Gemma explained: What’s new in Gemma 3

Gemma 3 represents a significant advancement in the Gemma model family, introducing vision-language capabilities and architectural enhancements for improved performance and efficiency. Unlike its predecessors, Gemma 3 employs a custom SigLIP vision encoder for interpreting visual inputs and uses a "Pan&Scan" algorithm to optimize image handling, despite increased computational demands. It also features interleaved attention mechanisms to manage both short- and long-range dependencies, reducing KV-cache memory usage and supporting extended context lengths up to 128k tokens. The model enhances multilingual capabilities with a revised data mixture and an improved tokenizer, while its bidirectional attention approach offers a complete contextual understanding of images. Gemma 3 outperforms previous models in various benchmarks, particularly in zero-shot vision tasks, and is optimized for on-device use, making it accessible for mobile and embedded systems. These innovations make Gemma 3 a versatile tool for researchers and developers, paving the way for more capable multimodal language models that function efficiently on standard hardware.

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