#EPYCprocessor

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G593-SD1 & ZD1 : High-Capacity, Liquid-Cooled GPU Servers

Customized Cooling for the G593 Series

With an 8-GPU baseboard specifically designed for it, the GPU-focused G593 series boasts both liquid and air cooling. The industry’s most easily scalable chassis, the 5U, can accommodate up to 64 GPUs in a single rack and sustain 100kW of IT infrastructure. This reduces the footprint of the data center by consolidating the IT hardware. Growing consumer desire for higher energy efficiency has led to the development of the G593 series servers for DLC. Liquids can quickly and efficiently remove heat from heated components to maintain lower operating temperatures since they have a higher thermal conductivity than air. Additionally, the data center uses less energy overall because it depends on heat and water exchangers.

“With the NVIDIA HGX H200 GPU, they provide an excellent AI scaling GIGABYTE solution,” stated Vincent Wang, vice president of sales at Giga Computing. “It is necessary to make sure the infrastructure can handle the computational demand and complexity of AI/ML, and data science models due to the complexity of business data centers. Increasing optimization is required due to the growing complexity. They are able to create and fund scalable AI infrastructure. Additionally, by working with the NVIDIA NVAIE platform, They can handle every facet of AI data center infra services, from software stack deployment to overall coverage.

For the NVIDIA HGX H200 and NVIDIA HGX H100 platforms, GIGABYTE has now launched variants of its G593 series that are air-cooled and DLC compatible. Future GIGABYTE servers with the NVIDIA HGX B200A architecture will additionally be available with liquid or air cooling. As a solution to the requirement for a full supercluster with 256x NVIDIA H100 GPUs, GIGABYTE has already launched GIGAPOD for rack-scale deployment of all these NVIDIA HGX systems. This system consists of five racks for DLC servers, four of which are filled with eight G593 servers apiece. Additionally, a nine-rack system may accommodate the same thirty-two G593-SD1 for air cooling.

NVIDIA NVLink and NVIDIA NVSwitch provide excellent interconnectivity, and systems are combined with InfiniBand to facilitate interconnectivity across cluster nodes. All things considered, a full cluster can handle scientific simulations, large-scale model training, and more with ease.

G593-ZD1-LAX3

GPU + CPU Direct cooling solution in liquid

GPU: NVIDIA HGXTM H200 8-GPU liquid-cooled

GPU-to-GPU bandwidth of 900GB/s using NVIDIA NVLink and NVSwitch

Two Processors AMD EPYC 9004 Series

24-piece DDR5 RDIMM with 12 channels

Architecture with Dual ROM

2 x 10Gb/s LAN ports through the Intel X710-AT2

2 x M.2 slots with x4 and x1 PCIe Gen3 interfaces

8 × 2.5″ Gen5 hot-swappable bays for SAS-4, SATA, and NVMe

Four FHHL Gen5 x16 PCIe slots

PCIe Gen5 x16 slots with 8 LPs

4+2 3000W 80 PLUS Titanium backup power sources

G593-SD1-LAX3

GPU + CPU Direct cooling solution in liquid

8-GPU NVIDIA HGX H200 liquid-cooled

GPU-to-GPU bandwidth of 900GB/s using NVIDIA NVLink and NVSwitch

Two Intel Xeon Scalable Processors, Generations 5 and 4

Intel Xeon Dual Core Max Series

32 DIMMs, 8-Channel DDR5 RDIMM

Architecture with Dual ROM

Compliant with SuperNICs and NVIDIA BlueField-3 DPUs

Intel X710-AT2 provides two 10Gb/s LAN ports.

8 × 2.5″ Gen5 hot-swappable bays for SAS-4, SATA, and NVMe

Four FHHL Gen5 x16 PCIe slots

PCIe Gen5 x16 slots with 8 LPs

4+2 3000W 80 PLUS Titanium backup power sources

Fueling the Next Wave of Energy Efficiency and Server Architecture

G593-ZD1

AMD EPYC 9004 Series processors continue the EPYC breakthroughs and chiplet designs that led to AMD’s 5nm ‘Zen 4’ architecture. The new EPYC processor family includes several new capabilities to target a wide range of applications, improving performance per watt and CPU performance. on a platform with double the throughput of PCIe 4.0 lanes and support for 50% more memory channels. With components designed to maximize the performance of EPYC-based systems that enable fast PCIe G593, Gen5 NVMe SSDs, and highly performant DDR5 memory, GIGABYTE is prepared for this new platform.

AMD EPYC 4th Generation Processors for SP5 Socket

5 nm architecture

More transistors crammed into a smaller space led to an improvement in compute density.

128 cores for the CPU

Zen 4c and Zen 4 cores have dedicated cores and intended workloads.

Big L3 cache

Specific CPUs for technical computing feature three times or more L3 cache.

Compatibility with SP5

There is a single platform that supports all 9004 series processors.

Twelve channels

Six terabytes of memory can fit in one socket.

DDR5 RAM

Increased DDR5 capacity per DIMM and increased memory throughput

PCIe 5.0 lanes

Enhanced IO throughput on PCIe x16 lanes, reaching 128GB/s bandwidth

Support for CXL 1.1+

Compute Express Link makes disaggregated compute architecture viable.

G593-SD1

Accelerating AI and Leading Efficiency

on business transformation, Intel has increased CPU performance by engineering richer features on a new platform. The 4th and 5th Gen Intel Xeon Scalable processors’ built-in AI acceleration engines boost AI and deep learning performance, while networking, storage, and analytics use other accelerators. Adding a host of new features to target a wide range of workloads, the new Intel Xeon processor families will deliver even better CPU performance and performance per watt Using a PCIe 5.0 platform with 2x the previous gen throughput to speed GPU-storage data transfer. Intel introduced the Intel Xeon CPU Max Series with HBM to boost memory-bound HPC and AI applications. GIGABYTE has solutions ready for Intel Xeon CPU-based systems with fast PCIe Gen5 accelerators, Gen5 NVMe SSDs, and high-performance DDR5 memory.

Why Opt for GIGABYTE Servers for Liquid Cooling?

Amazing Performance

Due to the great performance of liquid-cooled components that run well below CPU TDP, servers will operate with exceptional stability.

Energy Conservation

A liquid-cooled server can outperform an air-cooled server by requiring less electricity, fans, and speeds.

Reduced Noise

Numerous loud, high-speed fans are needed for servers. With fewer fans and a liquid cooling method, GIGABYTE has discovered a way to cut down on noise.

A Track record of success

The direct liquid cooling system supplier has served desktop PCs and data centers for 20 years. GIGABYTE has 20+ years of experience.

Dependability

Maintenance for liquid cooling solutions is low and visible. GIGABYTE and liquid cooling suppliers warranty components.

Usability

GIGABYTE liquid-cooled servers can be rack-mounted or connected to a building’s water supply. and provides dry, simple, and fast disconnects.

Elevated Efficiency

Compatible with NVIDIA HGX H200 8-GPU

High-speed interconnects and H200 Tensor Core GPUs are combined by the NVIDIA HGX H200 to provide every data center with exceptional performance, scalability, and security. With configurations of up to eight GPUs, the world’s most potent accelerated scale-up server platform for AI and HPC is created, offering unparalleled acceleration and an astounding 32 petaFLOPS of performance. Over 32 petaflops of FP8 deep learning computing and 1.1TB of aggregate high-bandwidth memory are offered by an eight-way HGX H200. In order to facilitate cloud networking, composable storage, zero-trust security, and GPU computing elasticity in hyperscale AI clouds, NVIDIA HGX H200 also incorporates NVIDIA BlueField-3 data processing units (DPUs).

Energy Efficiency

Controlled Fan Speed Automatically

Automatic Fan Speed Control is enabled on GIGABYTE servers to provide optimal cooling and power efficiency. Intelligently placed temperature sensors across servers will automatically adjust fan speeds.

Elevated Availability

Ride-through Smart (SmaRT)

In order to guard against data loss and server outages due to AC power outages, GIGABYTE has included SmaRT into all of server platforms. The system will throttle in response to such an occurrence, maintaining availability and lowering power consumption. Power supply capacitors can provide power for 10–20 ms, enough time to switch to a backup power source and continue running.

SCMP means Smart Crises Management and Protection

SCMP is patented by GIGABYTE and utilized in non-redundant PSU servers. SCMP puts the CPU in ultra-low power mode to prevent an unintended shutdown, component damage, and data loss. In the event of a malfunctioning PSU or overheated system

Architecture with Dual ROM

The backup BMC and/or BIOS will replace the primary BIOS upon system reset if the ROM cannot boot. The backup BMC’s ROM will immediately update the backup through synchronization as soon as the primary BMC is updated. Users can upgrade the BIOS based on firmware version.

Hardware Safety

TPM 2.0 Module Option

Passwords, encryption keys, and digital certificates are kept in a TPM module for hardware-based authentication to keep unauthorized users from accessing your data. There are two types of GIGABYTE TPM modules: Low Pin Count and Serial Peripheral Interface.

Easy to Use

Tool-free Drive Bays Style

A clip secures the drive. It takes seconds to install or swap out a new drive.

Management with Added Value

Gigabete provides free management programs with a dedicated tiny CPU integrated into the server.

Console for GIGABYTE Management

Every server comes with the GIGABYTE Management Console, which can manage a single server or a small cluster. After the servers are up and running, the browser-based graphical user interface allows IT workers to monitor and manage each server’s health in real time. Furthermore, the GIGABYTE Management Console offers:

Support for industry-standard IPMI specifications that allow open interface service integration onto a single platform.

Automatic event recording makes it simpler to decide what to do next by capturing system behavior up to 30 seconds before an event happens.

Integrate SAS/SATA/NVMe devices and RAID controller firmware into GIGABYTE Management Console to monitor and manage Broadcom MegaRAID adapters.

Management of GIGABYTE Servers (GSM)

A software suite called GSM can manage many server clusters online. Any GIGABYTE server can run GSM on Windows and Linux. GSM, available from GIGABYTE, meets Redfish and IPMI standards. The following tools are among the full set of system administration features that are included with GSM:

GSM Server: Software that runs on an administrator’s PC or a server in the cluster to enable real-time, remote control via a graphical user interface. Large server clusters can have easier maintenance thanks to the software.

GSM CLI: A command-line interface designed for remote management and monitoring.

GSM Agent: An application that is installed on every GIGABYTE server node and interfaces with GSM Server or GSM CLI to retrieve data from all systems and devices via the operating system.

GSM Mobile: An iOS and Android mobile application that gives administrators access to real-time system data.

The GSM Plugin is an application program interface that enables users to manage and monitor server clusters in real time using VMware vCenter.

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AMD EPYC 9754 Benchmark Beats NVIDIA Grace Superchip

AMD EPYC 9754 Benchmark

AMD EPYC 4th Gen Outperforms NVIDIA Grace Superchip in Performance and Efficiency Industry benchmarks indicate that single- and dual-socket 4th Gen AMD EPYC systems outperform 2P NVIDIA Grace CPU Superchip systems by ~2.00x to ~3.70x and have more than double the energy efficiency.

My previous blogs showed that 4th Gen AMD EPYC processors beat 5th Gen Intel Xeon Platinum and Ampere Altra Max M128-30  CPUs for critical applications. They’ll evaluate 4th Gen AMD EPYC and NVIDIA Grace CPU Superchip  CPUs’ performance and energy efficiency.

Continuous innovation drives 4th Gen AMD EPYC CPUs to create new standards in datacenter performance, power efficiency, security, and TCO. The 4th Gen AMD EPYC processor range provides cutting-edge on-premises and cloud-based solutions for today’s demanding, diverse workloads.

Over 250 server architectures and 800 cloud instances are supported by AMD EPYC. AMD EPYC processors have over 300 world records in commercial applications, technical computing, data management, data analytics, digital services, media and entertainment, and infrastructure solutions.

The Grace CPU Superchip from NVIDIA was released with eye-opening performance comparisons. These claims must be carefully assessed because their benchmark results are minimal and lack key system setup data. With comprehensive industry-standard benchmark releases, AMD continually shows superior performance and power efficiency.

AMD EPYC processors have 5927 official SPEC CPU 2017 publications, while NVIDIA Grace has none. As you will see, 4th Gen AMD EPYC processors outperform NVIDIA  CPUs in energy efficiency and performance.

Please note that this blog covers only a portion of the tested workloads. ARM-based NVIDIA Grace can run a limited number of workloads due to compatibility concerns with x86 processing architecture, which enables enterprise, cloud-native, and HPC applications.

AMD tested several systems with single-socket and dual-socket AMD EPYC 9754 (code name “Bergamo,” with 128 cores and 256 threads/vCPUs), dual-socket AMD EPYC 9654 (code name “Genoa,” with 96 cores and 192 threads/vCPUs), and NVIDIA Grace processors. Unless otherwise stated, each AMD EPYC system had 12 × 64GB DDR5-4800 memory per socket. NVIDIA used the highest server-supported LPDDR5-8532 memory of 480 GB.

Efficiency of Power

Modern data centres must handle rising demand while optimizing electricity use to cut costs and promote sustainability. By assessing the System Under Test’s power and performance, the Standard Performance Evaluation Corporation (SPEC) power ssj 2008 benchmark compares volume server class computers’ energy efficiency.

General-purpose computing

For computer system performance testing, SPEC created the SPEC CPU 2017 benchmark set. A leading industry standard for evaluating general-purpose computing infrastructure, SPECrate 2017_int_base ratings evaluate integer performance.

Single- and dual-socket AMD EPYC 9754 systems outscored NVIDIA Grace systems by approx. 1.33x and 2.64x, respectively. Additionally, a dual-socket AMD EPYC 9654 system outperformed the same NVIDIA system by ~2.43x in the same tests.

Java server-side

4th Gen AMD EPYC  CPUs provide cloud native workloads without sacrifice or costly architectural upgrades. Java is used everywhere in enterprise and cloud contexts. The SPE jbb 2015 benchmark models an e-commerce corporation with an IT infrastructure that handles point-of-sale requests, online purchases, and data-mining operations to evaluate Java-based application performance on server-class hardware.

Single- and dual-socket AMD EPYC 9754 computers outscored NVIDIA Grace by ~1.81x and ~3.58x, respectively. Additionally, the dual-socket AMD EPYC 9654 system outperformed the NVIDIA system by ~3.36x in SPECjbb2015-MultiJVM max-jOPS tests. AMD EPYC computers use SUSE Linux Enterprise Server 15 SP4 v15.14.21 with Java SE 21.0 for 9654 and 17.0 LTS for 9754. NVIDIA Grace system running Ubuntu 22.04.4 (kernel v15.15.0-105-generic) and Java SE 22.0.

Transactional Databases

MySQL is a popular open-source relational database technology in enterprise and cloud environments. AMD assessed online transaction processing using HammerDB TPROC-C. The HammerDB TPROC-C workload, generated from the TPC-C Benchmark Standard, does not match published TPC-C results as it does not meet the standard.

Single- and dual-socket AMD EPYC 9754 computers outscored NVIDIA Grace by ~1.58x and ~2.16x, respectively. Additionally, the dual-socket AMD EPYC 9654 system outperformed the NVIDIA system by ~2.17x in the same tests.

Ubuntu 22.04, MySQL 8.0.37, and HammerDB 4.4 were installed on the test systems. Multiple 16-core VMs were on each system. Three test runs’ medians were compared.

System Supporting Decisions

Decision Support System deployments use MySQL extensively. AMD evaluated Design Support System performance with HammerDB TPROC-H. HammerDB TPROC-H workload results do not meet with the TPC-H Benchmark Standard, hence they cannot be compared to published TPC-H results.

As shown in single- and dual-socket AMD EPYC 9754 systems outscored NVIDIA Grace systems by ~1.42x and ~2.98x, respectively. Additionally, the dual-socket AMD EPYC 9654 system outperformed the same system by ~2.62x in the same tests.

Ubuntu 22.04, MySQL 8.0.37, and HammerDB 4.4 were installed on the test systems. Multiple 16-core VMs were on each system. Three test runs’ medians were compared.

The Web Server

In Figure 6, single- and dual-socket AMD EPYC 9754 computers outscored NVIDIA Grace by ~1.27x and ~2.56x, respectively. Additionally, the dual-socket AMD EPYC 9654 system outperformed the NVIDIA system by ~1.89x in the same tests.

The server and client were on the same system in this benchmark test to reduce network delay and estimate CPU processing power. The systems ran Ubuntu 22.04 and NGINX 1.18.0. Multiple 8-core instances ran on each system. Each run assessed the workload for 90 seconds, and the median requests per second (rps) from 3 runs per platform were averaged to compare performance.

In-memory analytics

A powerful in-memory distributed key-value database, cache, and message broker with optional persistence, Redis. AMD used redis-benchmark to test Redis servers.

Single- and dual-socket AMD EPYC 9754 beat NVIDIA Grace by ~1.15x and ~2.29x, respectively (Figure 7). Additionally, the dual-socket AMD EPYC 9654 system outperformed the NVIDIA system by ~1.54x in the same tests.

Ubuntu 22.04, Redis 7.0.11, and redis-benchmark 7.2.3 were installed. Each client established 512 GET/SET connections with 1000-byte keys to its Redis server. Multiple 8-core instances ran on each system. Three runs of the workload test ran 10 million requests on each system, and the median requests per second (rps) statistics were aggregated to compare performance.

Cache Tier

The high-performance, distributed in-memory caching system Memcached stores key-value pairs for short amounts of arbitrary data like strings or objects. It caches rendered pages and database or API calls. AMD used the popular memtier benchmarking tool to evaluate latency and throughput improvements.

Single and dual-socket AMD EPYC 9754 computers outscored NVIDIA Grace by ~1.16x and ~2.26x, respectively. Additionally, the dual-socket AMD EPYC 9654 system outperformed the NVIDIA system by ~1.97x in the same tests.

The computers ran Ubuntu v22.04, Memcached v1.6.14, and memtier v1.4.0. Each memtier client had 10 connections, 8 pipelines, and a 1:10 SET/GET ratio with its Memcached server. Multiple instances with 8 cores ran on each system. To compare performance, the workload test processed 10 million requests on each system and averaged the median requests per second (rps) from three runs per platform.

High-performance computing

HPC impacts every sector of our lives where performance is critical, from manufacturing to life sciences. ARM processors excel at lighter workloads but struggle with HPC and crucial data-centric tasks. Some apps have been ported to ARM, however they lack the advanced features of x86 processors that increase HPC performance. Another factor is memory capacity: 4th Gen AMD EPYC  CPUs can support 3 TB, whereas ARM-based NVIDIA Grace can only support 480 GB.

Compiling HPC apps for ARM processors and diagnosing runtime errors is difficult. When testing common open-source HPC workloads for fast turnaround times, AMD engineers encountered the following issues:

Runtime issues occur despite source code updates in NAMD.

GROMACS compiles incorrectly and requires source adjustment.

OpenRadioss fails to compile and needs Pull Requests and adjustments for ARM instructions.

WRF and OpenFOAM dependencies do not compile.

AMD engineers easily compiled and tested these open-source HPC workloads:

HPL needs modest cmake adjustments to compile and run with the ARM performance math library.

Scalapack is not included in the ARM performance math library, yet Quantum ESPRESSO compiles and runs.

These issues highlight the importance of AMD EPYC chips’ x86 CPU architecture compatibility between generations. Compare the performance of these two workloads.

High-performance Linpack

Figure 9 shows that the dual-socket AMD EPYC 9754 machine outperformed NVIDIA Grace by ~2.34x. Additionally, a dual-socket AMD EPYC 9654 system outperformed the NVIDIA system by ~1.97x in the same tests.

NVIDIA Grace runs Red Hat Enterprise Linux 9.4 with kernel 5.14.0-427.18.1.el9_4.aarch64+64k and has 480GB of LPDDR5X-8532 memory, while AMD EPYC 9754 and 9654 have 1.5 TB of DDR5-4800.

Quantum ESPRESSO

Open-source Quantum ESPRESSO calculates nanoscale electronic structures and materials using density-functional theory, plane waves, and pseudo potentials. Comparing system performance with Quantum ESPRESSO 7.0 ausurf benchmark.

The dual-socket AMD EPYC 9754 computer outscored NVIDIA Grace by ~4.08x. Additionally, a dual-socket AMD EPYC 9654 machine outperformed the NVIDIA system by ~3.46x in the same tests.

NVIDIA Grace runs Red Hat Enterprise Linux 9.4 with kernel 5.14.0-427.18.1.el9_4.aarch64+64k and has 480GB of LPDDR5X-8532 memory, while AMD EPYC 9754 and 9654 have 1.5 TB of DDR5-4800.

Video Encoding

From classic to modern, FFmpeg can encode, decode, convert, stream, filter, and play most video formats. Its format conversion, video resizing, editing, and seamless streaming make it popular.

The systems ran Ubuntu 22.04 and FFmpeg 4.4.2. Each FFmpeg instance transcoded a 4K input file into raw video using the VP9 codec. Multiple 8-core instances ran on each system. Each system was evaluated by its median total frames processed per hour over three test runs.

AMD EPYC 9754 Price

The AMD EPYC 9754 processor costs around $11,900 at launch. Its high-end server capabilities and top-tier EPYC lineup position impact its pricing (CPU / processor comparison) (Club386).

Conclusion

General-purpose AMD EPYC 9654 and, most critically, cloud-native AMD EPYC 9764 processors outperformed NVIDIA Grace in eleven fundamental, SQL, and HPC applications. This and my June blog statistics show that 4th Gen AMD EPYC processors remain market leaders in performance and power efficiency.

With the upcoming release of 5th Gen AMD EPYC processors (codenamed “Turin”) with up to 192 cores per processor and a fully compatible x86 architecture that runs your current and future workloads, AMD will extend our performance and energy efficiency leads. Let’s keep this a secret until then.

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The Future of AI is Efficient: Why Choose AMD EPYC Servers

AMD EPYC Server to Power Sustainable AI

Artificial intelligence (AI) must undoubtedly be taken into consideration by every company creating a competitive roadmap. Many essential aspects of daily life are currently powered by artificial intelligence, including data center compute efficiency and consumer-focused productivity solutions.

Having said that, AI is clearly still in its infancy, and there are many uncertainties about the future. A lot of businesses are still planning how they will use the technology. Implementation presents the next difficulty for a company when it has a vision. For your AI use case, which computing environment is best? What fresh materials are you going to require to fuel your AI tools? In what way do you incorporate such resources into the surroundings you already have?

AI isn’t a single kind of tool. Different enterprises have distinct aims, goals, and technological issues. As a result, their AI workloads will differ and might have very distinct infrastructure needs. Most likely, the route is evolutionary.

EPYC Processors

The fact is that a large number of businesses will need to use both CPUs and GPUs. This is not surprising, considering the wide installed base of x86-based CPUs that have powered corporate computing for decades and are home to the enormous data repositories that companies will use AI methods to mine and develop. Moreover, the CPUs themselves will often meet the demand in a successful and economical manner. They think that many businesses would profit more from smaller, more focused models that operate on less powerful infrastructure, even while massive language models like the ChatGPT have a lot to offer and need a lot of processing capacity.

What position does the workload at your company occupy on this spectrum? Although it’s often the correct response, “it depends” is seldom a satisfactory one. But AMD can also guide you through it with assurance. When workload demands demand it, AMD provides the business with a balanced platform that can house leading high-performance GPUs in addition to high-performance, energy-efficient CPUs with its AMD EPYC Processor-based servers.

From the marketing of a top GPU vendor, you may have inferred that GPUs are the optimal solution for handling your AI tasks. Conversely, the marketing campaigns of a CPU manufacturer may imply that their CPUs are always and unquestionably the best choice. You will need a platform that can handle both alternatives and everything in between, such as AMD EPYC Processor-based servers, if you want to apply AI in a manner that makes the most sense for your business with a dynamic mix of AI- and non-AI enhanced workloads.

Allow AI to live there

Regardless of your AI goals, setting up space in your data center is often the first thing you need to do. In terms of available power, available space, or both, data centers these days are usually operating at or close to capacity. In the event that this is the case in your data center, consolidating your current workloads is one of the better options.

EPYC Processor

You can design and launch native AI or AI-enabled apps by moving current workloads to new systems, which may free up resources and space. Suppose, for illustration purposes, that your current data center is equipped with Intel Xeon 6143 “Sky Lake” processor-based servers that can achieve 80 thousand units of SPECint performance (a measure of CPU integer processing capability). You might save up to 70% on system rack space and up to 65% on power consumption if you swapped out your five-year-old x86 AMD EPYC servers with AMD EPYC 9334 processor-based systems to do the same amount of work (SP5TCO-055).

When you’re ready to go forward and have the necessary space and energy, AMD can assist you in selecting the appropriate computing alternatives. AMD EPYC Processors provide outstanding performance for small-to-medium models, traditional machine learning, and hybrid workloads (such as AI-augmented engineering simulation tools or AI-enhanced collaboration platforms). In situations when the cost and performance of additional GPUs are not justified or efficient, they are also useful for batch and small-scale real-time inference applications. CPUs may give good performance and efficiency choices at affordable costs, even if you’re developing a huge, custom language model with a few billion parameters, as compared to OpenAI’s GPT-3, which has 175 billion.

AMD EPYC servers are an attractive option for tasks that need the capability of GPUs, such large-scale real-time inference and medium-to-large models. There are more possibilities, of course, but the AMD Instinct and AMD Radeon GPU families are progressively showing themselves to be powerful solutions to allow great AI performance. You can now easily plug in your Nvidia accelerators with well-known, reliable AMD EPYC server manufacturers to achieve the speed and scalability you want.

An increasing number of AMD EPYC Processor-based servers are certified to operate a variety of Nvidia GPUs. You will receive not just the speed you want but also the memory capacity, bandwidth, and strong security features you desire with AMD EPYC processor-based servers, regardless of the accelerators used.

There is no one-size-fits-all path to AI enablement. Depending on their unique objectives, top commercial and technological priorities, and other factors, many businesses will take alternative routes. Yet AMD EPYC servers with AMD EPYC processors provide the infrastructure to take you there as your demands change, regardless of where your business is going in the AI future.

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