Cost-Effective AI with Ollama, GKE GPU Sharing, and vCluster

As organizations scale their AI workloads, two major challenges often emerge: the high cost of underutilized GPUs and the operational complexity of managing isolated environments for multiple teams. Traditionally, assigning a whole GPU to a single pod is inefficient, but managing separate clusters for every team is operationally heavy.

In this post, we’ll demonstrate how to solve both problems by combining Google Kubernetes Engine (GKE) GPU time-sharing with vCluster for multi-tenancy. We’ll deploy Ollama to serve open models (like Mistral) in isolated virtual environments that share the same physical GPU infrastructure.

The Architecture: Virtual Clusters on Shared Hardware

The architecture leverages GKE Autopilot to abstract away the physical infrastructure. Instead of managing nodes, you simply deploy workloads, and Autopilot provisions the necessary hardware on demand, including GPUs, drivers, etc.

This setup lets teams have their own isolated environments, APIs, and Ollama instances, and potentially different models, while running on the same cost-effective, shared GPU nodes. For example, Team A (e.g., Legal Research) and Team B (e.g., Customer Support) can work in separate environments while they share GPU resources.