Gemma 4 31B IT is an open multimodal model built by Google DeepMind that handles text and image inputs, can process video as sequences of frames, and generates text output. It is designed to deliver frontier-level performance for reasoning, agentic workflows, coding, and multimodal understanding on consumer GPUs and workstations, with a 256K-token context window and support for over 140 languages. The model uses a hybrid attention mechanism that interleaves local sliding-window and full global attention, with unified Keys and Values in global layers and Proportional RoPE (p-RoPE) to support long-context performance.
This model is ready for commercial/non-commercial use.
This model is not owned or developed by NVIDIA. This model has been developed and built to a third-party's requirements for this application and use case; see link to Non-NVIDIA Gemma 4 31B IT Model Card
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Global
Use Case: Designed for text generation, chatbots and conversational AI, text summarization, image data extraction, reasoning, coding, multimodal understanding, function calling, and research or educational use.
Build.NVIDIA.com: 04/02/2026 via link
Huggingface: 04/02/2026 via link
Architecture Type: Transformer
Network Architecture: Gemma 4
Total Parameters: 30.7B
Vocabulary Size: 262,144
Input Types: Text, Image, Video
Input Formats: String, Red, Green, Blue (RGB), Video (MP4/WebM)
Input Parameters: One-Dimensional (1D), Two-Dimensional (2D), Three-Dimensional (3D)
Other Input Properties: Supports variable image aspect ratios and resolutions, configurable visual token budgets of 70, 140, 280, 560, and 1120, and video inputs up to 60 seconds at one frame per second.
Input Context Length (ISL): 256K
Output Types: Text
Output Format: String
Output Parameters: One-Dimensional (1D)
Other Output Properties: Generates text responses for chat, reasoning, coding, multimodal understanding, and function-calling workflows.
Our AI models are designed and/or optimized to run on NVIDIA GPU-accelerated systems. By leveraging NVIDIA's hardware (e.g. GPU cores) and software frameworks (e.g., CUDA libraries), the model achieves faster training and inference times compared to CPU-only solutions.
Runtime Engines:
Supported Hardware:
Preferred Operating Systems: Linux
The integration of foundation and fine-tuned models into AI systems requires additional testing using use-case-specific data to ensure safe and effective deployment. Following the V-model methodology, iterative testing and validation at both unit and system levels are essential to mitigate risks, meet technical and functional requirements, and ensure compliance with safety and ethical standards before deployment.
Gemma 4 31B IT v1.0
Data Modality: Text, Image, Audio, Other (Code)
Training Data Collection: Automated
Training Labeling: Undisclosed
Training Properties: Large-scale multimodal pre-training data spanning web documents, code, images, and audio, with a cutoff date of January 2025 and coverage in over 140 languages. Data was filtered for CSAM, sensitive data, quality, and safety.
Testing Data Collection: Undisclosed
Testing Labeling: Undisclosed
Testing Properties: Undisclosed
Evaluation Benchmark Score: Benchmark results are provided in the Gemma 4 31B IT Model Card comparing Gemma 4 31B IT to similar sized models across text and vision tasks. Specific scores vary by benchmark.
Evaluation Data Collection: Hybrid: Automated, Human
Evaluation Labeling: Hybrid: Automated, Human
Evaluation Properties: Gemma 4 models were evaluated across reasoning, coding, vision, long-context, and safety tasks, with benchmark table results reported for instruction-tuned models. Evaluation Methodology Notes:
Tau2 is reported as an average over 3 runs.HLE is reported with and without search.OmniDocBench 1.5 uses average edit distance, where lower is better.MRCR v2 8 needle 128k is reported as an average.| Benchmark | Gemma 4 31B | Gemma 4 26B A4B | Gemma 4 E4B | Gemma 4 E2B | Gemma 3 27B (no think) |
|---|---|---|---|---|---|
| MMLU Pro | 85.2% | 82.6% | 69.4% | 60.0% | 67.6% |
| AIME 2026 no tools | 89.2% | 88.3% | 42.5% | 37.5% | 20.8% |
| LiveCodeBench v6 | 80.0% | 77.1% | 52.0% | 44.0% | 29.1% |
| Codeforces ELO | 2150 | 1718 | 940 | 633 | 110 |
| GPQA Diamond | 84.3% | 82.3% | 58.6% | 43.4% | 42.4% |
| Tau2 (average over 3) | 76.9% | 68.2% | 42.2% | 24.5% | 16.2% |
| HLE no tools | 19.5% | 8.7% | - | - | - |
| HLE with search | 26.5% | 17.2% | - | - | - |
| BigBench Extra Hard | 74.4% | 64.8% | 33.1% | 21.9% | 19.3% |
| MMMLU | 88.4% | 86.3% | 76.6% | 67.4% | 70.7% |
| Vision | |||||
| MMMU Pro | 76.9% | 73.8% | 52.6% | 44.2% | 49.7% |
| OmniDocBench 1.5 (average edit distance, lower is better) | 0.131 | 0.149 | 0.181 | 0.290 | 0.365 |
| MATH-Vision | 85.6% | 82.4% | 59.5% | 52.4% | 46.0% |
| MedXPertQA MM | 61.3% | 58.1% | 28.7% | 23.5% | - |
| Audio | |||||
| CoVoST | - | - | 35.54 | 33.47 | - |
| FLEURS (lower is better) | - | - | 0.08 | 0.09 | - |
| Long Context | |||||
| MRCR v2 8 needle 128k (average) | 66.4% | 44.1% | 25.4% | 19.1% | 13.5% |
Acceleration Engine: vLLM
Test Hardware: NVIDIA Hopper H100
For best performance, use the partner-recommended sampling configuration of temperature=1.0, top_p=0.95, and top_k=64 across use cases.
Compared to Gemma 3, Gemma 4 uses standard system, assistant, and user roles. Thinking is enabled by including the <|think|> token at the start of the system prompt and disabled by removing it. When thinking is enabled, the model outputs its internal reasoning followed by the final answer using the structure <|channel>thought\n[Internal reasoning]<channel|>. For models other than E2B and E4B, including 31B, disabling thinking still produces the tags with an empty thought block before the final answer: <|channel>thought\n<channel|>[Final answer]. Libraries such as Transformers and llama.cpp may handle chat-template details automatically.
In multi-turn conversations, historical model output should include only the final response. Thought content from previous turns should not be included before the next user turn begins.
For optimal multimodal performance, place image content before text in the prompt. For variable image resolution, Gemma 4 uses a configurable visual token budget with supported values of 70, 140, 280, 560, and 1120. Lower budgets are better suited to classification, captioning, or video understanding where faster inference and processing many frames are preferred, while higher budgets are better suited to OCR, document parsing, or reading small text.
The 31B model can process video as frames and supports video inputs up to 60 seconds when frames are processed at one frame per second.
These models have certain limitations that users should be aware of.
The Gemma 4 model card describes a broad intended-usage set for the Gemma 4 family across content creation, communication, research, and education. Example uses include text generation for creative and practical formats such as poems, scripts, code, marketing copy, and email drafts; chatbots and conversational AI for customer service, virtual assistants, or interactive applications; text summarization for corpora, research papers, or reports; image data extraction for interpreting and summarizing visual data into text communications; natural language processing and VLM research; language learning tools; and knowledge exploration over large bodies of text. It also notes audio processing and interaction for the smaller E2B and E4B models only.
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Please make sure you have proper rights and permissions for all input image and video content; if image or video includes people, personal health information, or intellectual property, the image or video generated will not blur or maintain proportions of image subjects included.
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