AMD EPYC 9575F Boosts AI Inference Performance on GPUs

GPUs have rightfully gained attention due to artificial intelligence (AI) workloads, but the host CPU is a crucial component that is frequently disregarded. New AMD research reveals how high-frequency CPUs drive AI performance gains faster inference with EPYC 9575F and top GPUs.

Host CPUs serve as the air traffic controller, coordinating all other tasks including as data transportation, inference request management, batching, and workload scheduling, while GPUs carry out model computations. Your GPUs won’t operate at their best if your CPU isn’t tuned for AI. Bottlenecks, underutilised GPUs, and lengthier inference times are all consequences of a slow CPU, which can raise expenses and limit scalability.

With the support of real-world benchmarks, AMD researchers examine the performance impact of host CPUs on GPU-based AI workloads in a new white paper titled, Maximise AI GPU Efficiency with AMD EPYC High Frequency Processors. Key findings are highlighted, the importance of high-frequency CPUs like the AMD EPYC 9575F is explained, the significance of the CPU in AI inference is broken down, and average inference timings on Nvidia H100 and AMD Instinct MI300 GPUs are shown to be 8 and 9% faster, respectively.

The Host CPU: The Unsung Hero of AI Inference

No GPU functions independently; it depends on the host CPU to:

• Retrieve and prepare the data
• Control batching and inference requests.
• Effectively schedule GPU execution
• Control memory paging to prevent bottlenecks. and
• Complete and provide users with the results.

GPUs lie idle and waste electricity and computation cycles if your CPU is unable to keep up. Finding the ideal mix between GPU and CPU is more important for AI efficiency than simply adding additional GPUs.

Understanding the Inference Pipeline: Why the CPU is a Bottleneck

The goal of a GPU-based AI system is to effectively manage the entire inference process, not just compute. The host CPU enters the picture.

The Inference API Server receives user-submitted inference requests, queues them, and then sends them to the Runtime Engine, a crucial CPU-running component. To keep the GPU fully utilised and reduce latency, the Runtime Engine carries out a number of optimisation activities, including as batching, graph orchestration, and K-V cache paging.

Following preparation and optimisation, the data is transferred to the GPU for inference. The CPU completes processing and gives the user the results.

The host CPU’s capacity to manage several concurrent AI queries without experiencing bottlenecks is essential to the operation of this entire pipeline. Insufficient CPU speed results in latency spikes, decreased GPU efficiency, and slower response times, all of which squander computational resources.

Enhancing AI Inference Which CPU Type Is Required?

Two CPU properties are necessary to guarantee optimal GPU utilisation:

• Memory Interface Speed and Capacity Are Important: AI inference relies on data velocity as well as computation capacity. Massive volumes of incoming data must be efficiently stored, retrieved, and processed by the CPU before being sent to the GPU. Larger batch sizes and more effective key-value (KV) caching are made possible by high-capacity memory, which also lowers fetching times. By ensuring that AI models can swiftly recover cached data and embeddings, high memory bandwidth helps to minimise bottlenecks.

With its high bandwidth and DDR5 memory, the AMD EPYC 9575F optimises AI inference by cutting down on sluggish data retrieval cycles.

• The central frequency CPUs that are faster Keep AI Pipelines Running: By guaranteeing quick batching, tokenisation, and GPU scheduling, a high-frequency CPU helps avoid bottlenecks in AI workloads. Increased clock speeds cut down on latency in scheduling, detokenization, and token processing. GPUs get data quickly when CPU reaction times are faster, allowing them to be fully utilised rather than waiting for commands.

The AMD EPYC 9575F guarantees AI tasks operate with less delay to its 5 GHz maximum clock and strong single-thread performance.

AI Performance Benchmarking: AMD EPYC vs. Intel Xeon

It compared AMD EPYC 9575F and Intel Xeon 8592+ in AMD Instinct MI300x and NVIDIA H100 GPU-based systems in order to measure the effect of host CPUs on AI inference. All AMD EPYC CPUs decreased inference latency, which resulted in more effective GPU use.

Key Findings

• 9% quicker inference speeds on average across AI models such as Llama 3.1 and Mixtral on the 8x AMD Instinct MI300 GPU-based platforms
• 8% faster inference times on average across AI models such as Llama 3.1 and Mixtral on the 8x Nvidia H100 GPU-based systems, respectively
• Increased GPU utilisation, lower costs, and less idle time

Table 1: Overview of Enhanced Host CPU Performance with AMD EPYC 9575F

Final Thoughts: Optimizing AI Workloads Beyond GPUs

By ensuring that GPUs are fully utilised, a high-performance host CPU can reduce inference latency, increase throughput, and improve overall AI efficiency.