Streamlining Qwen3:30B Benchmarking with Google Gemini

Recently, I dedicated an evening to benchmarking and fine-tuning the Qwen3:30B Mixture of Experts (MoE) model, specifically in its quantized version, on my 8 GB RTX 3070 laptop utilizing the Ollama framework. The journey was both challenging and enlightening, requiring numerous test runs, careful monitoring of VRAM usage, timing tokens per second, and meticulous adjustments of configuration files—along with an extensive array of logs.

Throughout this meticulous process, I found an invaluable ally in Google Gemini, which I dubbed my “AI lab assistant.” This tool significantly enhanced my workflow, as I input various logs, layer configurations, and test summaries while posing questions such as:

• “What does this decline in performance indicate?”
• “Can you summarize these four test runs in terms of speed and memory consumption?”
• “What insights can I derive from this VRAM spike?”

What stood out during this experience was not just the answers I received but how Gemini helped illuminate patterns in my analysis and thought process. The transformation from a chaotic document filled with raw notes to a coherent, organized summary was remarkable, allowing me to produce a structured breakdown that I could confidently share with the wider community.

I’m curious to hear from others—has anyone else leveraged Google Gemini in similar development or testing scenarios? It would be fascinating to exchange experiences on how you’ve integrated this tool into your workflow.