Google’s artificial intelligence ecosystem has been largely centered around Gemini, which powers some of the company’s most widely used applications across both software and hardware. However, alongside Gemini, Google has also been developing and releasing open-source AI models under the Gemma brand for more than a year.
Today, Google has introduced its third-generation open-source AI models, dubbed Gemma 3, boasting impressive enhancements. These models are available in four configurations—featuring 1 billion, 4 billion, 12 billion, and 27 billion parameters—designed to cater to a wide range of devices, from smartphones to high-performance workstations.
According to Google, the Gemma 3 series holds the title of the best single-accelerator AI model currently available. Unlike many large-scale AI models that require multiple GPUs or TPUs to operate, Gemma 3 is optimized to run efficiently on just one. This breakthrough means that a Gemma 3 model could potentially run directly on the dedicated Tensor Processing Unit (TPU) inside Google’s Pixel smartphones, much like the Gemini Nano model that functions natively on mobile devices.
What Sets Gemma 3 Apart?
One of the standout features of the Gemma 3 series is its open-source nature. Unlike the proprietary Gemini models, Gemma 3 allows developers to integrate and modify the AI system according to their specific needs, enabling custom AI-powered experiences across mobile apps and desktop platforms. Another key advantage is its extensive multilingual support—covering over 140 languages, with 35 of them pre-trained out of the box.
Moreover, similar to Google’s latest Gemini 2.0 models, Gemma 3 is designed for multimodal capabilities, meaning it can interpret and generate content in text, images, and video formats. Performance-wise, it is said to outperform other leading open-source AI models, including DeepSeek V3, OpenAI’s reasoning-optimized o3-mini, and Meta’s Llama-405B.
Functionality and Deployment
Beyond raw processing power, Gemma 3 is also built with advanced features such as function calling and structured output. This allows the model to interact with external datasets and function as an automated assistant, similar to how Gemini seamlessly integrates with platforms like Gmail and Google Docs.
Google has made the Gemma 3 models accessible through multiple deployment options. They can be utilized locally or hosted on cloud-based platforms such as Vertex AI. Additionally, the models are available via Google AI Studio, as well as third-party repositories like Hugging Face, Ollama, and Kaggle.
A Step Forward for Small Language Models (SLMs)
The release of Gemma 3 aligns with a broader indus
SLMs like Gem
Regarding input capabilities, Gemma 3 supports a context window of up to 128,000 tokens, which is sufficient to process the equivalent of a 200-page book in one go. For comparison, Google’s Gemini 2.0 Flash Lite model boasts a much larger window of 1 million tokens. Given that an average English word is roughly 1.3 tokens in AI processing, this marks a significant leap in handling extensive textual inputs.
With the launch of Gemma 3, Google continues to push the boundaries of open-source AI, making cutting-edge models more accessible for developers and businesses alike. Whether deployed on a smartphone or in the cloud, the Gemma 3 series promises enhanc
