Google has released Gemma 3, its newest collection of open AI models designed to run on a single GPU or TPU. This marks a major step forward in making advanced AI accessible to more developers and users.
What is Gemma 3?
Gemma 3 is a new set of AI models built on the same technology that powers Google’s Gemini 2.0 systems. The key feature that sets these models apart is their ability to run efficiently on limited hardware while still offering strong performance.
These models come in four sizes:
- 1B (1 billion parameters)
- 4B (4 billion parameters)
- 12B (12 billion parameters)
- 27B (27 billion parameters)
This range lets developers pick the right model for their specific needs and hardware limits. The smallest model works well on phones and laptops, while the largest model offers more advanced capabilities but still runs on just one high-end GPU.
Gemma 3 builds on the success of earlier versions. According to Google, the original Gemma has been downloaded over 100 million times, with users creating more than 60,000 different versions.
Key Features and Abilities
Works in Many Languages
Gemma 3 can handle over 140 languages, with direct support for more than 35 languages right out of the box. This helps developers create apps that work for users around the world.
Understands Images and Videos
Unlike prior versions, Gemma 3 can process images, text, and short videos. This makes it useful for creating more interactive and smart applications that can “see” and respond to visual content.
Handles More Context
Gemma 3 features a 128K-token context window (32K for the smallest model). This means it can process very long texts – almost an entire book at once. This helps the model better understand large amounts of information.
Function Calling
The model supports function calling and structured output, which helps developers create automated tasks and build agent-based systems that can take actions.
Runs Fast with Less Computing Power
Google has released official quantized versions of the models, which reduce their size and computing needs while keeping good accuracy. This makes them run faster on standard hardware.
Safety Measures
Google states they took a careful approach to safety when building Gemma 3. This included data review, alignment with safety rules through fine-tuning, and testing to check for risks.
Along with Gemma 3, Google has released ShieldGemma 2, a safety tool for image content. This 4B parameter model checks images across three safety categories:
- Dangerous content
- Sexually explicit material
- Violence
Developers can customize ShieldGemma 2 to fit their specific safety needs.
How to Use Gemma 3
Google has made Gemma 3 easy to use with many common tools and platforms:
Development Tools
Gemma 3 works with popular frameworks like Hugging Face Transformers, Ollama, JAX, Keras, PyTorch, Google AI Edge, and others. This gives developers the freedom to use tools they already know.
Quick Testing
Developers can try Gemma 3 right away in Google AI Studio, or download the models through Kaggle or Hugging Face.
Making It Your Own
Gemma 3 comes with tools for fine-tuning and changing the model to fit specific needs. Developers can train and adapt it using Google Colab, Vertex AI, or even a standard gaming GPU.
Running the Model
Gemma 3 can be used in many ways, including on Vertex AI, Cloud Run, the Google GenAI API, local setups, and other platforms.
Hardware Support
NVIDIA has directly made Gemma 3 run better on their GPUs, from small Jetson Nano devices to the newest Blackwell chips. The model is also set up to work well with Google Cloud TPUs and AMD GPUs.
Performance Compared to Other Models
According to Google, Gemma 3 performs very well for its size. The company states that it beats models like Llama-405B, DeepSeek-V3, and o3-mini in human preference tests on the LMArena leaderboard.
Sundar Pichai, Google’s CEO, highlighted the model’s efficiency in a tweet: “Gemma 3 is here! Our new open models are very efficient – the largest 27B model runs on just one H100 GPU. You’d need at least 10x the computing power to get similar performance from other models.”
The 4B version of Gemma 3 reportedly matches the performance of the previous 27B Gemma 2 model, while the 27B Gemma 3 model is said to offer similar ability to Google’s Gemini 1.5 Pro from last year.
The Growing “Gemmaverse”
Google mentions the “Gemmaverse,” an active community of developers using Gemma models to create new tools and systems. Examples include:
- AI Singapore’s SEA-LION v3, which helps communication across Southeast Asia
- INSAIT’s BgGPT, a Bulgarian language model
- Nexa AI’s OmniAudio, which brings advanced audio processing to everyday devices
To further support research, Google is launching the Gemma 3 Academic Program. Academic researchers can apply for Google Cloud credits worth $10,000 per award to help with their Gemma 3 research.
Getting Started
Interested developers have several ways to start using Gemma 3:
- Try it directly in a browser using Google AI Studio
- Get an API key from Google AI Studio and use Gemma 3 with the Google GenAI SDK
- Download models from Hugging Face, Ollama, or Kaggle
- Fine-tune and adapt the model using Hugging Face’s Transformers library
- Bring custom Gemma 3 creations to market using Vertex AI
- Run the model on Cloud Run with Ollama
- Use it with NVIDIA systems through the NVIDIA API Catalog
Why This Matters
The release of Gemma 3 shows a major trend in AI development: the push toward more efficient models that can run without massive computing resources. This shift makes advanced AI more accessible to independent developers, small businesses, and researchers who may not have access to large data centers.
As one comment on social media noted: “I’m just glad the competition is now about who uses less compute. All thanks to DeepSeek, AI organizations now see they can’t just throw compute at a problem to cover up for inefficiency.”
The multimodal abilities of Gemma 3 – handling text, images, and videos – also puts more advanced AI features into the hands of more people. Tasks that once needed costly cloud services can now run directly on phones, laptops, and workstations.
Google’s decision to release these models as open source, rather than keeping them behind a paywall or API, follows similar moves by companies like Meta with Llama and DeepSeek with their models. This growing trend toward open AI models helps speed up innovation as more developers can build on and improve these systems.
What’s Next?
As with previous open source AI models, we can expect to see many new applications built on Gemma 3 in the coming months. The community will likely create specialized versions for specific languages, tasks, and industries.
For developers working with limited resources, these models open new doors for creating smart, responsive applications that once needed much more computing power. And for users, this means more AI tools running directly on their devices, with better privacy and lower costs than cloud-based options.
Try out Gemma 3 today through Google AI Studio, or download it from platforms like Hugging Face, Ollama, or Kaggle to see what’s possible with this new generation of efficient AI models.
FAQs
What is Gemma 3?
Gemma 3 is Google’s newest collection of open AI models designed to run efficiently on a single GPU or TPU. It’s built on the same technology that powers Google’s Gemini 2.0 systems but optimized for more limited hardware configurations.
What sizes does Gemma 3 come in?
Gemma 3 is available in four sizes: 1B (1 billion parameters), 4B (4 billion parameters), 12B (12 billion parameters), and 27B (27 billion parameters). This range allows developers to select the appropriate model based on their specific requirements and hardware limitations.
Can Gemma 3 process images and videos?
Yes, unlike prior versions, Gemma 3 can process images, text, and short videos. This multimodal capability enables more interactive applications that can respond to visual content.
What hardware is required to run Gemma 3?
The models are designed for efficiency—the smallest model works well on phones and laptops, while the largest 27B model can run on a single high-end GPU. NVIDIA has optimized Gemma 3 to run on their GPUs, from small Jetson Nano devices to the newest Blackwell chips. The model also works well with Google Cloud TPUs and AMD GPUs.
Why is Gemma 3’s release significant for the AI field?
Gemma 3 represents a major trend in AI development toward more efficient models that can run without massive computing resources. This makes advanced AI more accessible to independent developers, small businesses, and researchers who may not have access to large data centers. The open-source nature of these models also helps accelerate innovation as more developers can build on and improve these systems.
