Technologist based in Toronto, Canada focused on Cloud and Container Security.
After getting Gemma 4 running on my home lab’s Iris Xe GPU, the next step was wiring it into my budgeting app. Answering “how much did I spend on coffee last month?” is easy for a model when it can write a few lines of Python against my transactions and run them. Getting the model to write that code is the easy part. The real question is where the code runs.
A few months ago, Google announced Gemma 4, its latest family of open models designed to run on a wide range of hardware while supporting a variety of generation tasks. The models that immediately caught my attention were the 2B and 4B variants. I wanted to see how well they would perform on hardware I already had running in my home lab.
I’ve been running a home lab for several years. It hosts services such as Plex, Pi-hole, and various personal projects, but I had never used it for AI workloads. My goal was to understand how difficult it would be to run a genuinely useful language model locally. Longer term, I’d like to integrate a local LLM into some of my personal applications, such as a budgeting app I maintain, rather than routing requests to providers like OpenAI, Anthropic, or Google.
After years of working with developers, one theme is consistent: they want to focus on writing code and shipping features. In modern CI/CD pipelines, a container image scan runs immediately after build and will block the pipeline if critical CVEs (vulnerabilities) are found. While this is an essential, non-negotiable safeguard, it often leaves developers or platform engineers digging through vulnerability reports and fixing packages before they can move forward - time that could otherwise be spent on feature delivery.