Google released the full version of Gemma 3n, its latest open-source model in the Gemma 3 family of artificial intelligence (AI) models, on Thursday. First announced in May, the new model is designed and optimised for on-device use cases and features several new architecture-based improvements. Interestingly, the large language model (LLM) can be run locally on just 2GB of RAM. This means the model can be deployed and operated even on a smartphone, provided it comes with AI-enabled processing power.
Gemma 3n Is a Multimodal AI Model
In a blog post, the Mountain View-based tech giant announced the release of the full version of Gemma 3n. The model follows the launch of the Gemma 3 and GemmaSign models and joins the Gemmaverse. Since it is an open-source model, the company has provided its model weights as well as the cookbook to the community. The model itself is available to use under a permissive Gemma license, which allows both academic and commercial usages.
Gemma 3n is a multimodal AI model. It natively supports image, audio, video, and text inputs. However, it can only generate text outputs. It is also a multilingual model and supports 140 languages for text, and 35 languages when the input is multimodal.
Google says that Gemma 3n has a “mobile-first architecture,” which is built on Matryoshka Transformer or MatFormer architecture. It is a nested transformer, named after the Russian nesting dolls, where one fits inside another. This architecture offers a unique way of training AI models with different parameter sizes.
Gemma 3n comes in two sizes — E2B and E4B — short for effective parameters. This means, despite being five billion and eight billion parameters in size, the active parameters are just two and four billion.
This is achieved using a technique called Per-Layer Embeddings (PLE), where only the most essential parameters are required to be loaded into the fast memory (VRAM). The rest remains in the extra layer embeddings and can be handled by the CPU.
So, with the MatFormer system, the E4B variant nests the E2B model, and when the larger model is being trained, it simultaneously trains the smaller model. This gives users the convenience of either using E4B for more advanced operations or E2B for faster outputs without finding any noticeable differences in the quality of the processing or output.
Google is also letting users create custom-sized models by tweaking certain internal parts. For this, the company is releasing the MatFormer Lab tool that will let developers test different combinations to help them find the custom model sizes.
Currently, Gemma 3n is available to download via Google’s Hugging Face listing and Kaggle listing. Users can also visit Google AI Studio to try Gemma 3n. Notably, Gemma models can also be deployed directly to Cloud Run from AI Studio.