Gemma 3 1B
Smallest text-only Gemma 3 for phones and IoT.
Overview
Smallest text-only Gemma 3 for phones and IoT.
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
- Text-only fast inference
Weaknesses
- No vision
- 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 | 0.7 GB | 2 GB |
Get the model
Ollama
One-line install
ollama run gemma3:1bRead 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 3 1B.
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 3 1B?
Can I use Gemma 3 1B commercially?
What's the context length of Gemma 3 1B?
How do I install Gemma 3 1B with Ollama?
Source: huggingface.co/google/gemma-3-1b-it
Reviewed by RunLocalAI Editorial. See our editorial policy for how we research and verify model claims.
Related — keep moving
Verify Gemma 3 1B runs on your specific hardware before committing money.