GPU Slicing & Kubernetes: How to Efficiently Share Expensive AI Resources
In modern IT infrastructure, the GPU has become the new CPU. Whether it's Large Language Models (LLMs), computer vision, or complex data analysis, the demand for computing power on graphics cards has massively increased in the mid-market. However, while CPUs have been efficiently virtualized and shared for decades, GPUs often present platform engineers with a dilemma: A high-end graphics card (like an NVIDIA H100 or A100) is often oversized for a single microservice, yet too expensive to leave idle.






















