Deploying Gemma 4 on TPU v6e-1 used to be a total headache for

I’ve been playing around with this setup where a Gemma 4 model is hosted on a TPU, but instead of wrestling with raw shell commands, I'm using a Python MCP (Model Context Protocol) suite paired with the Antigravity CLI. It basically acts as a DevOps/SRE assistant. If you're trying to provision Docker containers or manage model observability, this is significantly more stable than my old GPU-based workflows.

The real magic is how the Antigravity CLI handles the interface. It’s the successor to the Gemini CLI, and it’s built to be this terminal-driven, agent-assisted tool that actually understands the context of your deployment.

If you want to replicate this without the trial and error I went through, here is the workflow I used to get the dev environment stabilized:

First, you need the specific samples repo. I found that running the init script is the only way to ensure your shell environment variables don't get trashed during the setup:

cd ~
git clone https://github.com/xbill9/gemma4-tips

Once cloned, you have to source the setup script to handle the project IDs and environment variables. If your session times out (which happens a lot with GCP auth), don't just restart your terminal—use the reset script:

cd tpu-4B-v6e1-devops-agent
source init.sh

If things get wonky or your credentials expire, just run:

cd tpu-4B-v6e1-devops-agent
source set_env.sh

The clever part here is the MCP implementation. By using stdio transport, the Antigravity CLI (acting as the MCP client) can talk to a local Python MCP server. It abstracts the transport layer so the high-level tool logic stays the same whether you're running it locally or hitting a remote deployment. It’s a massive productivity boost for anyone doing heavy lifting with vLLM and TPU clusters.

For the actual SDK implementation, check out the official Python SDK for MCP:

https://github.com/modelcontextprotocol/python-sdk