Gemma 3 4B

Overview

Google's compact multimodal 4B with 128K context, vision input, and 140+ language coverage. The smallest Gemma 3 with the full feature set intact.

When to pick this model

• You need vision and long context on a low-VRAM machine or edge device
• You're shipping multilingual apps and need broad language coverage
• You want one small model for both text and image inputs
• You're prototyping before scaling to 12B or 27B

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

• Multimodal in a 4B footprint
• 140+ language coverage
• 128K context
• Sliding-window attention keeps memory in check

Limitations

• Gemma License — review terms before commercial use
• Trails the 12B and 27B on reasoning and code

Architecture & training

Architecture: Dense · multimodal text+vision · sliding-window attention (5:1 local:global)

Training: 4T tokens, 140+ languages.

Quick start

ollama run gemma3:4b

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