Generative AI on Kubernetes

Chapter 3. Kubernetes and GPUs

At its core, generative AI involves intensive mathematical computations, particularly linear algebra operations such as tensor multiplications. These operations demand significant computational power and memory capacity to process large datasets and models ranging from tens to hundreds of billions of parameters. Fortunately, specialized hardware known as Graphics Processing Units (GPUs) have emerged to optimize and accelerate these computational workloads.

Initially designed for rendering graphics and creating immersive gaming experiences, GPUs quickly found their place in the AI domain due to their massively parallel architecture. This capability perfectly aligns with the requirements of linear algebra-heavy tasks involved in AI and machine learning.

Today, GPUs are the most prevalent type of accelerator in the AI landscape, with NVIDIA leading the market by a large margin, followed by AMD and Intel as its primary competitors. While GPUs dominate, alternative technologies exist, each with unique strengths and ideal use cases. Google’s Tensor Processing Units, for example, offer compelling performance but are typically restricted to the Google ecosystem. Additionally, specialized AI-specific Application-Specific Integrated Circuits (ASICs), such as those developed by Cerebras and Graphcore, as well as Field Programmable Gate Arrays (FPGAs), represent emerging but still niche alternatives.

The primary reason GPUs remain the standard choice is their ...