Technical Specifications

System Requirements

VRAM requirements for different quantization methods and context sizes

Gemma 3 4B

Gemma 3 4B is a foundational vision-language model developed by Google, designed to process both text and image inputs while generating textual outputs. It is part of the Gemma 3 family of lightweight, state-of-the-art models built upon the same research and technology that powers Google's Gemini models. The 4 billion parameter variant is optimized for efficient performance across diverse hardware environments, ranging from cloud-scale deployments to on-device execution on workstations, laptops, and mobile devices.

Architecturally, Gemma 3 4B employs a decoder-only transformer design. Key innovations include an optimized attention mechanism featuring a 5:1 interleaving ratio of local sliding window self-attention layers with global self-attention layers, coupled with a reduced window size for local attention. This architectural modification aims to decrease KV-cache memory overhead, enabling efficient processing of extended context lengths without degrading perplexity. The model utilizes a custom SigLIP vision encoder, which transforms 896x896 pixel square images into tokens for the language model, with a "Pan&Scan" algorithm employed to handle images of varying aspect ratios or higher resolutions.

Gemma 3 4B is engineered for a wide array of generative AI tasks, including question answering, summarization, and complex reasoning. Its multimodal capabilities allow for comprehensive understanding and analysis of visual data, such as object identification or text extraction from images. The model supports a context window of 128,000 tokens and offers broad multilingual capabilities, handling over 140 languages. Additionally, it integrates function calling, enabling the creation of intelligent agents that can interact with external tools and application programming interfaces.