Gemma 4 - Google's open-source multimodal large model series

What is Gemma 4?

Gemma 4 is Google's open-source multimodal large model series, based on the Gemini 3 architecture. The models include four versions: E2B/E4B (runs on mobile phones/Raspberry Pi), 26B MoE (only 3.8B parameters are activated), and 31B Dense (ranked third in Arena open-source rating). The models support text, image, video, and audio input, as well as 256K long context, and feature built-in function calls and agent capabilities. Gemma 4 achieves performance exceeding that of models with tens of billions of parameters with extremely small parameters, primarily focusing on offline deployment and efficient inference on mobile devices.

Main features of Gemma 4

• Full Modal ProcessingSupports text, image, video, and native audio input (device version), and has OCR, chart understanding, and visual question answering capabilities.
• Agent native architectureIt features built-in function calls, structured JSON output, and system commands, enabling the direct construction of autonomous agent workflows and supporting multi-step inference and tool calls.
• Code and Mathematical ReasoningThe model supports high-quality code generation (80% in LiveCodeBench v6) and complex mathematical reasoning (89.2% in AIME 2026), and supports long codebase analysis (up to 256K context).
• Offline deployment on the device sideThe E2B/E4B versions can run completely offline on devices such as mobile phones, Raspberry Pi, and Jetson Orin Nano, processing voice and vision tasks with zero latency.
• Multilingual supportIt natively supports 140+ languages, making it suitable for global application development.
• Flexible hardware adaptationIt offers models ranging from 2B edge devices to 31B high-performance versions, covering everything from mobile phones to H100 workstations, and supports local execution on consumer-grade GPUs.

How to use Gemma 4

• Visit the Hugging Face website: Go to the model page to obtain the model identifier and download the weight file.
• Install dependencies: Execute in the terminal pip install transformers accelerate torch The command installs the Python environment required for model inference.
• Loading Model and TokenizerUse in code AutoModelForCausalLM.from_pretrained("google/gemma-4-31B-it") Initialize the model instance with the corresponding word segmenter.
• Execution reasoningThe input text is encoded into a tensor by a tokenizer and then fed into the model to generate a response. The output tensor is then decoded into readable text to complete the inference.

Project address for Gemma 4

• Project official website: https://blog.google/innovation-and-ai/technology/developers-tools/gemma-4/
• HuggingFace model libraryhttps://huggingface.co/collections/google/gemma-4

Key information and usage requirements for Gemma 4

• Model SpecificationsThere are 4 versions in total: E2B (device-side, 2B activated), E4B (device-side, 4B activated), 26B MoE (3.8B activated, Arena score 1441), and 31B Dense (Arena score 1452, third in open source).
• Technology baseBased on the Gemini 3 architecture, it supports 140+ languages, 256K context, and multimodal support for text/image/video/audio, and natively supports function calls and Agent workflows.
• Hardware threshold:
  • E2B/E4BMobile phone, Raspberry Pi, Jetson Orin Nano (completely offline).
  • 26B MoEThe quantized version can run on a 24GB MacBook/RTX 3090.
  • 31B DenseUnquantized version requires a single 80GB H100 image; quantized version supports consumer-grade GPUs.

Gemma 4's core advantages

• Extreme parameter efficiencyThe 31B dense model achieved an Arena score of 1452 (third among open-source models), surpassing Qwen3.5-397B and DeepSeek v3.2, which have 10-20 times more parameters; the 26B MoE model achieved the sixth level among open-source models by activating only 3.8B parameters, realizing a performance leap of "winning with small parameters".
• Full-scenario edge coverageE2B/E4B can run completely offline on mobile phones, Raspberry Pi, and Jetson Orin Nano, supporting 128K context and native audio/visual processing to achieve edge AI with zero latency and zero cloud dependency.
• True open source commercializationIt fully adopts the Apache 2.0 license (replacing previous restricted licenses), allowing for free commercial use, modification, and redistribution without payment. It also features built-in patent protection, addressing legal concerns for businesses.
• Agent native architectureBuilt-in function calls, structured JSON output, and system commands; supports 256K long context and multi-step inference; can directly build autonomous agent workflows without additional adaptation layers.

Gemma 4 Comparison with Similar Products

Application scenarios of Gemma 4

• End-side privacy computingThe E2B/E4B versions support completely offline operation on mobile phones, Raspberry Pi, and IoT devices, meeting the zero-latency intelligent agent requirements of privacy-sensitive scenarios such as initial screening of medical images and local processing of financial data.
• Enterprise AutomationThe model can build a global business agent system with private codebase question answering, automated API scheduling, and support for 140+ languages.
• Scientific Research and EducationThe model is suitable for local programming assistants (IDE plugins), bioinformatics analysis (such as Yale University's Gemma-based cancer target discovery project), and low-cost edge AI teaching experiments.
• Multimodal interactionThe model supports cross-modal intelligent applications such as OCR document digitization, real-time video content analysis, and speech-visual fusion.