arxiv Moonshot AI (arXiv) · 574d ago

Google releases Gemma 3 multimodal models with 128K context and improved efficiency

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Google introduces Gemma 3, a new family of lightweight open multimodal models ranging from 1 to 27 billion parameters. This update adds vision understanding, support for at least 128K tokens of context, and expanded multilingual coverage compared to previous versions.

Models utilize a tailored SigLIP vision encoder with a Pan & Scan method for flexible image resolution.
Architecture changes include a 5:1 interleaving of local to global attention layers to reduce KV-cache memory usage during long-context inference.
The 1B model supports 32K context, while larger models support 128K tokens.
Training employs knowledge distillation from Gemini frontier models and a novel post-training recipe.
Gemma3-4B-IT matches Gemma2-27B-IT performance, and Gemma3-27B-IT is comparable to Gemini-1.5-Pro on benchmarks.

The release provides all model variants to the community, enabling powerful multimodal capabilities on standard consumer-grade hardware.

Importance 3/3 Beats a top-lab benchmark Moonshot AI (arXiv) Google DeepMind Multimodal Open weights

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