The Crucial Role of Host CPUs in AI Workloads

While GPUs often take center stage in artificial intelligence (AI) workloads, the importance of host CPUs in ensuring optimal performance cannot be understated. According to AMD, host CPUs play a pivotal role in managing data movement, inference requests, and workload scheduling, all of which are critical to maximizing GPU efficiency.

The Role of Host CPUs in AI

GPUs are responsible for executing model computations, but it is the host CPU that acts as the orchestrator, akin to an air traffic controller. The CPU handles tasks such as data fetching, preprocessing, and managing inference requests. If the CPU is not optimized, it can create bottlenecks, leading to underutilized GPUs and increased inference times, which ultimately affect cost and scalability.

Key Findings from AMD’s White Paper

AMD's white paper, Maximize AI GPU Efficiency with AMD EPYC™ High Frequency Processors, highlights the significance of high-frequency CPUs like the AMD EPYC™ 9575F. Real-world benchmarks demonstrate that these CPUs can achieve up to 9% faster inference times on AMD Instinct™ MI300 GPUs and 8% on Nvidia H100 GPUs.

Understanding the Inference Pipeline

The inference process is not solely about computational power; it involves efficiently managing the entire pipeline. The host CPU's ability to handle multiple simultaneous AI queries without bottlenecks is crucial. When a user submits an inference request, the CPU's Runtime Engine optimizes it before sending it to the GPU. If the CPU lags, it results in latency spikes and reduced GPU efficiency.

Optimizing AI Inference with the Right CPU

Two critical characteristics define a CPU's ability to optimize AI inference: memory interface and core frequency. A high-capacity memory interface allows for larger batch sizes and efficient key-value caching, reducing data retrieval delays. Meanwhile, a high-frequency CPU ensures fast execution of tasks, minimizing latency and keeping GPUs fully utilized.

Benchmarking AMD EPYC™ vs. Intel Xeon

AMD conducted tests comparing the EPYC™ 9575F with Intel Xeon 8592+ in systems using AMD Instinct™ MI300x and NVIDIA H100 GPUs. The results showed that AMD's CPUs reduced inference latency and improved GPU utilization, leading to better performance and efficiency across various AI models.

Conclusion

To optimize AI workloads, it is essential to look beyond GPUs and focus on the supporting infrastructure, especially host CPUs. A well-balanced system with high-performance CPUs can significantly enhance AI efficiency, reducing latency and improving throughput.

For more details, the full white paper is available on the AMD community.