Launching today
Google Gemma 4 12B
Run multimodal AI locally with an encoder-free architecture
30 followers
Run multimodal AI locally with an encoder-free architecture
30 followers
Gemma 4 12B processes text, vision, and audio natively without separate encoders, running on 16GB VRAM. For developers building local agentic applications who need multimodal capability without cloud dependency.
Gemma 4 12B is Google DeepMind's latest open-source model that processes text, images, and audio natively on consumer hardware, running on just 16GB of VRAM.
Most multimodal models carry a hidden memory tax: separate encoder stacks for vision and audio that inflate overhead before a single token is generated. Gemma 4 12B removes the encoders entirely. Vision runs through a lightweight embedding module, audio is projected as raw signal directly into the token space, and the LLM backbone handles the rest.
The result is a model that benchmarks close to Google's larger 26B MoE variant while fitting comfortably on a consumer laptop.
Key capabilities include:
🧠 Encoder-free architecture for native text, vision, and audio processing
💻 Runs locally on 16GB VRAM or unified memory
🤖 Reasoning performance nearing the 26B MoE Gemma model
⚡ Multi-Token Prediction drafters for reduced local inference latency
📦 Apache 2.0 license, available now on Hugging Face and Kaggle
🛠️ Compatible with Ollama, LM Studio, llama.cpp, vLLM, and HF Transformers
It is built for ML engineers and AI developers building on-device or edge applications that need multimodal capability without a cloud API dependency. Download the weights on Hugging Face or Kaggle and start building today.
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