Use Gemini CLI to deploy cost-effective LLM workloads on GKE
google cloudblogDeploying LLM workloads can be complex and costly, often involving a lengthy, multi-step process. To solve this, Google Kubernetes Engine (GKE) offers Inference Quickstart.
With Inference Quickstart, you can replace months of manual trial-and-error with out-of-the-box manifests and data-driven insights. Inference Quickstart integrates with the Gemini CLI through native Model Context Protocol (MCP) support to offer tailored recommendations for your LLM workload cost and performance needs. Together, these tools empower you to analyze, select, and deploy your LLMs on GKE in a matter of minutes. Here’s how.
1. Select and serve your LLM on GKE via Gemini CLI
You can install the gemini cli and gke-mcp server with the following steps:
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Here are some example prompts that you can give Gemini CLI to select an LLM workload and generate the manifest needed to deploy the model to a GKE cluster:
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This video below shows an end-to-end example ...
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