Artificial intelligence (AI) workloads have deservedly placed GPUs in the spotlight, but there’s a clutch player often overlooked: the host CPU. 

KEY TAKEAWAYS

• Your GPU needs a fast CPU—it’s the air traffic controller keeping AI inference running smoothly.
• High-frequency CPUs like AMD EPYC 9575F improve GPU efficiency by reducing latency in key AI tasks.
• Benchmarks show up to 10% faster inference times with AMD EPYC 9575F over Intel Xeon.
• Maximize AI ROI by choosing the right CPU for your GPUs!

Ask a typical IT professional today whether they're leveraging AI, and there's a good chance they'll say yes–after all, they have reputations to protect! Kidding aside, many will report that their teams may use Web-based tools like ChatGPT or even have internal chatbots that serve their employee base on their intranet, but for that not much AI is really being implemented at the infrastructure level.  

Discover AMD AI solutions with day-0 support for Meta's Llama 3.1, delivering efficiency from cloud to edge

AMD invests significant financial resources to provide on-premises and multi-cloud EDA compute infrastructure for its engineers. At AMD IT, we continuously optimize our cloud/data center components. We have traditionally used machine learning (ML) and business intelligence (BI) tools to predict low-level capacity metrics and drive efficiencies. In this article, we will highlight the challenges in managing EDA compute forecast, our approach to the problem, and how we integrate ML/Generative AI in the compute investment optimization process in IT.

Artificial intelligence has enjoyed a meteoric rise in popularity, from science fiction to real-world use within just a few years. But how does AI work, behind the buzzwords? If you are confused about how terms like “artificial intelligence,” “deep learning,” and “neural net” relate to each other or want to better understand what sets generative AI apart from other types of AI, you’ve come to the right place.

I’m excited to share that today, AMD has completed the acquisition of Nod.ai - further expanding our open-source AI software capabilities. We are thrilled to welcome Nod.ai’s talented team of AI software experts to the AMD Artificial Intelligence Group (AIG).   

AMD is committed to industry standard benchmarks – fully transparent, peer-reviewed or independently audited, verifiable, and full disclosure reports to reproduce our results. We are proud of our 300+ world records across a diverse set of important workload areas.

We are happy to announce the new availability of the TensorFlow-ZenDNN plug-in for TensorFlow v2.12 and above, improving the accessibility of ZenDNN optimizations for the community via framework upstreaming. Learn more in this blog.

In 2021, AMD announced a new vision - a 30x25 goal - to achieve 30x energy efficiency improvement by 2025 from a 2020 baseline for our accelerated data center compute nodes. Built with AMD EPYC™ CPUs and AMD Instinct™ accelerators, these nodes are designed for some of the world’s fastest-growing computing needs in AI training and HPC Applications. We plan to share updates annually on where we stand, and this is our first progress report on 30x25.