Benchmarking Google Gemma 4: Comparing the 12B and 26B-A4B Architectures

A local performance evaluation comparing the Gemma 4 12B and 26B-A4B models reveals significant disparities in coding efficiency and inference speed when tasked with complex physics-based HTML5 canvas animations.

Experimental Setup and Methodology

The benchmark was conducted on a single NVIDIA RTX 4090 GPU to evaluate the real-world performance of two models from the Gemma 4 family. The objective was to test the models' ability to generate self-contained, library-free HTML5 canvas animations implementing real-world physics. The models were required to produce three distinct scenes in a single file: a Galton board, a collision simulation involving two blocks and a wall, and a chaotic triple pendulum.

Performance Metrics and Resource Utilization

The testing yielded the following technical results regarding VRAM consumption, token output, and throughput:

Gemma 4 26B-A4B

• VRAM Usage: 15 GB
• Output Length: 6.9k tokens
• Inference Speed: 138 tok/s

Gemma 4 12B

• VRAM Usage: 9 GB
• Output Length: 8.9k tokens
• Inference Speed: 80 tok/s

Analysis of Results

Despite the claims regarding the 12B model's performance relative to larger variants, the 26B-A4B model demonstrated clear superiority in this specific coding task. The 26B-A4B variant successfully completed every scene with higher accuracy and efficiency. Notably, the 26B-A4B model achieved an inference speed approximately 1.7x faster than the 12B model, despite utilizing only 4B active parameters, highlighting the efficiency of its architectural design.

While the 12B model required less VRAM (9 GB vs 15 GB), it produced a higher token count (8.9k vs 6.9k) to achieve the result, suggesting less concise code generation and lower overall efficiency compared to the 26B-A4B variant.

Note: This analysis is based on a community-led test; broader benchmark data across diverse datasets is required to fully validate these performance claims.