Gemma 3 model

Description

Gemma is a family of lightweight, state-of-the-art open models from Google,
built from the same research and technology used to create the Gemini models.
Gemma 3 models are multimodal, handling text and image input and generating text
output, with open weights for both pre-trained variants and instruction-tuned
variants. Gemma 3 has a large, 128K context window, multilingual support in over
140 languages, and is available in more sizes than previous versions. Gemma 3
models are well-suited for a variety of text generation and image understanding
tasks, including question answering, summarization, and reasoning. Their
relatively small size makes it possible to deploy them in environments with
limited resources such as laptops, desktops or your own cloud infrastructure,
democratizing access to state of the art AI models and helping foster innovation
for everyone. This model is ready for commercial use.

Third-Party Community Consideration

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 3 model card.

License/Terms of Use

GOVERNING TERMS: The trial service is governed by the NVIDIA API Trial Terms of Service; and the use of this model is governed by the NVIDIA Community Model License. ADDITIONAL INFORMATION: Gemma Terms of Use.

Deployment Geography

Global

Use Case

Models are well-suited for a variety of text generation and image understanding tasks, including question answering, summarization, and reasoning.

Benefits

At the time of release, this family of models provides high-performance open
vision-language model implementations designed from the ground up for
responsible AI development compared to similarly sized models.

Using the benchmark evaluation metrics described in this document, these models
have shown to provide superior performance to other, comparably-sized open model
alternatives.

Release Date

• Build.Nvidia.com - 3/11/2025 via https://build.nvidia.com/google/gemma-3-1b-it and https://build.nvidia.com/google/gemma-3-27b-it

References

Model Page: Gemma
Authors: Google DeepMind

• Gemma 3 Technical Report
• Responsible Generative AI Toolkit
• Gemma on Kaggle
• Gemma on Vertex Model Garden

Model Architecture

Architecture Type: Dense decoder-only Transformer model

Inputs and outputs

Input

Input Type(s): Text, Text+Image
Input Format(s):

• String
• Image: jpg

Input Parameters:

• Text: One-dimensional (1D)
• Image: Two-dimensional (2D)

Other Properties Related to Input:

• Text string, such as a question, a prompt, or a document to be summarized
• Images, normalized to 896 x 896 resolution and encoded to 256 tokens
  each
• Total input context of 128K tokens for the 4B, 12B, and 27B sizes, and
  32K tokens for the 1B size

Output

Output Type(s): Text
Output Format: String
Output Parameters: (1D)
Other Properties Related to Output:

• Generated text in response to the input, such as an answer to a
  question, analysis of image content, or a summary of a document
• Total output context of 8192 tokens

Software Integration

Runtime Engine(s): TRT-LLM
Supported Hardware Microarchitecture Compatibility: NVIDIA Ampere, NVIDIA Blackwell, NVIDIA Jetson, NVIDIA Hopper, NVIDIA Lovelace, NVIDIA Pascal, NVIDIA Turing, and NVIDIA Volta architectures
[Preferred/Supported] Operating System(s): Linux

Model Version(s):

• Gemma 3 IT 1B: 1.0 (3/12/2025)
• Gemma 3 IT 4B: 1.0 (3/12/2025)
• Gemma 3 IT 12B: 1.0 (3/12/2025)
• Gemma 3 IT 27B: 1.0 (3/12/2025)

Software

Training was done using JAX and ML Pathways.

JAX allows researchers to take advantage of the latest generation of hardware,
including TPUs, for faster and more efficient training of large models. ML
Pathways is Google's latest effort to build artificially intelligent systems
capable of generalizing across multiple tasks. This is especially suitable for
foundation models, including large language models like these ones.

Together, JAX and ML Pathways are used as described in the
paper about the Gemini family of models; "the 'single
controller' programming model of Jax and Pathways allows a single Python
process to orchestrate the entire training run, dramatically simplifying the
development workflow."

Training, Testing, and Evaluation Datasets

Training Dataset

Data Collection Method by dataset: Hybrid: Human, Automated
Labeling Method by dataset: Hybrid: Human, Automated

These models were trained on a dataset of text data that includes a wide variety
of sources. The 27B model was trained with 14 trillion tokens, the 12B model was
trained with 12 trillion tokens, 4B model was trained with 4 trillion tokens and
1B with 2 trillion tokens. Here are the key components:

• Web Documents: A diverse collection of web text ensures the model is
  exposed to a broad range of linguistic styles, topics, and vocabulary. The
  training dataset includes content in over 140 languages.
• Code: Exposing the model to code helps it to learn the syntax and
  patterns of programming languages, which improves its ability to generate
  code and understand code-related questions.
• Mathematics: Training on mathematical text helps the model learn logical
  reasoning, symbolic representation, and address mathematical queries.
• Images: A wide range of images enables the model to perform image
  analysis and visual data extraction tasks.

The combination of these diverse data sources is crucial for training a powerful
multimodal model that can handle a wide variety of different tasks and data
formats.

Data Preprocessing

Here are the key data cleaning and filtering methods applied to the training
data:

• CSAM Filtering: Rigorous CSAM (Child Sexual Abuse Material) filtering
  was applied at multiple stages in the data preparation process to ensure
  the exclusion of harmful and illegal content.
• Sensitive Data Filtering: As part of making Gemma pre-trained models
  safe and reliable, automated techniques were used to filter out certain
  personal information and other sensitive data from training sets.
• Additional methods: Filtering based on content quality and safety in
  line with Google Responsible AI policies.

Testing Dataset

Data Collection Method by dataset: Hybrid: Human, Automated
Labeling Method by dataset: Hybrid: Human, Automated

Evaluation Dataset

Data Collection Method by dataset: Hybrid: Human, Automated
Labeling Method by dataset: Hybrid: Human, Automated

Evaluation

Model evaluation metrics and results are highlighted below.

Benchmark Results

These models were evaluated against a large collection of different datasets and
metrics to cover different aspects of text generation:

Reasoning and factuality

STEM and code

Multilingual

Multimodal

Inference

Engine: Transformers
Test Hardware: NVIDIA Hopper

Ethics and Safety

Ethics and safety evaluation approach and results are highlighted below.

Evaluation Approach

The evaluation method included structured evaluations and internal red-teaming
testing of relevant content policies. Red-teaming was conducted by a number of
different teams, each with different goals and human evaluation metrics. These
models were evaluated against a number of different categories relevant to
ethics and safety, including:

• Child Safety: Evaluation of text-to-text and image to text prompts
  covering child safety policies, including child sexual abuse and
  exploitation.
• Content Safety: Evaluation of text-to-text and image to text prompts
  covering safety policies including, harassment, violence and gore, and hate
  speech.
• Representational Harms: Evaluation of text-to-text and image to text
  prompts covering safety policies including bias, stereotyping, and harmful
  associations or inaccuracies.

In addition to development level evaluations, assurance evaluations were conducted using the "arms-length" internal evaluations for responsibility
governance decision making. They are conducted separately from the model
development team, to inform decision making about release. High level findings
are fed back to the model team, but prompt sets are held-out to prevent
overfitting and preserve the results' ability to inform decision making.
Assurance evaluation results are reported to the Responsibility & Safety Council
as part of release review.

Evaluation Results

For all areas of safety testing, there were major improvements in the categories of
child safety, content safety, and representational harms relative to previous
Gemma models. All testing was conducted without safety filters to evaluate the
model capabilities and behaviors. For both text-to-text and image-to-text, and
across all model sizes, the model produced minimal policy violations, and showed
significant improvements over previous Gemma models' performance with respect
to ungrounded inferences. One limitation of the evaluation was that the models
incorporated only English language prompts.

Usage and Limitations

The potential limitations for these models are outlined below.

Intended Usage

Open vision-language models (VLMs) models have a wide range of applications
across various industries and domains. The following list of potential uses is
not comprehensive. The purpose of this list is to provide contextual information
about the possible use-cases that the model creators considered as part of model
training and development.

• Content Creation and Communication
  • Text Generation: These models can be used to generate creative text
    formats such as poems, scripts, code, marketing copy, and email drafts.
  • Chatbots and Conversational AI: Power conversational interfaces
    for customer service, virtual assistants, or interactive applications.
  • Text Summarization: Generate concise summaries of a text corpus,
    research papers, or reports.
  • Image Data Extraction: These models can be used to extract,
    interpret, and summarize visual data for text communications.
• Research and Education
  • Natural Language Processing (NLP) and VLM Research: These
    models can serve as a foundation for researchers to experiment with VLM
    and NLP techniques, develop algorithms, and contribute to the
    advancement of the field.
  • Language Learning Tools: Support interactive language learning
    experiences, aiding in grammar correction or providing writing practice.
  • Knowledge Exploration: Assist researchers in exploring large
    bodies of text by generating summaries or answering questions about
    specific topics.

Model Limitations

• Training Data
  • The quality and diversity of the training data significantly
    influence the model's capabilities. Biases or gaps in the training data
    can lead to limitations in the model's responses.
  • The scope of the training dataset determines the subject areas
    the model can handle effectively.
• Context and Task Complexity
  • Models are better at tasks that can be framed with clear
    prompts and instructions. Open-ended or highly complex tasks might be
    challenging.
  • A model's performance can be influenced by the amount of context
    provided (longer context generally leads to better outputs, up to a
    certain point).
• Language Ambiguity and Nuance
  • Natural language is inherently complex. Models might struggle
    to grasp subtle nuances, sarcasm, or figurative language.
• Factual Accuracy
  • Models generate responses based on information they learned
    from their training datasets, but they are not knowledge bases. They
    may generate incorrect or outdated factual statements.
• Common Sense
  • Models rely on statistical patterns in language. They might
    lack the ability to apply common sense reasoning in certain situations.

Identified risks and mitigations

• Perpetuation of biases: It's encouraged to perform continuous
  monitoring (using evaluation metrics, human review) and the exploration of
  de-biasing techniques during model training, fine-tuning, and other use
  cases.
• Generation of harmful content: Mechanisms and guidelines for content
  safety are essential. Developers are encouraged to exercise caution and
  implement appropriate content safety safeguards based on their specific
  product policies and application use cases.
• Misuse for malicious purposes: Technical limitations and developer
  and end-user education can help mitigate against malicious applications of
  VLMs. Educational resources and reporting mechanisms for users to flag
  misuse are provided. Prohibited uses of Gemma models are outlined in the
  Gemma Prohibited Use Policy.
• Privacy violations: Models were trained on data filtered for removal
  of certain personal information and other sensitive data. Developers are
  encouraged to adhere to privacy regulations with privacy-preserving
  techniques.

Ethical Considerations

NVIDIA believes Trustworthy AI is a shared responsibility and we have established policies and practices to enable development for a wide array of AI applications. When downloaded or used in accordance with our terms of service, developers should work with their internal model team to ensure this model meets requirements for the relevant industry and use case and addresses unforeseen product misuse.

Please report security vulnerabilities or NVIDIA AI Concerns here.