gke-inference

To deploy AI/ML inference workloads on GKE, you can automate deployment using Google's Inference Quickstart and best practices. This Skill configures GPU and TPU accelerators alongside model servers like vLLM or Triton for optimal inference.

For serving LLMs on Google Kubernetes Engine, you can choose from model servers like vLLM, Triton, and TensorRT-LLM. These options allow you to configure and optimize large language model inference directly within your GKE cluster.

Yes, you need the gcloud CLI installed and sufficient GPU or TPU quota in your Google Cloud project to run inference workloads on GKE. These prerequisites ensure your cluster can provision the necessary hardware accelerators.

You can implement autoscaling for LLM inference on GKE by applying best practices for LLM serving. This configuration allows your inference workloads to dynamically handle varying loads by adjusting resources based on demand.

The best way to serve large language models like Llama or Gemma on GKE is by automating deployment with Google's Inference Quickstart. This approach optimizes real-time inference using configured model servers and GPU or TPU acceleration.

When configuring TPU accelerators for model serving on GKE, you are limited by your available Google Cloud TPU quota. You must ensure your project has sufficient quota provisioned before attempting to deploy the inference workloads.