2.4 support: PyTorch is a machine learning library used for applications such as computer vision and natural language processing. Originally developed by Meta AI, it’s now part of the Linux Foundation umbrella.
  • Expanded OS support: This includes added support for Ubuntu 24.04.2 and 22.04.5; RHEL 9.5; and Oracle Linux 8.10. In addition, ROCm 6.3.1 includes support for both Debian 12 and the AMD Instinct MI325X accelerator.
  • Documentation updates: ROCm 6.3 offers clearer, more comprehensive guidance for a wider variety of use cases and user needs.

Super for Supermicro

Developers can use ROCm 6.3 to create tune workloads and create solutions for Supermicro GPU systems based on AMD Instinct MI300 accelerators.

Supermicro offers three such systems:

Are your customers building AI and HPC systems? Then tell them about the new features offered by AMD ROCm 6.3.

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The AMD Instinct MI300X Accelerator draws top marks from leading AI benchmark

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The AMD Instinct MI300X Accelerator draws top marks from leading AI benchmark

In the latest MLPerf testing, the AMD Instinct MI300X Accelerator with ROCm software stack beat the competition with strong GenAI inference performance. 

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New benchmarks using the AMD Instinct MI300X Accelerator show impressive performance that surpasses the competition.

This is great news for customers operating demanding AI workloads, especially those underpinned by large language models (LLMs) that require super-low latency.

Initial platform tests using MLPerf Inference v4.1 measured AMD’s flagship accelerator against the Llama 2 70B benchmark. This test is an indication for real-world applications, including natural language processing (NLP) and large-scale inferencing.

MLPerf is the industry’s leading benchmarking suite for measuring the performance of machine learning and AI workloads from domains that include vision, speech and NLP. It offers a set of open-source AI benchmarks, including rigorous tests focused on Generative AI and LLMs.

Gaining high marks from the MLPerf Inference benchmarking suite represents a significant milestone for AMD. It positions the AMD Instinct MI300X accelerator as a go-to solution for enterprise-level AI workloads.

Superior Instincts

The results of the LLaMA2-70B test are particularly significant. That’s due to the benchmark’s ability to produce an apples-to-apples comparison of competitive solutions.

In this benchmark, the AMD Instinct MI300X was compared with NVIDIA’s H100 Tensor Core GPU. The test concluded that AMD’s full-stack inference platform was better than the H100 at achieving high-performance LLMs, a workload that requires both robust parallel computing and a well-optimized software stack.

The testing also showed that because the AMD Instinct MI300X offers the largest GPU memory available—192GB of HBM3 memory—it was able to fit the entire LLaMA2-70B model into memory. Doing so helped to avoid network overhead by preventing model splitting. This, in turn, maximized inference throughput, producing superior results.

Software also played a big part in the success of the AMD Instinct series. The AMD ROCm software platform accompanies the AMD Instinct MI300X. This open software stack includes programming models, tools, compilers, libraries and runtimes for AI solution development on the AMD Instinct MI300 accelerator series and other AMD GPUs.

The testing showed that the scaling efficiency from a single AMD Instinct MI300X, combined with the ROCm software stack, to a complement of eight AMD Instinct accelerators was nearly linear. In other words, the system’s performance improved proportionally by adding more GPUs.

That test demonstrated the AMD Instinct MI300X’s ability to handle the largest MLPerf inference models to date, containing over 70 billion parameters.

Thinking Inside the Box

Benchmarking the AMD Instinct MI300X required AMD to create a complete hardware platform capable of addressing strenuous AI workloads. For this task, AMD engineers chose as their testbed the Supermicro AS -8125GS-TNMR2, a massive 8U complete system.

Supermicro’s GPU A+ Client Systems are designed for both versatility and redundancy. Designers can outfit the system with an impressive array of hardware, starting with two AMD EPYC 9004-series processors and up to 6TB of ECC DDR5 main memory.

Because AI workloads consume massive amounts of storage, Supermicro has also outfitted this 8U server with 12 front hot-swap 2.5-inch NVMe drive bays. There’s also the option to add four more drives via an additional storage controller.

The Supermicro AS -8125GS-TNMR2 also includes room for two hot-swap 2.5-inch SATA bays and two M.2 drives, each with a capacity of up to 3.84TB.

Power for all those components is delivered courtesy of six 3,000-watt redundant titanium-level power supplies.

Coming Soon: Even More AI power

AMD engineers continually push the limits of silicon and human ingenuity to expand the capabilities of their hardware. So it should come as little surprise that new iterations of the AMD Instinct series are expected to be released in the coming months. This past May, AMD officials said they plan to introduce AMD Instinct MI325, MI350 and MI400 accelerators.

Forthcoming Instinct accelerators, AMD says, will deliver advances including additional memory, support for lower-precision data types, and increased compute power.

New features are also coming to the AMD ROCm software stack. Those changes should include software enhancements including kernel improvements and advanced quantization support.

Are you customers looking for a high-powered, low-latency system to run their most demanding HPC and AI workloads? Tell them about these benchmarks and the AMD Instinct MI300X accelerators.

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Developing AI and HPC solutions? Check out the new AMD ROCm 6.2 release

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Developing AI and HPC solutions? Check out the new AMD ROCm 6.2 release

The latest release of AMD’s free and open software stack for developing AI and HPC solutions delivers 5 important enhancements. 

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If you develop AI and HPC solutions, you’ll want to know about the most recent release of AMD ROCm software, version 6.2.

ROCm, in case you’re unfamiliar with it, is AMD’s free and open software stack. It’s aimed at developers of artificial intelligence and high-performance computing (HPC) solutions on AMD Instinct accelerators. It's also great for developing AI and HPC solutions on AMD Instinct-powered servers from Supermicro. 

First introduced in 2016, ROCm open software now includes programming models, tools, compilers, libraries, runtimes and APIs for GPU programming.

ROCm version 6.2, announced recently by AMD, delivers 5 key enhancements:

  • Improved vLLM support 
  • Boosted memory efficiency & performance with Bitsandbytes
  • New Offline Installer Creator
  • New Omnitrace & Omniperf Profiler Tools (beta)
  • Broader FP8 support

Let’s look at each separately and in more detail.

LLM support

To enhance the efficiency and scalability of its Instinct accelerators, AMD is expanding vLLM support. vLLM is an easy-to-use library for the large language models (LLMs) that power Generative AI.

ROCm 6.2 lets AMD Instinct developers integrate vLLM into their AI pipelines. The benefits include improved performance and efficiency.

Bitsandbytes

Developers can now integrate Bitsandbytes with ROCm for AI model training and inference, reducing their memory and hardware requirements on AMD Instinct accelerators. 

Bitsandbytes is an open source Python library that enables LLMs while boosting memory efficiency and performance. AMD says this will let AI developers work with larger models on limited hardware, broadening access, saving costs and expanding opportunities for innovation.

Offline Installer Creator

The new ROCm Offline Installer Creator aims to simplify the installation process. This tool creates a single installer file that includes all necessary dependencies.

That makes deployment straightforward with a user-friendly GUI that allows easy selection of ROCm components and versions.

As the name implies, the Offline Installer Creator can be used on developer systems that lack internet access.

Omnitrace and Omniperf Profiler

The new Omnitrace and Omniperf Profiler Tools, both now in beta release, provide comprehensive performance analysis and a streamlined development workflow.

Omnitrace offers a holistic view of system performance across CPUs, GPUs, NICs and network fabrics. This helps developers ID and address bottlenecks.

Omniperf delivers detailed GPU kernel analysis for fine-tuning.

Together, these tools help to ensure efficient use of developer resources, leading to faster AI training, AI inference and HPC simulations.

FP8 Support

Broader FP8 support can improve the performance of AI inferencing.

FP8 is an 8-bit floating point format that provides a common, interchangeable format for both AI training and inference. It lets AI models operate and perform consistently across hardware platforms.

In ROCm, FP8 support improves the process of running AI models, particularly in inferencing. It does this by addressing key challenges such as the memory bottlenecks and high latency associated with higher-precision formats. In addition, FP8's reduced precision calculations can decrease the latency involved in data transfers and computations, losing little to no accuracy.  

ROCm 6.2 expands FP8 support across its ecosystem, from frameworks to libraries and more, enhancing performance and efficiency.

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Research Roundup, AI Edition: platform power, mixed signals on GenAI, smarter PCs

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Research Roundup, AI Edition: platform power, mixed signals on GenAI, smarter PCs

Catch the latest AI insights from leading researchers and market analysts.

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Sales of artificial intelligence platform software show no sign of a slowdown. The road to true Generative AI disruption could be bumpy. And PCs with built-in AI capabilities are starting to sell.

That’s some of the latest AI insights from leading market researchers, analysts and pollsters. And here’s your research roundup.

AI Platforms Maintain Momentum

Is the excitement around AI overblown? Not at all, says market watcher IDC.

“The AI platforms market shows no sign of slowing down,” says IDC VP Ritu Jyoti.

IDC now believes that the market for AI platform software will maintain its momentum through at least 2028.

By that year, IDC expects, worldwide revenue for AI software will reach $153 billion. If so, that would mark a five-year compound annual growth rate (CAGR) of nearly 41%.

The market really got underway last year. That’s when worldwide AI platform software revenue hit $27.9 billion, an annual increase of 44%, IDC says.

Since then, lots of progress has been made. Fully half the organizations now deploying GenAI in production have already selected an AI platform. And IDC says most of the rest will do so in the next six months.

All that has AI software suppliers looking pretty smart.

Mixed Signals on GenAI

There’s no question that GenAI is having a huge impact. The question is how difficult it will be for GenAI-using organizations to achieve their desired results.

GenAI use is already widespread. In a global survey conducted earlier this year by management consultants McKinsey & Co., 65% of respondents said they use GenAI on a regular basis.

That was nearly double the percentage from McKinsey’s previous survey, conducted just 10 months earlier.

Also, three quarters of McKinsey’s respondents said they expect GenAI will lead their industries to significant or disruptive changes.

However, the road to GenAI could be bumpy. Separately, researchers at Gartner are predicting that by the end of 2025, at least 30% of all GenAI projects will be abandoned after their proof-of-concept (PoC). 

The reason? Gartner points to several factors: poor data quality, inadequate risk controls, unclear business value, and escalating costs.

“Executives are impatient to see returns on GenAI investments,” says Gartner VP Rita Sallam. “Yet organizations are struggling to prove and realize value.”

One big challenge: Many organizations investing in GenAI want productivity enhancements. But as Gartner points out, those gains can be difficult to quantify.

Further, implementing GenAI is far from cheap. Gartner’s research finds that a typical GenAI deployment costs anywhere from $5 million to $20 million.

That wide range of costs is due to several factors. These include the use cases involved, the deployment approaches used, and whether an organization seeks to be a market disruptor.

Clearly, an intelligent approach to GenAI can be a money-saver.

PCs with AI? Yes, Please

Leading PC makers hope to boost their hardware sales by offering new, built-in AI capabilities. It seems to be working.

In the second quarter of this year, 8.8 million PCs—that’s 14% of all shipped globally in the quarter—were AI-capable, says market analysts Canalys.

Canalys defines “AI-capable” pretty simply: It’s any desktop or notebook system that includes a chipset or block for one or more dedicated AI workloads.

By operating system, nearly 40% of the AI-capable PC shipped in Q2 were Windows systems, 60% were Apple macOS systems, and just 1% ran ChromeOS, Canalys says.

For the full year 2024, Canalys expects some 44 million AI-capable PCs to be shipped worldwide. In 2025, the market watcher predicts, these shipments should more than double, rising to 103 million units worldwide. There's nothing artificial about that boost.

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Why Lamini offers LLM tuning software on Supermicro servers powered by AMD processors

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Why Lamini offers LLM tuning software on Supermicro servers powered by AMD processors

Lamini, provider of an LLM platform for developers, turns to Supermicro’s high-performance servers powered by AMD CPUs and GPUs to run its new Memory Tuning stack.

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Generative AI systems powered by large language models (LLMs) have a serious problem: Their answers can be inaccurate—and sometimes, in the case of AI “hallucinations,” even fictional.

For users, the challenge is equally serious: How do you get precise factual accuracy—that is, correct answers with zero hallucinations—while upholding the generalization capabilities that make LLMs so valuable?

A California-based company, Lamini, has come up with an innovative solution. And its software stack runs on Supermicro servers powered by AMD CPUs and GPUs.

Why Hallucinations Happen

Here’s the premise underlying Lamini’s solution: Hallucinations happen because the right answer is clustered with other, incorrect answers. As a result, the model doesn’t know that a nearly right answer is in fact wrong.

To address this issue, Lamini’s Memory Tuning solution teaches the model that getting the answer nearly right is the same as getting it completely wrong. Its software does this by tuning literally millions of expert adapters with precise facts on top of any open-source LLM, such as Llama 3 or Mistral 3.

The Lamini model retrieves only the most relevant experts from an index at inference time. The goal is high accuracy, high speed and low cost.

More than Fine-Tuning

Isn’t this just LLM fine-tuning? Lamini says no, its Memory Tuning is fundamentally different.

Fine-tuning can’t ensure that a model’s answers are faithful to the facts in its training data. By contrast, Lamini says, its solution has been designed to deliver output probabilities that are not just close, but exactly right.

More specifically, Lamini promises its solution can deliver 95% LLM accuracy with 10x fewer hallucinations.

In the real world, Lamini says one large customer used its solution and raised LLM accuracy from 50% to 95%, and reduced the rate of AI hallucinations from an unreliable 50% to just 5%.

Investors are certainly impressed. Earlier this year Lamini raised $25 million from an investment group that included Amplify Partners, Bernard Arnault and AMD Ventures. Lamini plans to use the funding to accelerate its expert AI development and expand its cloud infrastructure.

Supermicro Solution

As part of its push to offer superior LLM tuning, Lamini chose Supermicro’s GPU server — model number AS -8125S-TNMR2 — to train LLM models in a reasonable time.

This Supermicro 8U system is powered by dual AMD EPYC 9000 series CPUs and eight AMD Instinct MI300X GPUs.

The GPUs connect with CPUs via a standard PCIe 5 bus. This gives fast access when the CPU issues commands or sends data from host memory to the GPUs.

Lamini has also benefited from Supermicro’s capacity and quick delivery schedule. With other GPUs makers facing serious capacity issues, that’s an important benefit for both Lamini and its customers.

“We’re thrilled to be working with Supermicro,” says Lamini co-founder and CEO Sharon Zhou.

Could your customers be thrilled by Lamini, too? Check out the “do more” links below.

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Why CSPs Need Hyperscaling

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Why CSPs Need Hyperscaling

Today’s cloud service providers need IT infrastructures that can scale like never before.

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Hyperscaling IT infrastructure may be one of the toughest challenges facing cloud service providers (CSPs) today.

The term hyperscale refers to an IT architecture’s ability to scale in response to increased demand.

Hyperscaling is tricky, in large part because demand is a constantly moving target. Without much warning, a data center’s IT demand can increase exponentially due to a myriad of factors.

That could mean a public emergency, the failure of another CSP’s infrastructure, or simply the rampant proliferation of data—a common feature of today’s AI environment.

To meet this growing demand, CSPs have a lot to manage. That includes storage measured in exabytes, AI workloads of massive complexity, and whatever hardware is needed to keep system uptime as close to 100% as possible.

The hardware alone can be a real challenge. CSPs now oversee both air- and liquid-powered cooling systems, redundant power sources, diverse networking gear, and miles of copper and fiber-optic cabling. It’s a real handful.

Design with CSPs in Mind

To help CSPs cope with this seemingly overwhelming complexity, Supermicro offers purpose-built hardware designed to tackle the world’s most demanding workloads.

Enterprise-class servers like Supermicro’s H13 and A+ server series offer CSPs powerful platforms built to handle the rigors of resource-intensive AI workloads. They’ve been designed to scale quickly and efficiently as demand and data inevitably increase.

Take the Supermicro GrandTwin. This innovative solution puts the power and flexibility of multiple independent servers in a single enclosure.

The design helps lower operating expenses by enabling shared resources, including a space-saving 2U enclosure, heavy-duty cooling system, backplane and N+1 power supplies.

To help CSPs tackle the world’s most demanding AI workloads, Supermicro offers GPU server systems. These include a massive—and massively powerful—8U eight-GPU server.

Supermicro H13 GPU servers are powered by 4th-generation AMD EPYC processors. These cutting-edge chips are engineered to help high-end applications perform better and return faster.

To make good on those lofty promises, AMD included more and faster cores, higher bandwidth to GPUs and other devices, and the ability to address vast amounts of memory.

Theory Put to Practice

Capable and reliable hardware is a vital component for every modern CSP, but it’s not the only one. IT infrastructure architects must consider not just their present data center requirements but how to build a bridge to the requirements they’ll face tomorrow.

To help build that bridge, Supermicro offers an invaluable list: 10 essential steps for scaling the CSP data center.

A few highlights include:

  • Standardize and scale: Supermicro suggests CSPs standardize around a preferred configuration that offers the best compute, storage and networking capabilities.
  • Plan ahead for support: To operate a sophisticated data center 24/7 is to embrace the inevitability of technical issues. IT managers can minimize disruption and downtime when some-thing goes wrong by choosing a support partner who can solve problems quickly and efficiently.
  • Simplify your supply chain: Hyperscaling means maintaining the ability to move new infra-structure into place fast and without disruption. CSPs can stack the odds in their favor by choosing a partner that is ever ready to deliver solutions that are integrated, validated, and ready to work on day one.

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Hyperscaling for CSPs will be the focus of a session at the upcoming Supermicro Open Storage Summit ‘24, which streams live Aug. 13 - Aug. 29.

The CSP session, set for Aug. 20, will cover the ways in which CSPs can seamlessly scale their AI operations across thousands of GPUs while ensuring industry-leading reliability, security and compliance capabilities. The speakers will feature representatives from Supermicro, AMD, Vast Data and Solidigm.

Learn more and register now to attend the 2024 Supermicro Open Storage Summit.

 

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Tech Explainer: What is CXL — and how can it help you lower data-center latency?

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Tech Explainer: What is CXL — and how can it help you lower data-center latency?

High latency is a data-center manager’s worst nightmare. Help is here from an open-source solution known as CXL. It works by maintaining “memory coherence” between the CPU’s memory and memory on attached devices.

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Latency is a crucial measure for every data center. Because latency measures the time it takes for data to travel from one point in a system or network to another, lower is generally better. A network with high latency has slower response times—not good.

Fortunately, the industry has come up with an open-source solution that provides a low-latency link between processors, accelerators and memory devices such as RAM and SSD storage. It’s known as Compute Express Link, or CXL for short.

CXL is designed to solve a couple of common problems. Once a processor uses up the capacity of its direct-attached memory, it relies on an SSD. This introduces a three-order-of-magnitude latency gap that can hurt both performance and total cost of ownership (TCO).

Another problem is that multicore processors are starving for memory bandwidth. This has become an issue because processors have been scaling in terms of cores and frequencies faster than their main memory channels. The resulting deficit leads to suboptimal use of the additional processor cores, as the cores have to wait for data.

CXL overcomes these issues by introducing a low-latency, memory cache coherent interconnect. CXL works for processors, memory expansion and AI accelerators such as the AMD Instinct MI300 series. The interconnect provides more bandwidth and capacity to processors, which increases efficiency and enables data-center operators to get more value from their existing infrastructure.

Cache-coherence refers to IT architecture in which multiple processor cores share the same memory hierarchy, yet retain individual L1 caches. The CXL interconnect reduces latency and increases performance throughout the data center.

The latest iteration of CXL, version 3.1, adds features to help data centers keep up with high-performance computational workloads. Notable upgrades include new peer-to-peer direct memory access, enhancements to memory pooling, and CXL Fabric improvements.

3 Ways to CXL

Today, there are three main types of CXL devices:

  • Type 1: Any device without integrated local memory. CXL protocols enable these devices to communicate and transfer memory capacity from the host processor.
  • Type 2: These devices include integrated memory, but also share CPU memory. They leverage CXL to enable coherent memory-sharing between the CPU and the CXL device.
  • Type 3: A class of devices designed to augment existing CPU memory. CXL enables the CPU to access external sources for increased bandwidth and reduced latency.

Hardware Support

As data-center architectures evolve, more hardware manufacturers are supporting CXL devices. One such example is Supermicro’s All-Flash EDSFF and NVM3 servers.

Supermicro’s cutting-edge appliances are optimized for resource-intensive workloads, including data-center infrastructure, data warehousing, hyperscale/hyperconverged and software-defined storage. To facilitate these workloads, Supermicro has included support for up to eight CXL 2.0 devices for advanced memory-pool sharing.

Of course, CXL can be utilized only on server platforms designed to support communication between the CPU, memory and CXL devices. That’s why CXL is built into the 4th gen AMD EPYC server processors.

These AMD EPYC processors include up to 96 ‘Zen 4’ 5nm cores. Each core includes 32MB per CCD of L3 cache, as well as up to 12 DDR5 channels supporting as much as 12TB of memory.

CXL memory expansion is built into the AMD EPYC platform. That makes these CPUs ideally suited for advanced AI and GenAI workloads.

Crucially, AMD also includes 256-bit AES-XTS and secure multikey encryption. This enables hypervisors to encrypt address space ranges on CXL-attached memory.

The Near Future of CXL

Like many add-on devices, CXL devices are often connected via the PCI Express (PCIe) bus. However, implementing CXL over PCIe 5.0 in large data centers has some drawbacks.

Chief among them is the way its memory pools remain isolated from each other. This adds latency and hampers significant resource-sharing.

The next generation of PCIe, version 6.0, is coming soon and will offer a solution. CXL for PCIe6.0 will offer twice as much throughput as PCIe 5.0.

The new PCIe standard will also add new memory-sharing functionality within the transaction layer. This will help reduce system latency and improve accelerator performance.

CXL is also leading to the start of disaggregated computing. There, resources that reside in different physical enclosures can be available to several applications.

Are your customers suffering from too much latency? The solution could be CXL.

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