Gemma 4 12B: evaluating local multimodal AI

A new local candidate

On 3 June 2026, Google released Gemma 4 12B, a dense multimodal model with a unified architecture and no separate encoders. The practical question is how it behaves on a machine actually available to the team.

Build a small evaluation set

We recommend starting with a specific task, such as describing a screenshot or extracting information from a photographed document. Keep the same inputs and define a verifiable expected answer. Comparing only responses selected afterwards would make the result less informative.

For each trial, record memory use, time to the first response and reading mistakes. Repeating an identical request also helps reveal variation. These observations are more useful than an impression of responsiveness from a single demonstration.

Choose for the task

Local execution may address a need for control over data, but it also requires hardware and maintenance. Before integrating it, compare answer quality with operating costs and the ease of updates. External benchmark results cannot replace qualification using the project’s own inputs.

Official source.

Retrospective watch — June 2026.