AMD EPYC processor’s one-of-a-kind secrets revealed

The AI lifecycle consists of two of the most important parts, one is AI training and the other is AI reasoning.

One of them is AI training, which is about getting the model to recognize data patterns, and is the most data and processing intensive part, requiring massive arithmetic power.

In this phase, massively parallel GPU gas pedals or dedicated AI gas pedals are often prioritized, and sometimes ultra-high-performance CPU processors can be used depending on the situation.

AI inference, on the other hand, is based on trained models that process the input data in real time, requiring only a smaller amount of arithmetic power, closer to the actual location of the data, and placing more emphasis on continuous computing with low latency.

Therefore, it is most appropriate to use conventional CPUs at this stage, whose performance, energy efficiency, compatibility, and price/performance ratio perfectly match AI inference needs.

Of course, this also has a high demand for the comprehensive quality of the CPU, which is powerful enough and balanced performance, energy efficiency, and cost in order to bring high enough efficiency and effectiveness.

In general, GPU training, CPU reasoning, coupled with development frameworks and software support, constitute the most appropriate complete AI life cycle.

As the industry's only AMD with both high-performance GPUs, CPUs, and FPGA platform-based solutions, coupled with the continued maturity of the ROCm development platform, AMD is uniquely positioned to take advantage of the entire lifecycle of AI training and reasoning, especially with EPYC CPUs that simply do not have the loneliness of the enemy.

Today, AMD EPYC processors have become the server platform most often chosen for AI reasoning, especially the fourth generation Genoa EPYC 9004 series, the ability to perform AI reasoning has been another huge leap.

For example, the new Zen 4 architecture, which offers a ~14% increase in the number of instructions executed per clock cycle compared to its predecessor, coupled with higher frequencies, provides a huge performance boost.

For example, the advanced 5nm manufacturing process, which greatly improves processor integration, combined with the new architecture makes high performance and energy efficiency possible.

For example, the number of cores and threads has increased by half compared to the previous generation, up to 96, and supports synchronous multi-threading, which allows more inference operations to be performed without the need for multiplexing, and it is not a problem to handle the data inference needs of tens of thousands of sources at the same time, resulting in a combination of high concurrency and low latency.

For example, the flexible and efficient AVX-512 extended instruction set can efficiently perform a large number of matrix and vector calculations, significantly improving the speed of convolution and matrix multiplication, especially the BF16 data type can improve throughput and avoid the risk of quantization of INT8 data, and it is also a dual-cycle 256-bit pipeline design, which is more efficient and energy-efficient.

Such as more powerful memory and I/O, including the introduction of DDR5 memory and support for up to 12 lanes, and up to 128 PCIe 5.0 lanes, which becomes a highway for large-scale data transfer.

For example, the extremely high power efficiency, the thermal design power consumption of 96 cores is also only 360W, 84 cores can be controlled at 290W, thus significantly reducing the pressure on heat dissipation.

There is also the consistently excellent price/performance ratio, which can greatly reduce the TCO (total cost of ownership).

As well as don't forget that AMD EPYC is based on the x86 architecture instruction set, which is the most familiar and proficient, and is far less difficult and costly to deploy, develop, and apply than a variety of special architectures.

For AI, we usually pay more attention to AI training, especially the huge arithmetic demand, AI reasoning is the stage of the real landing experience after training, the importance of which is also self-evident, and also requires just the right hardware and software platform needs.

A server equipped with AMD EPYC provides an excellent platform for CPU-based AI reasoning.

The 96 cores, DDR5 memory and PCIe 5.0 extensions, and AVX-512 instructions achieve both performance and energy efficiency, while the libraries and primitives optimized for the processor provide a strong bailout.

Regardless of any model or scenario, AMD EPYC provides plenty of high performance, energy efficiency, and cost-effectiveness.