Gemma 4 E2B (Effective 2B)
Smallest Gemma 4. Designed for phones and Raspberry-Pi-class hardware.
Overview
Smallest Gemma 4. Designed for phones and Raspberry-Pi-class hardware.
Family & lineage
How this model relates to others in its lineage. Family members share architecture and training-data roots; parent / children edges record direct distillation or fine-tune relationships.
Strengths
- Phone-class footprint
- Multimodal
Weaknesses
- Limited reasoning
Quantization variants
Each quantization trades model quality for file size and VRAM. Q4_K_M is the most popular starting point.
| Quantization | File size | VRAM required |
|---|---|---|
| Q4_K_M | 1.3 GB | 3 GB |
| Q8_0 | 2.2 GB | 4 GB |
Get the model
Ollama
One-line install
ollama run gemma4:e2bRead our Ollama review →HuggingFace
Original weights
Source repository — direct quantization required.
Hardware that runs this
Cards with enough VRAM for at least one quantization of Gemma 4 E2B (Effective 2B).
Models worth comparing
Same parameter band, plus what's one tier above and below — so you can decide what actually fits your hardware.
Frequently asked
What's the minimum VRAM to run Gemma 4 E2B (Effective 2B)?
Can I use Gemma 4 E2B (Effective 2B) commercially?
What's the context length of Gemma 4 E2B (Effective 2B)?
How do I install Gemma 4 E2B (Effective 2B) with Ollama?
Does Gemma 4 E2B (Effective 2B) support images?
Source: huggingface.co/google/gemma-4-e2b-it
Reviewed by RunLocalAI Editorial. See our editorial policy for how we research and verify model claims.
Related — keep moving
Verify Gemma 4 E2B (Effective 2B) runs on your specific hardware before committing money.