Gemma 4 E4B

Overview

Google's 4B-effective multimodal Gemma variant tuned for laptops and edge devices, handling text, image, and audio across 140 languages with a 128K context.

When to pick this model

• Multimodal apps running on laptops or mobile
• Offline assistants that need image and audio input
• Multilingual edge deployments
• Low-power on-device inference
• Prototyping multimodal flows before scaling up

VRAM requirements by quantization

Quantization	VRAM required
Q4_K_M (recommended)	10 GB
Q5_K_M	12 GB
Q8_0	18 GB
FP16 (no quantization)	33 GB

VRAM figures include model weights plus a typical 8k KV cache and ~600 MB runtime overhead (Ollama / llama.cpp baseline). Add headroom for higher context lengths.

Strengths

• Full text + image + audio in a 4B model
• Runs comfortably on laptops and high-end phones
• 128K context is generous for the size class
• 140-language coverage in a small footprint

Limitations

• Gemma license restricts some commercial uses
• Quality clearly trails 12B+ multimodal models
• Audio reasoning is functional but not robust

Architecture & training

Architecture: Dense E4B (4B effective) · multimodal text+image+audio

Training: Edge/mobile edition of Gemma 4.

Quick start

ollama run gemma4:e4b

Or use the open-source MCP server to query this model from Claude Desktop, Cursor, or any MCP-compatible client.