#AMDEPYCprocessors

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Micron DDR5 128GB Memory And AMD EPYC 128-Core CPUs

Important lessons learned

A potent solution for AI and database infrastructures is provided by Micron DDR5 128GB memory and 5th Gen AMD EPYC CPUs with 128 cores. The computational complexity of huge model sizes and extensive datasets, which are features of AI, machine learning, data analytics, and IMDB applications, is successfully managed by this technique.SVM 1.3x Improves AI/ML support vector machine (SVM) performance by 1.3x.1.3x higher bandwidth use due to higher memory clock speeds on Micron 128GB RDIMMs, along with increased core counts on 5th Gen AMD EPYC processors.IMDB REDIS 1.2x Improves IMDB Redis performance by 1.2x for a set:get ratio of 1:10.30% Delivers 30% better average latency and 60% better p99 latency.SAP SD 201k users Improves SAP Sales and Distribution (SAP SD) benchmark. With 30% higher memory capacity and 30% higher clock, SAP SD scores higher than previous six-socket performance score.

Pairing Micron 128GB DDR5 RDIMMs with 5th Gen AMD EPYC processors (codenamed Turin).

As compared to Micron 96GB DDR5 RDIMMs and 4th Gen AMD EPYC processors with 96 cores (codenamed Genoa).

High-capacity memory and substantial processing power are necessary for modern data centers to support enterprise machine learning (ML) and artificial intelligence (AI) programs by running a range of workloads. Micron DDR5 128GB RDIMMs and 5th Gen AMD EPYC processors work together to provide exceptional performance and capabilities for a variety of server workloads that data centers handle, such as hosting demanding corporate applications and powering expansive cloud-based infrastructures.

In this blog, Micron present the outcomes of benchmark tests for Redis in-memory database (IMDB), SAP SD, and AI/ML support vector machine (SVM), where Its hardware configuration contrasted the following:

5th Gen AMD EPYC processors (codenamed Turin) with Micron DDR5 128GB DIMMs

With 4th Gen AMD EPYC processors (codenamed Genoa), Micron DDR5 96GB DIMMs

Its testing demonstrates that Micron DDR5 RDIMMs with increased capacity (128GB) and bandwidth (capable up to 8000 MT/s) improve performance for SVM, SAP SD, and Redis IMDB.

Hardware and system setup

The table below displays the specifics of the system architecture. Two systems, designated A and B in this blog, were compared. System A has Micron 96GB DDR5 DIMMs with 4th Gen AMD EPYC CPUs (96 cores), while System B had 128GB and 5th Gen. Both systems support a 12GB/core configuration; the 96-core CPU has 96GB across 12 memory channels, while the 128-core CPU has 128GB. System ASystem BHardware4th Gen AMD EPYC processors (codenamed Genoa)5th Gen AMD EPYC processors (codenamed Turin)MemoryMicron 96GB DDR5 4800 MT/sDual rank, 12 channelsMicron 128GB DDR5 6400 MT/sDual rank, 12 channelsCPUDual-socket AMD EPYC 9654 (96-core)Dual-socket AMD EPYC (128-core)Storage (for SVM)Micron 9400 8TB (3)Micron 9400 8TB (3)

Support Vector Machine in AI/ML

SVM is a kind of machine learning technique that is frequently used to prepare datasets for a variety of cloud-based data science applications. Its processed a 2TB dataset using the SparkML engine and the Intel Hi-Bench architecture in tests.

Faster execution time

System B outperformed system A by 30% in terms of SVM execution time. This is mostly because system B’s processor has more cores, the 128GB memory modules offer more capacity and bandwidth, and the bandwidth is used efficiently.

Higher bandwidth use

Because of the faster memory rate (6400 MT/s vs. 4800 MT/s) and the extra Zen5 cores made possible by the 5th Gen AMD EPYC processors with 128 cores, data indicate that SVM on system B uses 1.3 times more bandwidth than system A.

The SVM can keep more data in memory because to system B’s larger capacity (128GB vs. 96GB), which reduces storage input/output. For both setups, maintained a consistent memory capacity of 12GB per core. Compared to the baseline configuration (system A), this method allowed us to separate the effects of more computation capacity and faster clock speed (memory).

Redis

Applications that need minimal latency can store and retrieve data using Redis, a fast in-memory database (IMBD). Memtier simulates a multithreaded and multiclient execution model by benchmarking Redis with multiple set:get operations.

Running Redis on system B (128GB and 128 cores) results in a 1.2x speedup. Additionally, the identical combination reduces p99 latency by 60% and average latency by 30%. Higher core counts, such as the 5th Gen’s 128 cores, can better utilize the Micron DDR5 128GB DIMMs’ greater capacity and bandwidth than earlier AMD EPYC CPU generations. Enterprise data centers can easily serve more users with the increased throughput made possible by the additional cores.

SAP Sales and Distribution (SAP SD)

An enterprise resource planning (ERP) software suite that is frequently used is called Systems Application and Products (SAP). As a component of the SAP ecosystem, it is composed of several smaller parts. Setting a new performance world record of 201,000 users for the SAP SD benchmark on a two-socket system, the component that includes all of the operations and processes for SAP Sales and Distribution (SAP SD) was benchmarked against the Dell PowerEdge R6725 server and outfitted with Micron DDR5 128GB RDIMMs and 5th Gen AMD EPYC processors.

That surpasses the top six-socket score. The increased number of benchmark users suggests that using Micron memory in conjunction with 5th Gen AMD EPYC CPUs on Dell PowerEdge servers for database use cases offers a performance advantage.

AI in data centers

For data center infrastructures to efficiently manage the computational complexity, massive model sizes, and extensive datasets typical of AI, machine learning, data analytics, and IMDB applications, high-capacity memory, high memory bandwidth, and low latency are essential. Micron workload findings demonstrate that Micron DDR5 128GB memory modules combined with 5th Gen AMD EPYC processors provide a potent solution for these situations.

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AMD Instinct MI300X GPU Accelerators With Meta’s Llama 3.2

AMD applauds Meta for their most recent Llama 3.2 release. Llama 3.2 is intended to increase developer productivity by assisting them in creating the experiences of the future and reducing development time, while placing a stronger emphasis on data protection and ethical AI innovation. The focus on flexibility and openness has resulted in a tenfold increase in Llama model downloads this year over last, positioning it as a top option for developers looking for effective, user-friendly AI solutions.

Llama 3.2 and AMD Instinct MI300X GPU Accelerators

The world of multimodal AI models is changing with AMD Instinct MI300X accelerators. One example is Llama 3.2, which has 11B and 90B parameter models. To analyze text and visual data, they need a tremendous amount of processing power and memory capa

AMD and Meta have a long-standing cooperative relationship. Its is still working to improve AI performance for Meta models on all of AMD platforms, including Llama 3.2. AMD partnership with Meta allows Llama 3.2 developers to create novel, highly performant, and power-efficient agentic apps and tailored AI experiences on AI PCs and from the cloud to the edge.

AMD Instinct accelerators offer unrivaled memory capability, as demonstrated by the launch of Llama 3.1 in previous demonstrations. This allows a single server with 8 MI300X GPUs to fit the largest open-source project currently available with 405B parameters in FP16 datatype something that no other 8x GPU platform can accomplish. AMD Instinct MI300X GPUs are now capable of supporting both the latest and next iterations of these multimodal models with exceptional memory economy with the release of Llama 3.2.

By lowering the complexity of distributing memory across multiple devices, this industry-leading memory capacity makes infrastructure management easier. It also allows for quick training, real-time inference, and the smooth handling of large datasets across modalities, such as text and images, without compromising performance or adding network overhead from distributing across multiple servers.

With the powerful memory capabilities of the AMD Instinct MI300X platform, this may result in considerable cost savings, improved performance efficiency, and simpler operations for enterprises.

Throughout crucial phases of the development of Llama 3.2, Meta has also made use of AMD ROCm software and AMD Instinct MI300X accelerators, enhancing their long-standing partnership with AMD and their dedication to an open software approach to AI. AMD’s scalable infrastructure offers open-model flexibility and performance to match closed models, allowing developers to create powerful visual reasoning and understanding applications.

Developers now have Day-0 support for the newest frontier models from Meta on the most recent generation of AMD Instinct MI300X GPUs, with the release of the Llama 3.2 generation of models. This gives developers access to a wider selection of GPU hardware and an open software stack ROCm for future application development.

CPUs from AMD EPYC and Llama 3.2

Nowadays, a lot of AI tasks are executed on CPUs, either alone or in conjunction with GPUs. AMD EPYC processors provide the power and economy needed to power the cutting-edge models created by Meta, such as the recently released Llama 3.2. The rise of SLMs (small language models) is noteworthy, even if the majority of recent attention has been on LLM (long language model) breakthroughs with massive data sets.

These smaller models need far less processing resources, assist reduce risks related to the security and privacy of sensitive data, and may be customized and tailored to particular company datasets. These models are appropriate and well-sized for a variety of corporate and sector-specific applications since they are made to be nimble, efficient, and performant.

The Llama 3.2 version includes new capabilities that are representative of many mass market corporate deployment situations, particularly for clients investigating CPU-based AI solutions. These features include multimodal models and smaller model alternatives.

When consolidating their data center infrastructure, businesses can use the Llama 3.2 models’ leading AMD EPYC processors to achieve compelling performance and efficiency. These processors can also be used to support GPU- or CPU-based deployments for larger AI models, as needed, by utilizing AMD EPYC CPUs and AMD Instinct GPUs.

AMD AI PCs with Radeon and Ryzen powered by Llama 3.2

AMD and Meta have collaborated extensively to optimize the most recent versions of Llama 3.2 for AMD Ryzen AI PCs and AMD Radeon graphics cards, for customers who choose to use it locally on their own PCs. Llama 3.2 may also be run locally on devices accelerated by DirectML AI frameworks built for AMD on AMD AI PCs with AMD GPUs that support DirectML. Through AMD partner LM Studio, Windows users will soon be able to enjoy multimodal Llama 3.2 in an approachable package.

Up to 192 AI accelerators are included in the newest AMD Radeon, graphics cards, the AMD Radeon PRO W7900 Series with up to 48GB and the AMD Radeon RX 7900 Series with up to 24GB. These accelerators can run state-of-the-art models such Llama 3.2-11B Vision. Utilizing the same AMD ROCm 6.2 optimized architecture from the joint venture between AMD and Meta, customers may test the newest models on PCs that have these cards installed right now3.

AMD and Meta: Progress via Partnership

To sum up, AMD is working with Meta to advance generative AI research and make sure developers have everything they need to handle every new release smoothly, including Day-0 support for entire AI portfolio. Llama 3.2’s integration with AMD Ryzen AI, AMD Radeon GPUs, AMD EPYC CPUs, AMD Instinct MI300X GPUs, and AMD ROCm software offers customers a wide range of solution options to power their innovations across cloud, edge, and AI PCs.

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Top Oracle Exadata Databases Run Faster On AMD EPYC CPUs

The best databases in the world perform better on AMD EPYC processors.

Oracle Exadata

They at AMD think that their EPYC processors are the finest data center CPUs available. Their clients concur. EPYC processors have become widely used, as shown by the fact that they run the Frontier system at Oak Ridge National Laboratory, the most powerful supercomputer in the world, and that they are used in over 800 public cloud instances across all major cloud service providers. Businesses are using more and more EPYC CPUs to power their on-premise infrastructures across all business verticals.

Indeed, AMD EPYC now holds over 300 world records for efficiency and performance, both on-site and in the cloud. However, did you realize that data management and analytics account for more than 50 of those records? This indicates that AMD EPYC is the best option available for processing the most important and fundamental business tasks in the globe.

The two databases that big businesses use the most often are Oracle and Microsoft SQL Server. AMD’s 4th Gen EPYC CPUs provide impressive performance whether you’re using Microsoft SQL Server or the Oracle Exadata Database Platform. First, let’s examine the Oracle Exadata platform to understand how AMD EPYC has contributed to the advancement of database computing.

The Database Platform for Oracle Exadata

Managing workloads for Oracle Exadata databases is a major endeavor. Exadata offers real-time vector search capabilities, analytics, and fast transaction processing. Tasks that are vital to the mission may be handled by the Exadata system. These workloads allow for the instantaneous completion of online financial transactions while instantly detecting fraudulent activity. These database workloads help carriers maintain their networks and keep up with the increasing needs of the worldwide 5G network. They also keep the internet transactions flowing, particularly during the peak shopping seasons of Cyber Monday and Single’s Day.

Oracle developed the Oracle Exadata Database Platform to provide performance, scalability, availability, and security while supporting these workloads. This platform, which includes hardware, software, and an Oracle database, is simple to set up.

Oracle made a significant shift in 2023 after depending on Intel CPUs for years to power the Oracle Exadata Database Platform. Oracle decided to employ the 4th generation AMD EPYC CPU (previously code-named “Genoa”) in the most recent version X10M Exadata system in order to achieve better performance and energy efficiency.

Exadata Oracle

Think about the impact that EPYC CPUs have: Compared to the preceding Exadata X9M-2 system, the Exadata X10M Database Platform features three times more database server cores per socket from 32 to 96 cores thanks to AMD EPYC CPUs. More EPYC CPU cores simply translate into more database transactions and quicker analytics when combined with Oracle’s optimized software.

Meanwhile, the dual-socket X10M Database server can now tackle tasks that previously needed pricey and power-hungry 8-socket servers thanks to its remarkable core count. An organization may recover precious space in the data center and save energy by using fewer, smaller servers to do the same tasks.

X10M Database server

In order to ensure that database performance grows linearly with a core count of up to 192 per Database Server (2 X 96 core CPUs), AMD and Oracle worked together to enhance the performance of the new Exadata X10M server. Exadata system software, on the other hand, can encrypt and decrypt data quicker than other components can get it to the chip thanks to the 4th generation AMD EPYC CPU. Furthermore, the Exadata system software was tailored to take full use of AMD EPYC’s reliability, availability, and serviceability (RAS) features. One example of this is Platform First Error Handling, which improves the uptime of workloads related to key databases.

Microsoft SQL Server

Let’s now examine Microsoft SQL Server and see how AMD EPYC leads the industry in database transaction processing speed. Businesses often utilize the TPC (Transaction Processing Performance Council) benchmarks to assess processor performance as well as the overall performance of server systems. To guarantee the validity of the benchmark test findings, TPC.org has an audited library of benchmarks. Let’s examine two widely used TPC benchmarks to assess database performance: TPC-E and TPC-H.

Decision support systems that analyze vast amounts of data, carry out intricate queries, and respond to important business inquiries are evaluated using the TPC-H benchmark. According to this test, the best performance the most queries per hour is achieved by a non-clustered system running Microsoft SQL Server 2022 on a 64-core 4th Gen AMD EPYC CPU. Performance on the AMD-based system is 14% better than that of a system powered by the most recent 5th Gen Intel Xeon 8592+. To put it another way, the firm can evaluate data considerably more quickly and get business outcomes since there are 14% more inquiries answered in an hour.

The findings shown above pertain to a database with a 10TB size. Furthermore, the TPC-H findings show that AMD EPYC powered systems also provide the best non clustered performance for 3 TB (EPYCWR-869) and 1 TB databases (EPYCWR-865).

In the meanwhile, online transaction processing (OLTP) is measured by the TPC-E benchmark. There are twelve simultaneous transactions of varying kinds and complexity involved. Either online or time- or price-based criteria are used to initiate the transactions.

Once again, while running Microsoft SQL Server 2022, the AMD-based system achieves the greatest performance for a non-clustered server on the TPC-E benchmark. When compared to a system using a 5th generation Intel Xeon CPU, the system built around the 4th generation AMD EPYC processor performs 7% better.

In summary

AMD has shown that its EPYC CPUs provide the greatest performance available on the market when used with Microsoft SQL Server and Oracle Exadata databases. It is not worth taking shortcuts when it comes to organizing and evaluating data that is essential to the continuous operation of your organization.

AMD continues to set the standard in the server industry with the upcoming release of 5th Gen EPYC CPUs. For the highest database workloads, AMD’s unrivaled 4th Gen EPYC CPUs are the obvious option for the finest database performance in the interim.

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AMD EPYC Processors Widely Supported By Red Hat OpenShift

EPYC processors

AMD fundamentally altered the rules when it returned to the server market in 2017 with the EPYC chip. Record-breaking performance, robust ecosystem support, and platforms tailored for contemporary workflows allowed EPYC to seize market share fast. AMD EPYC began the year with a meagre 2% of the market, but according to estimates, it now commands more than 30% of the market. All of the main OEMs, including Dell, HPE, Cisco, Lenovo, and Supermicro, offer EPYC CPUs on a variety of platforms.

Best EPYC Processor

Given AMD EPYC’s extensive presence in the public cloud and enterprise server markets, along with its numerous performance and efficiency world records, it is evident that EPYC processors is more than capable of supporting Red Hat OpenShift, the container orchestration platform. EPYC is the finest option for enabling application modernization since it forms the basis of contemporary enterprise architecture and state-of-the-art cloud functionalities. Making EPYC processors argument and demonstrating why AMD EPYC should be taken into consideration for an OpenShift implementation at Red Hat Summit was a compelling opportunity.

Gaining market share while delivering top-notch results

Over the course of four generations, EPYC’s performance has raised the standard. The fastest data centre  CPU in the world is the AMD EPYC 4th Generation. For general purpose applications (SP5-175A), the 128-core EPYC provides 73% better performance at 1.53 times the performance per projected system watt than the 64-core Intel Xeon Platinum 8592+.

In addition, EPYC provides the leadership inference performance needed to manage the increasing ubiquity of  AI. For example, utilising the industry standard end-to-end AI benchmark TPCx-AI SF30, an Intel Xeon Platinum 8592+ (SP5-051A) server has almost 1.5 times the aggregate throughput compared to an AMD EPYC 9654 powered server.

A comprehensive array of data centres and cloud presence

You may be certain that the infrastructure you’re now employing is either AMD-ready or currently operates on AMD while you work to maximise the performance of your applications.

Red Hat OpenShift-certified servers are the best-selling and most suitable for the OpenShift market among all the main providers. Take a time to look through the Red Hat partner catalogue, if you’re intrigued, to see just how many AMD-powered choices are compatible with OpenShift.

On the cloud front, OpenShift certified AMD-powered instances are available on AWS and Microsoft Azure. For instance, the EPYC-powered EC2 instances on AWS are T3a, C5a, C5ad, C6a, M5a, M5ad, M6a, M7a, R5a, and R6a.

Supplying the energy for future tasks

The benefit AMD’s rising prominence in the server market offers enterprises is the assurance that their EPYC infrastructure will perform optimally whether workloads are executed on-site or in the cloud. This is made even more clear by the fact that an increasing number of businesses are looking to jump to the cloud when performance counts, such during Black Friday sales in the retail industry.

Modern applications increasingly incorporate or produce  AI elements for rich user benefits in addition to native scalability flexibility. Another benefit of using AMD EPYC CPUs is their shown ability to provide quick large language model inference responsiveness. A crucial factor in any AI implementation is the latency of LLM inference. At Red Hat Summit, AMD seized the chance to demonstrate exactly that.

AMD performed��Llama 2-7B-Chat-HF at bf16 precision​over Red Hat OpenShift on Red Hat Enterprise Linux CoreOS in order to showcase the performance of the 4th Gen AMD EPYC. AMD showcased the potential of EPYC on several distinct use cases, one of which was a chatbot for customer service. The Time to First Token in this instance was 219 milliseconds, easily satisfying the patience of a human user who probably anticipates a response in under a second.

The maximum performance needed by the majority of English readers is approximately 6.5 tokens per second, or 5 English words per second, but the throughput of tokens was 8 tokens per second. The model’s performance can readily produce words quicker than a fast reader can usually keep up, as evidenced by the 127 millisecond latency per token.

Meeting developers, partners, and customers at conferences like Red Hat Summit is always a pleasure, as is getting to hear directly from customers. AMD has worked hard to demonstrate that it provides infrastructure that is more than competitive for the development and deployment of contemporary applications. EPYC processors, EPYC-based commercial servers, and the Red Hat Enterprise Linux and OpenShift ecosystem surrounding them are reliable resources for OpenShift developers.

It was wonderful to interact with the community at the Summit, and it’s always positive to highlight AMD’s partnerships with industry titans like Red Hat. EPYC processors will return this autumn with an update, coinciding with Kubecon.

Red Hat OpenShift’s extensive use of AMD EPYC-based servers is evidence of their potent blend of affordability, effectiveness, and performance. As technology advances, they might expect a number of fascinating breakthroughs in this field:

Improved Efficiency and Performance

EPYC processors of the upcoming generation

AMD is renowned for its quick innovation cycle. It’s expected that upcoming EPYC processors would offer even more cores, faster clock rates, and cutting-edge capabilities like  AI acceleration. Better performance will result from these developments for demanding OpenShift workloads.

Better hardware-software integration

AMD, Red Hat, and hardware partners working together more closely will produce more refined optimizations that will maximize the potential of EPYC-based systems for OpenShift. This entails optimizing virtualization capabilities, I/O performance, and memory subsystems.

Increased Support for Workloads

Acceleration of AI and machine learning

EPYC-based servers equipped with dedicated AI accelerators will proliferate as AI and ML become more widespread. As a result, OpenShift environments will be better equipped to manage challenging AI workloads.

Data analytics and high-performance computing (HPC)

EPYC’s robust performance profile makes it appropriate for these types of applications. Platforms that are tailored for these workloads should be available soon, allowing for OpenShift simulations and sophisticated analytics.

Integration of Edge Computing and IoT

Reduced power consumption

EPYC processors of the future might concentrate on power efficiency, which would make them perfect for edge computing situations where power limitations are an issue. By doing this, OpenShift deployments can be made closer to data sources, which will lower latency and boost responsiveness.

IoT device management

EPYC-based servers have the potential to function as central hubs for the management and processing of data from Internet of Things devices. On these servers, OpenShift can offer a stable foundation for creating and implementing IoT applications.

Environments with Hybrid and Multiple Clouds

Uniform performance across clouds

major cloud providers will probably offer EPYC-based servers, which will guarantee uniform performance for hybrid and multi-cloud OpenShift setups.

Cloud-native apps that are optimised

EPYC-based platforms are designed to run cloud-native applications effectively by utilising microservices and containerisation.

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ThinkSystem SR685a V3 Rack Server Uses 4th Gen AMD EPYC

ThinkSystem SR685a V3

Many businesses are still looking for methods to use generative AI, even since ChatGPT sparked a lot of interest in the field some 20 months ago. Robert Daigle of Lenovo claims that utilising  AI can be difficult when dealing with large amounts of data. Often, expensive GPU deployments and sizable language models are required. But that’s only one possible outcome. According to Robert, some businesses require assistance in even knowing where to begin.

Although Lenovo is well-known worldwide for being a dominant producer of personal and business systems, the multinational corporation also offers AI services and solutions to companies in a variety of industries. Robert is in charge of Lenovo’s worldwide AI division, which helps customers set objectives, solve problems, and create infrastructures that maximise the benefits of AI.

AMD and Lenovo: Appropriately sized AI for businesses

Robert stated, “You don’t need a GPU for everything.” “Not every use case requires the greatest performance interconnected accelerators. That’s excellent if you’re attempting to train a model with more than 100 billion parameters from scratch. It’s a wise decision to have that much powerful computing power in a system with networked GPUs. However, many commercial clients will only be considering inferencing and perhaps some fine-tuning. and a tendency towards smaller language models is also present.”

CPUs and lightweight accelerators are more than sufficient for inferencing and smaller language models, according to Robert. Thus, giving customers additional choices and freedom is a crucial component of Lenovo’s “AI for All” agenda. Lenovo gives businesses more options by offering a large selection of both hardware and software.

AMD’s Instinct MI300X GPUs

Lenovo unveiled a line of HCI devices and servers in April that handle “compute intensive workloads” and hybrid AI-centric infrastructure systems. Among these is the ThinkSystem SR685a V3. AMD’s Instinct MI300X GPUs and various GPU alternatives, along with 4th generation AMD EPYC processors, power the ThinkSystem SR685a V3. The system is made to work with both public and on-premise AI cloud services.

Lenovo also contributes to addressing security, removing bias from  AI, and environmental protection issues. Lenovo discloses a multi-step assessment procedure that was originally developed for internal usage to help customers build ethical and responsible AI.

Rack server ThinkSystem SR685a V3

Image Credit To AMD

Designed with Compute-Heavy AI in Mind

Form Factor: Rack server (8U/2S)

Processor: Two AMD EPYC 4th Generation Processors

Supported GPUs: 8x NVIDIA H100/H200/B100 or AMD Instinct MI300x

Fastest GPU interconnect using NVLink from NVIDIA and AMD’s Infinity Fabric

Memory: 24x TruDDR5 RDIMM slots maximum

Extension Slots: Ten PCIe Gen5 x16 adapters maximum

Regarding energy usage, Robert stated that businesses are understandably concerned about electricity usage. The fact that some data centres consume as much electricity as small nations astounded him. He suggested that using energy-efficient processors, like AMD EPYC CPUs, and possibly liquid cooling are answers for businesses using LLMs.

ThinkSystem SR685a V3 Features

Boosted Processing for  AI and HPC

An 8U2S rack server designed for demanding AI and HPC tasks is the ThinkSystem SR685a V3. Utilising industry-leading 4th Generation AMD EPYC Processors, it is accelerated for intensive computation, supports 8x of the newest GPUs, and connects at the quickest transfer rate via AMD’s Infinity Fabric or NVIDIA’s NVLink. With its computer capability, it can handle tasks like scientific research, financial technology, modelling, training, and rendering.

Option and Adaptability

The variety of choice provided by the open architecture design of the ThinkSystem SR685a V3 allows it to be modified to meet specific workload requirements.

You’ll find everything you need, whether you decide to use the NVIDIA H100/H200/B100 or the MI300x to run AMD on AMD. Whichever suits you best will depend on your availability and workload requirements.

Superior User Interface

With the ThinkSystem SR685a V3, they at Lenovo go above and beyond the norm to provide a premium user experience. To do this by incorporating easy systems administration, robust power supply, and thermals for next-generation GPUs.Image Credit To AMD

With power supply that provide complete N+N redundancy without throttling, this workhorse offers the highest level of resilience.

This computer, which uses N+1 hot-swap fans for air cooling, has enough thermal headroom to support both current and next GPUs with greater power.

Lenovo XClarity systems software is pre-installed on the ThinkSystem SR685a V3 to facilitate deployment and management.

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VMware VMmark Wins, Powered By AMD EPYC Virtual Tasks

VMmark User Guide

After their introduction in 2017, AMD EPYC processors have become the preferred option for both public and private cloud deployments. High core counts, the fastest x86 memory bandwidth in the market, and cutting-edge security features make 4th generation AMD EPYC processors ideal for demanding private cloud workloads. AMD EPYC processors offer a strong platform for building private cloud infrastructures, as do VMware by Broadcom products including VMware Cloud Foundation (VCF) and VMware vSphere Foundation (VVF), which comprise programmes like vSphere, vCenter, vSAN, and NSX. The strategic partnership between AMD and VMware By Broadcom helps organisations install and manage virtualized environments that meet their performance and scalability needs while optimising energy efficiency and TCO.

Two typical deployment scenarios for VCF/VVF exist:

Conventional VVF infrastructure typically comprises discrete networking, storage, and computation gear. Running this model requires managing each part independently, which frequently calls for specialised knowledge and equipment, but it produces the best results. The computational component is virtualized using software like VMware vSphere, which is controlled by a virtual infrastructure administrator. This configuration’s storage component depends on specialised Storage Area Network (SAN) or Network Attached Storage (NAS) devices under the control of a Storage Administrator. A contemporary method of managing data centres is called hyper-converged infrastructure (HCI), which integrates networking, storage, and processing into a single, cohesive system. Typically, HCI systems pool storage resources throughout the entire infrastructure for simple scaling and management by a Virtual Infrastructure Administrator. This is made possible by Software Defined Storage (SDS), which is made possible by products like vSAN. Compared to conventional VCF/VVF installations, this simplified method can maximise savings while enhancing agility and scalability. VMmark Benchmark

By offering thorough performance measurements that mimic real-world workloads to evaluate CPU, memory, storage, and networking performance, the VMmark benchmark helps enterprises to evaluate the effectiveness and scalability of their virtualized systems. Businesses may decide on hardware configurations, resource allocation, and workload management methods with knowledge thanks to this comprehensive picture of system capabilities under various workload scenarios.

By applying the smallest unit, or “tile,” of load, VMmark3 employs a weighted scoring system to assess a server’s performance. Nineteen virtual machines are included of each tile, which represents a typical enterprise virtualization scenario by executing a wide collection of tasks both concurrently and collaboratively. Each tile’s static nature allows for the size and scalability of the amount of work for every distinct VMmark3 cluster.

With a growth in hosts, sockets, and cores, there are usually more tiles. Each tile also serves as a representation of the maximum theoretical score and a Quality of Service (QoS) metric, which are used to appropriately scale each publication: an excessive number of tiles exceeds the benchmark latency requirements, while an insufficient number of tiles limits the maximum score.

While application performance accounts for a piece of the total score, the quantity of tiles is essential to comprehending the potential of a VCF deployment. Many of the capabilities that are exclusive to VCF, like XvMotion, Storage vMotion, vMotion, and the Distributed Resource Scheduler (DRS), are also utilised by standard virtualized deployments. By utilising these capabilities, VMmark workloads offer a more comprehensive view of what can be accomplished with the entire system as opposed to just one server. Depending on the underlying technology, these infrastructure processes can vary in scope and duration, accounting for 20% of the total score.

Benefits of Generational Performance AMD is committed to innovation and keeps delivering notable gains in efficiency and performance for important applications. As can be seen in Figure 1, AMD EPYC processors have significantly improved in performance with each iteration when used in VCF scenarios. These assessments include single- and dual-processor installations in both normal settings and vSAN configurations. The consistent dedication of AMD towards meeting the dynamic and ever-increasing demands of contemporary virtualized infrastructures is demonstrated by the notable performance improvements attained with every new generation of AMD EPYC CPUs.

Performance of Throughput

Thanks to continued AMD processor technology advances and collaborative VMware By Broadcom optimisations, the preceding section of this blog revealed constant performance increases across successive AMD EPYC processor generations. The performance domination of AMD EPYC processors over several processing generations is demonstrated in Figure 2, which highlights their unwavering commitment. In VMmark3 performance tests conducted on clusters comprising two dual-socket servers for a total of four processor sockets, the 4th generation AMD EPYC 9654 CPU outperforms even the most recent 5th generation Intel Xeon Platinum 8592+ processor.

Per-Core Efficiency

When it comes to power, space, and server count optimisation, per-server throughput is a crucial measure. Additionally, because per-core performance is frequently a major issue in software licencing models based on core count, it is especially important for workloads that are SLA-critical.

The performance improvements of 32-core AMD EPYC 9374F processors over 32-core Intel Xeon Gold 6548Y+ processors and 64-core AMD EPYC 9554 processors over 64-core 5th Gen Intel Xeon 8592+ processors are shown in Figure 3.

Power Effectiveness

In order to minimise cooling requirements, maximise utility costs, and support sustainability goals, modern data centres must have efficient power consumption. In comparison to both 4th and 5th Gen Intel Xeon processors, Figure 4 illustrates how 4th Gen AMD EPYC 9654 CPUs provide higher power efficiency.

Space for a Data Centre VMmark Scores Data centres have tremendous incentives to optimise space, electricity, and cooling for sustainability and TCO. Less servers that may offer more virtual machine consolidation without sacrificing the speed required for corporate operations can be used to achieve these goals. Suppose AMD workloads need to achieve business objectives with resources and quality of service equal to a total VMmark3 score of 500. This aggregate score can be attained with just 26 servers running 4th Gen AMD EPYC 9654 processors, as shown in Figure 5.

AMD will require 38 servers if they choose to employ servers with 5th generation Intel Xeon 8592+ processors. It may take up to 112 servers for customers with 2nd Gen Intel Xeon 8280 processor-based servers to get the same aggregate score! That’s more than 4 times the amount of AMD EPYC 9654 processor-based servers required to meet the same workload in business! This comparison emphasises the significance of not only power efficiency but also making the best use of server rack space in datacenters.

VMmark Benchmark Results This article demonstrated how VMmark victories and the next generation of AMD EPYC processors can drive consistent performance leadership, giving organisations optimal hardware consolidation and unparalleled x86 workload performance that may help reduce total cost of ownership. Businesses can safely depend on AMD EPYC processors to satisfy their changing virtualized infrastructure demands while maximising available power and capacity. Together, AMD and VMware by Broadcom are still dedicated to promoting innovation that continuously yields record-breaking virtualized environment performance and efficiency. Businesses can achieve new levels of agility, scalability, security, and reliability in their IT infrastructures by utilising Broadcom’s combination of experience in AMD and VMware.

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Powerful AMD EPYC 9654 Beats 5th Gen Intel Xeon Processors

AMD EPYC Gen 4 vs Intel Xeon 5th Gen: A Battle for Server Supremacy

The recent availability of 5th Gen Intel Xeon processors allows testing of 4th Gen AMD EPYC CPUs on critical workloads. AMD EPYC 9654 beats competitors in data centre performance, power efficiency, and cost. AMD EPYC CPU hold over 300 performance world records, and Intel’s latest release maintains our lead. Over 250 server designs and 800 cloud instances comprise AMD EPYC.

SPECpower

Modern data centers manage power utilization to meet growing needs, save costs, and satisfy environmental goals. The System Under Test’s power and performance are used to compare volume server class computers’ energy efficiency in the SPECpower ssj 2008 benchmark. SPECpower was the first industry benchmark for single- and multi-node server power and performance.

Consumers can compare server and configuration energy efficiency using the SPECpower ssj 2008 benchmark. This benchmark applies to hardware, IT, OEM, and government companies. The SPECpower ssj 2008 statistic assesses SUT power efficiency as “overall ssj ops/watt”. It is the ratio of the overall throughput ssj ops, which is the sum of all target load scores, to their average watt power consumption.

AMD EPYC 9754

AMD EPYC 4th Gen excels at power efficiency. A dual-socket system with 128-core AMD EPYC 9754 processors outscored Intel Xeon Platinum 8592+ by 2.25x.

AMD EPYC 9554

Industry benchmarks for compute-intensive jobs include the SPEC CPU 2017 test, which stresses the processor, memory subsystem, and compiler on various computer systems. This blog discusses SPECrate 2017 Integer and Floating Point, two of SPEC CPU 2017’s four suites’ 43 benchmarks.

On SPECrate 2017, 32-core AMD EPYC 9374F machine surpasses Intel Xeon Scalable 8562Y+ by ~1.14x (base) and ~1.06x (64 cores). 2P 64-core AMD EPYC 9554 systems outperform Intel Xeon Scalable 8592+ in SPECrate 2017 and similar.

Top performer: SPECrate 2017 ranked 2P 64-core Intel Xeon Scalable 8592+ behind 2P 96-core AMD EPYC 9654. SPECjbb2015 was considered simulates a corporate IT infrastructure that handles point-of-sale requests, online transactions, and data-mining to test server-side Java applications. Because Java is so popular, JVM providers, hardware manufacturers, Java application developers, academics, and academia should evaluate this benchmark.

AMD EPYC 9554 64-core processor

64 cores A dual-socket 64-core AMD EPYC 9554 machine performs comparably to an Intel Xeon Scalable 8592+ on SPECjbb 2015 MultiJVM-maxJOPS.

Top of stack: A dual-socket 96-core general purpose AMD EPYC 9654 machine outperforms a 64-core Intel Xeon Scalable 8592+ system 1.48x on SPECjbb 2015 MultiJVM-maxJOPS.

Decision Support System

The decision support benchmark TPC Benchmark H (TPC-H) evaluates systems that handle complex business queries by running sophisticated queries over big datasets. This benchmark’s queries and data manipulations affect several industries.

TPC-H’s Composite Query-per-Hour Performance Metric (QphH Size) evaluates query processing. When handling concurrent user requests, database sizes, query stream processing, and query throughput are considered. A system’s price-performance ratio, or “bang for the buck,” is likewise assessed by TPC-H.

A 2P 64-core AMD EPYC 9554 system outperforms a 2P Intel Xeon Scalable 8592+ system by ~1.14x and ~1.22x at SF 10000 in price-performance TPC-H.

Business Uses

Key SAP ERP logistics module is SAP Sales and Distribution (SAP SD). SAP Application Performance Standard units’ database efficiency is measured using the SAP-SD 2-Tier benchmark to assess hardware performance. SAPS is hardware-independent and uses the Sales and Distribution (SD) benchmark to evaluate SAP systems. Recently, a 2P system with 96-core AMD EPYC 9654 processors outscored a 64-core Intel Xeon Platinum 8592+ by ~1.53x. For further information, refer to my blog post “4th Gen AMD EPYC CPUs Empower SAP SD 2-Tier Performance.”

In four generations of general-purpose AMD EPYC processors, “top of stack” CPU SKUs enhanced performance. It also indicates that AMD EPYC 2nd Gen and later outperform Intel Xeon processors.

The Virtualization Infrastructure

VMmark 3 benchmarks virtualized server performance, power efficiency, and scalability on physical hardware under intense loads. Server capabilities and virtualization solutions are compared for strengths and limitations.

AMD EPYC 9654

A 2P 64-core AMD EPYC 9554 machine outperforms a 2P Intel Xeon Scalable 8592+ system 1.29x on VMmark 3. In VMmark 3, a 2P 96-core AMD EPYC 9654 system outperforms a 2P 64-core Intel Xeon Scalable 8592+ system by ~1.60x.

Powerful computing

HPC predicts major climate events and enhances transit and infrastructure safety, making it part of almost every modern activity. Optimizing material use, simplifying designs, and lowering development costs lowers prices. Rapid virtual prototyping reduces physical testing and speeds market entry.

Demand for HPC workload performance rises. Performance increases enable faster simulations, shorter product development cycles, more scenario testing, and finer model tweaks, making products more effective and efficient. Explore 4th Gen AMD EPYC CPUs HPC dominance.

AMD EPYC 9374F

CFD models and studies fluid behavior like water flowing around a boat hull or air around a vehicle or aircraft using numerical analysis. Uses include industrial and consumer goods. Memory bandwidth limits computationally demanding CFD activities.

Altair AcuSolve: Without CFD, Altair AcuSolve allows organizations investigate ideas using flow, heat transfer, turbulence, and non-Newtonian material analysis. In AcuSolve’s acus-in test, a 2P 32-core AMD EPYC 9374F machine surpasses an Intel Xeon Scalable 8562Y+ system by ~1.46x. Ansys CFX: High-performance CFD software provides fast, reliable, and accurate solutions for a variety of CFD and Multiphysics applications. In several CFX tests, a 32-core AMD EPYC 9374F machine outperformed an Intel Xeon Scalable 8562Y+ system by ~1.48x. Ansys Fluent: Advanced physics modelling and industry-leading accuracy characterize this fluid simulation package. A 2P 32-core AMD EPYC 9374F machine outperformed a 2P Intel Xeon Scalable 8562Y+ system performance ~1.25x in chosen Fluent tests.

OpenFOAM CFD software is free and open-source. Academic and commercial institutions use it. In OpenFOAM tests, a 2P 64-core AMD EPYC 9554 system outperformed an Intel Xeon Scalable 8592+ and a top-of-stack general purpose system by ~1.14x. A 2P 96-core AMD EPYC 9654 system outscored a 64-core Intel Scalable 8592+ system by ~1.22 in FEA Clear.

An explicit Finite Element Analysis (FEA) numerical simulation evaluates structures and materials under dynamic situations like impacts, explosions, and crashes. The auto industry uses FEA to predict crash behavior and evaluate occupant safety. Cell phone manufacturers use FEA to simulate drop tests for durability. Simulations save manufacturers time and money by testing designs online instead of prototyping.

These simulations involve complex digital replicas of items like cars and phones. To reproduce dynamic events like impacts, models solve differential equations over time. Model part interactions are assessed for deformations or failures. The calculations demand lots of memory and processing power. Due to model interconnectivity, compute nodes must communicate to exchange information about model elements’ effects.

AMD EPYC 9374F Benchmark

A prominent explicit simulation programme is Ansys LS-DYNA. Complex short-duration events in automobile, aircraft, construction, military, manufacturing, and biotechnology are simulated. In some LS-DYNA tests, the 2P 32-core AMD EPYC 9374F machine outperformed the Intel Xeon Scalable 8562Y+ system by ~1.50x.

Altair Radioss measures impact or crash structures. It benchmarks hardware using common usage issues. The 2P 32-core AMD EPYC 9374F system outperformed the Intel Xeon Scalable 8562Y+ workstation by ~1.25x in some Radioss tests.

AMD EPYC 9654 96-core processor

Molecular Dynamics

Newton’s equations are solved using molecular dynamics to analyze atoms and molecules. Molecular systems can be studied by analyzing material behavior, protein folding, and chemical processes. For hundreds to millions of particles, GROMACS simulates Newtonian motion using modular dynamics. A 2P 64-core AMD EPYC 9554 system provides ~1.24x the average performance of an Intel Xeon Scalable 8592+ system, outperforming competing datacenter CPUs. A top-of-the-stack 2P 96-core AMD EPYC 9654 machine outperforms Intel CPU systems by ~1.63x in GROMACS tests.

AMD EPYC 9554 64-core processor

Quantum chemistry

Quantum chemistry illuminates bonding and reactions through molecule structure, energetics, and reactivity. In quantum chemistry and solid-state physics, CP2K duplicates atomic systems. 2P 64-core AMD EPYC 9554 system outscored Intel Xeon Scalable 8592+ by ~1.27x in H2O-dft-ls test, whereas top-of-stack The AMD EPYC 9654 outperformed the Intel processing system by ~1.62 (Weather Forecast).

The conus2.5km test showed a 2P 64-core AMD EPYC 9554 system outperforming a 2P Intel Xeon Scalable 8592+ system by ~1.30x, and a top-of-the-line 2P 96-core AMD EPYC 9654 system outperforming the Intel system by ~1.50.

Conclusion

This blog revealed that 4th Gen AMD EPYC processors outperformed 5th Gen Intel Xeons in critical tasks. The comparisons above show that 4th Gen AMD EPYC CPUs perform well and reliably in industry-critical tasks. 4th Gen AMD EPYC CPUs excel in virtualized environments, molecular dynamics, and quantum chemistry simulations. Leading operating systems, hypervisors, and cloud services support AMD EPYC CPUs. The AMD Documentation Hub offers BIOS and OS tunings for different workloads to improve performance.

AMD delivers innovative technology beyond processors:

AMD Instinct Accelerators: These accelerators assist scientists tackle complex challenges in various industries through exascale discoveries. They power scientific research, data analytics, machine learning, and other demanding applications with high-performance computing.

AMD Pensando DPUs enable cloud, computing, networking, storage, and security software-defined programming. These technologies improve productivity, performance, and scalability over present infrastructures wherever data is. Programmable infrastructure pieces provide data centre agility.

AMD FPGAs, hardware-adaptive SoCs, and ACAP processing platforms are adaptable and adaptive. These technologies boost endpoint, edge, and cloud infrastructure innovation.

Developers can adapt and optimize solutions for specific applications and use cases, enhancing processing efficiency.

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