Gemma 4 12B: A Multimodal Model Without Integrated Encoders

Gemma 4 12B is a mid-sized model designed to run agentic multimodal intelligence in laptop environments.

It adopts an encoder-free integrated architecture, passing image and audio inputs directly to the LLM backbone without separate multimodal encoders. Vision processing is replaced by a lightweight embedding module, and audio is projected into a dimensional space similar to text tokens, drastically reducing latency and memory usage.

Key features include:

• Performance and Efficiency: It delivers performance close to the 26B MoE model while using less than half the memory.
• Optimized for Local Execution: It runs on consumer laptops with 16GB RAM/VRAM and reduces latency through Multi-Token Prediction (MTP) drafters.
• Native Audio Support: It supports native audio input for the first time in the Gemma family.
• Developer-Friendly: It is provided under the Apache 2.0 license and supports various platforms such as Hugging Face, Ollama, and LM Studio.