How modern hardware is
revolutionising AI infrastructures

An infrastructure for every stage of AI

AI is more than just machine learning. Successful projects typically go through four distinct phases – each with its own demands on the underlying hardware:

1. Data preparation
   Before an AI model can learn, vast amounts of data must be structured, cleaned, and annotated. AMD EPYC™ processors, with their high I/O capacity and up to 192 cores, are ideal for these highly parallel tasks. They deliver impressive results, especially for in-memory analytics or when using frameworks like Apache Spark.
2. Model training
   This is where the heavy lifting begins: Deep learning models like GPT or Stable Diffusion process millions of parameters. AMD EPYC™ CPUs provide a stable foundation, while AMD Instinct™ GPUs take on the computational load – with massive bandwidth, large high-bandwidth memory, and excellent floating-point performance. The MI300 series is purpose-built for exactly this use case.
3. Fine-tuning
   After training comes fine-tuning – often on smaller, domain-specific datasets. This stage benefits from the flexibility of the AMD platform: depending on the model size and task, workloads can be optimised using CPUs alone or in tandem with GPUs.
4. Inference
   In live environments, models need to deliver lightning-fast responses – whether in medical diagnostics, online shopping, or fraud detection in financial transactions. Here, AMD EPYC™ CPUs shine with low latency, deep compute capabilities, and maximum energy efficiency. Studies show that for inference workloads such as XGBoost, the AMD EPYC™ 9005 Series delivers up to three times the performance of comparable Xeon CPUs (source).