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 theGemini 3architecture. The models include four versions: E2B/E4B (runnable on phones/Raspberry Pi), 26B MoE (activating only 3.8B parameters), and 31B Dense (ranked third among open-source models on the Arena leaderboard). The models support text, image, video, and audio input as well as a 256K long context, with built-in function calling and Agent capabilities. Gemma 4 achieves performance surpassing hundred-billion-level models with extremely small parameters, focusing on on-device offline deployment and efficient inference.

Main Features of Gemma 4

• Full-modal processing: Supports text, image, video, and native audio input (on-device versions), with OCR, chart understanding, and visual question answering capabilities.
• Agent-native architecture: Built-in function calling, structured JSON output, and system instructions enable direct construction of autonomous Agent workflows, supporting multi-step reasoning and tool calling.
• Code and mathematical reasoning: The models support high-quality code generation (80% on LiveCodeBench v6) and complex mathematical reasoning (89.2% on AIME 2026), and support long codebase analysis (up to 256K context).
• On-device offline deployment""The E2B/E4B versions can run completely offline on devices such as phones, Raspberry Pi, and Jetson Orin Nano, processing voice and vision tasks with zero latency.
• Multilingual support""Native support for 140+ languages, suitable for globalized application development.
• Flexible hardware adaptation: Offers options from 2B on-device models to 31B high-performance versions, covering everything from phones to H100 workstations, and supports local running on consumer-grade GPUs.

How to Use Gemma 4

• Visit the Hugging Face website: Go to the model page to get the model identifier and download the weight files.
• Install dependency libraries: Run the pip install transformers accelerate torch command in the terminal to install the Python environment required for model inference.
• Load the model and tokenizer: Use AutoModelForCausalLM.from_pretrained("google/gemma-4-31B-it") and the corresponding tokenizer in your code to initialize the model instance.
• Run inference: Encode the input text into tensors through the tokenizer and pass them to the model to generate a response, then decode the output tensors into readable text to complete inference.

Gemma 4 Project Address

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

Key Information and Usage Requirements for Gemma 4

• Model specifications: 4 versions in total—E2B (on-device, 2B activated), E4B (on-device, 4B activated), 26B MoE (3.8B activated, Arena score 1441), 31B Dense (Arena score 1452, third among open-source models).
• Technical foundation: Based on the Gemini 3 architecture, supports 140+ languages, 256K context, text/image/video/audio multimodality, and natively supports function calling and Agent workflows.
• Hardware requirements：
  • E2B/E4B: Phones, Raspberry Pi, Jetson Orin Nano (completely offline).
  • 26B MoE: Quantized versions can run on a 24GB MacBook/RTX 3090.
  • 31B Dense: Unquantized requires a single 80GB H100; quantized versions support consumer-grade GPUs.

Core Advantages of Gemma 4

• Extreme parameter efficiency: 31B dense model achieves 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 reaches the sixth-best open-source level while activating only 3.8B parameters, achieving a performance leap of "defeating larger with smaller".
• Full-scenario on-device coverage: E2B/E4B can run completely offline on phones, Raspberry Pi, and Jetson Orin Nano, supporting 128K context and native audio/vision processing, enabling zero-latency, zero-cloud-dependency on-device AI.
• Truly open-source commercialization: Fully adopts the Apache 2.0 license (replacing previous restricted licenses), allowing arbitrary commercial use, modification, and redistribution without fees, with built-in patent protection, addressing enterprise legal concerns.
• Agent-native architecture: Built-in function calling, structured JSON output, and system instructions, supporting 256K long context and multi-step reasoning, enabling direct construction of autonomous Agent workflows without additional adaptation layers.

Comparison of Gemma 4 with similar competitors

Application scenarios of Gemma 4

• On-device privacy computing: The E2B/E4B versions support completely offline operation on phones, Raspberry Pi, and IoT devices, meeting the zero-latency agent needs of privacy-sensitive scenarios such as preliminary medical imaging screening and local processing of financial data.
• Enterprise automation: The model can build private codebase Q&A, automated API scheduling, and global commercial Agent systems supporting 140+ languages.
• Scientific research and education: The model is suitable for local programming assistants (in the form of IDE plugins), bioinformatics analysis (such as Yale University's Gemma-based cancer target discovery project), and low-cost edge AI teaching experiments.
• Multimodal interaction: The model supports OCR document digitization, real-time video content analysis, and cross-modal intelligent applications integrating speech and vision.