## Choosing the Right Fractional GPU Strategy for Cloud Providers

As demand for GPU-accelerated workloads soars across industries, cloud providers are under increasing pressure to offer flexible, cost-efficient, and isolated access to GPUs. While full GPU allocation remains the norm, it often leads to resource waste—especially for lightweight or intermittent workloads.

In the [previous blog](https://docs.rafay.co/blog/2025/07/08/demystifying-fractional-gpus-in-kubernetes-mig-time-slicing-and-custom-schedulers/), we described the three primary technical approaches for fractional GPUs. In this blog, we'll explore the most viable approaches to offering fractional GPUs in a **GPU-as-a-Service (GPUaaS)** model, and evaluate their suitability for **cloud providers serving end customers**.

## Demystifying Fractional GPUs in Kubernetes: MIG, Time Slicing, and Custom Schedulers

As GPU acceleration becomes central to modern AI/ML workloads, Kubernetes has emerged as the orchestration platform of choice. However, allocating full GPUs for many real-world workloads is an overkill resulting in under utilization and soaring costs.

Enter the need for **fractional GPUs**: ways to share a physical GPU among multiple containers without compromising performance or isolation.

In this post, we'll walk through **three approaches to achieve fractional GPU access** in Kubernetes:

1. **MIG (Multi-Instance GPU)**
2. **Time Slicing**
3. **Custom Schedulers (e.g., KAI)**

For each, we’ll break down how it works, its pros and cons, and when to use it.