In the realm of AI, there are two crucial phases: training and inference.
AI training involves teaching models to recognize data patterns, which is the most data and processing-intensive part requiring substantial computational power.
During this phase, large-scale parallel GPU accelerators or dedicated AI accelerators are often preferred, though high-performance CPU processors can also be utilized depending on the scenario.
On the other hand, AI inference involves processing input data in real-time based on the trained models, requiring relatively smaller computational power, emphasizing continuous operation and low latency, and operating closer to the data's actual location.
Hence, conventional CPUs are the most suitable for this stage, offering optimal performance, energy efficiency, compatibility, and cost-effectiveness tailored to the demands of AI inference.
Of course, this demands comprehensive qualities from the CPU, with a balance of robust performance, energy efficiency, and cost-effectiveness to deliver high efficiency and benefits.
Generally, GPU training, CPU inference, along with development frameworks and software support, constitute the most suitable complete AI lifecycle.
As the industry's only solution provider with high-performance GPU, CPU, and FPGA platforms, AMD stands out, especially with the maturation of its ROCm development platform. Throughout the entire lifecycle of AI training and inference, AMD enjoys a unique advantage, particularly with the EPYC CPU, which seems to be unbeatably lonely at the top.
Today, AMD EPYC processors have become the go-to server platform for AI inference, especially with the fourth-generation Genoa EPYC 9004 series, which has seen a significant leap in AI inference performance.
For instance, the all-new Zen 4 architecture boasts about a 14% increase in instructions executed per clock cycle compared to its predecessor, coupled with higher frequencies, resulting in substantial performance gains.
Moreover, the advanced 5nm manufacturing process dramatically enhances processor integration. Combined with the new architecture, it makes high-performance, high-efficiency computing achievable.
Furthermore, with more cores and threads - up to 96 cores in the latest generation, a fifty percent increase over the previous one, supporting simultaneous multithreading - executing multiple inference operations without the need for parallelism is now feasible. Handling the inference demands of tens of thousands of data sources concurrently is now achievable, ensuring high concurrency and low latency.
Additionally, the flexible and efficient AVX-512 extension instruction set efficiently executes numerous matrix and vector calculations, significantly boosting the speed of convolutions and matrix multiplications. The BF16 data type improves throughput and mitigates the quantization risks of INT8 data. Moreover, with a dual-cycle 256-bit pipeline design, both efficiency and energy efficiency are enhanced.
The enhancements also extend to memory and I/O capabilities, including the introduction of DDR5 memory supporting up to 12 channels and up to 128 PCIe 5.0 lanes, serving as high-speed highways for massive data transfers.
Despite its impressive capabilities, the power efficiency remains high. Even with 96 cores, the thermal design power is only 360W, while 84 cores can be kept under control at 290W, significantly reducing cooling pressure.
Furthermore, AMD EPYC offers outstanding value for money, significantly reducing the Total Cost of Ownership (TCO).
Let's not forget that AMD EPYC is based on the x86 instruction set architecture, which is the most familiar and proficient for everyone. Deployment, development, and application entail significantly lower difficulty and cost compared to various specialized architectures.
When it comes to AI, we often focus more on AI training, especially the immense computational power required. However, AI inference, which occurs after training and is where the real-world applications take place, is equally crucial and demands suitable hardware and software platforms.
Servers powered by AMD EPYC processors provide an excellent platform for CPU-based AI inference tasks.
With 96 cores, DDR5 memory, PCIe 5.0 expansion, AVX-512 instructions, and other features, AMD EPYC servers deliver a dual boost in performance and energy efficiency. Furthermore, optimized libraries and primitives for the processor offer robust support.
In any model or scenario, AMD EPYC ensures ample high performance, energy efficiency, and cost-effectiveness.
