Google's Gemma 4 12B challenges 26B model with half the VRAM cost

Google's Gemma 4 lineup has sparked buzz with two distinct models—the 26B-A4B (a mixture-of-experts variant with only 4B active parameters at any time) and the 12B dense model. Tested locally on a single RTX 4090, both were tasked with generating a self-contained HTML5 canvas animation featuring a Galton board, block collisions, and a chaotic triple pendulum—all with real physics and no external libraries. The 26B-A4B won every scene, consuming 15GB VRAM and achieving 6.9k tokens at 138 tok/s, roughly 1.7x faster than the 12B. However, the 12B held its own with 8.9k tokens at 80 tok/s on only 9GB VRAM, staying competitive in output quality while using almost half the memory.

For professionals running AI models on consumer hardware, the Gemma 4 12B is a sweet spot. It fits comfortably in a 16GB laptop GPU and delivers near-26B performance for complex coding and creative tasks. The 26B-A4B remains the raw power leader, but its VRAM appetite limits it to high-end setups. Both models are open source and available via atomic.chat, highlighting Google’s push to make advanced local AI more accessible. This test confirms that smaller, efficiently designed models can close the gap with larger counterparts—great news for anyone wanting capable AI without a data center budget.