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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