From the model card
Fields below are copied from the tags and counters on the HuggingFace repository Qwen/Qwen3-VL-8B-Instruct at our last fetch. They are set by the uploader, not verified by us; rows with no tag are omitted. How this page is made.
- Publisher (HF namespace)
- Qwen
- Pipeline tag
- image-text-to-text
- Library
- Transformers
- Weight formats
- safetensors
- License tag
apache-2.0— read the license file in the repo before relying on it- Papers cited
- arXiv:2505.09388, arXiv:2502.13923, arXiv:2409.12191, arXiv:2308.12966
- Downloads (HF counter at last fetch)
- 9,810,595
- Likes (HF counter at last fetch)
- 1,081
- Model card
- https://huggingface.co/Qwen/Qwen3-VL-8B-Instruct
Use cases
- Visual document understanding and structured extraction at mid-tier scale
- Image-grounded QA requiring stronger reasoning than 2-4B VLMs
- Server-side VLM inference on single A40/RTX 4090-class GPU
- Multimodal RAG where the generator must also interpret retrieved images
- Video frame analysis with text queries
Pros
- Apache 2.0 license for commercial deployment
- 8B VLM scale provides substantially stronger visual reasoning than 2-4B alternatives
- Part of Qwen3-VL series with active development
- Handles diverse visual input types (documents, natural images, charts)
Cons
- 8B VLM requires 20-24GB VRAM at FP16 for image-inclusive inference
- Inference speed on high-resolution inputs is slower than text-only 8B models
- Performance gaps vs. 30B+ VLMs on complex multi-image document analysis
- Instruction following on ambiguous visual queries less reliable than larger models
- Benchmark coverage at time of writing is still growing
Tags
transformerssafetensorsqwen3_vlimage-text-to-textconversationalarxiv:2505.09388arxiv:2502.13923arxiv:2409.12191arxiv:2308.12966license:apache-2.0eval-resultsendpoints_compatibleregion:usdeploy:sagemakerdeploy:azure