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An in-depth introduction to the Google Gemma 4 open-source multimodal AI model family, covering model features (from 2B to 31B parameters), multimodal capabilities, local deployment solutions, and usage methods for the free online platform gemma4.run
Section 01
An in-depth introduction to the Google Gemma 4 open-source multimodal AI model family, covering model features (from 2B to 31B parameters), multimodal capabilities, local deployment solutions, and usage methods for the free online platform gemma4.run
Section 02
Gemma 4 is Google's latest open-source multimodal AI model family, distilled from the Gemini 3 architecture. Unlike the closed Gemini API, Gemma 4 is fully open-source under the Apache 2.0 license, allowing developers to deploy and use it commercially freely. The project not only provides the models themselves but also builds a free online platform gemma4.run, enabling users to experience Gemma 4's powerful capabilities without registration or API keys
Section 03
The Gemma 4 series includes four main models, covering various deployment scenarios from edge devices to servers:
Section 04
A lightweight model designed for mobile devices and embedded systems, supporting text and image understanding. It only requires about 1.5GB of VRAM to run, making it suitable for AI application development in resource-constrained environments
Section 05
A medium-scale model for laptops and edge deployments, also supporting text and image dual modalities. It requires about 2.8GB of VRAM and provides better inference quality while maintaining a small size
Section 06
A large-scale model using a Mixture of Experts (MoE) architecture, with only about 4 billion active parameters. It supports three modalities: text, image, and audio. Requiring about 15GB of VRAM, it is suitable for server deployment and daily conversation scenarios. The MoE architecture significantly reduces computing costs while ensuring inference speed
Section 07
The flagship model of the Gemma 4 family, using a dense architecture and supporting full three-modal understanding. Requiring about 18GB of VRAM, it is designed for complex reasoning, in-depth analysis, and demanding workloads, making it the first choice for those pursuing the highest output quality
Section 08
All Gemma 4 models support a 256K token context window, far exceeding the average level of similar open-source models. This means it can process entire technical documents, complete codebases, or long research papers in one go without segmentation, greatly improving the efficiency of long-document analysis and code understanding
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