Who should use this model?
- Searching screenshots, image-heavy PDFs, product photos, videos, and mixed text-image collections locally with GPUs available.
Choose something else when
- You only index plain text, or you need simple Ollama-first beginner setup today at home.
What hardware makes sense?
The download is only part of the memory needed. Leave room for the app, the input it processes, and other open programs. Document-search memory also depends on input length, batch size, and the document index; a chat-memory estimate does not confirm that workload.
- CPU only
- Use a smaller model for this memory limit.
- 8GB system memory
- Check this model with your exact hardware and settings before choosing it.
- 16GB system memory
- Check this model with your exact hardware and settings before choosing it.
- 32GB system memory
- Check this model with your exact hardware and settings before choosing it.
- 8GB GPU memory
- Check this model with your exact hardware and settings before choosing it.
- 12GB GPU memory
- Check this model with your exact hardware and settings before choosing it.
- 24GB GPU memory
- Check this model with your exact hardware and settings before choosing it.
- Apple unified memory
- Check this model with your exact hardware and settings before choosing it.
Model facts that change the decision
- Parameters
- 2B
- Input
- Text · Image · Screenshot · Video · Mixed modal input
- Published context limit
- 32,768 tokens
- Model format
- safetensors · sentence-transformers
- Languages
- 30+ languages
Using this model for document search
Select this model for the document-search step in an app that supports it. Keep the model that generates answers separate. Check document chunk length and any required query or document prefixes in the model’s instructions.
Changing the embedding model changes the document vectors. Rebuild the document index rather than mixing vectors from different models.
Using image input
This model needs an app or library that supports its image-processing components. Follow the exact model’s instructions for preparing image inputs.
Check the setup instructions
A ready-to-follow setup for a local chat app is not supplied here. Follow the official model card for this exact model and task.
Conditions that change the answer
- This model needs an advanced setup for searching mixed text and images.
- Use the model’s own setup instructions. Do not assume Ollama, LM Studio, or a GGUF download supports it.
- Qwen's quantization support language appears to refer to embedding deployment and vector output behavior, not necessarily downloadable GGUF model-weight quantizations.
- The 8B variant should be treated as workstation or server-class unless local tooling improves.
- Commercial use: Yes under Apache-2.0.
- Qwen3-VL Embedding 2B is for visual retrieval, not normal chat. It embeds text, images, screenshots, video, or mixed inputs into one search space, making it useful for PDF images and visual archives in advanced document search.
Read the controlling license text →