Ollama LLM On Kubernetes Locally (Run It On Your Laptop)

Now that we’ve gotten over the “buzz lifecycle” of AI and LLMs, it’s time to start thinking about how to run the workloads in our environments. Wiping away all of the said “buzz”, there’s a solid use case for running training Models and other LLM/AI workloads on Kubernetes. One of the biggest reasons is the decoupling of memory, CPU, and GPUs.

In this blog post, you’ll learn how to get started with running an LLM on a local Kubernetes cluster.

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This will work on a production cluster and/or a cloud-based cluster (AKS, EKS, GKE, etc.) as well.

Prerequisites

To follow along from a hands-on perspective with this blog post, you should have the following:

1. A code editor like VS Code.


2. Minikube installed. You can find the installation
   
   
   

   Congrats! You’ve successfully deployed an LLM to Kubernetes.

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