Adapters

Qwen3-VL-2B-Instruct PC with NPU

Qwen3-VL-2B-Instruct PC with NPU

To install this model locally in the shortest time, opt for Docker.

Please follow the instructions listed below to get started.

1-click setup: the app automatically fetches the large weight files.

The smart installation system will instantly find the perfect configuration for your specific hardware.

🔒 Hash checksum: 73cdaf3204487fb86f96e78e389fa177 • 📆 Last updated: 2026-06-25



  • Processor: high single-core performance needed for token latency
  • RAM: required: 16 GB absolute minimum for small models
  • Disk Space: required: fast PCIe 4.0 drive for instant boots
  • Graphics: TensorRT-LLM / vLLM inference engine compatible chip

The Qwen3-VL-2B-Instruct model is a compact yet powerful vision‑language AI designed for versatile multimodal tasks. It leverages a hybrid architecture that combines a vision transformer with a language model to process images and text in a unified context. The model supports high‑resolution inputs up to 1024×1024 pixels and can understand complex instructions ranging from caption generation to OCR. Its efficient parameter count of 2 billion enables fast inference on consumer‑grade hardware while maintaining competitive performance. A quick glance at its core specifications is provided below.

Parameters 2 B
Input Modalities Text + Images
Max Resolution 1024×1024 pixels
Key Capabilities Captioning, OCR, VQA, Instruction Following

Users appreciate its balanced trade‑off between size and capability, making it suitable for both research prototyping and production deployments.

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