Gemma 4 12B deleted the encoders

Gemma 4 12B just deleted the vision and audio encoders. Most multimodal models still bolt on three separate networks. Google ships one weight space.

How it actually works:

Vision. Raw images → 48×48 patches → a single matrix multiply into the LLM's embedding space. No standalone ViT, no cross-attention adapter.

Audio. Raw 16kHz frames → linear projection into that same text embedding space. RoPE already handles the timing, so the 12 conformer layers older Gemma needed are simply gone.

Result. Three encoder stacks collapse to one. Apache 2.0, Q4, runs on a 16GB laptop. Text, image, audio and video in native.

Google published no benchmarks at launch. The "near-26B at half the memory" line going around is community-reported — treat it as a hypothesis, then benchmark your own task.

The trap: one weight space means your single Q4 build is the whole multimodal stack, and LoRA now updates vision, audio and text in one pass. Great — until you quantize. There's no separate encoder to keep at higher precision, so a sloppy quant degrades every modality at once.

When the encoders disappear, your quantization config becomes your entire multimodal strategy.