SmolVLM Instruct
SmolVLM-Instruct is Hugging Face's compact vision-language model built on the Idefics3 architecture, pairing SmolLM2-1.7B-Instruct with a SigLIP-SO400M vision encoder. It is engineered for minimum VRAM footprint and ships under Apache-2.0 with full training-data disclosure (the_cauldron, Docmatix).
The 'minimum viable VLM' for teams that want a vision model with a clean Apache-2.0 license and a fully published recipe.
Overview
SmolVLM-Instruct is Hugging Face's compact vision-language model built on the Idefics3 architecture, pairing SmolLM2-1.7B-Instruct with a SigLIP-SO400M vision encoder. It is engineered for minimum VRAM footprint and ships under Apache-2.0 with full training-data disclosure (the_cauldron, Docmatix).
Strengths
- Smallest credible open VLM — runs in ~5GB VRAM at int8
- Apache-2.0 with published training datasets
- Strong document understanding from Docmatix training
- Active follow-ons (SmolVLM2, SmolVLM-256M/500M) show ongoing investment
Weaknesses
- Behind Qwen2-VL-2B on most public VLM benchmarks
- Idefics3 image preprocessing is finicky to wire up correctly
- 8K context limits multi-page document workflows
- Smaller community than Qwen-VL line means fewer integrations
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 | 1.2 GB | 2 GB |
Get the model
HuggingFace
Original weights
Source repository — direct quantization required.
Hardware that runs this
Cards with enough VRAM for at least one quantization of SmolVLM Instruct.
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 SmolVLM Instruct?
Can I use SmolVLM Instruct commercially?
What's the context length of SmolVLM Instruct?
Source: huggingface.co/HuggingFaceTB/SmolVLM-Instruct
Reviewed by RunLocalAI Editorial. See our editorial policy for how we research and verify model claims.
Related — keep moving
Verify SmolVLM Instruct runs on your specific hardware before committing money.