For Everyday AI, Use AMD EPYC CPUs
Since they have reputations to uphold, it’s likely that a typical IT professional will respond in the affirmative when asked if they are utilising AI. Putting aside the jokes, many may mention that their teams may use Web-based solutions like ChatGPT or even have internal chatbots that service their employee base on their intranet, but that infrastructure-level AI implementation is still lacking.
The real answer, it turns out, is somewhat different. Even many IT professionals may not be aware of how commonplace AI tools and approaches are; they have become deeply ingrained in typical company workloads. Inspections driven by computer vision are now a part of assembly line operations. AI is used by supply chains to estimate demand, which speeds up corporate operations. Naturally, AI note-taking and meeting summaries are integrated into almost every version of collaboration and meeting software.
Recommendation engines, virtual agents, and other AI-enabled support are becoming more and more integrated into essential enterprise IT solutions.
AI is genuinely spreading throughout daily business as a supplementary tool. Today’s businesses must simultaneously manage a hybrid environment where cutting-edge AI-driven jobs coexist with conventional, mission-critical workloads. The workload environment of “mixed enterprise and AI” necessitates infrastructure that can manage both processing kinds with ease.
To meet this need, strong, all-purpose CPUs, such as the AMD EPYC CPUs, are made to be strong, safe, and adaptable. In addition to handling routine duties like managing databases, web servers, and ERP systems, they also provide robust security measures that are essential for business operations enhanced by AI workloads.
Creating a healthy environment is the fundamental goal of contemporary enterprise infrastructure. This balance is mostly achieved by AMD EPYC CPUs, which provide excellent performance, efficiency, and security characteristics that support both sophisticated AI operations and conventional enterprise workloads.
When it makes reasonable to use CPU inference
There are four possible use case features that can be used to determine whether workloads are suitable for CPU inference:
- Large Memory Capacity: More memory capacity to store more detailed state information and larger models during inference.
- Minimal Latency: Real-time, intermittent, or low concurrent inference requests for small and medium-sized models.
- Offline/Batch Processing: Unrestricted latency or situations in which high volume workloads can be handled by batch processing.
- Energy Efficiency and Cost: Cost and energy consumption sensitivity for both CAPEX and OPEX.
The 5th Gen AMD EPYC processors are a smart choice for managing AI inference because of these features. The fact that x86 CPUs, which have the most cores in the industry, are able to handle the parallelized architectures that are essential to AI models is not a coincidence. Additionally, AI models may quickly and easily access the key value cache to the memory’s closeness, speed, and overall capacity, which aids in the models’ efficient operation.
It should come as no surprise that AMD EPYC CPUs have set hundreds of world records for efficiency and performance, proving to be the best at a variety of general-purpose computing workloads.
Workloads that fit for CPU inference
As it have seen, a workload’s suitability for a CPU will depend on its attributes. Classical machine learning, recommendation systems, natural language processing, generative AI (such as language models), and collaborative prompt-based pre-processing are the most prevalent workload categories that are frequently executed on CPUs. To examine each of these in further detail and explain why 5th Gen AMD EPYC CPUs are a strong fit for inference.
Traditional Machine Learning
Decision trees and linear regression models are typical examples of machine learning models. Compared to deep neural networks, these algorithms usually use rule-based logic and matrix operations, and their architecture is more sequential than that of AI models.
CPUs are well-suited to managing branching logic and scalar operations well. Furthermore, organized datasets that fit in memory are used by traditional machine learning techniques. CPUs offer excellent performance optimization because of their enormous memory capacity and reduced memory access latency.
Recommendation Systems
Think about how recommendations are used to curate social media feeds and online purchases. These systems process a wide range of data sets, including item attributes, user demographics, and interaction history, and employ a number of algorithms, such as collaborative filtering and content-based filtering. CPUs are perfect for providing the flexibility needed to support this large range. CPUs are ideally suited for recommendation systems, which also need extensive, low latency memory access to store whole datasets and embed tables in memory for quick, frequent access.
Natural Language Processing
Natural language processing models are frequently used in chatbots and text-to-speech or speech-to-text applications. These models are small and designed to function in a conversational environment in real time. These applications are excellent candidates for CPU inference since, from a computational perspective, they are not particularly latency sensitive, requiring answers that are less than a millisecond, whereas human response times are within seconds.
Additionally, by utilising 5th generation AMD EPYC CPUs with a large core count, numerous concurrent instances may be accommodated on a single CPU, providing a compelling price-performance efficiency for certain applications.
Generative AI Including Language Models
Generative models are currently being used by many enterprise applications that have transitioned from tiny chatbot applications to expedite and simplify content generation. Language models are the most prevalent kind of generative model. CPUs can handle small and medium language models with ease.
The 5th generation AMD EPYC processors’ large core count and memory capacity allow for real-time inference that is responsive enough for the majority of typical use cases, such as chatbots or search engines, and perfect for batch/offline inference with slack reaction time requirements. AMD EPYC-optimized libraries can boost performance throughput by offering more parallelism and the ability to execute multiple instances.
Collaborative Prompt Based Pre-Processing
A more recent class of models known as collaborative models are tiny and effective for pre-processing data or user prompts to expedite the inference process for a larger model later on. These tiny models, which are utilized in speculative decoding and retrieval augmented generation (RAG) AI solutions, are well suited for CPU operation and are frequently employed in “mixed” scenarios where the host CPU does inference while the GPUs handle heavy inference workloads.
Each of these workloads has a wide range of applications in various industry sectors. These workloads can be used in an infinite number of end applications, which means that there are an infinite number of uses for CPU-based inference. CPUs power everyday AI inference, whether it’s streamlining supply chains with demand forecasting driven by time series and traditional machine learning models, reducing carbon footprints with predictive analysis like XGBoost to forecast emissions, or enhancing customer experience with in-store deals and coupon delivery.
Although each of these workload types can coexist peacefully on a CPU, 5th Gen AMD EPYC processors are the best option for CPU inference in each case due to their high core count, high memory capacity architecture designed to balance serialized and parallelized workloads, and flexibility in supporting multiple workloads and data types.
Speaking of adaptability, high frequency 5th Gen AMD EPYC processors become the ideal host CPU once you do begin utilising accelerators. AMD EPYC 9575F CPUs have up to 50% more memory bandwidth capacity (8 channels vs. 12 channels), 1.6 times the high-speed PCIe Gen 5 lanes (80 vs. 128 in single socket configurations), and an 8% higher maximum core frequency (3.9GHz vs. 5.0GHz) than the Intel Xeon 8592+.
In addition, AMD provides a wide range of solutions, such as the AMD Instinct GPU, to provide the optimal combination of compute engines. You have the option to run the infrastructure you desire because an increasing number of servers with AMD EPYC CPUs are certified to run Nvidia GPUs.
Solutions for the Evolving Spectrum of AI
AMD EPYC CPUs can allow you to develop and grow. They provide flexibility to match your AI workload requirements, independent of size and scale, in addition to helping you consolidate legacy servers in your data centre to free up power and space. 5th Gen AMD EPYC CPUs offer outstanding price-performance efficiency for smaller-scale AI installations, and they enable you get the most throughput out of your AI workload for larger-scale deployments that need one or hundreds of thousands of GPUs.
Development never stops. Since the future is uncertain, 5th Gen AMD EPYC CPUs provide flexibility to adjust to the changing AI landscape, regardless of whether models become smaller and more efficient or larger and more competent (or both!).
You need to be flexible if you want to provide your clients with the greatest goods and services at the most competitive cost. A server with an AMD EPYC CPU will be flexible enough to grow with you.
